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import numpy as np
from omegaconf import DictConfig, OmegaConf
from rfdiffusion.RoseTTAFoldModel import RoseTTAFoldModule
from rfdiffusion.kinematics import get_init_xyz, xyz_to_t2d
from rfdiffusion.diffusion import Diffuser
from rfdiffusion.chemical import seq2chars
from rfdiffusion.util_module import ComputeAllAtomCoords
from rfdiffusion.contigs import ContigMap
from rfdiffusion.inference import utils as iu, symmetry
from rfdiffusion.potentials.manager import PotentialManager
import logging
import torch.nn.functional as nn
from rfdiffusion import util
from hydra.core.hydra_config import HydraConfig
import os
from rfdiffusion.model_input_logger import pickle_function_call
import sys
SCRIPT_DIR=os.path.dirname(os.path.realpath(__file__))
TOR_INDICES = util.torsion_indices
TOR_CAN_FLIP = util.torsion_can_flip
REF_ANGLES = util.reference_angles
class Sampler:
def __init__(self, conf: DictConfig):
"""
Initialize sampler.
Args:
conf: Configuration.
"""
self.initialized = False
self.initialize(conf)
def initialize(self, conf: DictConfig) -> None:
"""
Initialize sampler.
Args:
conf: Configuration
- Selects appropriate model from input
- Assembles Config from model checkpoint and command line overrides
"""
self._log = logging.getLogger(__name__)
if torch.cuda.is_available():
self.device = torch.device('cuda')
else:
self.device = torch.device('cpu')
needs_model_reload = not self.initialized or conf.inference.ckpt_override_path != self._conf.inference.ckpt_override_path
# Assign config to Sampler
self._conf = conf
################################
### Select Appropriate Model ###
################################
if conf.inference.model_directory_path is not None:
model_directory = conf.inference.model_directory_path
else:
model_directory = f"{SCRIPT_DIR}/../../models"
print(f"Reading models from {model_directory}")
# Initialize inference only helper objects to Sampler
if conf.inference.ckpt_override_path is not None:
self.ckpt_path = conf.inference.ckpt_override_path
print("WARNING: You're overriding the checkpoint path from the defaults. Check that the model you're providing can run with the inputs you're providing.")
else:
if conf.contigmap.inpaint_seq is not None or conf.contigmap.provide_seq is not None:
# use model trained for inpaint_seq
if conf.contigmap.provide_seq is not None:
# this is only used for partial diffusion
assert conf.diffuser.partial_T is not None, "The provide_seq input is specifically for partial diffusion"
if conf.scaffoldguided.scaffoldguided:
self.ckpt_path = f'{model_directory}/InpaintSeq_Fold_ckpt.pt'
else:
self.ckpt_path = f'{model_directory}/InpaintSeq_ckpt.pt'
elif conf.ppi.hotspot_res is not None and conf.scaffoldguided.scaffoldguided is False:
# use complex trained model
self.ckpt_path = f'{model_directory}/Complex_base_ckpt.pt'
elif conf.scaffoldguided.scaffoldguided is True:
# use complex and secondary structure-guided model
self.ckpt_path = f'{model_directory}/Complex_Fold_base_ckpt.pt'
else:
# use default model
self.ckpt_path = f'{model_directory}/Base_ckpt.pt'
# for saving in trb file:
assert self._conf.inference.trb_save_ckpt_path is None, "trb_save_ckpt_path is not the place to specify an input model. Specify in inference.ckpt_override_path"
self._conf['inference']['trb_save_ckpt_path']=self.ckpt_path
#######################
### Assemble Config ###
#######################
if needs_model_reload:
# Load checkpoint, so that we can assemble the config
self.load_checkpoint()
self.assemble_config_from_chk()
# Now actually load the model weights into RF
self.model = self.load_model()
else:
self.assemble_config_from_chk()
# self.initialize_sampler(conf)
self.initialized=True
# Initialize helper objects
self.inf_conf = self._conf.inference
self.contig_conf = self._conf.contigmap
self.denoiser_conf = self._conf.denoiser
self.ppi_conf = self._conf.ppi
self.potential_conf = self._conf.potentials
self.diffuser_conf = self._conf.diffuser
self.preprocess_conf = self._conf.preprocess
if conf.inference.schedule_directory_path is not None:
schedule_directory = conf.inference.schedule_directory_path
else:
schedule_directory = f"{SCRIPT_DIR}/../../schedules"
# Check for cache schedule
if not os.path.exists(schedule_directory):
os.mkdir(schedule_directory)
self.diffuser = Diffuser(**self._conf.diffuser, cache_dir=schedule_directory)
###########################
### Initialise Symmetry ###
###########################
if self.inf_conf.symmetry is not None:
self.symmetry = symmetry.SymGen(
self.inf_conf.symmetry,
self.inf_conf.recenter,
self.inf_conf.radius,
self.inf_conf.model_only_neighbors,
)
else:
self.symmetry = None
self.allatom = ComputeAllAtomCoords().to(self.device)
if self.inf_conf.input_pdb is None:
# set default pdb
script_dir=os.path.dirname(os.path.realpath(__file__))
self.inf_conf.input_pdb=os.path.join(script_dir, '../../examples/input_pdbs/1qys.pdb')
self.target_feats = iu.process_target(self.inf_conf.input_pdb, parse_hetatom=True, center=False)
self.chain_idx = None
##############################
### Handle Partial Noising ###
##############################
if self.diffuser_conf.partial_T:
assert self.diffuser_conf.partial_T <= self.diffuser_conf.T
self.t_step_input = int(self.diffuser_conf.partial_T)
else:
self.t_step_input = int(self.diffuser_conf.T)
@property
def T(self):
'''
Return the maximum number of timesteps
that this design protocol will perform.
