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new file mode 100644
index 0000000000000000000000000000000000000000..fc80eaf276f7268a1fa4cd582955481339821959
--- /dev/null
+++ b/iterative_alignment_experiment_structure/TrpB4/base_model_96/trpb_post_rd_2_4.pt
@@ -0,0 +1,3 @@
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diff --git a/iterative_alignment_experiment_structure/TrpB4/base_model_96/trpb_post_rd_2_5.pt b/iterative_alignment_experiment_structure/TrpB4/base_model_96/trpb_post_rd_2_5.pt
new file mode 100644
index 0000000000000000000000000000000000000000..9ca571a65965ec353182eaf951212de7690c9180
--- /dev/null
+++ b/iterative_alignment_experiment_structure/TrpB4/base_model_96/trpb_post_rd_2_5.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 645912
diff --git a/iterative_alignment_experiment_structure/TrpB4/base_model_96/trpb_post_rd_2_6.pt b/iterative_alignment_experiment_structure/TrpB4/base_model_96/trpb_post_rd_2_6.pt
new file mode 100644
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+++ b/iterative_alignment_experiment_structure/TrpB4/base_model_96/trpb_post_rd_2_6.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 645912
diff --git a/iterative_alignment_experiment_structure/TrpB4/base_model_96/trpb_post_rd_2_7.pt b/iterative_alignment_experiment_structure/TrpB4/base_model_96/trpb_post_rd_2_7.pt
new file mode 100644
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/iterative_alignment_experiment_structure/TrpB4/base_model_96/trpb_post_rd_2_8.pt b/iterative_alignment_experiment_structure/TrpB4/base_model_96/trpb_post_rd_2_8.pt
new file mode 100644
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/iterative_alignment_experiment_structure/TrpB4/base_model_96/trpb_post_rd_2_9.pt b/iterative_alignment_experiment_structure/TrpB4/base_model_96/trpb_post_rd_2_9.pt
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 645912
diff --git a/iterative_alignment_experiment_structure/compute_updated_logps.py b/iterative_alignment_experiment_structure/compute_updated_logps.py
new file mode 100644
index 0000000000000000000000000000000000000000..582f9777a4001cd647c9908a0f797d6a41b15d5b
--- /dev/null
+++ b/iterative_alignment_experiment_structure/compute_updated_logps.py
@@ -0,0 +1,246 @@
+import os
+import torch
+import numpy as np
+import pandas as pd
+import h5py
+import re
+from omegaconf import OmegaConf
+import h5py
+import lightning as L
+from pera.nn import BidirectionalModel, sample_components_from_bidirectional_transformer, sample_perturbations, sample_embedding_perturbations
+from esm.tokenization.sequence_tokenizer import EsmSequenceTokenizer
+from Bio.Seq import Seq
+from Bio.PDB import PDBList, PDBParser, is_aa
+
+device = torch.device("cuda:0")
+
+# Optional: map 3-letter residue names to 1-letter codes
+three_to_one = {
+    'ALA': 'A', 'ARG': 'R', 'ASN': 'N', 'ASP': 'D',
+    'CYS': 'C', 'GLN': 'Q', 'GLU': 'E', 'GLY': 'G',
+    'HIS': 'H', 'ILE': 'I', 'LEU': 'L', 'LYS': 'K',
+    'MET': 'M', 'PHE': 'F', 'PRO': 'P', 'SER': 'S',
+    'THR': 'T', 'TRP': 'W', 'TYR': 'Y', 'VAL': 'V',
+    'SEC': 'U', 'PYL': 'O', 'ASX': 'B', 'GLX': 'Z',
+    'XLE': 'J', 'UNK': 'X'
+}
+
+def get_backbone_coords_from_local_pdb(pdb_path, chain_id='A', sequence_length=None, target="data", device=device):
+    """
+    Load backbone coordinates and residue types from a local PDB file.
+
+    Returns:
+        coords_tensor: torch.Tensor of shape (1, N, 3, 3)
+        residue_types: List of one-letter residue codes
+    """
+    parser = PDBParser(QUIET=True)
+    structure = parser.get_structure("local_structure", pdb_path)
+
+    coords = []
+    residue_types = []
+    model = structure[0]
+
+    if chain_id not in model:
+        raise ValueError(f"Chain {chain_id} not found in {pdb_path}")
+
+    chain = model[chain_id]
+
+    for residue in chain:
+        if sequence_length is not None and len(coords) >= sequence_length:
+            break
+        if not is_aa(residue):
+            continue
+        try:
+            n = residue['N'].get_coord()
+            ca = residue['CA'].get_coord()
+            c = residue['C'].get_coord()
+            coords.append([n, ca, c])
+            resname = residue.get_resname().upper()
+            residue_types.append(three_to_one.get(resname, 'X'))  # default to 'X' if unknown
+        except KeyError:
+            continue
+
+    if not coords:
+        raise ValueError("No residues with complete backbone atoms found.")
+
+    # Add infinity-padding before and after
+    pad = [[float('inf')]*3, [float('inf')]*3, [float('inf')]*3]
+    coords.insert(0, pad)
+    coords.append(pad)
+
+    if target == "ParD2":
+        coords = [pad, pad] + coords + [pad, pad]
+    elif target == "ParD3":
+        coords = [pad]*2 + coords + [pad]*6
+    elif target == "TrpB4":
+        coords = [pad] + coords
+
+    coords_tensor = torch.tensor(coords, device=device).unsqueeze(0)  # (1, N, 3, 3)
+
+    return coords_tensor, residue_types
+
+sequence_tokenizer = EsmSequenceTokenizer()
+
+import argparse
+# set up parser
+parser = parser = argparse.ArgumentParser(description="Calculating the log-likelihood of a sequence")
+parser.add_argument('--target', type=str, required=True, help='Dataset as a string')
+parser.add_argument('--num_samples', type=int, required=False, default=384, help='Number of samples to process (default: 100000)')
+parser.add_argument('--alignment_round', type=int, required=False, default=1, help='Alignment round as an integer')
+parser.add_argument('--version_number', type=str, required=False, default=1, help='Version number as a string')
+parser.add_argument('--replicate', type=int, required=False, default=1, help='Replicate number as an integer')
+args = parser.parse_args()
+
+target = args.target
+alignment_round = args.alignment_round
+version_number = args.version_number
+num_samples = args.num_samples
+replicate = args.replicate
+
+datasets = [f"{target}/base_model_{num_samples}"]
+for i in range(alignment_round):
+    datasets.append(f"{target}/aligned_{i}_{num_samples}_{replicate}")
+
+data_root_path = "/global/cfs/projectdirs/m4235/sebastian/data"
+
+sequence_tokenizer = EsmSequenceTokenizer()
+
+cfg_filename = f"{target}/lightning_logs/{version_number}/config.yaml"
+network_filename = f"{target}/lightning_logs/{version_number}/checkpoints/best_model.ckpt"
+
+cfg = OmegaConf.load(cfg_filename)
+# sampling_temperature = cfg["train"]["lightning_model_args"]["sampling_temperature"]
+sampling_temperature=1
+OmegaConf.update(cfg, "train.lightning_model_args.sampling_temperature", sampling_temperature)
+esm_model = BidirectionalModel(cfg["nn"]["model"], 
+                                cfg["nn"]["model_args"],
+                                **cfg["train"]["lightning_model_args"]).to(device)
+esm_model.load_model_from_ckpt(network_filename)
+esm_model.eval()
+print("")
+mask_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["mask"]
+bos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["bos"]
+eos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["eos"]
+pad_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["pad"]
+
+for data in datasets:
+    save_folder_name = data
+    
+    data = data.split("/")[0]
+    
+    os.makedirs(save_folder_name, exist_ok=True)
+    if not data.startswith("TrpB") and not data.startswith("DHFR"):
+        df = pd.read_csv(f"{data_root_path}/{data}/scale2max/{data}.csv")
+        with open(f"{data_root_path}/{data}/{data}.fasta", "r") as file:
+            parent_sequence_decoded = file.readlines()[1].strip()
+    elif data.startswith("DHFR"):
+        print("Loading DHFR data...")
+        df = pd.read_csv(f"{data_root_path}/{data}/scale2max/{data}.csv")
+        with open(f"{data_root_path}/{data}/{data}.fasta", "r") as file:
+            nucleotide_seq = file.readlines()[1].strip()
+        nucleotide_seq = Seq(nucleotide_seq)
+        parent_sequence_decoded = str(nucleotide_seq.translate())  # Translate to amino acid sequence
+    else:
+        df = pd.read_csv(f"{data_root_path}/TrpB/scale2max/{data}.csv")
+        with open(f"{data_root_path}/TrpB/TrpB.fasta", "r") as file:
+            parent_sequence_decoded = file.readlines()[1].strip()
+            
+    if data != "GB1":        
+        muts = df["muts"].iloc[0]
+    else:
+        muts = df["muts"].iloc[100000]
+    
+    numbers = re.findall(r'\d+', muts)
+    mask_indices = list(map(int, numbers))
+    num_masks_per_sequence = num_samples // 4
+    num_to_generate_per_mask = 4
+    
+
+    parent_sequence = torch.tensor(sequence_tokenizer.encode(parent_sequence_decoded, 
+                                                                add_special_tokens=True), device=device).unsqueeze(0).long()
+    sequence_length = parent_sequence.shape[1]
+    print(sequence_length, parent_sequence.shape, parent_sequence_decoded)
+
+    print(save_folder_name)
+
+    trpb = torch.load(f"./{save_folder_name}/trpb_{replicate}.pt")
+    all_unmasked_sequences_decoded = trpb["all_unmasked_sequences_decoded"]
+    all_unmasked_sequences = trpb["all_unmasked_sequences"]
+    all_masked_sequences = trpb["all_masked_sequences"]
+    all_unmasked_sequences = all_unmasked_sequences.reshape(-1, all_unmasked_sequences.shape[-1])
+    
+    all_logps = []
+
+    print(all_masked_sequences.shape)
+
+    for i in range(0, all_masked_sequences.shape[0], num_to_generate_per_mask):
+        masked_sequences = all_masked_sequences[i:i+num_to_generate_per_mask]
+        unmasked_sequences = all_unmasked_sequences[i:i+num_to_generate_per_mask]
+        
+        sequence_id = torch.ones((num_to_generate_per_mask, sequence_length), device=device).long() * 1
+        
+        structure_tokens = torch.ones((num_to_generate_per_mask, sequence_length), device=device).long() * 4096
+        structure_tokens[:, 0] = 4098
+        structure_tokens[:, -1] = 4097
+
+        coords, residue_types = get_backbone_coords_from_local_pdb(f"{data_root_path}/{data}/{data}.pdb", chain_id='A', sequence_length=sequence_length-2, target=data) if not data.startswith("TrpB") else get_backbone_coords_from_local_pdb(f"{data_root_path}/TrpB/TrpB.pdb", chain_id='A', sequence_length=sequence_length-2, target=data)
+
+        # parent sequence sanity check
+        coords_trimmed = coords[:, 1:-1]  # shape: (1, N-2, 3, 3)
+
+        # Step 2: Determine mask of non-padding residues (i.e., not all coords are inf)
+        valid_mask = ~(torch.isinf(coords_trimmed).view(-1, 9).any(dim=1))  # shape: (N-2,)
+        residues_to_compare = [r for r, valid in zip(list(parent_sequence_decoded), valid_mask) if valid]
+
+        if residue_types != residues_to_compare:
+            print("Residue mismatch detected!")
+            for i, (ref, pdb) in enumerate(zip(residues_to_compare, residue_types)):
+                if ref != pdb:
+                    print(f"Position {i}: expected {ref}, got {pdb}")
+        else:
+            print("Residues match.")
