import os
import gradio as gr
import re
import pandas as pd
from io import StringIO
import rdkit
from rdkit import Chem
from rdkit.Chem import AllChem, Draw
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from io import BytesIO
import tempfile
from rdkit import Chem
class PeptideAnalyzer:
def __init__(self):
self.bond_patterns = [
(r'OC\(=O\)', 'ester'), # Ester bond
(r'N\(C\)C\(=O\)', 'n_methyl'), # N-methylated peptide bond
(r'N[0-9]C\(=O\)', 'proline'), # Proline peptide bond
(r'NC\(=O\)', 'peptide'), # Standard peptide bond
(r'C\(=O\)N\(C\)', 'n_methyl_reverse'), # Reverse N-methylated
(r'C\(=O\)N[12]?', 'peptide_reverse') # Reverse peptide bond
]
# Three to one letter code mapping
self.three_to_one = {
'Ala': 'A', 'Cys': 'C', 'Asp': 'D', 'Glu': 'E',
'Phe': 'F', 'Gly': 'G', 'His': 'H', 'Ile': 'I',
'Lys': 'K', 'Leu': 'L', 'Met': 'M', 'Asn': 'N',
'Pro': 'P', 'Gln': 'Q', 'Arg': 'R', 'Ser': 'S',
'Thr': 'T', 'Val': 'V', 'Trp': 'W', 'Tyr': 'Y'
}
def is_peptide(self, smiles):
"""Check if the SMILES represents a peptide structure"""
mol = Chem.MolFromSmiles(smiles)
if mol is None:
return False
# Look for peptide bonds: NC(=O) pattern
peptide_bond_pattern = Chem.MolFromSmarts('[NH][C](=O)')
if mol.HasSubstructMatch(peptide_bond_pattern):
return True
# Look for N-methylated peptide bonds: N(C)C(=O) pattern
n_methyl_pattern = Chem.MolFromSmarts('[N;H0;$(NC)](C)[C](=O)')
if mol.HasSubstructMatch(n_methyl_pattern):
return True
return False
def is_cyclic(self, smiles):
"""Improved cyclic peptide detection"""
# Check for C-terminal carboxyl
if smiles.endswith('C(=O)O'):
return False, [], []
# Find all numbers used in ring closures
ring_numbers = re.findall(r'(?:^|[^c])[0-9](?=[A-Z@\(\)])', smiles)
# Find aromatic ring numbers
aromatic_matches = re.findall(r'c[0-9](?:ccccc|c\[nH\]c)[0-9]', smiles)
aromatic_cycles = []
for match in aromatic_matches:
numbers = re.findall(r'[0-9]', match)
aromatic_cycles.extend(numbers)
# Numbers that aren't part of aromatic rings are peptide cycles
peptide_cycles = [n for n in ring_numbers if n not in aromatic_cycles]
is_cyclic = len(peptide_cycles) > 0 and not smiles.endswith('C(=O)O')
return is_cyclic, peptide_cycles, aromatic_cycles
def split_on_bonds(self, smiles):
positions = []
used = set()
# Find Gly pattern first
gly_pattern = r'NCC\(=O\)'
for match in re.finditer(gly_pattern, smiles):
if not any(p in range(match.start(), match.end()) for p in used):
positions.append({
'start': match.start(),
'end': match.end(),
'type': 'gly',
'pattern': match.group()
})
used.update(range(match.start(), match.end()))
for pattern, bond_type in self.bond_patterns:
for match in re.finditer(pattern, smiles):
if not any(p in range(match.start(), match.end()) for p in used):
positions.append({
'start': match.start(),
'end': match.end(),
'type': bond_type,
'pattern': match.group()
})
used.update(range(match.start(), match.end()))
# Sort by position
positions.sort(key=lambda x: x['start'])
# Create segments
segments = []
if positions:
# First segment
if positions[0]['start'] > 0:
segments.append({
'content': smiles[0:positions[0]['start']],
'bond_after': positions[0]['pattern']
})
# Process segments
for i in range(len(positions)-1):
current = positions[i]
next_pos = positions[i+1]
if current['type'] == 'gly':
segments.append({
'content': 'NCC(=O)',
'bond_before': positions[i-1]['pattern'] if i > 0 else None,
'bond_after': next_pos['pattern']
})
segments.append({
'content': smiles[current['start']+7:next_pos['start']],
'bond_before': 'gly_bond',
'bond_after': next_pos['pattern']
})
else:
content = smiles[current['end']:next_pos['start']]
if content:
segments.append({
'content': content,
'bond_before': current['pattern'],
'bond_after': next_pos['pattern']
})
# Last segment
if positions[-1]['end'] < len(smiles):
segments.append({
'content': smiles[positions[-1]['end']:],
'bond_before': positions[-1]['pattern']
})
return segments
def clean_terminal_carboxyl(self, segment):
"""Remove C-terminal carboxyl only if it's the true terminus"""
content = segment['content']
# Only clean if:
# 1. Contains C(=O)O
# 2. No bond_after exists (meaning it's the last segment)
# 3. C(=O)O is at the end of the content
if 'C(=O)O' in content and not segment.get('bond_after'):
print('recognized?')