Output:
T (int): The maximum number of timesteps to perform
'''
return self.diffuser_conf.T
def load_checkpoint(self) -> None:
"""Loads RF checkpoint, from which config can be generated."""
self._log.info(f'Reading checkpoint from {self.ckpt_path}')
print('This is inf_conf.ckpt_path')
print(self.ckpt_path)
self.ckpt = torch.load(
self.ckpt_path, map_location=self.device)
def assemble_config_from_chk(self) -> None:
"""
Function for loading model config from checkpoint directly.
Takes:
- config file
Actions:
- Replaces all -model and -diffuser items
- Throws a warning if there are items in -model and -diffuser that aren't in the checkpoint
This throws an error if there is a flag in the checkpoint 'config_dict' that isn't in the inference config.
This should ensure that whenever a feature is added in the training setup, it is accounted for in the inference script.
"""
# get overrides to re-apply after building the config from the checkpoint
overrides = []
if HydraConfig.initialized():
overrides = HydraConfig.get().overrides.task
print("Assembling -model, -diffuser and -preprocess configs from checkpoint")
for cat in ['model','diffuser','preprocess']:
for key in self._conf[cat]:
try:
print(f"USING MODEL CONFIG: self._conf[{cat}][{key}] = {self.ckpt['config_dict'][cat][key]}")
self._conf[cat][key] = self.ckpt['config_dict'][cat][key]
except:
pass
# add overrides back in again
for override in overrides:
if override.split(".")[0] in ['model','diffuser','preprocess']:
print(f'WARNING: You are changing {override.split("=")[0]} from the value this model was trained with. Are you sure you know what you are doing?')
mytype = type(self._conf[override.split(".")[0]][override.split(".")[1].split("=")[0]])
self._conf[override.split(".")[0]][override.split(".")[1].split("=")[0]] = mytype(override.split("=")[1])
def load_model(self):
"""Create RosettaFold model from preloaded checkpoint."""
# Read input dimensions from checkpoint.
self.d_t1d=self._conf.preprocess.d_t1d
self.d_t2d=self._conf.preprocess.d_t2d
model = RoseTTAFoldModule(**self._conf.model, d_t1d=self.d_t1d, d_t2d=self.d_t2d, T=self._conf.diffuser.T).to(self.device)
if self._conf.logging.inputs:
pickle_dir = pickle_function_call(model, 'forward', 'inference')
print(f'pickle_dir: {pickle_dir}')
model = model.eval()
self._log.info(f'Loading checkpoint.')
model.load_state_dict(self.ckpt['model_state_dict'], strict=True)
return model
def construct_contig(self, target_feats):
"""
Construct contig class describing the protein to be generated
"""
self._log.info(f'Using contig: {self.contig_conf.contigs}')
return ContigMap(target_feats, **self.contig_conf)
def construct_denoiser(self, L, visible):
"""Make length-specific denoiser."""
denoise_kwargs = OmegaConf.to_container(self.diffuser_conf)
denoise_kwargs.update(OmegaConf.to_container(self.denoiser_conf))
denoise_kwargs.update({
'L': L,
'diffuser': self.diffuser,
'potential_manager': self.potential_manager,
})
return iu.Denoise(**denoise_kwargs)
def sample_init(self, return_forward_trajectory=False):
"""
Initial features to start the sampling process.
Modify signature and function body for different initialization
based on the config.