+            print(coords.shape)
+
+        assert coords.shape[1] == sequence_length, f"Coords length {coords.shape[1]} does not match sequence length {sequence_length}"
+
+        # Repeat the coords tensor to match the batch size (num_to_generate_per_mask)
+        coords = coords.repeat(num_to_generate_per_mask, 1, 1, 1)  # Shape becomes (num_to_generate_per_mask, sequence_length, 3, 3)
+
+        average_plddt = torch.ones((num_to_generate_per_mask), device=device)
+
+        per_res_plddt = torch.zeros((num_to_generate_per_mask, sequence_length), device=device)
+        ss8_tokens = torch.zeros((num_to_generate_per_mask, sequence_length), device=device).long()
+        sasa_tokens = torch.zeros((num_to_generate_per_mask, sequence_length), device=device).long()
+
+        function_tokens = torch.zeros((num_to_generate_per_mask, sequence_length, 8), device=device).long()
+        residue_annotation_tokens = torch.zeros((num_to_generate_per_mask, sequence_length, 16), device=device).long()
+        masked_indices = (masked_sequences == mask_token_sequence).float()
+        
+        with torch.no_grad():
+            logits = esm_model.nn(sequence_tokens=masked_sequences,
+                                    structure_tokens=structure_tokens,
+                                    average_plddt=average_plddt,
+                                    per_res_plddt=per_res_plddt,
+                                    ss8_tokens=ss8_tokens,
+                                    sasa_tokens=sasa_tokens,
+                                    function_tokens=function_tokens,
+                                    residue_annotation_tokens=residue_annotation_tokens,
+                                    sequence_id=sequence_id,
+                                    bb_coords=coords)["sequence_logits"].detach()
+            logps = torch.nn.functional.log_softmax(logits/sampling_temperature, dim=-1)
+            logps = torch.gather(logps, dim=-1, index=unmasked_sequences.unsqueeze(-1)).squeeze(-1)
+            logps = (logps * masked_indices).sum(-1).detach()
+
+        all_logps.append(logps)
+
+    all_logps = torch.cat(all_logps).view(-1)
+    
+    print(all_logps.shape)
+
+
+    to_save = {"parent_sequence": parent_sequence,
+                "all_masked_sequences": all_masked_sequences,
+                "all_unmasked_sequences": all_unmasked_sequences,
+                "all_unmasked_sequences_decoded": all_unmasked_sequences_decoded,
+                "all_logps": all_logps}
+    torch.save(to_save, f"{save_folder_name}/trpb_post_rd_{alignment_round}_{replicate}.pt")
\ No newline at end of file
diff --git a/iterative_alignment_experiment_structure/config.yaml b/iterative_alignment_experiment_structure/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..32a1daca17a707f224c31441b90be8d74f580fe4
--- /dev/null
+++ b/iterative_alignment_experiment_structure/config.yaml
@@ -0,0 +1,162 @@
+nn:
+  model: GeometricTransformer
+  dataset: GeometricTransformerDataset
+  dtype: float32
+  device: cuda
+  data_in_memory: false
+  load_model: null
+  batch_size: 128
+  num_workers: 4
+  collate_fn: geometric_transformer_collate_fn
+  model_args:
+    dim_model: 1536
+    unified_transformer_args:
+      n_layers: 48
+      geom_layer_indices:
+      - 0
+      ida_layer_indices: []
+      mha_layer_indices:
+      - 0
+      - 1
+      - 2
+      - 3
+      - 4
+      - 5
+      - 6
+      - 7
+      - 8
+      - 9
+      - 10
+      - 11
+      - 12
+      - 13
+      - 14
+      - 15
+      - 16
+      - 17
+      - 18
+      - 19
+      - 20
+      - 21
+      - 22
+      - 23
+      - 24
+      - 25
+      - 26
+      - 27
+      - 28
+      - 29
+      - 30
+      - 31
+      - 32
+      - 33
+      - 34
+      - 35
+      - 36
+      - 37
+      - 38
+      - 39
+      - 40
+      - 41
+      - 42
+      - 43
+      - 44
+      - 45
+      - 46
+      - 47
+      bias: false
+      mha_args:
+        num_heads: 24
+        bias: false
+        qk_layernorm: true
+      gha_args:
+        num_heads: 256
+        num_vector_messages: 1
+        mask_and_zero_frameless: true
+        bias: false
+      scaling_factor: 1.1547005383792515
+      ffn_type: swiglu
+      norm_type: layer_norm
+      expansion_ratio: 2.66666666667
+    struc_token_info:
+      mask: 4096
+      eos: 4097
+      bos: 4098
+      pad: 4099
+      total: 5001
+      max_non_special_token: 4095
+    residue_token_info:
+      mask: 32
+      eos: 2
+      bos: 0
+      pad: 1
+      total: 33
+      max_non_special_token: null
+    sasa_token_info:
+      mask: 0
+      eos: 0
+      bos: 0
+      pad: 0
+      total: null
+      max_non_special_token: null
+    sec_struct_token_info:
+      mask: 0
+      eos: 0
+      bos: 0
+      pad: 0
+      total: null
+      max_non_special_token: null
+    res_annot_token_info:
+      mask: 0
+      eos: 0
+      bos: 0
+      pad: 0
+      total: null
+      max_non_special_token: null
+  dataset_split_args:
+    train: 0.8
+    val: 0.2
+    test: 0.0
+train:
+  lightning_model: TransformerModel
+  resume_training_path: null
+  lightning_model_args:
+    eval_type: sft
+    beta: null
+    gamma: null
+    sampling_temperature: null
+    optimizer: Adam
+    optimizer_args:
+      lr: 0.0004
+      betas:
+      - 0.9
+      - 0.95
+      weight_decay: 0.01
+    lr_scheduler: LinearWarmupCosineAnnealingLR
+    lr_scheduler_args:
+      warmup_epochs: 250000
+      max_epochs: 2500000
+      eta_min: 4.0e-05
+    interval: step
+    monitor: val/CELoss
+    sync_dist: true
+    on_step: true
+  trainer_args:
+    eval_type: era
+    accelerator: cuda
+    devices: 1
+    strategy: auto
+    log_every_n_steps: 500
+    max_epochs: 10000
+    enable_progress_bar: false
+    gradient_clip_val: 1.0
+  logger:
+    loggertype: TensorBoard
+  seed_args:
+    seed: 42
+    workers: true
+global_args:
+  dataset_filename: /scratch/group_scratch/era/directed_evolution/datasets/gb1/gb1_tokenized.h5
+  keys_to_test:
+  - nn.model
+  - nn.model_args
diff --git a/iterative_alignment_experiment_structure/create_alignment_dataset_first_round.py b/iterative_alignment_experiment_structure/create_alignment_dataset_first_round.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2ed2c46bd456ca245acf9e3d0285352c1b5a6a5
--- /dev/null
+++ b/iterative_alignment_experiment_structure/create_alignment_dataset_first_round.py
@@ -0,0 +1,237 @@
+import torch
+import re
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import h5py
+from omegaconf import OmegaConf
+from esm.tokenization.sequence_tokenizer import EsmSequenceTokenizer
+from Bio.PDB import PDBList, PDBParser, is_aa
+
+device = torch.device("cuda:0")
+
+# Optional: map 3-letter residue names to 1-letter codes
+three_to_one = {
+    'ALA': 'A', 'ARG': 'R', 'ASN': 'N', 'ASP': 'D',
+    'CYS': 'C', 'GLN': 'Q', 'GLU': 'E', 'GLY': 'G',
+    'HIS': 'H', 'ILE': 'I', 'LEU': 'L', 'LYS': 'K',
+    'MET': 'M', 'PHE': 'F', 'PRO': 'P', 'SER': 'S',
+    'THR': 'T', 'TRP': 'W', 'TYR': 'Y', 'VAL': 'V',
+    'SEC': 'U', 'PYL': 'O', 'ASX': 'B', 'GLX': 'Z',
+    'XLE': 'J', 'UNK': 'X'
+}
+
+def get_backbone_coords_from_local_pdb(pdb_path, chain_id='A', sequence_length=None, target="data", device=device):
+    """
+    Load backbone coordinates and residue types from a local PDB file.
+
+    Returns:
+        coords_tensor: torch.Tensor of shape (1, N, 3, 3)
+        residue_types: List of one-letter residue codes
+    """
+    parser = PDBParser(QUIET=True)
+    structure = parser.get_structure("local_structure", pdb_path)
+
+    coords = []
+    residue_types = []
+    model = structure[0]
+
+    if chain_id not in model:
+        raise ValueError(f"Chain {chain_id} not found in {pdb_path}")
+
+    chain = model[chain_id]
+
+    for residue in chain:
+        if sequence_length is not None and len(coords) >= sequence_length:
+            break
+        if not is_aa(residue):
+            continue
+        try:
+            n = residue['N'].get_coord()
+            ca = residue['CA'].get_coord()
+            c = residue['C'].get_coord()
+            coords.append([n, ca, c])
+            resname = residue.get_resname().upper()
+            residue_types.append(three_to_one.get(resname, 'X'))  # default to 'X' if unknown
+        except KeyError:
+            continue
+
+    if not coords:
+        raise ValueError("No residues with complete backbone atoms found.")
+
+    # Add infinity-padding before and after
+    pad = [[float('inf')]*3, [float('inf')]*3, [float('inf')]*3]
+    coords.insert(0, pad)
+    coords.append(pad)
+
+    if target == "ParD2":
+        coords = [pad, pad] + coords + [pad, pad]
+    elif target == "ParD3":
+        coords = [pad]*2 + coords + [pad]*6
+    elif target == "TrpB4":
+        coords = [pad] + coords
+
+    coords_tensor = torch.tensor(coords, device=device).unsqueeze(0)  # (1, N, 3, 3)
+
+    return coords_tensor, residue_types
+
+num_replicates = 10
+campaign_number = 0 # change this according to the campaign we are interested in
+dataset_size = 96 # change this according to the dataset size we are interested in
+
+sequence_tokenizer = EsmSequenceTokenizer()
+
+datasets = ["TrpB4"]
+data_root_path = "/global/cfs/projectdirs/m4235/sebastian/data"
+
+for data in datasets:
+    print(data)
+    for i in range(num_replicates):
+        cfg_filename = f"./config.yaml"
+        cfg = OmegaConf.load(cfg_filename)
+        sampling_temperature=1
+        OmegaConf.update(cfg, "train.lightning_model_args.sampling_temperature", sampling_temperature)
+        mask_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["mask"]
+        bos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["bos"]
+        eos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["eos"]
+        pad_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["pad"]
+        
+        if not data.startswith("TrpB"):
+            df = pd.read_csv(f"{data_root_path}/{data}/scale2max/{data}.csv")
+            with open(f"{data_root_path}/{data}/{data}.fasta", "r") as file:
+                parent_sequence_decoded = file.readlines()[1].strip()
+                
+        else:
+            df = pd.read_csv(f"{data_root_path}/TrpB/scale2max/{data}.csv")
+            with open(f"{data_root_path}/TrpB/TrpB.fasta", "r") as file:
+                parent_sequence_decoded = file.readlines()[1].strip()
+                
+        if data != "GB1":        
+            muts = df["muts"].iloc[0]
+        else:
+            muts = df["muts"].iloc[100000]
+        
+        numbers = re.findall(r'\d+', muts)
+        mask_indices = list(map(int, numbers))
+        # mask_indices = [i-1 for i in mask_indices] #convert to 0-based indexing
+        
+        fitness_scores = []
+        
+        # Load from base_model_{dataset_size}
+        trpb_base = torch.load(f"./{data}/base_model_{dataset_size}/trpb_{i}.pt")
+        all_unmasked_sequences_decoded_base = trpb_base["all_unmasked_sequences_decoded"]
+        all_unmasked_sequences_base = trpb_base["all_unmasked_sequences"]
+        all_masked_sequences_base = trpb_base["all_masked_sequences"]
+        all_unmasked_sequences_base = all_unmasked_sequences_base.reshape(-1, all_unmasked_sequences_base.shape[-1])
+        all_logps_base = trpb_base["all_logps"]
+        
+        for unmasked_sequence_decoded, unmasked_sequence in zip(all_unmasked_sequences_decoded_base, all_unmasked_sequences_base):
+            index_residue_0 = unmasked_sequence_decoded[mask_indices[0]-1]
+            index_residue_1 = unmasked_sequence_decoded[mask_indices[1]-1]
+            index_residue_2 = unmasked_sequence_decoded[mask_indices[2]-1]
+            try:
+                index_residue_3 = unmasked_sequence_decoded[mask_indices[3]-1]
+                mutations = [index_residue_0, index_residue_1, index_residue_2, index_residue_3]
+                muts = ''.join(mutations)
+            except:
+                mutations = [index_residue_0, index_residue_1, index_residue_2]
+                muts = ''.join(mutations)
+            
+            df_filtered = df[df["AAs"] == muts]
+            
+            if len(df_filtered) == 0:
+                if torch.any((unmasked_sequence[1:-1] > 23) | (unmasked_sequence[1:-1] < 4)):
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+                else:
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+            else:
+                fitness_score = df_filtered["fitness"].values[0]
+            fitness_scores.append(fitness_score)
+
+        
+        # Concatenate the sequences and logps from all models
+        all_unmasked_sequences = all_unmasked_sequences_base.clone()
+        all_masked_sequences = all_masked_sequences_base.clone()
+        all_logps = all_logps_base.clone()
+
+        all_fitness_scores = fitness_scores
+        
+        # Check for duplicates in all_unmasked_sequences
+        unique_sequences, counts = torch.unique(all_unmasked_sequences, dim=0, return_counts=True)
+        num_duplicates = torch.sum(counts > 1).item()
+        print(f"Number of duplicate sequences: {num_duplicates}")
+        
+        all_fitness_scores = np.array(all_fitness_scores)
+        all_fitness_scores = np.where(all_fitness_scores > 0, -np.log(all_fitness_scores), 10)
+        
+        sampling_temperature = 1 # hard-coding a sampling temperature of 1 for mixed-temperature alignment
+
+        sequence_length = all_unmasked_sequences.shape[1]
+
+        sequence_id = torch.ones((all_unmasked_sequences.shape[0], sequence_length), device=device).long() * 1
+
+        structure_tokens = torch.ones((1, sequence_length), device=device).long() * 4096
+        structure_tokens[:, 0] = 4098
+        structure_tokens[:, -1] = 4097
+
+        coords, residue_types = get_backbone_coords_from_local_pdb(f"{data_root_path}/{data}/{data}.pdb", chain_id='A', sequence_length=sequence_length-2, target=data) if not data.startswith("TrpB") else get_backbone_coords_from_local_pdb(f"{data_root_path}/TrpB/TrpB.pdb", chain_id='A', sequence_length=sequence_length-2, target=data)
+
+        # parent sequence sanity check
+        coords_trimmed = coords[:, 1:-1]  # shape: (1, N-2, 3, 3)
+
+        # Step 2: Determine mask of non-padding residues (i.e., not all coords are inf)
+        valid_mask = ~(torch.isinf(coords_trimmed).view(-1, 9).any(dim=1))  # shape: (N-2,)
+        residues_to_compare = [r for r, valid in zip(list(parent_sequence_decoded), valid_mask) if valid]
+
+        if residue_types != residues_to_compare:
+            print("Residue mismatch detected!")
+            for i, (ref, pdb) in enumerate(zip(residues_to_compare, residue_types)):
+                if ref != pdb:
+                    print(f"Position {i}: expected {ref}, got {pdb}")
+        else:
+            print("Residues match.")