# Remove C(=O)O pattern regardless of position
cleaned = re.sub(r'\(C\(=O\)O\)', '', content)
# Remove any leftover empty parentheses
cleaned = re.sub(r'\(\)', '', cleaned)
print(cleaned)
return cleaned
return content
def identify_residue(self, segment):
"""Identify residue with Pro reconstruction"""
# Only clean terminal carboxyl if this is the last segment
content = self.clean_terminal_carboxyl(segment)
mods = self.get_modifications(segment)
# Proline (P) - flexible ring numbers
if any([
# Check for any ring number in bond patterns
(segment.get('bond_after', '').startswith(f'N{n}C(=O)') and 'CCC' in content and
any(f'[C@@H]{n}' in content or f'[C@H]{n}' in content for n in '123456789'))
for n in '123456789'
]) or any([(segment.get('bond_before', '').startswith(f'C(=O)N{n}') and 'CCC' in content and
any(f'CCC{n}' for n in '123456789'))
for n in '123456789'
]) or any([
# Check ending patterns with any ring number
(f'CCCN{n}' in content and content.endswith('=O') and
any(f'[C@@H]{n}' in content or f'[C@H]{n}' in content for n in '123456789'))
for n in '123456789'
]) or any([
# Handle CCC[C@H]n patterns
(content == f'CCC[C@H]{n}' and segment.get('bond_before', '').startswith(f'C(=O)N{n}')) or
(content == f'CCC[C@@H]{n}' and segment.get('bond_before', '').startswith(f'C(=O)N{n}')) or
# N-terminal Pro with any ring number
(f'N{n}CCC[C@H]{n}' in content) or
(f'N{n}CCC[C@@H]{n}' in content)
for n in '123456789'
]):
return 'Pro', mods
# Tryptophan (W) - more specific indole pattern
if re.search(r'c[0-9]c\[nH\]c[0-9]ccccc[0-9][0-9]', content) and \
'c[nH]c' in content.replace(' ', ''):
return 'Trp', mods
# Lysine (K)
if '[C@@H](CCCCN)' in content or '[C@H](CCCCN)' in content:
return 'Lys', mods
# Arginine (R)
if '[C@@H](CCCNC(=N)N)' in content or '[C@H](CCCNC(=N)N)' in content:
return 'Arg', mods
if ('NCC(=O)' in content) or (content == 'C'):
if segment.get('bond_before') and segment.get('bond_after'):
if ('C(=O)N' in segment['bond_before'] or 'C(=O)N(C)' in segment['bond_before']):
return 'Gly', mods
elif segment.get('bond_before') and segment.get('bond_before').startswith('C(=O)N'):
return 'Gly', mods
if 'CC(C)C[C@H]' in content or 'CC(C)C[C@@H]' in content or '[C@@H](CC(C)C)' in content or '[C@H](CC(C)C)' in content or (('N[C@H](CCC(C)C)' in content or 'N[C@@H](CCC(C)C)' in content) and segment.get('bond_before') is None):
return 'Leu', mods
if '[C@@H]([C@@H](C)O)' in content or '[C@H]([C@H](C)O)' in content:
return 'Thr', mods
if re.search(r'\[C@H\]\(Cc\d+ccccc\d+\)', content) or re.search(r'\[C@@H\]\(Cc\d+ccccc\d+\)', content):
return 'Phe', mods
if ('[C@H](C(C)C)' in content or
'[C@@H](C(C)C)' in content or
'[C@H]C(C)C' in content or
'[C@@H]C(C)C' in content
):
if not any(p in content for p in ['CC(C)C[C@H]', 'CC(C)C[C@@H]']): # Still check not Leu
return 'Val', mods
if any([
'CC[C@H](C)' in content,
'CC[C@@H](C)' in content,
'[C@@H](CC)C' in content,
'[C@H](CC)C' in content,
'C(C)C[C@H]' in content and 'CC(C)C' not in content,
'C(C)C[C@@H]' in content and 'CC(C)C' not in content
]):
return 'Ile', mods
if ('[C@H](C)' in content or '[C@@H](C)' in content):
if not any(p in content for p in ['C(C)C', 'COC', 'CN(', 'C(C)O', 'CC[C@H]', 'CC[C@@H]']):
return 'Ala', mods
# Tyrosine (Tyr) - 4-hydroxybenzyl side chain
if re.search(r'Cc[0-9]ccc\(O\)cc[0-9]', content):
return 'Tyr', mods
# Serine (Ser) - Hydroxymethyl side chain
if '[C@H](CO)' in content or '[C@@H](CO)' in content:
if not ('C(C)O' in content or 'COC' in content):
return 'Ser', mods
# Threonine (Thr) - 1-hydroxyethyl side chain
if '[C@@H]([C@@H](C)O)' in content or '[C@H]([C@H](C)O)' in content or '[C@@H](C)O' in content or '[C@H](C)O' in content:
return 'Thr', mods
# Cysteine (Cys) - Thiol side chain
if '[C@H](CS)' in content or '[C@@H](CS)' in content:
return 'Cys', mods
# Methionine (Met) - Methylthioethyl side chain
if ('CCSC' in content):
return 'Met', mods
# Glutamine (Gln) - Carbamoylethyl side chain
if (content == '[C@@H](CC' or content == '[C@H](CC' and segment.get('bond_before')=='C(=O)N' and segment.get('bond_after')=='C(=O)N') or ('CCC(=O)N' in content) or ('CCC(N)=O' in content):
return 'Gln', mods
# Asparagine (Asn) - Carbamoylmethyl side chain
if (content == '[C@@H](C' or content == '[C@H](C' and segment.get('bond_before')=='C(=O)N' and segment.get('bond_after')=='C(=O)N') or ('CC(=O)N' in content) or ('CCN(=O)' in content) or ('CC(N)=O' in content):
