File size: 10,588 Bytes
813c6b1 |
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 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 |
import torch
import pandas as pd
import torch.nn as nn
import esm
from tokenizer.my_tokenizers import SMILES_SPE_Tokenizer
from transformers import AutoModelForMaskedLM, AutoModelForCausalLM, AutoTokenizer, AutoModel
base_path = "/scratch/pranamlab/sophtang/home/scoring/PeptiVerse"
class ImprovedBindingPredictor(nn.Module):
def __init__(self,
esm_dim=1280,
smiles_dim=768,
hidden_dim=512,
n_heads=8,
n_layers=3,
dropout=0.1):
super().__init__()
# Define binding thresholds
self.tight_threshold = 7.5 # Kd/Ki/IC50 ≤ ~30nM
self.weak_threshold = 6.0 # Kd/Ki/IC50 > 1μM
# Project to same dimension
self.smiles_projection = nn.Linear(smiles_dim, hidden_dim)
self.protein_projection = nn.Linear(esm_dim, hidden_dim)
self.protein_norm = nn.LayerNorm(hidden_dim)
self.smiles_norm = nn.LayerNorm(hidden_dim)
# Cross attention blocks with layer norm
self.cross_attention_layers = nn.ModuleList([
nn.ModuleDict({
'attention': nn.MultiheadAttention(hidden_dim, n_heads, dropout=dropout),
'norm1': nn.LayerNorm(hidden_dim),
'ffn': nn.Sequential(
nn.Linear(hidden_dim, hidden_dim * 4),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim * 4, hidden_dim)
),
'norm2': nn.LayerNorm(hidden_dim)
}) for _ in range(n_layers)
])
# Prediction heads
self.shared_head = nn.Sequential(
nn.Linear(hidden_dim * 2, hidden_dim),
nn.ReLU(),
nn.Dropout(dropout),
)
# Regression head
self.regression_head = nn.Linear(hidden_dim, 1)
# Classification head (3 classes: tight, medium, loose binding)
self.classification_head = nn.Linear(hidden_dim, 3)
def get_binding_class(self, affinity):
"""Convert affinity values to class indices
0: tight binding (>= 7.5)
1: medium binding (6.0-7.5)
2: weak binding (< 6.0)
"""
if isinstance(affinity, torch.Tensor):
tight_mask = affinity >= self.tight_threshold
weak_mask = affinity < self.weak_threshold
medium_mask = ~(tight_mask | weak_mask)
classes = torch.zeros_like(affinity, dtype=torch.long)
classes[medium_mask] = 1
classes[weak_mask] = 2
return classes
else:
if affinity >= self.tight_threshold:
return 0 # tight binding
elif affinity < self.weak_threshold:
return 2 # weak binding
else:
return 1 # medium binding
def forward(self, protein_emb, smiles_emb):
protein = self.protein_norm(self.protein_projection(protein_emb))
smiles = self.smiles_norm(self.smiles_projection(smiles_emb))
#protein = protein.transpose(0, 1)
#smiles = smiles.transpose(0, 1)
# Cross attention layers
for layer in self.cross_attention_layers:
# Protein attending to SMILES
attended_protein = layer['attention'](
protein, smiles, smiles
)[0]
protein = layer['norm1'](protein + attended_protein)
protein = layer['norm2'](protein + layer['ffn'](protein))
# SMILES attending to protein
attended_smiles = layer['attention'](
smiles, protein, protein
)[0]
smiles = layer['norm1'](smiles + attended_smiles)