Returns:
xt: Starting positions with a portion of them randomly sampled.
seq_t: Starting sequence with a portion of them set to unknown.
"""
#######################
### Parse input pdb ###
#######################
self.target_feats = iu.process_target(self.inf_conf.input_pdb, parse_hetatom=True, center=False)
################################
### Generate specific contig ###
################################
# Generate a specific contig from the range of possibilities specified at input
self.contig_map = self.construct_contig(self.target_feats)
self.mappings = self.contig_map.get_mappings()
self.mask_seq = torch.from_numpy(self.contig_map.inpaint_seq)[None,:]
self.mask_str = torch.from_numpy(self.contig_map.inpaint_str)[None,:]
self.binderlen = len(self.contig_map.inpaint)
####################
### Get Hotspots ###
####################
self.hotspot_0idx=iu.get_idx0_hotspots(self.mappings, self.ppi_conf, self.binderlen)
#####################################
### Initialise Potentials Manager ###
#####################################
self.potential_manager = PotentialManager(self.potential_conf,
self.ppi_conf,
self.diffuser_conf,
self.inf_conf,
self.hotspot_0idx,
self.binderlen)
###################################
### Initialize other attributes ###
###################################
xyz_27 = self.target_feats['xyz_27']
mask_27 = self.target_feats['mask_27']
seq_orig = self.target_feats['seq'].long()
L_mapped = len(self.contig_map.ref)
contig_map=self.contig_map
self.diffusion_mask = self.mask_str
self.chain_idx=['A' if i < self.binderlen else 'B' for i in range(L_mapped)]
####################################
### Generate initial coordinates ###
####################################
if self.diffuser_conf.partial_T:
assert xyz_27.shape[0] == L_mapped, f"there must be a coordinate in the input PDB for \
each residue implied by the contig string for partial diffusion. length of \
input PDB != length of contig string: {xyz_27.shape[0]} != {L_mapped}"
assert contig_map.hal_idx0 == contig_map.ref_idx0, f'for partial diffusion there can \
be no offset between the index of a residue in the input and the index of the \
residue in the output, {contig_map.hal_idx0} != {contig_map.ref_idx0}'
# Partially diffusing from a known structure
xyz_mapped=xyz_27
atom_mask_mapped = mask_27
else:
# Fully diffusing from points initialised at the origin
# adjust size of input xt according to residue map
xyz_mapped = torch.full((1,1,L_mapped,27,3), np.nan)
xyz_mapped[:, :, contig_map.hal_idx0, ...] = xyz_27[contig_map.ref_idx0,...]
xyz_motif_prealign = xyz_mapped.clone()
motif_prealign_com = xyz_motif_prealign[0,0,:,1].mean(dim=0)
self.motif_com = xyz_27[contig_map.ref_idx0,1].mean(dim=0)
xyz_mapped = get_init_xyz(xyz_mapped).squeeze()
# adjust the size of the input atom map
atom_mask_mapped = torch.full((L_mapped, 27), False)
atom_mask_mapped[contig_map.hal_idx0] = mask_27[contig_map.ref_idx0]
# Diffuse the contig-mapped coordinates
if self.diffuser_conf.partial_T:
assert self.diffuser_conf.partial_T <= self.diffuser_conf.T, "Partial_T must be less than T"
self.t_step_input = int(self.diffuser_conf.partial_T)
else:
self.t_step_input = int(self.diffuser_conf.T)
t_list = np.arange(1, self.t_step_input+1)
#################################
### Generate initial sequence ###
#################################
seq_t = torch.full((1,L_mapped), 21).squeeze() # 21 is the mask token
seq_t[contig_map.hal_idx0] = seq_orig[contig_map.ref_idx0]
# Unmask sequence if desired
if self._conf.contigmap.provide_seq is not None:
seq_t[self.mask_seq.squeeze()] = seq_orig[self.mask_seq.squeeze()]
seq_t[~self.mask_seq.squeeze()] = 21
seq_t = torch.nn.functional.one_hot(seq_t, num_classes=22).float() # [L,22]
seq_orig = torch.nn.functional.one_hot(seq_orig, num_classes=22).float() # [L,22]
fa_stack, xyz_true = self.diffuser.diffuse_pose(
xyz_mapped,
torch.clone(seq_t),
atom_mask_mapped.squeeze(),
diffusion_mask=self.diffusion_mask.squeeze(),
t_list=t_list)
xT = fa_stack[-1].squeeze()[:,:14,:]
xt = torch.clone(xT)
self.denoiser = self.construct_denoiser(len(self.contig_map.ref), visible=self.mask_seq.squeeze())
######################
### Apply Symmetry ###
######################
if self.symmetry is not None:
xt, seq_t = self.symmetry.apply_symmetry(xt, seq_t)
self._log.info(f'Sequence init: {seq2chars(torch.argmax(seq_t, dim=-1))}')
self.msa_prev = None
self.pair_prev = None
self.state_prev = None
#########################################
### Parse ligand for ligand potential ###
#########################################
if self.potential_conf.guiding_potentials is not None:
if any(list(filter(lambda x: "substrate_contacts" in x, self.potential_conf.guiding_potentials))):
assert len(self.target_feats['xyz_het']) > 0, "If you're using the Substrate Contact potential, \
you need to make sure there's a ligand in the input_pdb file!"