return 'Asn', mods
# Glutamic acid (Glu) - Carboxyethyl side chain
if ('CCC(=O)O' in content):
return 'Glu', mods
# Aspartic acid (Asp) - Carboxymethyl side chain
if ('CC(=O)O' in content):
return 'Asp', mods
# Arginine (Arg) - 3-guanidinopropyl side chain
if ('CCCNC(=N)N' in content):
return 'Arg', mods
# Histidine (His) - Imidazole side chain
if re.search(r'Cc\d+c\[nH\]cn\d+', content) or re.search(r'Cc\d+cnc\[nH\]\d+', content):
return 'His', mods
############UAA
if '[C@H](COC(C)(C)C)' in content or '[C@@H](COC(C)(C)C)' in content:
return 'O-tBu', mods
if re.search(r'c\d+ccccc\d+', content):
if '[C@@H](c1ccccc1)' in content or '[C@H](c1ccccc1)' in content:
return '4', mods # Base phenylglycine
if ('C[C@H](CCCC)' in content or 'C[C@@H](CCCC)' in content) and 'CC(C)' not in content:
return 'Nle', mods
# Ornithine (Orn) - 3-carbon chain with NH2
if ('C[C@H](CCCN)' in content or 'C[C@@H](CCCN)' in content) and 'CC(C)' not in content:
return 'Orn', mods
# 2-Naphthylalanine (2Nal)
if ('Cc3cc2ccccc2c3' in content):
return '2Nal', mods
# Cyclohexylalanine (Cha)
if 'N2CCCCC2' in content or 'CCCCC2' in content:
return 'Cha', mods
# Aminobutyric acid (Abu) - 2-carbon chain
if ('C[C@H](CC)' in content or 'C[C@@H](CC)' in content) and not any(p in content for p in ['CC(C)', 'CCCC', 'CCC(C)']):
return 'Abu', mods
# Pipecolic acid (Pip)
if ('N3CCCCC3' in content or 'CCCCC3' in content):
return 'Pip', mods
# Cyclohexylglycine (Chg) - direct cyclohexyl without CH2
if ('C[C@H](C1CCCCC1)' in content or 'C[C@@H](C1CCCCC1)' in content):
return 'Chg', mods
# 4-Fluorophenylalanine (4F-Phe)
if ('Cc2ccc(F)cc2' in content):
return '4F-Phe', mods
# 4-substituted phenylalanines
if 'Cc1ccc' in content:
if 'OMe' in content or 'OCc1ccc' in content:
return '0A1', mods # 4-methoxy-Phenylalanine
elif 'Clc1ccc' in content:
return '200', mods # 4-chloro-Phenylalanine
elif 'Brc1ccc' in content:
return '4BF', mods # 4-Bromo-phenylalanine
elif 'C#Nc1ccc' in content:
return '4CF', mods # 4-cyano-phenylalanine
elif 'Ic1ccc' in content:
return 'PHI', mods # 4-Iodo-phenylalanine
elif 'Fc1ccc' in content:
return 'PFF', mods # 4-Fluoro-phenylalanine
# Modified tryptophans
if 'c[nH]c2' in content:
if 'Oc2cccc2' in content:
return '0AF', mods # 7-hydroxy-tryptophan
elif 'Fc2cccc2' in content:
return '4FW', mods # 4-fluoro-tryptophan
elif 'Clc2cccc2' in content:
return '6CW', mods # 6-chloro-tryptophan
elif 'Brc2cccc2' in content:
return 'BTR', mods # 6-bromo-tryptophan
elif 'COc2cccc2' in content:
return 'MOT5', mods # 5-Methoxy-tryptophan
elif 'Cc2cccc2' in content:
return 'MTR5', mods # 5-Methyl-tryptophan
# Special amino acids
if 'CC(C)(C)[C@@H]' in content or 'CC(C)(C)[C@H]' in content:
return 'BUG', mods # Tertleucine
if 'CCCNC(=N)N' in content:
return 'CIR', mods # Citrulline
if '[SeH]' in content:
return 'CSE', mods # Selenocysteine
if '[NH3]CC[C@@H]' in content or '[NH3]CC[C@H]' in content:
return 'DAB', mods # Diaminobutyric acid
if 'C1CCCCC1' in content:
if 'C1CCCCC1[C@@H]' in content or 'C1CCCCC1[C@H]' in content:
return 'CHG', mods # Cyclohexylglycine
elif 'C1CCCCC1C[C@@H]' in content or 'C1CCCCC1C[C@H]' in content:
return 'ALC', mods # 3-cyclohexyl-alanine
# Naphthalene derivatives
if 'c1cccc2c1cccc2' in content:
if 'c1cccc2c1cccc2[C@@H]' in content or 'c1cccc2c1cccc2[C@H]' in content:
return 'NAL', mods # 2-Naphthyl-alanine
# Heteroaromatic derivatives
if 'c1cncc' in content:
return 'PYR4', mods # 3-(4-Pyridyl)-alanine
if 'c1cscc' in content:
return 'THA3', mods # 3-(3-thienyl)-alanine
if 'c1nnc' in content:
return 'TRZ4', mods # 3-(1,2,4-Triazol-1-yl)-alanine
# Modified serines and threonines
if 'OP(O)(O)O' in content:
if '[C@@H](COP' in content or '[C@H](COP' in content:
return 'SEP', mods # phosphoserine
elif '[C@@H](OP' in content or '[C@H](OP' in content:
return 'TPO', mods # phosphothreonine
# Specialized ring systems
if 'c1c2ccccc2cc2c1cccc2' in content:
return 'ANTH', mods # 3-(9-anthryl)-alanine
if 'c1csc2c1cccc2' in content:
return 'BTH3', mods # 3-(3-benzothienyl)-alanine
if '[C@]12C[C@H]3C[C@@H](C2)C[C@@H](C1)C3' in content:
return 'ADAM', mods # Adamanthane
# Fluorinated derivatives
if 'FC(F)(F)' in content:
if 'CC(F)(F)F' in content:
return 'FLA', mods # Trifluoro-alanine