+            print(coords.shape)
+
+        assert coords.shape[1] == sequence_length, f"Coords length {coords.shape[1]} does not match sequence length {sequence_length}"
+
+        average_plddt = torch.ones((1), device=device)
+
+        per_res_plddt = torch.zeros((1, sequence_length), device=device)
+        ss8_tokens = torch.zeros((1, sequence_length), device=device).long()
+        sasa_tokens = torch.zeros((1, sequence_length), device=device).long()
+
+        function_tokens = torch.zeros((1, sequence_length, 8), device=device).long()
+        residue_annotation_tokens = torch.zeros((1, sequence_length, 16), device=device).long()
+                
+        
+        with h5py.File(f"./{data}/alignment_dataset_{campaign_number}_{dataset_size}_from_ESM3_{i}.hdf5", "w") as f:
+            masked_sequence_tokens = f.create_dataset("masked_sequence_tokens", data=all_masked_sequences.cpu().numpy())
+            unmasked_sequence_tokens = f.create_dataset("unmasked_sequence_tokens", data=all_unmasked_sequences.cpu().numpy())
+            sequence_id = f.create_dataset("sequence_id", data=sequence_id.cpu().numpy())
+            structure_tokens = f.create_dataset("structural_tokens", data=structure_tokens.cpu().numpy())
+            coords = f.create_dataset("bb_coords", data=coords.cpu().numpy())
+            average_plddt = f.create_dataset("average_plddt", data=average_plddt.cpu().numpy())
+            per_res_plddt = f.create_dataset("per_res_plddt", data=per_res_plddt.cpu().numpy())
+            ss8_tokens = f.create_dataset("ss8_tokens", data=ss8_tokens.cpu().numpy())
+            sasa_tokens = f.create_dataset("sasa_tokens", data=sasa_tokens.cpu().numpy())
+            function_tokens = f.create_dataset("function_tokens", data=function_tokens.cpu().numpy())
+            residue_annotation_tokens = f.create_dataset("residue_annotation_tokens", data=residue_annotation_tokens.cpu().numpy())
+
+            ref_logps = f.create_dataset("ref_logps", data=all_logps.cpu().numpy())
+            energies = f.create_dataset("energies", data=all_fitness_scores)
+
+
+            f.attrs["num_prompts"] = 1
+            f.attrs["num_examples_per_prompt"] = masked_sequence_tokens.shape[0]
+            f.attrs["fixed_bb_coords"] = True
+            f.attrs["fixed_average_plddt"] = True
+            f.attrs["fixed_per_res_plddt"] = True
+            f.attrs["fixed_ss8_tokens"] = True
+            f.attrs["fixed_sasa_tokens"] = True
+            f.attrs["fixed_function_tokens"] = True
+            f.attrs["fixed_residue_annotation_tokens"] = True
+            f.attrs["fixed_structural_tokens"] = True
+            f.attrs["sampling_temperature"] = sampling_temperature
+    
diff --git a/iterative_alignment_experiment_structure/create_alignment_dataset_fourth_round.py b/iterative_alignment_experiment_structure/create_alignment_dataset_fourth_round.py
new file mode 100644
index 0000000000000000000000000000000000000000..be445b233e936a158d47fde81405ca3b127b26ba
--- /dev/null
+++ b/iterative_alignment_experiment_structure/create_alignment_dataset_fourth_round.py
@@ -0,0 +1,337 @@
+import torch
+import re
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import h5py
+from omegaconf import OmegaConf
+from esm.tokenization.sequence_tokenizer import EsmSequenceTokenizer
+from Bio.PDB import PDBList, PDBParser, is_aa
+
+device = torch.device("cuda:0")
+
+# Optional: map 3-letter residue names to 1-letter codes
+three_to_one = {
+    'ALA': 'A', 'ARG': 'R', 'ASN': 'N', 'ASP': 'D',
+    'CYS': 'C', 'GLN': 'Q', 'GLU': 'E', 'GLY': 'G',
+    'HIS': 'H', 'ILE': 'I', 'LEU': 'L', 'LYS': 'K',
+    'MET': 'M', 'PHE': 'F', 'PRO': 'P', 'SER': 'S',
+    'THR': 'T', 'TRP': 'W', 'TYR': 'Y', 'VAL': 'V',
+    'SEC': 'U', 'PYL': 'O', 'ASX': 'B', 'GLX': 'Z',
+    'XLE': 'J', 'UNK': 'X'
+}
+
+def get_backbone_coords_from_local_pdb(pdb_path, chain_id='A', sequence_length=None, target="data", device=device):
+    """
+    Load backbone coordinates and residue types from a local PDB file.
+
+    Returns:
+        coords_tensor: torch.Tensor of shape (1, N, 3, 3)
+        residue_types: List of one-letter residue codes
+    """
+    parser = PDBParser(QUIET=True)
+    structure = parser.get_structure("local_structure", pdb_path)
+
+    coords = []
+    residue_types = []
+    model = structure[0]
+
+    if chain_id not in model:
+        raise ValueError(f"Chain {chain_id} not found in {pdb_path}")
+
+    chain = model[chain_id]
+
+    for residue in chain:
+        if sequence_length is not None and len(coords) >= sequence_length:
+            break
+        if not is_aa(residue):
+            continue
+        try:
+            n = residue['N'].get_coord()
+            ca = residue['CA'].get_coord()
+            c = residue['C'].get_coord()
+            coords.append([n, ca, c])
+            resname = residue.get_resname().upper()
+            residue_types.append(three_to_one.get(resname, 'X'))  # default to 'X' if unknown
+        except KeyError:
+            continue
+
+    if not coords:
+        raise ValueError("No residues with complete backbone atoms found.")
+
+    # Add infinity-padding before and after
+    pad = [[float('inf')]*3, [float('inf')]*3, [float('inf')]*3]
+    coords.insert(0, pad)
+    coords.append(pad)
+
+    if target == "ParD2":
+        coords = [pad, pad] + coords + [pad, pad]
+    elif target == "ParD3":
+        coords = [pad]*2 + coords + [pad]*6
+    elif target == "TrpB4":
+        coords = [pad] + coords
+
+    coords_tensor = torch.tensor(coords, device=device).unsqueeze(0)  # (1, N, 3, 3)
+
+    return coords_tensor, residue_types
+
+num_replicates = 10
+campaign_number = 3 # change this according to the campaign we are interested in
+dataset_size = 96 # change this according to the dataset size we are interested in
+
+sequence_tokenizer = EsmSequenceTokenizer()
+
+datasets = ["TrpB4"]
+data_root_path = "/global/cfs/projectdirs/m4235/sebastian/data"
+
+for data in datasets:
+    print(data)
+    for i in range(num_replicates):
+        cfg_filename = f"./config.yaml"
+        cfg = OmegaConf.load(cfg_filename)
+        sampling_temperature=1
+        OmegaConf.update(cfg, "train.lightning_model_args.sampling_temperature", sampling_temperature)
+        mask_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["mask"]
+        bos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["bos"]
+        eos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["eos"]
+        pad_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["pad"]
+        
+        if not data.startswith("TrpB"):
+            df = pd.read_csv(f"{data_root_path}/{data}/scale2max/{data}.csv")
+            with open(f"{data_root_path}/{data}/{data}.fasta", "r") as file:
+                parent_sequence_decoded = file.readlines()[1].strip()
+                
+        else:
+            df = pd.read_csv(f"{data_root_path}/TrpB/scale2max/{data}.csv")
+            with open(f"{data_root_path}/TrpB/TrpB.fasta", "r") as file:
+                parent_sequence_decoded = file.readlines()[1].strip()
+                
+        if data != "GB1":        
+            muts = df["muts"].iloc[0]
+        else:
+            muts = df["muts"].iloc[100000]
+        
+        numbers = re.findall(r'\d+', muts)
+        mask_indices = list(map(int, numbers))
+        # mask_indices = [i-1 for i in mask_indices] #convert to 0-based indexing
+        
+        fitness_scores = []
+        
+        # Load from base_model_{dataset_size}
+        trpb_base = torch.load(f"./{data}/base_model_{dataset_size}/trpb_post_rd_{campaign_number-1}_{i}.pt")
+        all_unmasked_sequences_decoded_base = trpb_base["all_unmasked_sequences_decoded"]
+        all_unmasked_sequences_base = trpb_base["all_unmasked_sequences"]
+        all_masked_sequences_base = trpb_base["all_masked_sequences"]
+        all_unmasked_sequences_base = all_unmasked_sequences_base.reshape(-1, all_unmasked_sequences_base.shape[-1])
+        all_logps_base = trpb_base["all_logps"]
+        
+        for unmasked_sequence_decoded, unmasked_sequence in zip(all_unmasked_sequences_decoded_base, all_unmasked_sequences_base):
+            index_residue_0 = unmasked_sequence_decoded[mask_indices[0]-1]
+            index_residue_1 = unmasked_sequence_decoded[mask_indices[1]-1]
+            index_residue_2 = unmasked_sequence_decoded[mask_indices[2]-1]
+            try:
+                index_residue_3 = unmasked_sequence_decoded[mask_indices[3]-1]
+                mutations = [index_residue_0, index_residue_1, index_residue_2, index_residue_3]
+                muts = ''.join(mutations)
+            except:
+                mutations = [index_residue_0, index_residue_1, index_residue_2]
+                muts = ''.join(mutations)
+            
+            df_filtered = df[df["AAs"] == muts]
+            
+            if len(df_filtered) == 0:
+                if torch.any((unmasked_sequence[1:-1] > 23) | (unmasked_sequence[1:-1] < 4)):
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+                else:
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+            else:
+                fitness_score = df_filtered["fitness"].values[0]
+            fitness_scores.append(fitness_score)
+        
+        # Load from aligned_0_{dataset_size}
+        trpb_aligned = torch.load(f"./{data}/aligned_{campaign_number-3}_{dataset_size}_{i}/trpb_post_rd_{campaign_number-1}_{i}.pt")
+        all_unmasked_sequences_decoded_aligned_0 = trpb_aligned["all_unmasked_sequences_decoded"]
+        all_unmasked_sequences_aligned_0 = trpb_aligned["all_unmasked_sequences"]
+        all_masked_sequences_aligned_0 = trpb_aligned["all_masked_sequences"]
+        all_unmasked_sequences_aligned_0 = all_unmasked_sequences_aligned_0.reshape(-1, all_unmasked_sequences_aligned_0.shape[-1])
+        all_logps_aligned_0 = trpb_aligned["all_logps"]
+        
+        for unmasked_sequence_decoded, unmasked_sequence in zip(all_unmasked_sequences_decoded_aligned_0, all_unmasked_sequences_aligned_0):
+            index_residue_0 = unmasked_sequence_decoded[mask_indices[0]-1]
+            index_residue_1 = unmasked_sequence_decoded[mask_indices[1]-1]
+            index_residue_2 = unmasked_sequence_decoded[mask_indices[2]-1]
+            try:
+                index_residue_3 = unmasked_sequence_decoded[mask_indices[3]-1]
+                mutations = [index_residue_0, index_residue_1, index_residue_2, index_residue_3]
+                muts = ''.join(mutations)
+            except:
+                mutations = [index_residue_0, index_residue_1, index_residue_2]
+                muts = ''.join(mutations)
+            
+            df_filtered = df[df["AAs"] == muts]
+            
+            if len(df_filtered) == 0:
+                if torch.any((unmasked_sequence[1:-1] > 23) | (unmasked_sequence[1:-1] < 4)):
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+                else:
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+            else:
+                fitness_score = df_filtered["fitness"].values[0]
+            fitness_scores.append(fitness_score)
+        
+        # Load from aligned_1_{dataset_size}
+        trpb_aligned_1 = torch.load(f"./{data}/aligned_{campaign_number-2}_{dataset_size}_{i}/trpb_post_rd_{campaign_number-1}_{i}.pt")
+        all_unmasked_sequences_decoded_aligned_1 = trpb_aligned_1["all_unmasked_sequences_decoded"]
+        all_unmasked_sequences_aligned_1 = trpb_aligned_1["all_unmasked_sequences"]
+        all_masked_sequences_aligned_1 = trpb_aligned_1["all_masked_sequences"]
+        all_unmasked_sequences_aligned_1 = all_unmasked_sequences_aligned_1.reshape(-1, all_unmasked_sequences_aligned_1.shape[-1])
+        all_logps_aligned_1 = trpb_aligned_1["all_logps"]
+
+        for unmasked_sequence_decoded, unmasked_sequence in zip(all_unmasked_sequences_decoded_aligned_1, all_unmasked_sequences_aligned_1):
+            index_residue_0 = unmasked_sequence_decoded[mask_indices[0]-1]
+            index_residue_1 = unmasked_sequence_decoded[mask_indices[1]-1]
+            index_residue_2 = unmasked_sequence_decoded[mask_indices[2]-1]
+            try:
+                index_residue_3 = unmasked_sequence_decoded[mask_indices[3]-1]
+                mutations = [index_residue_0, index_residue_1, index_residue_2, index_residue_3]
+                muts = ''.join(mutations)
+            except:
+                mutations = [index_residue_0, index_residue_1, index_residue_2]
+                muts = ''.join(mutations)
+
+            df_filtered = df[df["AAs"] == muts]
+
+            if len(df_filtered) == 0:
+                if torch.any((unmasked_sequence[1:-1] > 23) | (unmasked_sequence[1:-1] < 4)):
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+                else:
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+            else:
+                fitness_score = df_filtered["fitness"].values[0]
+            fitness_scores.append(fitness_score)
+            
+        # Load from aligned_2_{dataset_size}
+        trpb_aligned_2 = torch.load(f"./{data}/aligned_{campaign_number-1}_{dataset_size}_{i}/trpb_{i}.pt")
+        all_unmasked_sequences_decoded_aligned_2 = trpb_aligned_2["all_unmasked_sequences_decoded"]
+        all_unmasked_sequences_aligned_2 = trpb_aligned_2["all_unmasked_sequences"]
+        all_masked_sequences_aligned_2 = trpb_aligned_2["all_masked_sequences"]
+        all_unmasked_sequences_aligned_2 = all_unmasked_sequences_aligned_2.reshape(-1, all_unmasked_sequences_aligned_2.shape[-1])
+        all_logps_aligned_2 = trpb_aligned_2["all_logps"]
+
+        for unmasked_sequence_decoded, unmasked_sequence in zip(all_unmasked_sequences_decoded_aligned_2, all_unmasked_sequences_aligned_2):
+            index_residue_0 = unmasked_sequence_decoded[mask_indices[0]-1]
+            index_residue_1 = unmasked_sequence_decoded[mask_indices[1]-1]
+            index_residue_2 = unmasked_sequence_decoded[mask_indices[2]-1]
+            try:
+                index_residue_3 = unmasked_sequence_decoded[mask_indices[3]-1]
+                mutations = [index_residue_0, index_residue_1, index_residue_2, index_residue_3]
+                muts = ''.join(mutations)
+            except:
+                mutations = [index_residue_0, index_residue_1, index_residue_2]
+                muts = ''.join(mutations)
+
+            df_filtered = df[df["AAs"] == muts]
+
+            if len(df_filtered) == 0:
+                if torch.any((unmasked_sequence[1:-1] > 23) | (unmasked_sequence[1:-1] < 4)):
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+                else:
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+            else:
+                fitness_score = df_filtered["fitness"].values[0]
+            fitness_scores.append(fitness_score)
+
+        
+        # Concatenate the sequences and logps from all models
+        all_unmasked_sequences = torch.cat((all_unmasked_sequences_base, all_unmasked_sequences_aligned_0, all_unmasked_sequences_aligned_1, all_unmasked_sequences_aligned_2),dim=0)#
+        all_masked_sequences = torch.cat((all_masked_sequences_base, all_masked_sequences_aligned_0, all_masked_sequences_aligned_1, all_masked_sequences_aligned_2),dim=0)#
+        print(all_logps_base.shape, all_logps_aligned_0.shape, all_logps_aligned_1.shape, all_logps_aligned_2.shape)#
+        all_logps = torch.cat((all_logps_base, all_logps_aligned_0, all_logps_aligned_1, all_logps_aligned_2),dim=0)#
+
+        all_fitness_scores = fitness_scores
+        
+        # Check for duplicates in all_unmasked_sequences
+        unique_sequences, counts = torch.unique(all_unmasked_sequences, dim=0, return_counts=True)
+        num_duplicates = torch.sum(counts > 1).item()
+        print(f"Number of duplicate sequences: {num_duplicates}")
+        
+        all_fitness_scores = np.array(all_fitness_scores)
+        all_fitness_scores = np.where(all_fitness_scores > 0, -np.log(all_fitness_scores), 10)
+        
+        sampling_temperature = 1 # hard-coding a sampling temperature of 1 for mixed-temperature alignment
+
+        sequence_length = all_unmasked_sequences.shape[1]
+
+        sequence_id = torch.ones((all_unmasked_sequences.shape[0], sequence_length), device=device).long() * 1
+
+        structure_tokens = torch.ones((1, sequence_length), device=device).long() * 4096
+        structure_tokens[:, 0] = 4098
+        structure_tokens[:, -1] = 4097
+
+        coords, residue_types = get_backbone_coords_from_local_pdb(f"{data_root_path}/{data}/{data}.pdb", chain_id='A', sequence_length=sequence_length-2, target=data) if not data.startswith("TrpB") else get_backbone_coords_from_local_pdb(f"{data_root_path}/TrpB/TrpB.pdb", chain_id='A', sequence_length=sequence_length-2, target=data)
+
+        # parent sequence sanity check
+        coords_trimmed = coords[:, 1:-1]  # shape: (1, N-2, 3, 3)
+
+        # Step 2: Determine mask of non-padding residues (i.e., not all coords are inf)
+        valid_mask = ~(torch.isinf(coords_trimmed).view(-1, 9).any(dim=1))  # shape: (N-2,)
+        residues_to_compare = [r for r, valid in zip(list(parent_sequence_decoded), valid_mask) if valid]
+
+        if residue_types != residues_to_compare:
+            print("Residue mismatch detected!")