het_names = np.array([i['name'].strip() for i in self.target_feats['info_het']])
xyz_het = self.target_feats['xyz_het'][het_names == self._conf.potentials.substrate]
xyz_het = torch.from_numpy(xyz_het)
assert xyz_het.shape[0] > 0, f'expected >0 heteroatoms from ligand with name {self._conf.potentials.substrate}'
xyz_motif_prealign = xyz_motif_prealign[0,0][self.diffusion_mask.squeeze()]
motif_prealign_com = xyz_motif_prealign[:,1].mean(dim=0)
xyz_het_com = xyz_het.mean(dim=0)
for pot in self.potential_manager.potentials_to_apply:
pot.motif_substrate_atoms = xyz_het
pot.diffusion_mask = self.diffusion_mask.squeeze()
pot.xyz_motif = xyz_motif_prealign
pot.diffuser = self.diffuser
return xt, seq_t
def _preprocess(self, seq, xyz_t, t, repack=False):
"""
Function to prepare inputs to diffusion model
seq (L,22) one-hot sequence
msa_masked (1,1,L,48)
msa_full (1,1,L,25)
xyz_t (L,14,3) template crds (diffused)
t1d (1,L,28) this is the t1d before tacking on the chi angles:
- seq + unknown/mask (21)
- global timestep (1-t/T if not motif else 1) (1)
MODEL SPECIFIC:
- contacting residues: for ppi. Target residues in contact with binder (1)
- empty feature (legacy) (1)
- ss (H, E, L, MASK) (4)
t2d (1, L, L, 45)
- last plane is block adjacency
"""
L = seq.shape[0]
T = self.T
binderlen = self.binderlen
target_res = self.ppi_conf.hotspot_res
##################
### msa_masked ###
##################
msa_masked = torch.zeros((1,1,L,48))
msa_masked[:,:,:,:22] = seq[None, None]
msa_masked[:,:,:,22:44] = seq[None, None]
msa_masked[:,:,0,46] = 1.0
msa_masked[:,:,-1,47] = 1.0
################
### msa_full ###
################
msa_full = torch.zeros((1,1,L,25))
msa_full[:,:,:,:22] = seq[None, None]
msa_full[:,:,0,23] = 1.0
msa_full[:,:,-1,24] = 1.0
###########
### t1d ###
###########
# Here we need to go from one hot with 22 classes to one hot with 21 classes (last plane is missing token)
t1d = torch.zeros((1,1,L,21))
seqt1d = torch.clone(seq)
for idx in range(L):
if seqt1d[idx,21] == 1:
seqt1d[idx,20] = 1
seqt1d[idx,21] = 0
t1d[:,:,:,:21] = seqt1d[None,None,:,:21]
# Set timestep feature to 1 where diffusion mask is True, else 1-t/T
timefeature = torch.zeros((L)).float()
timefeature[self.mask_str.squeeze()] = 1
timefeature[~self.mask_str.squeeze()] = 1 - t/self.T
timefeature = timefeature[None,None,...,None]
t1d = torch.cat((t1d, timefeature), dim=-1).float()
#############
### xyz_t ###
#############
if self.preprocess_conf.sidechain_input:
xyz_t[torch.where(seq == 21, True, False),3:,:] = float('nan')
else:
xyz_t[~self.mask_str.squeeze(),3:,:] = float('nan')
xyz_t=xyz_t[None, None]
xyz_t = torch.cat((xyz_t, torch.full((1,1,L,13,3), float('nan'))), dim=3)
###########
### t2d ###
###########
t2d = xyz_to_t2d(xyz_t)
###########
### idx ###
###########
idx = torch.tensor(self.contig_map.rf)[None]
###############
### alpha_t ###
###############
seq_tmp = t1d[...,:-1].argmax(dim=-1).reshape(-1,L)
alpha, _, alpha_mask, _ = util.get_torsions(xyz_t.reshape(-1, L, 27, 3), seq_tmp, TOR_INDICES, TOR_CAN_FLIP, REF_ANGLES)
alpha_mask = torch.logical_and(alpha_mask, ~torch.isnan(alpha[...,0]))
alpha[torch.isnan(alpha)] = 0.0
alpha = alpha.reshape(1,-1,L,10,2)
alpha_mask = alpha_mask.reshape(1,-1,L,10,1)
alpha_t = torch.cat((alpha, alpha_mask), dim=-1).reshape(1, -1, L, 30)
#put tensors on device
msa_masked = msa_masked.to(self.device)