if 'C(F)(F)F)c1' in content:
if 'c1ccccc1C(F)(F)F' in content:
return 'TFG2', mods # 2-(Trifluoromethyl)-phenylglycine
if 'c1cccc(c1)C(F)(F)F' in content:
return 'TFG3', mods # 3-(Trifluoromethyl)-phenylglycine
if 'c1ccc(cc1)C(F)(F)F' in content:
return 'TFG4', mods # 4-(Trifluoromethyl)-phenylglycine
# Multiple halogen patterns
if 'F' in content and 'c1' in content:
if 'c1ccc(c(c1)F)F' in content:
return 'F2F', mods # 3,4-Difluoro-phenylalanine
if 'cc(F)cc(c1)F' in content:
return 'WFP', mods # 3,5-Difluoro-phenylalanine
if 'Cl' in content and 'c1' in content:
if 'c1ccc(cc1Cl)Cl' in content:
return 'CP24', mods # 2,4-dichloro-phenylalanine
if 'c1ccc(c(c1)Cl)Cl' in content:
return 'CP34', mods # 3,4-dichloro-phenylalanine
# Hydroxy and amino derivatives
if 'O' in content and 'c1' in content:
if 'c1cc(O)cc(c1)O' in content:
return '3FG', mods # (2s)-amino(3,5-dihydroxyphenyl)-ethanoic acid
if 'c1ccc(c(c1)O)O' in content:
return 'DAH', mods # 3,4-Dihydroxy-phenylalanine
# Modified histidines
if 'c1cnc' in content:
if '[C@@H]1CN[C@@H](N1)F' in content:
return '2HF', mods # 2-fluoro-l-histidine
if 'c1cnc([nH]1)F' in content:
return '2HF1', mods # 2-fluoro-l-histidine variant
if 'c1c[nH]c(n1)F' in content:
return '2HF2', mods # 2-fluoro-l-histidine variant
if '[SeH]' in content:
return 'CSE', mods # Selenocysteine
if 'S' in content:
if 'CSCc1ccccc1' in content:
return 'BCS', mods # benzylcysteine
if 'CCSC' in content:
return 'ESC', mods # Ethionine
if 'CCS' in content:
return 'HCS', mods # homocysteine
if 'CN=[N]=N' in content:
return 'AZDA', mods # azido-alanine
if '[NH]=[C](=[NH2])=[NH2]' in content:
if 'CCC[NH]=' in content:
return 'AGM', mods # 5-methyl-arginine
if 'CC[NH]=' in content:
return 'GDPR', mods # 2-Amino-3-guanidinopropionic acid
# Others
if 'C1CCCC1' in content:
return 'CPA3', mods # 3-Cyclopentyl-alanine
if 'C1CCCCC1' in content:
if 'CC1CCCCC1' in content:
return 'ALC', mods # 3-cyclohexyl-alanine
else:
return 'CHG', mods # Cyclohexylglycine
if 'CCC[C@@H]' in content or 'CCC[C@H]' in content:
return 'NLE', mods # Norleucine
if 'CC[C@@H]' in content or 'CC[C@H]' in content:
if not any(x in content for x in ['CC(C)', 'COC', 'CN(']):
return 'ABA', mods # 2-Aminobutyric acid
if 'CCON' in content:
return 'CAN', mods # canaline
if '[C@@H]1C=C[C@@H](C=C1)' in content:
return 'ACZ', mods # cis-amiclenomycin
if 'CCC(=O)[NH3]' in content:
return 'ONL', mods # 5-oxo-l-norleucine
if 'c1ccncc1' in content:
return 'PYR4', mods # 3-(4-Pyridyl)-alanine
if 'c1ccco1' in content:
return 'FUA2', mods # (2-furyl)-alanine
if 'c1ccc' in content:
if 'c1ccc(cc1)c1ccccc1' in content:
return 'BIF', mods # 4,4-biphenylalanine
if 'c1ccc(cc1)C(=O)c1ccccc1' in content:
return 'PBF', mods # 4-benzoyl-phenylalanine
if 'c1ccc(cc1)C(C)(C)C' in content:
return 'TBP4', mods # 4-tert-butyl-phenylalanine
if 'c1ccc(cc1)[C](=[NH2])=[NH2]' in content:
return '0BN', mods # 4-carbamimidoyl-l-phenylalanine
if 'c1cccc(c1)[C](=[NH2])=[NH2]' in content:
return 'APM', mods # m-amidinophenyl-3-alanine
if 'O' in content:
if '[C@H]([C@H](C)O)O' in content:
return 'ILX', mods # 4,5-dihydroxy-isoleucine
if '[C@H]([C@@H](C)O)O' in content:
return 'ALO', mods # Allo-threonine
if '[C@H](COP(O)(O)O)' in content:
return 'SEP', mods # phosphoserine
if '[C@H]([C@@H](C)OP(O)(O)O)' in content:
return 'TPO', mods # phosphothreonine
if '[C@H](c1ccc(O)cc1)O' in content:
return 'OMX', mods # (betar)-beta-hydroxy-l-tyrosine
if '[C@H](c1ccc(c(Cl)c1)O)O' in content:
return 'OMY', mods # (betar)-3-chloro-beta-hydroxy-l-tyrosine
if 'n1' in content:
if 'n1cccn1' in content:
return 'PYZ1', mods # 3-(1-Pyrazolyl)-alanine
if 'n1nncn1' in content:
return 'TEZA', mods # 3-(2-Tetrazolyl)-alanine
if 'c2c(n1)cccc2' in content:
return 'QU32', mods # 3-(2-Quinolyl)-alanine
if 'c1cnc2c(c1)cccc2' in content:
return 'QU33', mods # 3-(3-quinolyl)-alanine
if 'c1ccnc2c1cccc2' in content:
return 'QU34', mods # 3-(4-quinolyl)-alanine
if 'c1ccc2c(c1)nccc2' in content:
return 'QU35', mods # 3-(5-Quinolyl)-alanine
if 'c1ccc2c(c1)cncc2' in content:
return 'QU36', mods # 3-(6-Quinolyl)-alanine
if 'c1cnc2c(n1)cccc2' in content:
return 'QX32', mods # 3-(2-quinoxalyl)-alanine
if 'N' in content:
if '[NH3]CC[C@@H]' in content:
return 'DAB', mods # Diaminobutyric acid
if '[NH3]C[C@@H]' in content:
return 'DPP', mods # 2,3-Diaminopropanoic acid