+            for i, (ref, pdb) in enumerate(zip(residues_to_compare, residue_types)):
+                if ref != pdb:
+                    print(f"Position {i}: expected {ref}, got {pdb}")
+        else:
+            print("Residues match.")
+            print(coords.shape)
+
+        assert coords.shape[1] == sequence_length, f"Coords length {coords.shape[1]} does not match sequence length {sequence_length}"
+
+        average_plddt = torch.ones((1), device=device)
+
+        per_res_plddt = torch.zeros((1, sequence_length), device=device)
+        ss8_tokens = torch.zeros((1, sequence_length), device=device).long()
+        sasa_tokens = torch.zeros((1, sequence_length), device=device).long()
+
+        function_tokens = torch.zeros((1, sequence_length, 8), device=device).long()
+        residue_annotation_tokens = torch.zeros((1, sequence_length, 16), device=device).long()
+                
+        
+        with h5py.File(f"./{data}/alignment_dataset_{campaign_number}_{dataset_size}_from_ESM3_{i}.hdf5", "w") as f:
+            masked_sequence_tokens = f.create_dataset("masked_sequence_tokens", data=all_masked_sequences.cpu().numpy())
+            unmasked_sequence_tokens = f.create_dataset("unmasked_sequence_tokens", data=all_unmasked_sequences.cpu().numpy())
+            sequence_id = f.create_dataset("sequence_id", data=sequence_id.cpu().numpy())
+            structure_tokens = f.create_dataset("structural_tokens", data=structure_tokens.cpu().numpy())
+            coords = f.create_dataset("bb_coords", data=coords.cpu().numpy())
+            average_plddt = f.create_dataset("average_plddt", data=average_plddt.cpu().numpy())
+            per_res_plddt = f.create_dataset("per_res_plddt", data=per_res_plddt.cpu().numpy())
+            ss8_tokens = f.create_dataset("ss8_tokens", data=ss8_tokens.cpu().numpy())
+            sasa_tokens = f.create_dataset("sasa_tokens", data=sasa_tokens.cpu().numpy())
+            function_tokens = f.create_dataset("function_tokens", data=function_tokens.cpu().numpy())
+            residue_annotation_tokens = f.create_dataset("residue_annotation_tokens", data=residue_annotation_tokens.cpu().numpy())
+
+            ref_logps = f.create_dataset("ref_logps", data=all_logps.cpu().numpy())
+            energies = f.create_dataset("energies", data=all_fitness_scores)
+
+
+            f.attrs["num_prompts"] = 1
+            f.attrs["num_examples_per_prompt"] = masked_sequence_tokens.shape[0]
+            f.attrs["fixed_bb_coords"] = True
+            f.attrs["fixed_average_plddt"] = True
+            f.attrs["fixed_per_res_plddt"] = True
+            f.attrs["fixed_ss8_tokens"] = True
+            f.attrs["fixed_sasa_tokens"] = True
+            f.attrs["fixed_function_tokens"] = True
+            f.attrs["fixed_residue_annotation_tokens"] = True
+            f.attrs["fixed_structural_tokens"] = True
+            f.attrs["sampling_temperature"] = sampling_temperature
+    
diff --git a/iterative_alignment_experiment_structure/create_alignment_dataset_second_round.py b/iterative_alignment_experiment_structure/create_alignment_dataset_second_round.py
new file mode 100644
index 0000000000000000000000000000000000000000..3639beb38306dce5846f467774edcd125b0a9c07
--- /dev/null
+++ b/iterative_alignment_experiment_structure/create_alignment_dataset_second_round.py
@@ -0,0 +1,271 @@
+import torch
+import re
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import h5py
+from omegaconf import OmegaConf
+from esm.tokenization.sequence_tokenizer import EsmSequenceTokenizer
+from Bio.PDB import PDBList, PDBParser, is_aa
+
+device = torch.device("cuda:0")
+
+# Optional: map 3-letter residue names to 1-letter codes
+three_to_one = {
+    'ALA': 'A', 'ARG': 'R', 'ASN': 'N', 'ASP': 'D',
+    'CYS': 'C', 'GLN': 'Q', 'GLU': 'E', 'GLY': 'G',
+    'HIS': 'H', 'ILE': 'I', 'LEU': 'L', 'LYS': 'K',
+    'MET': 'M', 'PHE': 'F', 'PRO': 'P', 'SER': 'S',
+    'THR': 'T', 'TRP': 'W', 'TYR': 'Y', 'VAL': 'V',
+    'SEC': 'U', 'PYL': 'O', 'ASX': 'B', 'GLX': 'Z',
+    'XLE': 'J', 'UNK': 'X'
+}
+
+def get_backbone_coords_from_local_pdb(pdb_path, chain_id='A', sequence_length=None, target="data", device=device):
+    """
+    Load backbone coordinates and residue types from a local PDB file.
+
+    Returns:
+        coords_tensor: torch.Tensor of shape (1, N, 3, 3)
+        residue_types: List of one-letter residue codes
+    """
+    parser = PDBParser(QUIET=True)
+    structure = parser.get_structure("local_structure", pdb_path)
+
+    coords = []
+    residue_types = []
+    model = structure[0]
+
+    if chain_id not in model:
+        raise ValueError(f"Chain {chain_id} not found in {pdb_path}")
+
+    chain = model[chain_id]
+
+    for residue in chain:
+        if sequence_length is not None and len(coords) >= sequence_length:
+            break
+        if not is_aa(residue):
+            continue
+        try:
+            n = residue['N'].get_coord()
+            ca = residue['CA'].get_coord()
+            c = residue['C'].get_coord()
+            coords.append([n, ca, c])
+            resname = residue.get_resname().upper()
+            residue_types.append(three_to_one.get(resname, 'X'))  # default to 'X' if unknown
+        except KeyError:
+            continue
+
+    if not coords:
+        raise ValueError("No residues with complete backbone atoms found.")
+
+    # Add infinity-padding before and after
+    pad = [[float('inf')]*3, [float('inf')]*3, [float('inf')]*3]
+    coords.insert(0, pad)
+    coords.append(pad)
+
+    if target == "ParD2":
+        coords = [pad, pad] + coords + [pad, pad]
+    elif target == "ParD3":
+        coords = [pad]*2 + coords + [pad]*6
+    elif target == "TrpB4":
+        coords = [pad] + coords
+
+    coords_tensor = torch.tensor(coords, device=device).unsqueeze(0)  # (1, N, 3, 3)
+
+    return coords_tensor, residue_types
+
+num_replicates = 10
+campaign_number = 1 # change this according to the campaign we are interested in
+dataset_size = 96 # change this according to the dataset size we are interested in
+
+sequence_tokenizer = EsmSequenceTokenizer()
+
+datasets = ["GB1", "TrpB4"]
+data_root_path = "/global/cfs/projectdirs/m4235/sebastian/data"
+
+for data in datasets:
+    print(data)
+    for i in range(num_replicates):
+        cfg_filename = f"./config.yaml"
+        cfg = OmegaConf.load(cfg_filename)
+        sampling_temperature=1
+        OmegaConf.update(cfg, "train.lightning_model_args.sampling_temperature", sampling_temperature)
+        mask_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["mask"]
+        bos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["bos"]
+        eos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["eos"]
+        pad_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["pad"]
+        
+        if not data.startswith("TrpB"):
+            df = pd.read_csv(f"{data_root_path}/{data}/scale2max/{data}.csv")
+            with open(f"{data_root_path}/{data}/{data}.fasta", "r") as file:
+                parent_sequence_decoded = file.readlines()[1].strip()
+                
+        else:
+            df = pd.read_csv(f"{data_root_path}/TrpB/scale2max/{data}.csv")
+            with open(f"{data_root_path}/TrpB/TrpB.fasta", "r") as file:
+                parent_sequence_decoded = file.readlines()[1].strip()
+                
+        if data != "GB1":        
+            muts = df["muts"].iloc[0]
+        else:
+            muts = df["muts"].iloc[100000]
+        
+        numbers = re.findall(r'\d+', muts)
+        mask_indices = list(map(int, numbers))
+        # mask_indices = [i-1 for i in mask_indices] #convert to 0-based indexing
+        
+        fitness_scores = []
+        
+        # Load from base_model_{dataset_size}
+        trpb_base = torch.load(f"./{data}/base_model_{dataset_size}/trpb_post_rd_{campaign_number-1}_{i}.pt")
+        all_unmasked_sequences_decoded_base = trpb_base["all_unmasked_sequences_decoded"]
+        all_unmasked_sequences_base = trpb_base["all_unmasked_sequences"]
+        all_masked_sequences_base = trpb_base["all_masked_sequences"]
+        all_unmasked_sequences_base = all_unmasked_sequences_base.reshape(-1, all_unmasked_sequences_base.shape[-1])
+        all_logps_base = trpb_base["all_logps"]
+        
+        for unmasked_sequence_decoded, unmasked_sequence in zip(all_unmasked_sequences_decoded_base, all_unmasked_sequences_base):
+            index_residue_0 = unmasked_sequence_decoded[mask_indices[0]-1]
+            index_residue_1 = unmasked_sequence_decoded[mask_indices[1]-1]
+            index_residue_2 = unmasked_sequence_decoded[mask_indices[2]-1]
+            try:
+                index_residue_3 = unmasked_sequence_decoded[mask_indices[3]-1]
+                mutations = [index_residue_0, index_residue_1, index_residue_2, index_residue_3]
+                muts = ''.join(mutations)
+            except:
+                mutations = [index_residue_0, index_residue_1, index_residue_2]
+                muts = ''.join(mutations)
+            
+            df_filtered = df[df["AAs"] == muts]
+            
+            if len(df_filtered) == 0:
+                if torch.any((unmasked_sequence[1:-1] > 23) | (unmasked_sequence[1:-1] < 4)):
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+                else:
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+            else:
+                fitness_score = df_filtered["fitness"].values[0]
+            fitness_scores.append(fitness_score)
+        
+        # Load from aligned_0_{dataset_size}
+        trpb_aligned = torch.load(f"./{data}/aligned_{campaign_number-1}_{dataset_size}_{i}/trpb_{i}.pt")
+        all_unmasked_sequences_decoded_aligned_0 = trpb_aligned["all_unmasked_sequences_decoded"]
+        all_unmasked_sequences_aligned_0 = trpb_aligned["all_unmasked_sequences"]
+        all_masked_sequences_aligned_0 = trpb_aligned["all_masked_sequences"]
+        all_unmasked_sequences_aligned_0 = all_unmasked_sequences_aligned_0.reshape(-1, all_unmasked_sequences_aligned_0.shape[-1])
+        all_logps_aligned_0 = trpb_aligned["all_logps"]
+        
+        for unmasked_sequence_decoded, unmasked_sequence in zip(all_unmasked_sequences_decoded_aligned_0, all_unmasked_sequences_aligned_0):
+            index_residue_0 = unmasked_sequence_decoded[mask_indices[0]-1]
+            index_residue_1 = unmasked_sequence_decoded[mask_indices[1]-1]
+            index_residue_2 = unmasked_sequence_decoded[mask_indices[2]-1]
+            try:
+                index_residue_3 = unmasked_sequence_decoded[mask_indices[3]-1]
+                mutations = [index_residue_0, index_residue_1, index_residue_2, index_residue_3]
+                muts = ''.join(mutations)
+            except:
+                mutations = [index_residue_0, index_residue_1, index_residue_2]
+                muts = ''.join(mutations)
+            
+            df_filtered = df[df["AAs"] == muts]
+            
+            if len(df_filtered) == 0:
+                if torch.any((unmasked_sequence[1:-1] > 23) | (unmasked_sequence[1:-1] < 4)):
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+                else:
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+            else:
+                fitness_score = df_filtered["fitness"].values[0]
+            fitness_scores.append(fitness_score)
+
+        
+        # Concatenate the sequences and logps from all models
+        all_unmasked_sequences = torch.cat((all_unmasked_sequences_base, all_unmasked_sequences_aligned_0),dim=0)
+        all_masked_sequences = torch.cat((all_masked_sequences_base, all_masked_sequences_aligned_0),dim=0)
+        print(all_logps_base.shape, all_logps_aligned_0.shape)
+        all_logps = torch.cat((all_logps_base, all_logps_aligned_0),dim=0)
+
+        all_fitness_scores = fitness_scores
+        
+        # Check for duplicates in all_unmasked_sequences
+        unique_sequences, counts = torch.unique(all_unmasked_sequences, dim=0, return_counts=True)
+        num_duplicates = torch.sum(counts > 1).item()
+        print(f"Number of duplicate sequences: {num_duplicates}")
+        
+        all_fitness_scores = np.array(all_fitness_scores)
+        all_fitness_scores = np.where(all_fitness_scores > 0, -np.log(all_fitness_scores), 10)
+        
+        sampling_temperature = 1 # hard-coding a sampling temperature of 1 for mixed-temperature alignment
+
+        sequence_length = all_unmasked_sequences.shape[1]
+
+        sequence_id = torch.ones((all_unmasked_sequences.shape[0], sequence_length), device=device).long() * 1
+
+        structure_tokens = torch.ones((1, sequence_length), device=device).long() * 4096
+        structure_tokens[:, 0] = 4098
+        structure_tokens[:, -1] = 4097
+
+        coords, residue_types = get_backbone_coords_from_local_pdb(f"{data_root_path}/{data}/{data}.pdb", chain_id='A', sequence_length=sequence_length-2, target=data) if not data.startswith("TrpB") else get_backbone_coords_from_local_pdb(f"{data_root_path}/TrpB/TrpB.pdb", chain_id='A', sequence_length=sequence_length-2, target=data)
+
+        # parent sequence sanity check
+        coords_trimmed = coords[:, 1:-1]  # shape: (1, N-2, 3, 3)
+
+        # Step 2: Determine mask of non-padding residues (i.e., not all coords are inf)
+        valid_mask = ~(torch.isinf(coords_trimmed).view(-1, 9).any(dim=1))  # shape: (N-2,)
+        residues_to_compare = [r for r, valid in zip(list(parent_sequence_decoded), valid_mask) if valid]
+
+        if residue_types != residues_to_compare:
+            print("Residue mismatch detected!")