msa_full = msa_full.to(self.device)
seq = seq.to(self.device)
xyz_t = xyz_t.to(self.device)
idx = idx.to(self.device)
t1d = t1d.to(self.device)
t2d = t2d.to(self.device)
alpha_t = alpha_t.to(self.device)
######################
### added_features ###
######################
if self.preprocess_conf.d_t1d >= 24: # add hotspot residues
hotspot_tens = torch.zeros(L).float()
if self.ppi_conf.hotspot_res is None:
print("WARNING: you're using a model trained on complexes and hotspot residues, without specifying hotspots.\
If you're doing monomer diffusion this is fine")
hotspot_idx=[]
else:
hotspots = [(i[0],int(i[1:])) for i in self.ppi_conf.hotspot_res]
hotspot_idx=[]
for i,res in enumerate(self.contig_map.con_ref_pdb_idx):
if res in hotspots:
hotspot_idx.append(self.contig_map.hal_idx0[i])
hotspot_tens[hotspot_idx] = 1.0
# Add blank (legacy) feature and hotspot tensor
t1d=torch.cat((t1d, torch.zeros_like(t1d[...,:1]), hotspot_tens[None,None,...,None].to(self.device)), dim=-1)
return msa_masked, msa_full, seq[None], torch.squeeze(xyz_t, dim=0), idx, t1d, t2d, xyz_t, alpha_t
def sample_step(self, *, t, x_t, seq_init, final_step):
'''Generate the next pose that the model should be supplied at timestep t-1.
Args:
t (int): The timestep that has just been predicted
seq_t (torch.tensor): (L,22) The sequence at the beginning of this timestep
x_t (torch.tensor): (L,14,3) The residue positions at the beginning of this timestep
seq_init (torch.tensor): (L,22) The initialized sequence used in updating the sequence.
Returns:
px0: (L,14,3) The model's prediction of x0.
x_t_1: (L,14,3) The updated positions of the next step.
seq_t_1: (L,22) The updated sequence of the next step.
tors_t_1: (L, ?) The updated torsion angles of the next step.
plddt: (L, 1) Predicted lDDT of x0.
'''
msa_masked, msa_full, seq_in, xt_in, idx_pdb, t1d, t2d, xyz_t, alpha_t = self._preprocess(
seq_init, x_t, t)
N,L = msa_masked.shape[:2]
if self.symmetry is not None:
idx_pdb, self.chain_idx = self.symmetry.res_idx_procesing(res_idx=idx_pdb)
msa_prev = None
pair_prev = None
state_prev = None
with torch.no_grad():
msa_prev, pair_prev, px0, state_prev, alpha, logits, plddt = self.model(msa_masked,
msa_full,
seq_in,
xt_in,
idx_pdb,
t1d=t1d,
t2d=t2d,
xyz_t=xyz_t,
alpha_t=alpha_t,
msa_prev = msa_prev,
pair_prev = pair_prev,
state_prev = state_prev,
t=torch.tensor(t),
return_infer=True,
motif_mask=self.diffusion_mask.squeeze().to(self.device))
# prediction of X0
_, px0 = self.allatom(torch.argmax(seq_in, dim=-1), px0, alpha)
px0 = px0.squeeze()[:,:14]
#####################
### Get next pose ###
#####################
if t > final_step:
seq_t_1 = nn.one_hot(seq_init,num_classes=22).to(self.device)
x_t_1, px0 = self.denoiser.get_next_pose(
xt=x_t,
px0=px0,
t=t,
diffusion_mask=self.mask_str.squeeze(),
align_motif=self.inf_conf.align_motif
)
else:
x_t_1 = torch.clone(px0).to(x_t.device)
seq_t_1 = torch.clone(seq_init)
px0 = px0.to(x_t.device)
if self.symmetry is not None:
x_t_1, seq_t_1 = self.symmetry.apply_symmetry(x_t_1, seq_t_1)
return px0, x_t_1, seq_t_1, plddt
class SelfConditioning(Sampler):
"""
Model Runner for self conditioning
pX0[t+1] is provided as a template input to the model at time t
"""
def sample_step(self, *, t, x_t, seq_init, final_step):
'''
Generate the next pose that the model should be supplied at timestep t-1.