if '[NH3]CCCCCC[C@@H]' in content:
return 'HHK', mods # (2s)-2,8-diaminooctanoic acid
if 'CCC[NH]=[C](=[NH2])=[NH2]' in content:
return 'GBUT', mods # 2-Amino-4-guanidinobutryric acid
if '[NH]=[C](=S)=[NH2]' in content:
return 'THIC', mods # Thio-citrulline
if 'CC' in content:
if 'CCCC[C@@H]' in content:
return 'AHP', mods # 2-Aminoheptanoic acid
if 'CCC([C@@H])(C)C' in content:
return 'I2M', mods # 3-methyl-l-alloisoleucine
if 'CC[C@H]([C@@H])C' in content:
return 'IIL', mods # Allo-Isoleucine
if '[C@H](CCC(C)C)' in content:
return 'HLEU', mods # Homoleucine
if '[C@@H]([C@@H](C)O)C' in content:
return 'HLU', mods # beta-hydroxyleucine
if '[C@@H]' in content:
if '[C@@H](C[C@@H](F))' in content:
return 'FGA4', mods # 4-Fluoro-glutamic acid
if '[C@@H](C[C@@H](O))' in content:
return '3GL', mods # 4-hydroxy-glutamic-acid
if '[C@@H](C[C@H](C))' in content:
return 'LME', mods # (3r)-3-methyl-l-glutamic acid
if '[C@@H](CC[C@H](C))' in content:
return 'MEG', mods # (3s)-3-methyl-l-glutamic acid
if 'S' in content:
if 'SCC[C@@H]' in content:
return 'HSER', mods # homoserine
if 'SCCN' in content:
return 'SLZ', mods # thialysine
if 'SC(=O)' in content:
return 'CSA', mods # s-acetonylcysteine
if '[S@@](=O)' in content:
return 'SME', mods # Methionine sulfoxide
if 'S(=O)(=O)' in content:
return 'OMT', mods # Methionine sulfone
if 'C=' in content:
if 'C=C[C@@H]' in content:
return '2AG', mods # 2-Allyl-glycine
if 'C=C[C@@H]' in content:
return 'LVG', mods # vinylglycine
if 'C=Cc1ccccc1' in content:
return 'STYA', mods # Styrylalanine
if '[C@@H]1Cc2c(C1)cccc2' in content:
return 'IGL', mods # alpha-amino-2-indanacetic acid
if '[C](=[C](=O)=O)=O' in content:
return '26P', mods # 2-amino-6-oxopimelic acid
if '[C](=[C](=O)=O)=C' in content:
return '2NP', mods # l-2-amino-6-methylene-pimelic acid
if 'c1cccc2c1cc(O)cc2' in content:
return 'NAO1', mods # 5-hydroxy-1-naphthalene
if 'c1ccc2c(c1)cc(O)cc2' in content:
return 'NAO2', mods # 6-hydroxy-2-naphthalene
return None, mods
def get_modifications(self, segment):
"""Get modifications based on bond types"""
mods = []
if segment.get('bond_after'):
if 'N(C)' in segment['bond_after'] or segment['bond_after'].startswith('C(=O)N(C)'):
mods.append('N-Me')
if 'OC(=O)' in segment['bond_after']:
mods.append('O-linked')
return mods
def analyze_structure(self, smiles):
"""Main analysis function with debug output"""
print("\nAnalyzing structure:", smiles)
# Split into segments
segments = self.split_on_bonds(smiles)
print("\nSegment Analysis:")
sequence = []
for i, segment in enumerate(segments):
print(f"\nSegment {i}:")
print(f"Content: {segment['content']}")
print(f"Bond before: {segment.get('bond_before', 'None')}")
print(f"Bond after: {segment.get('bond_after', 'None')}")
residue, mods = self.identify_residue(segment)
if residue:
if mods:
sequence.append(f"{residue}({','.join(mods)})")
else:
sequence.append(residue)
print(f"Identified as: {residue}")
print(f"Modifications: {mods}")
else:
print(f"Warning: Could not identify residue in segment: {segment['content']}")
# Check if cyclic
is_cyclic, peptide_cycles, aromatic_cycles = self.is_cyclic(smiles)
three_letter = '-'.join(sequence)
one_letter = ''.join(self.three_to_one.get(aa.split('(')[0], 'X') for aa in sequence)
if is_cyclic:
three_letter = f"cyclo({three_letter})"
one_letter = f"cyclo({one_letter})"
print(f"\nFinal sequence: {three_letter}")
print(f"One-letter code: {one_letter}")
print(f"Is cyclic: {is_cyclic}")
#print(f"Peptide cycles: {peptide_cycles}")
#print(f"Aromatic cycles: {aromatic_cycles}")
return {
'three_letter': three_letter,
'one_letter': one_letter,
'is_cyclic': is_cyclic
}
def annotate_cyclic_structure(mol, sequence):
"""Create structure visualization"""
AllChem.Compute2DCoords(mol)
drawer = Draw.rdMolDraw2D.MolDraw2DCairo(2000, 2000)
# Draw molecule first
drawer.drawOptions().addAtomIndices = False
drawer.DrawMolecule(mol)
drawer.FinishDrawing()
# Convert to PIL Image
img = Image.open(BytesIO(drawer.GetDrawingText()))
draw = ImageDraw.Draw(img)
try:
small_font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 60)
except OSError:
try:
small_font = ImageFont.truetype("arial.ttf", 60)
except OSError:
print("Warning: TrueType fonts not available, using default font")
small_font = ImageFont.load_default()
# Header
seq_text = f"Sequence: {sequence}"