smiles = layer['norm2'](smiles + layer['ffn'](smiles))
# Get sequence-level representations
protein_pool = torch.mean(protein, dim=0)
smiles_pool = torch.mean(smiles, dim=0)
# Concatenate both representations
combined = torch.cat([protein_pool, smiles_pool], dim=-1)
# Shared features
shared_features = self.shared_head(combined)
regression_output = self.regression_head(shared_features)
classification_logits = self.classification_head(shared_features)
return regression_output, classification_logits
class BindingAffinity:
def __init__(self, prot_seq, model_type='PeptideCLM'):
super().__init__()
# peptide embeddings
self.pep_model = AutoModelForMaskedLM.from_pretrained('aaronfeller/PeptideCLM-23M-all').roformer
self.pep_tokenizer = SMILES_SPE_Tokenizer(f'{base_path}/functions/tokenizer/new_vocab.txt',
f'{base_path}/functions/tokenizer/new_splits.txt')
self.model = ImprovedBindingPredictor()
checkpoint = torch.load(f'{base_path}/src/binding/best_model.pt', weights_only=False)
self.model.load_state_dict(checkpoint['model_state_dict'])
self.model.eval()
self.esm_model, alphabet = esm.pretrained.esm2_t33_650M_UR50D() # load ESM-2 model
self.prot_tokenizer = alphabet.get_batch_converter() # load esm tokenizer
data = [("target", prot_seq)]
# get tokenized protein
_, _, prot_tokens = self.prot_tokenizer(data)
with torch.no_grad():
results = self.esm_model.forward(prot_tokens, repr_layers=[33]) # Example with ESM-2
prot_emb = results["representations"][33]
self.prot_emb = prot_emb[0]
self.prot_emb = torch.mean(self.prot_emb, dim=0, keepdim=True)
def forward(self, input_seqs):
with torch.no_grad():
scores = []
for seq in input_seqs:
pep_tokens = self.pep_tokenizer(seq, return_tensors='pt', padding=True)
with torch.no_grad():
emb = self.pep_model(input_ids=pep_tokens['input_ids'],
attention_mask=pep_tokens['attention_mask'],
output_hidden_states=True)
#emb = self.pep_model(input_ids=pep_tokens['input_ids'], attention_mask=pep_tokens['attention_mask'])
pep_emb = emb.last_hidden_state.squeeze(0)
pep_emb = torch.mean(pep_emb, dim=0, keepdim=True)
score, logits = self.model.forward(self.prot_emb, pep_emb)
scores.append(score.item())
return scores
def __call__(self, input_seqs: list):
return self.forward(input_seqs)
def unittest():
amhr = 'MLGSLGLWALLPTAVEAPPNRRTCVFFEAPGVRGSTKTLGELLDTGTELPRAIRCLYSRCCFGIWNLTQDRAQVEMQGCRDSDEPGCESLHCDPSPRAHPSPGSTLFTCSCGTDFCNANYSHLPPPGSPGTPGSQGPQAAPGESIWMALVLLGLFLLLLLLLGSIILALLQRKNYRVRGEPVPEPRPDSGRDWSVELQELPELCFSQVIREGGHAVVWAGQLQGKLVAIKAFPPRSVAQFQAERALYELPGLQHDHIVRFITASRGGPGRLLSGPLLVLELHPKGSLCHYLTQYTSDWGSSLRMALSLAQGLAFLHEERWQNGQYKPGIAHRDLSSQNVLIREDGSCAIGDLGLALVLPGLTQPPAWTPTQPQGPAAIMEAGTQRYMAPELLDKTLDLQDWGMALRRADIYSLALLLWEILSRCPDLRPDSSPPPFQLAYEAELGNTPTSDELWALAVQERRRPYIPSTWRCFATDPDGLRELLEDCWDADPEARLTAECVQQRLAALAHPQESHPFPESCPRGCPPLCPEDCTSIPAPTILPCRPQRSACHFSVQQGPCSRNPQPACTLSPV'