Args:
t (int): The timestep that has just been predicted
seq_t (torch.tensor): (L,22) The sequence at the beginning of this timestep
x_t (torch.tensor): (L,14,3) The residue positions at the beginning of this timestep
seq_init (torch.tensor): (L,22) The initialized sequence used in updating the sequence.
Returns:
px0: (L,14,3) The model's prediction of x0.
x_t_1: (L,14,3) The updated positions of the next step.
seq_t_1: (L) The sequence to the next step (== seq_init)
plddt: (L, 1) Predicted lDDT of x0.
'''
msa_masked, msa_full, seq_in, xt_in, idx_pdb, t1d, t2d, xyz_t, alpha_t = self._preprocess(
seq_init, x_t, t)
B,N,L = xyz_t.shape[:3]
##################################
######## Str Self Cond ###########
##################################
if (t < self.diffuser.T) and (t != self.diffuser_conf.partial_T):
zeros = torch.zeros(B,1,L,24,3).float().to(xyz_t.device)
xyz_t = torch.cat((self.prev_pred.unsqueeze(1),zeros), dim=-2) # [B,T,L,27,3]
t2d_44 = xyz_to_t2d(xyz_t) # [B,T,L,L,44]
else:
xyz_t = torch.zeros_like(xyz_t)
t2d_44 = torch.zeros_like(t2d[...,:44])
# No effect if t2d is only dim 44
t2d[...,:44] = t2d_44
if self.symmetry is not None:
idx_pdb, self.chain_idx = self.symmetry.res_idx_procesing(res_idx=idx_pdb)
####################
### Forward Pass ###
####################
with torch.no_grad():
msa_prev, pair_prev, px0, state_prev, alpha, logits, plddt = self.model(msa_masked,
msa_full,
seq_in,
xt_in,
idx_pdb,
t1d=t1d,
t2d=t2d,
xyz_t=xyz_t,
alpha_t=alpha_t,
msa_prev = None,
pair_prev = None,
state_prev = None,
t=torch.tensor(t),
return_infer=True,
motif_mask=self.diffusion_mask.squeeze().to(self.device))
if self.symmetry is not None and self.inf_conf.symmetric_self_cond:
px0 = self.symmetrise_prev_pred(px0=px0,seq_in=seq_in, alpha=alpha)[:,:,:3]
self.prev_pred = torch.clone(px0)
# prediction of X0
_, px0 = self.allatom(torch.argmax(seq_in, dim=-1), px0, alpha)
px0 = px0.squeeze()[:,:14]
###########################
### Generate Next Input ###
###########################
seq_t_1 = torch.clone(seq_init)
if t > final_step:
x_t_1, px0 = self.denoiser.get_next_pose(
xt=x_t,
px0=px0,
t=t,
diffusion_mask=self.mask_str.squeeze(),
align_motif=self.inf_conf.align_motif,
include_motif_sidechains=self.preprocess_conf.motif_sidechain_input
)
self._log.info(
f'Timestep {t}, input to next step: { seq2chars(torch.argmax(seq_t_1, dim=-1).tolist())}')
else:
x_t_1 = torch.clone(px0).to(x_t.device)
px0 = px0.to(x_t.device)
######################
### Apply symmetry ###
######################
if self.symmetry is not None:
x_t_1, seq_t_1 = self.symmetry.apply_symmetry(x_t_1, seq_t_1)
return px0, x_t_1, seq_t_1, plddt
def symmetrise_prev_pred(self, px0, seq_in, alpha):
"""
Method for symmetrising px0 output for self-conditioning
"""
_,px0_aa = self.allatom(torch.argmax(seq_in, dim=-1), px0, alpha)
px0_sym,_ = self.symmetry.apply_symmetry(px0_aa.to('cpu').squeeze()[:,:14], torch.argmax(seq_in, dim=-1).squeeze().to('cpu'))
px0_sym = px0_sym[None].to(self.device)
return px0_sym
class ScaffoldedSampler(SelfConditioning):
"""
Model Runner for Scaffold-Constrained diffusion
"""
def __init__(self, conf: DictConfig):
"""
Initialize scaffolded sampler.