+            for i, (ref, pdb) in enumerate(zip(residues_to_compare, residue_types)):
+                if ref != pdb:
+                    print(f"Position {i}: expected {ref}, got {pdb}")
+        else:
+            print("Residues match.")
+            print(coords.shape)
+
+        assert coords.shape[1] == sequence_length, f"Coords length {coords.shape[1]} does not match sequence length {sequence_length}"
+
+        average_plddt = torch.ones((1), device=device)
+
+        per_res_plddt = torch.zeros((1, sequence_length), device=device)
+        ss8_tokens = torch.zeros((1, sequence_length), device=device).long()
+        sasa_tokens = torch.zeros((1, sequence_length), device=device).long()
+
+        function_tokens = torch.zeros((1, sequence_length, 8), device=device).long()
+        residue_annotation_tokens = torch.zeros((1, sequence_length, 16), device=device).long()
+                
+        
+        with h5py.File(f"./{data}/alignment_dataset_{campaign_number}_{dataset_size}_from_ESM3_{i}.hdf5", "w") as f:
+            masked_sequence_tokens = f.create_dataset("masked_sequence_tokens", data=all_masked_sequences.cpu().numpy())
+            unmasked_sequence_tokens = f.create_dataset("unmasked_sequence_tokens", data=all_unmasked_sequences.cpu().numpy())
+            sequence_id = f.create_dataset("sequence_id", data=sequence_id.cpu().numpy())
+            structure_tokens = f.create_dataset("structural_tokens", data=structure_tokens.cpu().numpy())
+            coords = f.create_dataset("bb_coords", data=coords.cpu().numpy())
+            average_plddt = f.create_dataset("average_plddt", data=average_plddt.cpu().numpy())
+            per_res_plddt = f.create_dataset("per_res_plddt", data=per_res_plddt.cpu().numpy())
+            ss8_tokens = f.create_dataset("ss8_tokens", data=ss8_tokens.cpu().numpy())
+            sasa_tokens = f.create_dataset("sasa_tokens", data=sasa_tokens.cpu().numpy())
+            function_tokens = f.create_dataset("function_tokens", data=function_tokens.cpu().numpy())
+            residue_annotation_tokens = f.create_dataset("residue_annotation_tokens", data=residue_annotation_tokens.cpu().numpy())
+
+            ref_logps = f.create_dataset("ref_logps", data=all_logps.cpu().numpy())
+            energies = f.create_dataset("energies", data=all_fitness_scores)
+
+
+            f.attrs["num_prompts"] = 1
+            f.attrs["num_examples_per_prompt"] = masked_sequence_tokens.shape[0]
+            f.attrs["fixed_bb_coords"] = True
+            f.attrs["fixed_average_plddt"] = True
+            f.attrs["fixed_per_res_plddt"] = True
+            f.attrs["fixed_ss8_tokens"] = True
+            f.attrs["fixed_sasa_tokens"] = True
+            f.attrs["fixed_function_tokens"] = True
+            f.attrs["fixed_residue_annotation_tokens"] = True
+            f.attrs["fixed_structural_tokens"] = True
+            f.attrs["sampling_temperature"] = sampling_temperature
+    
diff --git a/iterative_alignment_experiment_structure/create_alignment_dataset_third_round.py b/iterative_alignment_experiment_structure/create_alignment_dataset_third_round.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c2021967d3f1da0a4f2e38b297773684fe4084a
--- /dev/null
+++ b/iterative_alignment_experiment_structure/create_alignment_dataset_third_round.py
@@ -0,0 +1,303 @@
+import torch
+import re
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import h5py
+from omegaconf import OmegaConf
+from esm.tokenization.sequence_tokenizer import EsmSequenceTokenizer
+from Bio.PDB import PDBList, PDBParser, is_aa
+
+device = torch.device("cuda:0")
+
+# Optional: map 3-letter residue names to 1-letter codes
+three_to_one = {
+    'ALA': 'A', 'ARG': 'R', 'ASN': 'N', 'ASP': 'D',
+    'CYS': 'C', 'GLN': 'Q', 'GLU': 'E', 'GLY': 'G',
+    'HIS': 'H', 'ILE': 'I', 'LEU': 'L', 'LYS': 'K',
+    'MET': 'M', 'PHE': 'F', 'PRO': 'P', 'SER': 'S',
+    'THR': 'T', 'TRP': 'W', 'TYR': 'Y', 'VAL': 'V',
+    'SEC': 'U', 'PYL': 'O', 'ASX': 'B', 'GLX': 'Z',
+    'XLE': 'J', 'UNK': 'X'
+}
+
+def get_backbone_coords_from_local_pdb(pdb_path, chain_id='A', sequence_length=None, target="data", device=device):
+    """
+    Load backbone coordinates and residue types from a local PDB file.
+
+    Returns:
+        coords_tensor: torch.Tensor of shape (1, N, 3, 3)
+        residue_types: List of one-letter residue codes
+    """
+    parser = PDBParser(QUIET=True)
+    structure = parser.get_structure("local_structure", pdb_path)
+
+    coords = []
+    residue_types = []
+    model = structure[0]
+
+    if chain_id not in model:
+        raise ValueError(f"Chain {chain_id} not found in {pdb_path}")
+
+    chain = model[chain_id]
+
+    for residue in chain:
+        if sequence_length is not None and len(coords) >= sequence_length:
+            break
+        if not is_aa(residue):
+            continue
+        try:
+            n = residue['N'].get_coord()
+            ca = residue['CA'].get_coord()
+            c = residue['C'].get_coord()
+            coords.append([n, ca, c])
+            resname = residue.get_resname().upper()
+            residue_types.append(three_to_one.get(resname, 'X'))  # default to 'X' if unknown
+        except KeyError:
+            continue
+
+    if not coords:
+        raise ValueError("No residues with complete backbone atoms found.")
+
+    # Add infinity-padding before and after
+    pad = [[float('inf')]*3, [float('inf')]*3, [float('inf')]*3]
+    coords.insert(0, pad)
+    coords.append(pad)
+
+    if target == "ParD2":
+        coords = [pad, pad] + coords + [pad, pad]
+    elif target == "ParD3":
+        coords = [pad]*2 + coords + [pad]*6
+    elif target == "TrpB4":
+        coords = [pad] + coords
+
+    coords_tensor = torch.tensor(coords, device=device).unsqueeze(0)  # (1, N, 3, 3)
+
+    return coords_tensor, residue_types
+
+num_replicates = 10
+campaign_number = 2 # change this according to the campaign we are interested in
+dataset_size = 96 # change this according to the dataset size we are interested in
+
+sequence_tokenizer = EsmSequenceTokenizer()
+
+datasets = ["GB1", "TrpB4"]
+data_root_path = "/global/cfs/projectdirs/m4235/sebastian/data"
+
+for data in datasets:
+    print(data)
+    for i in range(num_replicates):
+        cfg_filename = f"./config.yaml"
+        cfg = OmegaConf.load(cfg_filename)
+        sampling_temperature=1
+        OmegaConf.update(cfg, "train.lightning_model_args.sampling_temperature", sampling_temperature)
+        mask_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["mask"]
+        bos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["bos"]
+        eos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["eos"]
+        pad_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["pad"]
+        
+        if not data.startswith("TrpB"):
+            df = pd.read_csv(f"{data_root_path}/{data}/scale2max/{data}.csv")
+            with open(f"{data_root_path}/{data}/{data}.fasta", "r") as file:
+                parent_sequence_decoded = file.readlines()[1].strip()
+                
+        else:
+            df = pd.read_csv(f"{data_root_path}/TrpB/scale2max/{data}.csv")
+            with open(f"{data_root_path}/TrpB/TrpB.fasta", "r") as file:
+                parent_sequence_decoded = file.readlines()[1].strip()
+                
+        if data != "GB1":        
+            muts = df["muts"].iloc[0]
+        else:
+            muts = df["muts"].iloc[100000]
+        
+        numbers = re.findall(r'\d+', muts)
+        mask_indices = list(map(int, numbers))
+        # mask_indices = [i-1 for i in mask_indices] #convert to 0-based indexing
+        
+        fitness_scores = []
+        
+# Load from base_model_{dataset_size}
+        trpb_base = torch.load(f"./{data}/base_model_{dataset_size}/trpb_post_rd_{campaign_number-1}_{i}.pt")
+        all_unmasked_sequences_decoded_base = trpb_base["all_unmasked_sequences_decoded"]
+        all_unmasked_sequences_base = trpb_base["all_unmasked_sequences"]
+        all_masked_sequences_base = trpb_base["all_masked_sequences"]
+        all_unmasked_sequences_base = all_unmasked_sequences_base.reshape(-1, all_unmasked_sequences_base.shape[-1])
+        all_logps_base = trpb_base["all_logps"]
+        
+        for unmasked_sequence_decoded, unmasked_sequence in zip(all_unmasked_sequences_decoded_base, all_unmasked_sequences_base):
+            index_residue_0 = unmasked_sequence_decoded[mask_indices[0]-1]
+            index_residue_1 = unmasked_sequence_decoded[mask_indices[1]-1]
+            index_residue_2 = unmasked_sequence_decoded[mask_indices[2]-1]
+            try:
+                index_residue_3 = unmasked_sequence_decoded[mask_indices[3]-1]
+                mutations = [index_residue_0, index_residue_1, index_residue_2, index_residue_3]
+                muts = ''.join(mutations)
+            except:
+                mutations = [index_residue_0, index_residue_1, index_residue_2]
+                muts = ''.join(mutations)
+            
+            df_filtered = df[df["AAs"] == muts]
+            
+            if len(df_filtered) == 0:
+                if torch.any((unmasked_sequence[1:-1] > 23) | (unmasked_sequence[1:-1] < 4)):
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+                else:
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+            else:
+                fitness_score = df_filtered["fitness"].values[0]
+            fitness_scores.append(fitness_score)
+        
+        # Load from aligned_0_{dataset_size}
+        trpb_aligned = torch.load(f"./{data}/aligned_{campaign_number-2}_{dataset_size}_{i}/trpb_post_rd_{campaign_number-1}_{i}.pt")
+        all_unmasked_sequences_decoded_aligned_0 = trpb_aligned["all_unmasked_sequences_decoded"]
+        all_unmasked_sequences_aligned_0 = trpb_aligned["all_unmasked_sequences"]
+        all_masked_sequences_aligned_0 = trpb_aligned["all_masked_sequences"]
+        all_unmasked_sequences_aligned_0 = all_unmasked_sequences_aligned_0.reshape(-1, all_unmasked_sequences_aligned_0.shape[-1])
+        all_logps_aligned_0 = trpb_aligned["all_logps"]
+        
+        for unmasked_sequence_decoded, unmasked_sequence in zip(all_unmasked_sequences_decoded_aligned_0, all_unmasked_sequences_aligned_0):
+            index_residue_0 = unmasked_sequence_decoded[mask_indices[0]-1]
+            index_residue_1 = unmasked_sequence_decoded[mask_indices[1]-1]
+            index_residue_2 = unmasked_sequence_decoded[mask_indices[2]-1]
+            try:
+                index_residue_3 = unmasked_sequence_decoded[mask_indices[3]-1]
+                mutations = [index_residue_0, index_residue_1, index_residue_2, index_residue_3]
+                muts = ''.join(mutations)
+            except:
+                mutations = [index_residue_0, index_residue_1, index_residue_2]
+                muts = ''.join(mutations)
+            
+            df_filtered = df[df["AAs"] == muts]
+            
+            if len(df_filtered) == 0:
+                if torch.any((unmasked_sequence[1:-1] > 23) | (unmasked_sequence[1:-1] < 4)):
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+                else:
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+            else:
+                fitness_score = df_filtered["fitness"].values[0]
+            fitness_scores.append(fitness_score)
+        
+        # Load from aligned_1_{dataset_size}
+        trpb_aligned_1 = torch.load(f"./{data}/aligned_{campaign_number-1}_{dataset_size}_{i}/trpb_{i}.pt")
+        all_unmasked_sequences_decoded_aligned_1 = trpb_aligned_1["all_unmasked_sequences_decoded"]
+        all_unmasked_sequences_aligned_1 = trpb_aligned_1["all_unmasked_sequences"]