bbox = draw.textbbox((1000, 100), seq_text, font=small_font)
padding = 10
draw.rectangle([bbox[0]-padding, bbox[1]-padding,
bbox[2]+padding, bbox[3]+padding],
fill='white', outline='white')
draw.text((1000, 100), seq_text,
font=small_font, fill='black', anchor="mm")
return img
def create_enhanced_linear_viz(sequence, smiles):
""""Linear visualization"""
analyzer = PeptideAnalyzer()
fig = plt.figure(figsize=(15, 10))
gs = fig.add_gridspec(2, 1, height_ratios=[1, 2])
ax_struct = fig.add_subplot(gs[0])
ax_detail = fig.add_subplot(gs[1])
if sequence.startswith('cyclo('):
residues = sequence[6:-1].split('-')
else:
residues = sequence.split('-')
segments = analyzer.split_on_bonds(smiles)
print(f"Number of residues: {len(residues)}")
print(f"Number of segments: {len(segments)}")
ax_struct.set_xlim(0, 10)
ax_struct.set_ylim(0, 2)
num_residues = len(residues)
spacing = 9.0 / (num_residues - 1) if num_residues > 1 else 9.0
y_pos = 1.5
for i in range(num_residues):
x_pos = 0.5 + i * spacing
rect = patches.Rectangle((x_pos-0.3, y_pos-0.2), 0.6, 0.4,
facecolor='lightblue', edgecolor='black')
ax_struct.add_patch(rect)
if i < num_residues - 1:
segment = segments[i] if i < len(segments) else None
if segment:
bond_type = 'ester' if 'O-linked' in segment.get('bond_after', '') else 'peptide'
is_n_methylated = 'N-Me' in segment.get('bond_after', '')
bond_color = 'red' if bond_type == 'ester' else 'black'
linestyle = '--' if bond_type == 'ester' else '-'
ax_struct.plot([x_pos+0.3, x_pos+spacing-0.3], [y_pos, y_pos],
color=bond_color, linestyle=linestyle, linewidth=2)
mid_x = x_pos + spacing/2
bond_label = f"{bond_type}"
if is_n_methylated:
bond_label += "\n(N-Me)"
ax_struct.text(mid_x, y_pos+0.1, bond_label,
ha='center', va='bottom', fontsize=10,
color=bond_color)
ax_struct.text(x_pos, y_pos-0.5, residues[i],
ha='center', va='top', fontsize=14)
ax_detail.set_ylim(0, len(segments)+1)
ax_detail.set_xlim(0, 1)
segment_y = len(segments)
for i, segment in enumerate(segments):
y = segment_y - i
# Check if this is a bond or residue
residue, mods = analyzer.identify_residue(segment)
if residue:
text = f"Residue {i+1}: {residue}"
if mods:
text += f" ({', '.join(mods)})"
color = 'blue'
else:
# Must be a bond
text = f"Bond {i}: "
if 'O-linked' in segment.get('bond_after', ''):
text += "ester"
elif 'N-Me' in segment.get('bond_after', ''):
text += "peptide (N-methylated)"
else:
text += "peptide"
color = 'red'
ax_detail.text(0.05, y, text, fontsize=12, color=color)
ax_detail.text(0.5, y, f"SMILES: {segment.get('content', '')}", fontsize=10, color='gray')
# If cyclic, add connection indicator
if sequence.startswith('cyclo('):
ax_struct.annotate('', xy=(9.5, y_pos), xytext=(0.5, y_pos),
arrowprops=dict(arrowstyle='<->', color='red', lw=2))
ax_struct.text(5, y_pos+0.3, 'Cyclic Connection',
ha='center', color='red', fontsize=14)
ax_struct.set_title("Peptide Structure Overview", pad=20)
ax_detail.set_title("Segment Analysis Breakdown", pad=20)
for ax in [ax_struct, ax_detail]:
ax.set_xticks([])
ax.set_yticks([])
ax.axis('off')
plt.tight_layout()
return fig
class PeptideStructureGenerator:
"""Generate 3D structures of peptides using different embedding methods"""
@staticmethod
def prepare_molecule(smiles):
"""Prepare molecule with proper hydrogen handling"""
mol = Chem.MolFromSmiles(smiles, sanitize=False)
if mol is None:
raise ValueError("Failed to create molecule from SMILES")
for atom in mol.GetAtoms():
atom.UpdatePropertyCache(strict=False)
# Sanitize with reduced requirements
Chem.SanitizeMol(mol,
sanitizeOps=Chem.SANITIZE_FINDRADICALS|
Chem.SANITIZE_KEKULIZE|
Chem.SANITIZE_SETAROMATICITY|
Chem.SANITIZE_SETCONJUGATION|
Chem.SANITIZE_SETHYBRIDIZATION|
Chem.SANITIZE_CLEANUPCHIRALITY)
mol = Chem.AddHs(mol)
return mol
@staticmethod
def get_etkdg_params(attempt=0):
"""Get ETKDG parameters"""
params = AllChem.ETKDGv3()
params.randomSeed = -1
params.maxIterations = 200
params.numThreads = 4 # Reduced for web interface
params.useBasicKnowledge = True
params.enforceChirality = True
params.useExpTorsionAnglePrefs = True
params.useSmallRingTorsions = True
params.useMacrocycleTorsions = True
params.ETversion = 2
params.pruneRmsThresh = -1
params.embedRmsThresh = 0.5
if attempt > 10:
params.bondLength = 1.5 + (attempt - 10) * 0.02
params.useExpTorsionAnglePrefs = False
return params