Two basic approaches here:
i) Given a block adjacency/secondary structure input, generate a fold (in the presence or absence of a target)
- This allows easy generation of binders or specific folds
- Allows simple expansion of an input, to sample different lengths
ii) Providing a contig input and corresponding block adjacency/secondary structure input
- This allows mixed motif scaffolding and fold-conditioning.
- Adjacency/secondary structure inputs must correspond exactly in length to the contig string
"""
super().__init__(conf)
# initialize BlockAdjacency sampling class
self.blockadjacency = iu.BlockAdjacency(conf, conf.inference.num_designs)
#################################################
### Initialize target, if doing binder design ###
#################################################
if conf.scaffoldguided.target_pdb:
self.target = iu.Target(conf.scaffoldguided, conf.ppi.hotspot_res)
self.target_pdb = self.target.get_target()
if conf.scaffoldguided.target_ss is not None:
self.target_ss = torch.load(conf.scaffoldguided.target_ss).long()
self.target_ss = torch.nn.functional.one_hot(self.target_ss, num_classes=4)
if self._conf.scaffoldguided.contig_crop is not None:
self.target_ss=self.target_ss[self.target_pdb['crop_mask']]
if conf.scaffoldguided.target_adj is not None:
self.target_adj = torch.load(conf.scaffoldguided.target_adj).long()
self.target_adj=torch.nn.functional.one_hot(self.target_adj, num_classes=3)
if self._conf.scaffoldguided.contig_crop is not None:
self.target_adj=self.target_adj[self.target_pdb['crop_mask']]
self.target_adj=self.target_adj[:,self.target_pdb['crop_mask']]
else:
self.target = None
self.target_pdb=False
def sample_init(self):
"""
Wrapper method for taking secondary structure + adj, and outputting xt, seq_t
"""
##########################
### Process Fold Input ###
##########################
self.L, self.ss, self.adj = self.blockadjacency.get_scaffold()
self.adj = nn.one_hot(self.adj.long(), num_classes=3)
##############################
### Auto-contig generation ###
##############################
if self.contig_conf.contigs is None:
# process target
xT = torch.full((self.L, 27,3), np.nan)
xT = get_init_xyz(xT[None,None]).squeeze()
seq_T = torch.full((self.L,),21)
self.diffusion_mask = torch.full((self.L,),False)
atom_mask = torch.full((self.L,27), False)
self.binderlen=self.L
if self.target:
target_L = np.shape(self.target_pdb['xyz'])[0]
# xyz
target_xyz = torch.full((target_L, 27, 3), np.nan)
target_xyz[:,:14,:] = torch.from_numpy(self.target_pdb['xyz'])
xT = torch.cat((xT, target_xyz), dim=0)
# seq
seq_T = torch.cat((seq_T, torch.from_numpy(self.target_pdb['seq'])), dim=0)
# diffusion mask
self.diffusion_mask = torch.cat((self.diffusion_mask, torch.full((target_L,), True)),dim=0)
# atom mask
mask_27 = torch.full((target_L, 27), False)
mask_27[:,:14] = torch.from_numpy(self.target_pdb['mask'])
atom_mask = torch.cat((atom_mask, mask_27), dim=0)
self.L += target_L
# generate contigmap object
contig = []
for idx,i in enumerate(self.target_pdb['pdb_idx'][:-1]):
if idx==0:
start=i[1]
if i[1] + 1 != self.target_pdb['pdb_idx'][idx+1][1] or i[0] != self.target_pdb['pdb_idx'][idx+1][0]:
contig.append(f'{i[0]}{start}-{i[1]}/0 ')
start = self.target_pdb['pdb_idx'][idx+1][1]
contig.append(f"{self.target_pdb['pdb_idx'][-1][0]}{start}-{self.target_pdb['pdb_idx'][-1][1]}/0 ")
contig.append(f"{self.binderlen}-{self.binderlen}")
contig = ["".join(contig)]
else:
contig = [f"{self.binderlen}-{self.binderlen}"]
self.contig_map=ContigMap(self.target_pdb, contig)
self.mappings = self.contig_map.get_mappings()
self.mask_seq = self.diffusion_mask
self.mask_str = self.diffusion_mask
L_mapped=len(self.contig_map.ref)
############################
### Specific Contig mode ###
############################
else:
# get contigmap from command line
assert self.target is None, "Giving a target is the wrong way of handling this is you're doing contigs and secondary structure"
# process target and reinitialise potential_manager. This is here because the 'target' is always set up to be the second chain in out inputs.