+        all_masked_sequences_aligned_1 = trpb_aligned_1["all_masked_sequences"]
+        all_unmasked_sequences_aligned_1 = all_unmasked_sequences_aligned_1.reshape(-1, all_unmasked_sequences_aligned_1.shape[-1])
+        all_logps_aligned_1 = trpb_aligned_1["all_logps"]
+
+        for unmasked_sequence_decoded, unmasked_sequence in zip(all_unmasked_sequences_decoded_aligned_1, all_unmasked_sequences_aligned_1):
+            index_residue_0 = unmasked_sequence_decoded[mask_indices[0]-1]
+            index_residue_1 = unmasked_sequence_decoded[mask_indices[1]-1]
+            index_residue_2 = unmasked_sequence_decoded[mask_indices[2]-1]
+            try:
+                index_residue_3 = unmasked_sequence_decoded[mask_indices[3]-1]
+                mutations = [index_residue_0, index_residue_1, index_residue_2, index_residue_3]
+                muts = ''.join(mutations)
+            except:
+                mutations = [index_residue_0, index_residue_1, index_residue_2]
+                muts = ''.join(mutations)
+
+            df_filtered = df[df["AAs"] == muts]
+
+            if len(df_filtered) == 0:
+                if torch.any((unmasked_sequence[1:-1] > 23) | (unmasked_sequence[1:-1] < 4)):
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+                else:
+                    print(f"Invalid sequence {muts}")
+                    fitness_score = -2
+            else:
+                fitness_score = df_filtered["fitness"].values[0]
+            fitness_scores.append(fitness_score)
+            
+        # Concatenate the sequences and logps from all models
+        all_unmasked_sequences = torch.cat((all_unmasked_sequences_base, all_unmasked_sequences_aligned_0, all_unmasked_sequences_aligned_1),dim=0)
+        all_masked_sequences = torch.cat((all_masked_sequences_base, all_masked_sequences_aligned_0, all_masked_sequences_aligned_1),dim=0)
+        print(all_logps_base.shape, all_logps_aligned_0.shape, all_logps_aligned_1.shape)
+        all_logps = torch.cat((all_logps_base, all_logps_aligned_0, all_logps_aligned_1),dim=0)
+
+        all_fitness_scores = fitness_scores
+        
+        # Check for duplicates in all_unmasked_sequences
+        unique_sequences, counts = torch.unique(all_unmasked_sequences, dim=0, return_counts=True)
+        num_duplicates = torch.sum(counts > 1).item()
+        print(f"Number of duplicate sequences: {num_duplicates}")
+        
+        all_fitness_scores = np.array(all_fitness_scores)
+        all_fitness_scores = np.where(all_fitness_scores > 0, -np.log(all_fitness_scores), 10)
+        
+        sampling_temperature = 1 # hard-coding a sampling temperature of 1 for mixed-temperature alignment
+
+        sequence_length = all_unmasked_sequences.shape[1]
+
+        sequence_id = torch.ones((all_unmasked_sequences.shape[0], sequence_length), device=device).long() * 1
+
+        structure_tokens = torch.ones((1, sequence_length), device=device).long() * 4096
+        structure_tokens[:, 0] = 4098
+        structure_tokens[:, -1] = 4097
+
+        coords, residue_types = get_backbone_coords_from_local_pdb(f"{data_root_path}/{data}/{data}.pdb", chain_id='A', sequence_length=sequence_length-2, target=data) if not data.startswith("TrpB") else get_backbone_coords_from_local_pdb(f"{data_root_path}/TrpB/TrpB.pdb", chain_id='A', sequence_length=sequence_length-2, target=data)
+
+        # parent sequence sanity check
+        coords_trimmed = coords[:, 1:-1]  # shape: (1, N-2, 3, 3)
+
+        # Step 2: Determine mask of non-padding residues (i.e., not all coords are inf)
+        valid_mask = ~(torch.isinf(coords_trimmed).view(-1, 9).any(dim=1))  # shape: (N-2,)
+        residues_to_compare = [r for r, valid in zip(list(parent_sequence_decoded), valid_mask) if valid]
+
+        if residue_types != residues_to_compare:
+            print("Residue mismatch detected!")
+            for i, (ref, pdb) in enumerate(zip(residues_to_compare, residue_types)):
+                if ref != pdb:
+                    print(f"Position {i}: expected {ref}, got {pdb}")
+        else:
+            print("Residues match.")
+            print(coords.shape)
+
+        assert coords.shape[1] == sequence_length, f"Coords length {coords.shape[1]} does not match sequence length {sequence_length}"
+
+        average_plddt = torch.ones((1), device=device)
+
+        per_res_plddt = torch.zeros((1, sequence_length), device=device)
+        ss8_tokens = torch.zeros((1, sequence_length), device=device).long()
+        sasa_tokens = torch.zeros((1, sequence_length), device=device).long()
+
+        function_tokens = torch.zeros((1, sequence_length, 8), device=device).long()
+        residue_annotation_tokens = torch.zeros((1, sequence_length, 16), device=device).long()
+                
+        
+        with h5py.File(f"./{data}/alignment_dataset_{campaign_number}_{dataset_size}_from_ESM3_{i}.hdf5", "w") as f:
+            masked_sequence_tokens = f.create_dataset("masked_sequence_tokens", data=all_masked_sequences.cpu().numpy())
+            unmasked_sequence_tokens = f.create_dataset("unmasked_sequence_tokens", data=all_unmasked_sequences.cpu().numpy())
+            sequence_id = f.create_dataset("sequence_id", data=sequence_id.cpu().numpy())
+            structure_tokens = f.create_dataset("structural_tokens", data=structure_tokens.cpu().numpy())
+            coords = f.create_dataset("bb_coords", data=coords.cpu().numpy())
+            average_plddt = f.create_dataset("average_plddt", data=average_plddt.cpu().numpy())
+            per_res_plddt = f.create_dataset("per_res_plddt", data=per_res_plddt.cpu().numpy())
+            ss8_tokens = f.create_dataset("ss8_tokens", data=ss8_tokens.cpu().numpy())
+            sasa_tokens = f.create_dataset("sasa_tokens", data=sasa_tokens.cpu().numpy())
+            function_tokens = f.create_dataset("function_tokens", data=function_tokens.cpu().numpy())
+            residue_annotation_tokens = f.create_dataset("residue_annotation_tokens", data=residue_annotation_tokens.cpu().numpy())
+
+            ref_logps = f.create_dataset("ref_logps", data=all_logps.cpu().numpy())
+            energies = f.create_dataset("energies", data=all_fitness_scores)
+
+
+            f.attrs["num_prompts"] = 1
+            f.attrs["num_examples_per_prompt"] = masked_sequence_tokens.shape[0]
+            f.attrs["fixed_bb_coords"] = True
+            f.attrs["fixed_average_plddt"] = True
+            f.attrs["fixed_per_res_plddt"] = True
+            f.attrs["fixed_ss8_tokens"] = True
+            f.attrs["fixed_sasa_tokens"] = True
+            f.attrs["fixed_function_tokens"] = True
+            f.attrs["fixed_residue_annotation_tokens"] = True
+            f.attrs["fixed_structural_tokens"] = True
+            f.attrs["sampling_temperature"] = sampling_temperature
+    
diff --git a/iterative_alignment_experiment_structure/create_sbatch_scripts.sh b/iterative_alignment_experiment_structure/create_sbatch_scripts.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e8eedac2a97eb4454b2baf57b4d071923fba13ca
--- /dev/null
+++ b/iterative_alignment_experiment_structure/create_sbatch_scripts.sh
@@ -0,0 +1,55 @@
+#!/bin/bash
+
+# # Define the base directory containing subdirectories
+BASE_DIR="./lightning_logs_round_1"
+
+# # Get a sorted list of subdirectories
+subdirs=($(ls -d "$BASE_DIR"/*/ | sort))
+
+# Generate sbatch scripts
+for i in {0..9}; do
+    # Extract the version number (subdirectory name)
+    version_number=$(basename "${subdirs[$i]}")
+
+    # Define the sbatch filename
+    sbatch_filename="alignment_dataset_2_96_from_ESM3_${i}.sh"
+
+    # Write the sbatch file
+    cat <<EOL > "$sbatch_filename"
+#!/bin/bash
+#SBATCH -N 1
+#SBATCH -G 4
+#SBATCH -C gpu&hbm80g
+#SBATCH -q regular
+#SBATCH -t 12:00:00
+#SBATCH -A m4235
+#SBATCH -J alignment
+
+source /global/u2/s/si264/mambaforge/etc/profile.d/conda.sh
+conda activate proera
+
+pera_train \\
+    "train.lightning_model_args.eval_type=era" \\
+    "train.lightning_model_args.beta=10.0" \\
+    "train.lightning_model_args.gamma=0" \\
+    "train.trainer_args.devices=4" \\
+    "train.trainer_args.max_epochs=25" \\
+    "train.trainer_args.log_every_n_steps=1" \\
+    "train.trainer_args.enable_progress_bar=True" \\
+    "train.lightning_model_args.interval=epoch" \\
+    "train.lightning_model_args.monitor=val/ERALoss" \\
+    "train.lightning_model_args.lr_scheduler=ReduceLROnPlateau" \\
+    "++train.lightning_model_args.lr_scheduler_args.patience=5" \\
+    "train.lightning_model_args.optimizer=AdamW" \\
+    "train.lightning_model_args.optimizer_args.lr=1.0e-6" \\
+    "++train.lightning_model_args.optimizer_args.betas=[0.9,0.99]" \\
+    "++train.lightning_model_args.optimizer_args.weight_decay=0.01" \\
+    "train.lightning_model_args.on_step=false" \\
+    "global_args.dataset_filename=alignment_dataset_2_96_from_ESM3_${i}.hdf5" \\
+    "nn.batch_size=4" \\
+    "nn.load_model=$BASE_DIR/$version_number/checkpoints/best_model.ckpt" \\
+    "++nn.model_args.unified_transformer_args.ida_layer_indices=[]" \\
+EOL
+
+    echo "Created $sbatch_filename"
+done
diff --git a/iterative_alignment_experiment_structure/sample_esm.py b/iterative_alignment_experiment_structure/sample_esm.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b04cd6d7a0d1292728742b7b0c47fbf0e53c265
--- /dev/null
+++ b/iterative_alignment_experiment_structure/sample_esm.py
@@ -0,0 +1,294 @@
+import os
+import torch
+import numpy as np
+import pandas as pd
+import h5py
+import re
+from omegaconf import OmegaConf
+import h5py
+import lightning as L
+from pera.nn import BidirectionalModel, sample_components_from_bidirectional_transformer, sample_perturbations, sample_embedding_perturbations
+from esm.tokenization.sequence_tokenizer import EsmSequenceTokenizer
+from Bio.Seq import Seq
+from Bio.PDB import PDBList, PDBParser, is_aa
+
+device = torch.device("cuda:0")
+
+# Optional: map 3-letter residue names to 1-letter codes
+three_to_one = {
+    'ALA': 'A', 'ARG': 'R', 'ASN': 'N', 'ASP': 'D',
+    'CYS': 'C', 'GLN': 'Q', 'GLU': 'E', 'GLY': 'G',
+    'HIS': 'H', 'ILE': 'I', 'LEU': 'L', 'LYS': 'K',
+    'MET': 'M', 'PHE': 'F', 'PRO': 'P', 'SER': 'S',
+    'THR': 'T', 'TRP': 'W', 'TYR': 'Y', 'VAL': 'V',
+    'SEC': 'U', 'PYL': 'O', 'ASX': 'B', 'GLX': 'Z',
+    'XLE': 'J', 'UNK': 'X'
+}
+
+def get_backbone_coords_from_local_pdb(pdb_path, chain_id='A', sequence_length=None, target="data", device=device):
+    """
+    Load backbone coordinates and residue types from a local PDB file.
+
+    Returns:
+        coords_tensor: torch.Tensor of shape (1, N, 3, 3)
+        residue_types: List of one-letter residue codes
+    """
+    parser = PDBParser(QUIET=True)
+    structure = parser.get_structure("local_structure", pdb_path)
+
+    coords = []
+    residue_types = []
+    model = structure[0]
+
+    if chain_id not in model:
+        raise ValueError(f"Chain {chain_id} not found in {pdb_path}")
+
+    chain = model[chain_id]
+
+    for residue in chain:
+        if sequence_length is not None and len(coords) >= sequence_length:
+            break
+        if not is_aa(residue):
+            continue
+        try:
+            n = residue['N'].get_coord()
+            ca = residue['CA'].get_coord()
+            c = residue['C'].get_coord()
+            coords.append([n, ca, c])
+            resname = residue.get_resname().upper()
+            residue_types.append(three_to_one.get(resname, 'X'))  # default to 'X' if unknown
+        except KeyError:
+            continue
+
+    if not coords:
+        raise ValueError("No residues with complete backbone atoms found.")