def generate_structure_etkdg(self, smiles, max_attempts=20):
"""Generate 3D structure using ETKDG without UFF optimization"""
success = False
mol = None
for attempt in range(max_attempts):
try:
mol = self.prepare_molecule(smiles)
params = self.get_etkdg_params(attempt)
if AllChem.EmbedMolecule(mol, params) == 0:
success = True
break
except Exception as e:
continue
if not success:
raise ValueError("Failed to generate structure with ETKDG")
return mol
def generate_structure_uff(self, smiles, max_attempts=20):
"""Generate 3D structure using ETKDG followed by UFF optimization"""
best_mol = None
lowest_energy = float('inf')
for attempt in range(max_attempts):
try:
test_mol = self.prepare_molecule(smiles)
params = self.get_etkdg_params(attempt)
if AllChem.EmbedMolecule(test_mol, params) == 0:
res = AllChem.UFFOptimizeMolecule(test_mol, maxIters=2000,
vdwThresh=10.0, confId=0,
ignoreInterfragInteractions=True)
if res == 0:
ff = AllChem.UFFGetMoleculeForceField(test_mol)
if ff:
current_energy = ff.CalcEnergy()
if current_energy < lowest_energy:
lowest_energy = current_energy
best_mol = Chem.Mol(test_mol)
except Exception:
continue
if best_mol is None:
raise ValueError("Failed to generate optimized structure")
return best_mol
@staticmethod
def mol_to_sdf_bytes(mol):
"""Convert RDKit molecule to SDF file bytes"""
sio = StringIO()
writer = Chem.SDWriter(sio)
writer.write(mol)
writer.close()
return sio.getvalue().encode('utf-8')
def process_input(smiles_input=None, file_obj=None, show_linear=False,
show_segment_details=False, generate_3d=False, use_uff=False):
"""Process input and create visualizations using PeptideAnalyzer"""
analyzer = PeptideAnalyzer()
temp_dir = tempfile.mkdtemp() if generate_3d else None
structure_files = []
# Handle direct SMILES input
if smiles_input:
smiles = smiles_input.strip()
if not analyzer.is_peptide(smiles):
return "Error: Input SMILES does not appear to be a peptide structure.", None, None
try:
mol = Chem.MolFromSmiles(smiles)
if mol is None:
return "Error: Invalid SMILES notation.", None, None
if generate_3d:
generator = PeptideStructureGenerator()
try:
# Generate ETKDG structure
mol_etkdg = generator.generate_structure_etkdg(smiles)
etkdg_path = os.path.join(temp_dir, "structure_etkdg.sdf")
writer = Chem.SDWriter(etkdg_path)
writer.write(mol_etkdg)
writer.close()
structure_files.append(etkdg_path)
# Generate UFF structure if requested
if use_uff:
mol_uff = generator.generate_structure_uff(smiles)
uff_path = os.path.join(temp_dir, "structure_uff.sdf")
writer = Chem.SDWriter(uff_path)
writer.write(mol_uff)
writer.close()
structure_files.append(uff_path)
except Exception as e:
return f"Error generating 3D structures: {str(e)}", None, None, None
segments = analyzer.split_on_bonds(smiles)
sequence_parts = []
output_text = ""
# Only include segment analysis in output if requested
if show_segment_details:
output_text += "Segment Analysis:\n"
for i, segment in enumerate(segments):
output_text += f"\nSegment {i}:\n"
output_text += f"Content: {segment['content']}\n"
output_text += f"Bond before: {segment.get('bond_before', 'None')}\n"
output_text += f"Bond after: {segment.get('bond_after', 'None')}\n"
residue, mods = analyzer.identify_residue(segment)
if residue:
if mods:
sequence_parts.append(f"{residue}({','.join(mods)})")
else:
sequence_parts.append(residue)
output_text += f"Identified as: {residue}\n"
output_text += f"Modifications: {mods}\n"
else:
output_text += f"Warning: Could not identify residue in segment: {segment['content']}\n"
output_text += "\n"
else:
for segment in segments:
residue, mods = analyzer.identify_residue(segment)
if residue:
if mods:
sequence_parts.append(f"{residue}({','.join(mods)})")
else:
sequence_parts.append(residue)
is_cyclic, peptide_cycles, aromatic_cycles = analyzer.is_cyclic(smiles)
three_letter = '-'.join(sequence_parts)
one_letter = ''.join(analyzer.three_to_one.get(aa.split('(')[0], 'X') for aa in sequence_parts)
if is_cyclic:
three_letter = f"cyclo({three_letter})"
one_letter = f"cyclo({one_letter})"
img_cyclic = annotate_cyclic_structure(mol, three_letter)
# Create linear representation if requested
img_linear = None
if show_linear:
fig_linear = create_enhanced_linear_viz(three_letter, smiles)
buf = BytesIO()
fig_linear.savefig(buf, format='png', bbox_inches='tight', dpi=300)
buf.seek(0)