self.target_feats = iu.process_target(self.inf_conf.input_pdb)
self.contig_map = self.construct_contig(self.target_feats)
self.mappings = self.contig_map.get_mappings()
self.mask_seq = torch.from_numpy(self.contig_map.inpaint_seq)[None,:]
self.mask_str = torch.from_numpy(self.contig_map.inpaint_str)[None,:]
self.binderlen = len(self.contig_map.inpaint)
target_feats = self.target_feats
contig_map = self.contig_map
xyz_27 = target_feats['xyz_27']
mask_27 = target_feats['mask_27']
seq_orig = target_feats['seq']
L_mapped = len(self.contig_map.ref)
seq_T=torch.full((L_mapped,),21)
seq_T[contig_map.hal_idx0] = seq_orig[contig_map.ref_idx0]
seq_T[~self.mask_seq.squeeze()] = 21
assert L_mapped==self.adj.shape[0]
diffusion_mask = self.mask_str
self.diffusion_mask = diffusion_mask
xT = torch.full((1,1,L_mapped,27,3), np.nan)
xT[:, :, contig_map.hal_idx0, ...] = xyz_27[contig_map.ref_idx0,...]
xT = get_init_xyz(xT).squeeze()
atom_mask = torch.full((L_mapped, 27), False)
atom_mask[contig_map.hal_idx0] = mask_27[contig_map.ref_idx0]
####################
### Get hotspots ###
####################
self.hotspot_0idx=iu.get_idx0_hotspots(self.mappings, self.ppi_conf, self.binderlen)
#########################
### Set up potentials ###
#########################
self.potential_manager = PotentialManager(self.potential_conf,
self.ppi_conf,
self.diffuser_conf,
self.inf_conf,
self.hotspot_0idx,
self.binderlen)
self.chain_idx=['A' if i < self.binderlen else 'B' for i in range(self.L)]
########################
### Handle Partial T ###
########################
if self.diffuser_conf.partial_T:
assert self.diffuser_conf.partial_T <= self.diffuser_conf.T
self.t_step_input = int(self.diffuser_conf.partial_T)
else:
self.t_step_input = int(self.diffuser_conf.T)
t_list = np.arange(1, self.t_step_input+1)
seq_T=torch.nn.functional.one_hot(seq_T, num_classes=22).float()
fa_stack, xyz_true = self.diffuser.diffuse_pose(
xT,
torch.clone(seq_T),
atom_mask.squeeze(),
diffusion_mask=self.diffusion_mask.squeeze(),
t_list=t_list,
include_motif_sidechains=self.preprocess_conf.motif_sidechain_input)
#######################
### Set up Denoiser ###
#######################
self.denoiser = self.construct_denoiser(self.L, visible=self.mask_seq.squeeze())
xT = torch.clone(fa_stack[-1].squeeze()[:,:14,:])
return xT, seq_T
def _preprocess(self, seq, xyz_t, t):
msa_masked, msa_full, seq, xyz_prev, idx_pdb, t1d, t2d, xyz_t, alpha_t = super()._preprocess(seq, xyz_t, t, repack=False)
###################################
### Add Adj/Secondary Structure ###
###################################
assert self.preprocess_conf.d_t1d == 28, "The checkpoint you're using hasn't been trained with sec-struc/block adjacency features"
assert self.preprocess_conf.d_t2d == 47, "The checkpoint you're using hasn't been trained with sec-struc/block adjacency features"
#####################
### Handle Target ###
#####################
if self.target:
blank_ss = torch.nn.functional.one_hot(torch.full((self.L-self.binderlen,), 3), num_classes=4)
full_ss = torch.cat((self.ss, blank_ss), dim=0)
if self._conf.scaffoldguided.target_ss is not None:
full_ss[self.binderlen:] = self.target_ss
else:
full_ss = self.ss
t1d=torch.cat((t1d, full_ss[None,None].to(self.device)), dim=-1)
t1d = t1d.float()
###########
### t2d ###
###########
if self.d_t2d == 47:
if self.target:
full_adj = torch.zeros((self.L, self.L, 3))
full_adj[:,:,-1] = 1. #set to mask
full_adj[:self.binderlen, :self.binderlen] = self.adj
if self._conf.scaffoldguided.target_adj is not None:
full_adj[self.binderlen:,self.binderlen:] = self.target_adj
else:
full_adj = self.adj
t2d=torch.cat((t2d, full_adj[None,None].to(self.device)),dim=-1)
###########
### idx ###
###########
if self.target:
idx_pdb[:,self.binderlen:] += 200
return msa_masked, msa_full, seq, xyz_prev, idx_pdb, t1d, t2d, xyz_t, alpha_t
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