+
+    # Add infinity-padding before and after
+    pad = [[float('inf')]*3, [float('inf')]*3, [float('inf')]*3]
+    coords.insert(0, pad)
+    coords.append(pad)
+
+    if target == "ParD2":
+        coords = [pad, pad] + coords + [pad, pad]
+    elif target == "ParD3":
+        coords = [pad]*2 + coords + [pad]*6
+    elif target == "TrpB4":
+        coords = [pad] + coords
+
+    coords_tensor = torch.tensor(coords, device=device).unsqueeze(0)  # (1, N, 3, 3)
+
+    return coords_tensor, residue_types
+
+sequence_tokenizer = EsmSequenceTokenizer()
+
+import argparse
+
+# set up parser
+parser = parser = argparse.ArgumentParser(description="Calculating the log-likelihood of a sequence")
+parser.add_argument('--target', type=str, required=True, help='Dataset as a string')
+parser.add_argument('--num_samples', type=int, required=False, default=384, help='Number of samples to process (default: 100000)')
+parser.add_argument('--alignment_round', type=int, required=False, default=1, help='Alignment round as an integer')
+parser.add_argument('--version_number', type=str, required=False, default=1, help='Version number as a string')
+parser.add_argument('--replicate', type=int, required=False, default=1, help='Replicate number as an integer')
+args = parser.parse_args()
+
+target = args.target
+alignment_round = args.alignment_round
+version_number = args.version_number
+num_samples = args.num_samples
+replicate = args.replicate
+
+cfg_filename = f"{target}/lightning_logs/{version_number}/config.yaml"
+network_filename = f"{target}/lightning_logs/{version_number}/checkpoints/best_model.ckpt"
+save_folder_name = f"{target}/aligned_{alignment_round}_{num_samples}_{replicate}"
+
+cfg = OmegaConf.load(cfg_filename)
+sampling_temperature=1
+OmegaConf.update(cfg, "train.lightning_model_args.sampling_temperature", sampling_temperature)
+esm_model = BidirectionalModel(cfg["nn"]["model"], 
+                                cfg["nn"]["model_args"],
+                                **cfg["train"]["lightning_model_args"]).to(device)
+esm_model.load_model_from_ckpt(network_filename)
+esm_model.eval()
+print("")
+mask_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["mask"]
+bos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["bos"]
+eos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["eos"]
+pad_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["pad"]
+
+
+os.makedirs(save_folder_name, exist_ok=True)
+
+past_generations =[f"{target}/base_model_{num_samples}"]
+for i in range(alignment_round):
+    past_generations.append(f"{target}/aligned_{i}_{num_samples}_{replicate}")
+
+previous_unmasked_sequences_decoded = []
+
+for round in past_generations:
+    trpb = torch.load(f"{round}/trpb_{replicate}.pt")
+    previous_unmasked_sequences_decoded.extend(trpb['all_unmasked_sequences_decoded'])
+    
+assert len(previous_unmasked_sequences_decoded) == len(set(previous_unmasked_sequences_decoded)), "There are duplicate sequences in previous_unmasked_sequences_decoded"
+print("All elements in previous_unmasked_sequences_decoded are unique.")
+
+data = target #  "GB1", "ParD2", "TEV", "TrpB3F", "TrpB3I", "TrpB4"
+data_root_path = "/global/cfs/cdirs/m4235/sebastian/data"
+
+sequence_tokenizer = EsmSequenceTokenizer()
+
+if data.startswith("TrpB"):
+    df = pd.read_csv(f"{data_root_path}/TrpB/scale2max/{data}.csv")
+    with open(f"{data_root_path}/TrpB/TrpB.fasta", "r") as file:
+        parent_sequence_decoded = file.readlines()[1].strip()
+    
+elif data == "DHFR":
+    df = pd.read_csv(f"{data_root_path}/{data}/scale2max/{data}.csv")
+    with open(f"{data_root_path}/{data}/{data}.fasta", "r") as file:
+        nucleotide_seq = file.readlines()[1].strip()
+    nucleotide_seq = Seq(nucleotide_seq)
+    parent_sequence_decoded = str(nucleotide_seq.translate())  # Translate to amino acid sequence
+    
+else:
+    df = pd.read_csv(f"{data_root_path}/{data}/scale2max/{data}.csv")
+    with open(f"{data_root_path}/{data}/{data}.fasta", "r") as file:
+        parent_sequence_decoded = file.readlines()[1].strip()
+        
+if data != "GB1":        
+    muts = df["muts"].iloc[0]
+else:
+    muts = df["muts"].iloc[100000]
+
+numbers = re.findall(r'\d+', muts)
+mask_indices = list(map(int, numbers))
+num_masks_per_sequence = num_samples // 4
+num_to_generate_per_mask = 4
+
+
+parent_sequence = torch.tensor(sequence_tokenizer.encode(parent_sequence_decoded, 
+                                                            add_special_tokens=True), device=device).unsqueeze(0).long()
+sequence_length = parent_sequence.shape[1]
+
+
+all_masked_sequences = []
+all_unmasked_sequences_decoded = []
+all_unmasked_sequences = []
+all_logps = []
+
+
+while len(all_unmasked_sequences_decoded) < num_samples:
+    
+    print(len(all_unmasked_sequences_decoded))
+
+    masked_sequences = parent_sequence.clone().repeat(num_to_generate_per_mask, 1)
+    masked_sequences[:, mask_indices] = mask_token_sequence
+
+
+
+
+    
+    sequence_id = torch.ones((num_to_generate_per_mask, sequence_length), device=device).long() * 1
+    
+    structure_tokens = torch.ones((num_to_generate_per_mask, sequence_length), device=device).long() * 4096
+    structure_tokens[:, 0] = 4098
+    structure_tokens[:, -1] = 4097
+
+    coords, residue_types = get_backbone_coords_from_local_pdb(f"{data_root_path}/{data}/{data}.pdb", chain_id='A', sequence_length=sequence_length-2, target=data) if not data.startswith("TrpB") else get_backbone_coords_from_local_pdb(f"{data_root_path}/TrpB/TrpB.pdb", chain_id='A', sequence_length=sequence_length-2, target=data)
+
+    # parent sequence sanity check
+    coords_trimmed = coords[:, 1:-1]  # shape: (1, N-2, 3, 3)
+
+    # Step 2: Determine mask of non-padding residues (i.e., not all coords are inf)
+    valid_mask = ~(torch.isinf(coords_trimmed).view(-1, 9).any(dim=1))  # shape: (N-2,)
+    residues_to_compare = [r for r, valid in zip(list(parent_sequence_decoded), valid_mask) if valid]
+
+    if residue_types != residues_to_compare:
+        print("Residue mismatch detected!")
+        for i, (ref, pdb) in enumerate(zip(residues_to_compare, residue_types)):
+            if ref != pdb:
+                print(f"Position {i}: expected {ref}, got {pdb}")
+    else:
+        print("Residues match.")
+        print(coords.shape)
+
+    assert coords.shape[1] == sequence_length, f"Coords length {coords.shape[1]} does not match sequence length {sequence_length}"
+
+    # Repeat the coords tensor to match the batch size (num_to_generate_per_mask)
+    coords = coords.repeat(num_to_generate_per_mask, 1, 1, 1)  # Shape becomes (num_to_generate_per_mask, sequence_length, 3, 3)
+
+    average_plddt = torch.ones((num_to_generate_per_mask), device=device)
+
+    per_res_plddt = torch.zeros((num_to_generate_per_mask, sequence_length), device=device)
+    ss8_tokens = torch.zeros((num_to_generate_per_mask, sequence_length), device=device).long()
+    sasa_tokens = torch.zeros((num_to_generate_per_mask, sequence_length), device=device).long()
+
+    function_tokens = torch.zeros((num_to_generate_per_mask, sequence_length, 8), device=device).long()
+    residue_annotation_tokens = torch.zeros((num_to_generate_per_mask, sequence_length, 16), device=device).long()
+
+
+
+
+    with torch.no_grad():
+        unmasked_sequences = sample_components_from_bidirectional_transformer(transformer_model=esm_model,
+                                                                                masked_sequence_tokens=masked_sequences,
+                                                                                structure_tokens=structure_tokens,
+                                                                                average_plddt=average_plddt,
+                                                                                per_res_plddt=per_res_plddt,
+                                                                                ss8_tokens=ss8_tokens,
+                                                                                sasa_tokens=sasa_tokens,
+                                                                                function_tokens=function_tokens,
+                                                                                residue_annotation_tokens=residue_annotation_tokens,
+                                                                                bb_coords=coords,
+                                                                                sequence_id=sequence_id,
+                                                                                mask_token_sequence=mask_token_sequence,
+                                                                                bos_token_sequence=bos_token_sequence,
+                                                                                eos_token_sequence=eos_token_sequence,
+                                                                                pad_token_sequence=pad_token_sequence,
+                                                                                inference_batch_size=1)
+
+
+        
+        masked_indices = (masked_sequences == mask_token_sequence).float()
+        logits = esm_model.nn(sequence_tokens=masked_sequences,
+                                structure_tokens=structure_tokens,
+                                average_plddt=average_plddt,
+                                per_res_plddt=per_res_plddt,
+                                ss8_tokens=ss8_tokens,
+                                sasa_tokens=sasa_tokens,
+                                function_tokens=function_tokens,
+                                residue_annotation_tokens=residue_annotation_tokens,
+                                sequence_id=sequence_id,
+                                bb_coords=coords)["sequence_logits"].detach()
+        logps = torch.nn.functional.log_softmax(logits/sampling_temperature, dim=-1)
+        logps = torch.gather(logps, dim=-1, index=unmasked_sequences.unsqueeze(-1)).squeeze(-1)
+        logps = (logps * masked_indices).sum(-1).detach()
+        
+        decoded_seqs = [sequence.replace(" ", "") for sequence in sequence_tokenizer.batch_decode(unmasked_sequences[:, 1:-1])]
+        for seq, logp, masked_seq, unmasked_seq in zip(decoded_seqs, logps, masked_sequences, unmasked_sequences):
+            if seq in all_unmasked_sequences_decoded or seq in previous_unmasked_sequences_decoded:
+                continue
+            else:
+                all_unmasked_sequences_decoded.append(seq)
+                all_logps.append(logp)
+                all_masked_sequences.append(masked_seq)
+                all_unmasked_sequences.append(unmasked_seq)
+
+
+
+all_unmasked_sequences_decoded = all_unmasked_sequences_decoded[:num_samples]
+all_masked_sequences = all_masked_sequences[:num_samples]
+all_unmasked_sequences = all_unmasked_sequences[:num_samples]
+all_logps = all_logps[:num_samples]
+    
+all_masked_sequences = torch.stack(all_masked_sequences, dim=0)
+all_unmasked_sequences = torch.stack(all_unmasked_sequences, dim=0)
+all_logps = torch.stack(all_logps, dim=0)
+
+
+
+to_save = {"parent_sequence": parent_sequence,
+            "all_masked_sequences": all_masked_sequences,
+            "all_unmasked_sequences": all_unmasked_sequences,
+            "all_unmasked_sequences_decoded": all_unmasked_sequences_decoded,
+            "all_logps": all_logps}
+torch.save(to_save, f"{save_folder_name}/trpb_{replicate}.pt")
\ No newline at end of file
diff --git a/iterative_alignment_experiment_structure/sample_esm_first_round.py b/iterative_alignment_experiment_structure/sample_esm_first_round.py
new file mode 100644
index 0000000000000000000000000000000000000000..f944658b4fdcebcc02ef315cc91ac5d5dd2dfcb9
--- /dev/null
+++ b/iterative_alignment_experiment_structure/sample_esm_first_round.py
@@ -0,0 +1,278 @@
+import os
+import torch
+import numpy as np
+import pandas as pd
+import h5py
+import re
+from omegaconf import OmegaConf
+import h5py
+import lightning as L
+from pera.nn import BidirectionalModel, sample_components_from_bidirectional_transformer, sample_perturbations, sample_embedding_perturbations
+from esm.tokenization.sequence_tokenizer import EsmSequenceTokenizer
+from Bio.Seq import Seq
+from Bio.PDB import PDBList, PDBParser, is_aa
+
+device = torch.device("cuda:0")
+
+# Optional: map 3-letter residue names to 1-letter codes
+three_to_one = {
+    'ALA': 'A', 'ARG': 'R', 'ASN': 'N', 'ASP': 'D',
+    'CYS': 'C', 'GLN': 'Q', 'GLU': 'E', 'GLY': 'G',
+    'HIS': 'H', 'ILE': 'I', 'LEU': 'L', 'LYS': 'K',
+    'MET': 'M', 'PHE': 'F', 'PRO': 'P', 'SER': 'S',
+    'THR': 'T', 'TRP': 'W', 'TYR': 'Y', 'VAL': 'V',
+    'SEC': 'U', 'PYL': 'O', 'ASX': 'B', 'GLX': 'Z',
+    'XLE': 'J', 'UNK': 'X'
+}
+
+def get_backbone_coords_from_local_pdb(pdb_path, chain_id='A', sequence_length=None, target="data", device=device):
+    """
+    Load backbone coordinates and residue types from a local PDB file.
+
+    Returns:
+        coords_tensor: torch.Tensor of shape (1, N, 3, 3)
+        residue_types: List of one-letter residue codes
+    """
+    parser = PDBParser(QUIET=True)
+    structure = parser.get_structure("local_structure", pdb_path)
+
+    coords = []
+    residue_types = []
+    model = structure[0]
+
+    if chain_id not in model:
+        raise ValueError(f"Chain {chain_id} not found in {pdb_path}")
+
+    chain = model[chain_id]
+
+    for residue in chain:
+        if sequence_length is not None and len(coords) >= sequence_length:
+            break
+        if not is_aa(residue):
+            continue
+        try:
+            n = residue['N'].get_coord()
+            ca = residue['CA'].get_coord()
+            c = residue['C'].get_coord()
+            coords.append([n, ca, c])
+            resname = residue.get_resname().upper()
+            residue_types.append(three_to_one.get(resname, 'X'))  # default to 'X' if unknown
+        except KeyError:
+            continue
+
+    if not coords:
+        raise ValueError("No residues with complete backbone atoms found.")