img_linear = Image.open(buf)
plt.close(fig_linear)
summary = "Summary:\n"
summary += f"Sequence: {three_letter}\n"
summary += f"One-letter code: {one_letter}\n"
summary += f"Is Cyclic: {'Yes' if is_cyclic else 'No'}\n"
#if is_cyclic:
#summary += f"Peptide Cycles: {', '.join(peptide_cycles)}\n"
#summary += f"Aromatic Cycles: {', '.join(aromatic_cycles)}\n"
if structure_files:
summary += "\n3D Structures Generated:\n"
for filepath in structure_files:
summary += f"- {os.path.basename(filepath)}\n"
return summary + output_text, img_cyclic, img_linear, structure_files if structure_files else None
except Exception as e:
return f"Error processing SMILES: {str(e)}", None, None, None
# Handle file input
if file_obj is not None:
try:
if hasattr(file_obj, 'name'):
with open(file_obj.name, 'r') as f:
content = f.read()
else:
content = file_obj.decode('utf-8') if isinstance(file_obj, bytes) else str(file_obj)
output_text = ""
for line in content.splitlines():
smiles = line.strip()
if smiles:
if not analyzer.is_peptide(smiles):
output_text += f"Skipping non-peptide SMILES: {smiles}\n"
continue
segments = analyzer.split_on_bonds(smiles)
sequence_parts = []
if show_segment_details:
output_text += f"\nSegment Analysis for SMILES: {smiles}\n"
for i, segment in enumerate(segments):
output_text += f"\nSegment {i}:\n"
output_text += f"Content: {segment['content']}\n"
output_text += f"Bond before: {segment.get('bond_before', 'None')}\n"
output_text += f"Bond after: {segment.get('bond_after', 'None')}\n"
residue, mods = analyzer.identify_residue(segment)
if residue:
if mods:
sequence_parts.append(f"{residue}({','.join(mods)})")
else:
sequence_parts.append(residue)
output_text += f"Identified as: {residue}\n"
output_text += f"Modifications: {mods}\n"
else:
for segment in segments:
residue, mods = analyzer.identify_residue(segment)
if residue:
if mods:
sequence_parts.append(f"{residue}({','.join(mods)})")
else:
sequence_parts.append(residue)
is_cyclic, peptide_cycles, aromatic_cycles = analyzer.is_cyclic(smiles)
sequence = f"cyclo({'-'.join(sequence_parts)})" if is_cyclic else '-'.join(sequence_parts)
output_text += f"\nSummary for SMILES: {smiles}\n"
output_text += f"Sequence: {sequence}\n"
output_text += f"Is Cyclic: {'Yes' if is_cyclic else 'No'}\n"
if is_cyclic:
output_text += f"Peptide Cycles: {', '.join(peptide_cycles)}\n"
output_text += "-" * 50 + "\n"
return output_text, None, None
except Exception as e:
return f"Error processing file: {str(e)}", None, None
return "No input provided.", None, None
iface = gr.Interface(
fn=process_input,
inputs=[
gr.Textbox(
label="Enter SMILES string",
placeholder="Enter SMILES notation of peptide...",
lines=2
),
gr.File(
label="Or upload a text file with SMILES",
file_types=[".txt"]
),
gr.Checkbox(
label="Show linear representation",
value=False
),
gr.Checkbox(
label="Show segment details",
value=False
),
gr.Checkbox(
label="Generate 3D structure (sdf file format)",
value=False
),
gr.Checkbox(
label="Use UFF optimization (may take long)",
value=False
)
],
outputs=[
gr.Textbox(
label="Analysis Results",
lines=10
),
gr.Image(
label="2D Structure with Annotations",
type="pil"
),
gr.Image(
label="Linear Representation",
type="pil"
),
gr.File(
label="3D Structure Files",
file_count="multiple"
)
],
title="Peptide Structure Analyzer and Visualizer",
description="""
Analyze and visualize peptide structures from SMILES notation:
1. Validates if the input is a peptide structure
2. Determines if the peptide is cyclic
3. Parses the amino acid sequence
4. Creates 2D structure visualization with residue annotations
5. Optional linear representation
6. Optional 3D structure generation (ETKDG and UFF methods)
Input: Either enter a SMILES string directly or upload a text file containing SMILES strings
Example SMILES strings (copy and paste):
```
CC(C)C[C@@H]1NC(=O)[C@@H](CC(C)C)N(C)C(=O)[C@@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H]2CCCN2C1=O
```
```
C(C)C[C@@H]1NC(=O)[C@@H]2CCCN2C(=O)[C@@H](CC(C)C)NC(=O)[C@@H](CC(C)C)N(C)C(=O)[C@H](C)NC(=O)[C@H](Cc2ccccc2)NC1=O
```
```
CC(C)C[C@H]1C(=O)N(C)[C@@H](Cc2ccccc2)C(=O)NCC(=O)N[C@H](C(=O)N2CCCCC2)CC(=O)N(C)CC(=O)N[C@@H]([C@@H](C)O)C(=O)N(C)[C@@H](C)C(=O)N[C@@H](COC(C)(C)C)C(=O)N(C)[C@@H](Cc2ccccc2)C(=O)N1C
```
""",
flagging_mode="never"
)
if __name__ == "__main__":
iface.launch(share=True)