tfr = 'MMDQARSAFSNLFGGEPLSYTRFSLARQVDGDNSHVEMKLAVDEEENADNNTKANVTKPKRCSGSICYGTIAVIVFFLIGFMIGYLGYCKGVEPKTECERLAGTESPVREEPGEDFPAARRLYWDDLKRKLSEKLDSTDFTGTIKLLNENSYVPREAGSQKDENLALYVENQFREFKLSKVWRDQHFVKIQVKDSAQNSVIIVDKNGRLVYLVENPGGYVAYSKAATVTGKLVHANFGTKKDFEDLYTPVNGSIVIVRAGKITFAEKVANAESLNAIGVLIYMDQTKFPIVNAELSFFGHAHLGTGDPYTPGFPSFNHTQFPPSRSSGLPNIPVQTISRAAAEKLFGNMEGDCPSDWKTDSTCRMVTSESKNVKLTVSNVLKEIKILNIFGVIKGFVEPDHYVVVGAQRDAWGPGAAKSGVGTALLLKLAQMFSDMVLKDGFQPSRSIIFASWSAGDFGSVGATEWLEGYLSSLHLKAFTYINLDKAVLGTSNFKVSASPLLYTLIEKTMQNVKHPVTGQFLYQDSNWASKVEKLTLDNAAFPFLAYSGIPAVSFCFCEDTDYPYLGTTMDTYKELIERIPELNKVARAAAEVAGQFVIKLTHDVELNLDYERYNSQLLSFVRDLNQYRADIKEMGLSLQWLYSARGDFFRATSRLTTDFGNAEKTDRFVMKKLNDRVMRVEYHFLSPYVSPKESPFRHVFWGSGSHTLPALLENLKLRKQNNGAFNETLFRNQLALATWTIQGAANALSGDVWDIDNEF'
gfap = 'MERRRITSAARRSYVSSGEMMVGGLAPGRRLGPGTRLSLARMPPPLPTRVDFSLAGALNAGFKETRASERAEMMELNDRFASYIEKVRFLEQQNKALAAELNQLRAKEPTKLADVYQAELRELRLRLDQLTANSARLEVERDNLAQDLATVRQKLQDETNLRLEAENNLAAYRQEADEATLARLDLERKIESLEEEIRFLRKIHEEEVRELQEQLARQQVHVELDVAKPDLTAALKEIRTQYEAMASSNMHEAEEWYRSKFADLTDAAARNAELLRQAKHEANDYRRQLQSLTCDLESLRGTNESLERQMREQEERHVREAASYQEALARLEEEGQSLKDEMARHLQEYQDLLNVKLALDIEIATYRKLLEGEENRITIPVQTFSNLQIRETSLDTKSVSEGHLKRNIVVKTVEMRDGEVIKESKQEHKDVM'
glp1 = 'MAGAPGPLRLALLLLGMVGRAGPRPQGATVSLWETVQKWREYRRQCQRSLTEDPPPATDLFCNRTFDEYACWPDGEPGSFVNVSCPWYLPWASSVPQGHVYRFCTAEGLWLQKDNSSLPWRDLSECEESKRGERSSPEEQLLFLYIIYTVGYALSFSALVIASAILLGFRHLHCTRNYIHLNLFASFILRALSVFIKDAALKWMYSTAAQQHQWDGLLSYQDSLSCRLVFLLMQYCVAANYYWLLVEGVYLYTLLAFSVLSEQWIFRLYVSIGWGVPLLFVVPWGIVKYLYEDEGCWTRNSNMNYWLIIRLPILFAIGVNFLIFVRVICIVVSKLKANLMCKTDIKCRLAKSTLTLIPLLGTHEVIFAFVMDEHARGTLRFIKLFTELSFTSFQGLMVAILYCFVNNEVQLEFRKSWERWRLEHLHIQRDSSMKPLKCPTSSLSSGATAGSSMYTATCQASCS'
glast = 'MTKSNGEEPKMGGRMERFQQGVRKRTLLAKKKVQNITKEDVKSYLFRNAFVLLTVTAVIVGTILGFTLRPYRMSYREVKYFSFPGELLMRMLQMLVLPLIISSLVTGMAALDSKASGKMGMRAVVYYMTTTIIAVVIGIIIVIIIHPGKGTKENMHREGKIVRVTAADAFLDLIRNMFPPNLVEACFKQFKTNYEKRSFKVPIQANETLVGAVINNVSEAMETLTRITEELVPVPGSVNGVNALGLVVFSMCFGFVIGNMKEQGQALREFFDSLNEAIMRLVAVIMWYAPVGILFLIAGKIVEMEDMGVIGGQLAMYTVTVIVGLLIHAVIVLPLLYFLVTRKNPWVFIGGLLQALITALGTSSSSATLPITFKCLEENNGVDKRVTRFVLPVGATINMDGTALYEALAAIFIAQVNNFELNFGQIITISITATAASIGAAGIPQAGLVTMVIVLTSVGLPTDDITLIIAVDWFLDRLRTTTNVLGDSLGAGIVEHLSRHELKNRDVEMGNSVIEENEMKKPYQLIAQDNETEKPIDSETKM'
ncam = 'LQTKDLIWTLFFLGTAVSLQVDIVPSQGEISVGESKFFLCQVAGDAKDKDISWFSPNGEKLTPNQQRISVVWNDDSSSTLTIYNANIDDAGIYKCVVTGEDGSESEATVNVKIFQKLMFKNAPTPQEFREGEDAVIVCDVVSSLPPTIIWKHKGRDVILKKDVRFIVLSNNYLQIRGIKKTDEGTYRCEGRILARGEINFKDIQVIVNVPPTIQARQNIVNATANLGQSVTLVCDAEGFPEPTMSWTKDGEQIEQEEDDEKYIFSDDSSQLTIKKVDKNDEAEYICIAENKAGEQDATIHLKVFAKPKITYVENQTAMELEEQVTLTCEASGDPIPSITWRTSTRNISSEEKASWTRPEKQETLDGHMVVRSHARVSSLTLKSIQYTDAGEYICTASNTIGQDSQSMYLEVQYAPKLQGPVAVYTWEGNQVNITCEVFAYPSATISWFRDGQLLPSSNYSNIKIYNTPSASYLEVTPDSENDFGNYNCTAVNRIGQESLEFILVQADTPSSPSIDQVEPYSSTAQVQFDEPEATGGVPILKYKAEWRAVGEEVWHSKWYDAKEASMEGIVTIVGLKPETTYAVRLAALNGKGLGEISAASEF'
binding = BindingAffinity(tfr)
seq = ["CC[C@H](C)[C@H](NC(=O)[C@H](C)NC(=O)[C@@H](N)Cc1c[nH]cn1)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](Cc1c[nH]cn1)C(=O)O"]
scores = binding(seq)
print(scores)
if __name__ == '__main__':
unittest() |