+
+    # Add infinity-padding before and after
+    pad = [[float('inf')]*3, [float('inf')]*3, [float('inf')]*3]
+    coords.insert(0, pad)
+    coords.append(pad)
+
+    if target == "ParD2":
+        coords = [pad, pad] + coords + [pad, pad]
+    elif target == "ParD3":
+        coords = [pad]*2 + coords + [pad]*6
+    elif target == "TrpB4":
+        coords = [pad] + coords
+
+    coords_tensor = torch.tensor(coords, device=device).unsqueeze(0)  # (1, N, 3, 3)
+
+    return coords_tensor, residue_types
+
+sequence_tokenizer = EsmSequenceTokenizer()
+
+import argparse
+
+# set up parser
+parser = parser = argparse.ArgumentParser(description="Calculating the log-likelihood of a sequence")
+parser.add_argument('--target', type=str, required=True, help='Dataset as a string')
+parser.add_argument('--num_samples', type=int, required=False, default=384, help='Number of samples to process (default: 100000)')
+parser.add_argument('--version_number', type=str, required=False, default=1, help='Version number as a string')
+parser.add_argument('--replicate', type=int, required=False, default=1, help='Replicate number as an integer')
+args = parser.parse_args()
+
+target = args.target
+num_samples = args.num_samples
+replicate = args.replicate
+
+cfg_filename = "./config.yaml"
+network_filename = "/global/cfs/cdirs/m4235/sebastian/models/esm3clm/esm3_clm.pt"
+save_folder_name = f"{target}/base_model_{num_samples}"
+
+cfg = OmegaConf.load(cfg_filename)
+sampling_temperature=1
+OmegaConf.update(cfg, "train.lightning_model_args.sampling_temperature", sampling_temperature)
+esm_model = BidirectionalModel(cfg["nn"]["model"], 
+                                cfg["nn"]["model_args"],
+                                **cfg["train"]["lightning_model_args"]).to(device)
+esm_model.load_model_from_ckpt(network_filename)
+esm_model.eval()
+print("")
+mask_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["mask"]
+bos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["bos"]
+eos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["eos"]
+pad_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["pad"]
+
+
+os.makedirs(save_folder_name, exist_ok=True)
+
+data = target #  "GB1", "ParD2", "TEV", "TrpB3F", "TrpB3I", "TrpB4"
+data_root_path = "/global/cfs/projectdirs/m4235/sebastian/data"
+
+sequence_tokenizer = EsmSequenceTokenizer()
+
+if data.startswith("TrpB"):
+    df = pd.read_csv(f"{data_root_path}/TrpB/scale2max/{data}.csv")
+    with open(f"{data_root_path}/TrpB/TrpB.fasta", "r") as file:
+        parent_sequence_decoded = file.readlines()[1].strip()
+    
+elif data == "DHFR":
+    df = pd.read_csv(f"{data_root_path}/{data}/scale2max/{data}.csv")
+    with open(f"{data_root_path}/{data}/{data}.fasta", "r") as file:
+        nucleotide_seq = file.readlines()[1].strip()
+    nucleotide_seq = Seq(nucleotide_seq)
+    parent_sequence_decoded = str(nucleotide_seq.translate())  # Translate to amino acid sequence
+    
+else:
+    df = pd.read_csv(f"{data_root_path}/{data}/scale2max/{data}.csv")
+    with open(f"{data_root_path}/{data}/{data}.fasta", "r") as file:
+        parent_sequence_decoded = file.readlines()[1].strip()
+        
+if data != "GB1":        
+    muts = df["muts"].iloc[0]
+else:
+    muts = df["muts"].iloc[100000]
+
+numbers = re.findall(r'\d+', muts)
+mask_indices = list(map(int, numbers))
+num_masks_per_sequence = num_samples // 4
+num_to_generate_per_mask = 4
+
+
+parent_sequence = torch.tensor(sequence_tokenizer.encode(parent_sequence_decoded, 
+                                                            add_special_tokens=True), device=device).unsqueeze(0).long()
+sequence_length = parent_sequence.shape[1]
+
+
+all_masked_sequences = []
+all_unmasked_sequences_decoded = []
+all_unmasked_sequences = []
+all_logps = []
+
+
+while len(all_unmasked_sequences_decoded) < num_samples:
+    
+    print(len(all_unmasked_sequences_decoded))
+
+    masked_sequences = parent_sequence.clone().repeat(num_to_generate_per_mask, 1)
+    masked_sequences[:, mask_indices] = mask_token_sequence
+
+
+
+
+    
+    sequence_id = torch.ones((num_to_generate_per_mask, sequence_length), device=device).long() * 1
+    
+    structure_tokens = torch.ones((num_to_generate_per_mask, sequence_length), device=device).long() * 4096
+    structure_tokens[:, 0] = 4098
+    structure_tokens[:, -1] = 4097
+
+    coords, residue_types = get_backbone_coords_from_local_pdb(f"{data_root_path}/{data}/{data}.pdb", chain_id='A', sequence_length=sequence_length-2, target=data) if not data.startswith("TrpB") else get_backbone_coords_from_local_pdb(f"{data_root_path}/TrpB/TrpB.pdb", chain_id='A', sequence_length=sequence_length-2, target=data)
+
+    # parent sequence sanity check
+    coords_trimmed = coords[:, 1:-1]  # shape: (1, N-2, 3, 3)
+
+    # Step 2: Determine mask of non-padding residues (i.e., not all coords are inf)
+    valid_mask = ~(torch.isinf(coords_trimmed).view(-1, 9).any(dim=1))  # shape: (N-2,)
+    residues_to_compare = [r for r, valid in zip(list(parent_sequence_decoded), valid_mask) if valid]
+
+    if residue_types != residues_to_compare:
+        print("Residue mismatch detected!")
+        for i, (ref, pdb) in enumerate(zip(residues_to_compare, residue_types)):
+            if ref != pdb:
+                print(f"Position {i}: expected {ref}, got {pdb}")
+    else:
+        print("Residues match.")
+        print(coords.shape)
+
+    assert coords.shape[1] == sequence_length, f"Coords length {coords.shape[1]} does not match sequence length {sequence_length}"
+
+    # Repeat the coords tensor to match the batch size (num_to_generate_per_mask)
+    coords = coords.repeat(num_to_generate_per_mask, 1, 1, 1)  # Shape becomes (num_to_generate_per_mask, sequence_length, 3, 3)
+
+    average_plddt = torch.ones((num_to_generate_per_mask), device=device)
+
+    per_res_plddt = torch.zeros((num_to_generate_per_mask, sequence_length), device=device)
+    ss8_tokens = torch.zeros((num_to_generate_per_mask, sequence_length), device=device).long()
+    sasa_tokens = torch.zeros((num_to_generate_per_mask, sequence_length), device=device).long()
+
+    function_tokens = torch.zeros((num_to_generate_per_mask, sequence_length, 8), device=device).long()
+    residue_annotation_tokens = torch.zeros((num_to_generate_per_mask, sequence_length, 16), device=device).long()
+
+
+
+
+    with torch.no_grad():
+        unmasked_sequences = sample_components_from_bidirectional_transformer(transformer_model=esm_model,
+                                                                                masked_sequence_tokens=masked_sequences,
+                                                                                structure_tokens=structure_tokens,
+                                                                                average_plddt=average_plddt,
+                                                                                per_res_plddt=per_res_plddt,
+                                                                                ss8_tokens=ss8_tokens,
+                                                                                sasa_tokens=sasa_tokens,
+                                                                                function_tokens=function_tokens,
+                                                                                residue_annotation_tokens=residue_annotation_tokens,
+                                                                                bb_coords=coords,
+                                                                                sequence_id=sequence_id,
+                                                                                mask_token_sequence=mask_token_sequence,
+                                                                                bos_token_sequence=bos_token_sequence,
+                                                                                eos_token_sequence=eos_token_sequence,
+                                                                                pad_token_sequence=pad_token_sequence,
+                                                                                inference_batch_size=1)
+
+
+        
+        masked_indices = (masked_sequences == mask_token_sequence).float()
+        logits = esm_model.nn(sequence_tokens=masked_sequences,
+                                structure_tokens=structure_tokens,
+                                average_plddt=average_plddt,
+                                per_res_plddt=per_res_plddt,
+                                ss8_tokens=ss8_tokens,
+                                sasa_tokens=sasa_tokens,
+                                function_tokens=function_tokens,
+                                residue_annotation_tokens=residue_annotation_tokens,
+                                sequence_id=sequence_id,
+                                bb_coords=coords)["sequence_logits"].detach()
+        logps = torch.nn.functional.log_softmax(logits/sampling_temperature, dim=-1)
+        logps = torch.gather(logps, dim=-1, index=unmasked_sequences.unsqueeze(-1)).squeeze(-1)
+        logps = (logps * masked_indices).sum(-1).detach()
+        
+        decoded_seqs = [sequence.replace(" ", "") for sequence in sequence_tokenizer.batch_decode(unmasked_sequences[:, 1:-1])]
+        for seq, logp, masked_seq, unmasked_seq in zip(decoded_seqs, logps, masked_sequences, unmasked_sequences):
+            if seq in all_unmasked_sequences_decoded:
+                continue
+            else:
+                all_unmasked_sequences_decoded.append(seq)
+                all_logps.append(logp)
+                all_masked_sequences.append(masked_seq)
+                all_unmasked_sequences.append(unmasked_seq)
+
+
+
+all_unmasked_sequences_decoded = all_unmasked_sequences_decoded[:num_samples]
+all_masked_sequences = all_masked_sequences[:num_samples]
+all_unmasked_sequences = all_unmasked_sequences[:num_samples]
+all_logps = all_logps[:num_samples]
+    
+all_masked_sequences = torch.stack(all_masked_sequences, dim=0)
+all_unmasked_sequences = torch.stack(all_unmasked_sequences, dim=0)
+all_logps = torch.stack(all_logps, dim=0)
+
+
+
+to_save = {"parent_sequence": parent_sequence,
+            "all_masked_sequences": all_masked_sequences,
+            "all_unmasked_sequences": all_unmasked_sequences,
+            "all_unmasked_sequences_decoded": all_unmasked_sequences_decoded,
+            "all_logps": all_logps}
+torch.save(to_save, f"{save_folder_name}/trpb_{replicate}.pt")
\ No newline at end of file
diff --git a/iterative_alignment_experiment_structure/submit_sampling.sh b/iterative_alignment_experiment_structure/submit_sampling.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d7155f5c86a529d1e1c6f5a0dcd26491af4418f5
--- /dev/null
+++ b/iterative_alignment_experiment_structure/submit_sampling.sh
@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH -N 1
+#SBATCH -G 4
+#SBATCH -C gpu&hbm80g
+#SBATCH -q regular
+#SBATCH -t 01:00:00
+#SBATCH -A m4235
+#SBATCH -J sampling
+
+source /global/u2/s/si264/mambaforge/etc/profile.d/conda.sh
+conda activate proera
+
+target="TrpB4"
+
+BASE_DIR="./$target/lightning_logs"
+
+# Get a sorted list of subdirectories
+subdirs=($(ls -d "$BASE_DIR"/*/ | sort))
+
+# Run the script 10 times
+for i in {0..9}; do
+    # Extract the version number (subdirectory name)
+    version_number=$(basename "${subdirs[$i]}")
+
+    # Run the Python script with the specified arguments
+    python sample_esm.py \
+        --target $target \
+        --num_samples 96 \
+        --alignment_round 3 \
+        --version_number "$version_number" \
+        --replicate "$i"
+done
\ No newline at end of file
diff --git a/iterative_alignment_experiment_structure/submit_sampling_first_round.sh b/iterative_alignment_experiment_structure/submit_sampling_first_round.sh
new file mode 100644
index 0000000000000000000000000000000000000000..cd150c3e68c79d53dba144690ff7e341e67b02d5
--- /dev/null
+++ b/iterative_alignment_experiment_structure/submit_sampling_first_round.sh
@@ -0,0 +1,23 @@
+#!/bin/bash
+#SBATCH -N 1
+#SBATCH -G 4
+#SBATCH -C gpu&hbm80g
+#SBATCH -q regular
+#SBATCH -t 12:00:00
+#SBATCH -A m4235
+#SBATCH -J sampling
+
+source /global/u2/s/si264/mambaforge/etc/profile.d/conda.sh
+conda activate proera
+
+target="TrpB4"
+
+# Run the script 10 times
+for i in {0..9}; do
+    echo "Running iteration $i for target $target"
+    # Run the Python script with the specified arguments
+    python sample_esm_first_round.py \
+        --target $target \
+        --num_samples 96 \
+        --replicate "$i"
+done
\ No newline at end of file
diff --git a/iterative_alignment_experiment_structure/submit_updating.sh b/iterative_alignment_experiment_structure/submit_updating.sh
new file mode 100644
index 0000000000000000000000000000000000000000..fbeaf68ea0571dc718aba802c5bf9bf6950d8a5c
--- /dev/null
+++ b/iterative_alignment_experiment_structure/submit_updating.sh
@@ -0,0 +1,36 @@
+#!/bin/bash
+#SBATCH -N 1
+#SBATCH -G 4
+#SBATCH -C gpu&hbm80g
+#SBATCH -q regular
+#SBATCH -t 01:00:00
+#SBATCH -A m4235
+#SBATCH -J sampling
+
+source /global/u2/s/si264/mambaforge/etc/profile.d/conda.sh
+conda activate proera
+
+# Define the directory containing subdirectories
+target="TrpB4"  # Replace with your actual target string if needed
+BASE_DIR="./$target/lightning_logs"
+
+# Get a sorted list of subdirectories
+subdirs=($(ls -d "$BASE_DIR"/*/ | sort))
+
+replicates=(0 1 2 3 4 5 6 7 8 9)
+
+# Run the script 10 times
+for i in {0..9}; do
+    # Extract the version number (subdirectory name)
+    version_number=$(basename "${subdirs[$i]}")
+
+    replicate="${replicates[$i]}"
+
+    # Run the Python script with the specified arguments
+    python compute_updated_logps.py \
+        --target $target \
+        --num_samples 96 \
+        --alignment_round 2 \
+        --version_number "$version_number" \
+        --replicate "$replicate"
+done
\ No newline at end of file