"""
Library of functions to perform sequence and structural alignments.
Main functions
--------------
* :py:func:`calc_rmsd`
* :py:func:`centroid`
* :py:func:`kabsch`
* :py:func:`load_coords`
* :py:func:`pdb2fastadic`
* :py:func:`get_atoms`
* :py:func:`get_align`
* :py:func:`align_struct`
* :py:func:`align_seq`
* :py:func:`make_range`
* :py:func:`dump_as_izone`
"""
import os
import shlex
import subprocess
from functools import partial
from pathlib import Path
import numpy as np
from Bio import Align
from Bio.Align import substitution_matrices
from Bio.Seq import Seq
from haddock import log
from haddock.core.typing import AtomsDict, FilePath, Literal, NDFloat, Optional
from haddock.libs.libio import pdb_path_exists
from haddock.libs.libontology import PDBFile, PDBPath
from haddock.libs.libpdb import (
slc_chainid,
slc_element,
slc_name,
slc_resname,
slc_resseq,
slc_x,
slc_y,
slc_z,
split_by_chain,
)
RES_TO_BE_IGNORED = ["SHA", "WAT"]
PROT_RES = [
"ALA",
"ARG",
"ASN",
"ASP",
"CYS",
"GLN",
"GLU",
"GLY",
"HIS",
"ILE",
"LEU",
"LYS",
"MET",
"PHE",
"PRO",
"SER",
"THR",
"TRP",
"TYR",
"VAL",
]
DNA_RES = ["DA", "DC", "DT", "DG"]
# Backbone
PROT_ATOMS = ["C", "N", "CA", "O"]
# Side chains
PROT_SIDE_CHAINS_DICT = {
"ALA": ["C", "N", "CA", "O", "CB"],
"ARG": ["C", "N", "CA", "O", "CB", "CG", "CD", "NE", "CZ", "NH1", "NH2"],
"ASN": ["C", "N", "CA", "O", "CB", "CG", "OD1", "ND2"],
"ASP": ["C", "N", "CA", "O", "CB", "CG", "OD1", "OD2"],
"CYS": ["C", "N", "CA", "O", "CB", "SG"],
"GLN": ["C", "N", "CA", "O", "CB", "CG", "CD", "OE1", "NE2"],
"GLU": ["C", "N", "CA", "O", "CB", "CG", "CD", "OE1", "OE2"],
"GLY": ["C", "N", "CA", "O"],
"HIS": ["C", "N", "CA", "O", "CB", "CG", "ND1", "CD2", "CE1", "NE2"],
"ILE": ["C", "N", "CA", "O", "CB", "CG1", "CG2", "CD1"],
"LEU": ["C", "N", "CA", "O", "CB", "CG", "CD1", "CD2"],
"LYS": ["C", "N", "CA", "O", "CB", "CG", "CD", "CE", "NZ"],
"MET": ["C", "N", "CA", "O", "CB", "CG", "SD", "CE"],
"PHE": ["C", "N", "CA", "O", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ"],
"PRO": ["C", "N", "CA", "O", "CB", "CG", "CD"],
"SER": ["C", "N", "CA", "O", "CB", "OG"],
"THR": ["C", "N", "CA", "O", "CB", "OG1", "CG2"],
"TRP": [
"C",
"N",
"CA",
"O",
"CB",
"CG",
"CD1",
"CD2",
"NE1",
"CE2",
"CE3",
"CZ2",
"CZ3",
"CH2",
],
"TYR": [
"C",
"N",
"CA",
"O",
"CB",
"CG",
"CD1",
"CD2",
"CE1",
"CE2",
"CZ",
"OH",
],
"VAL": ["C", "N", "CA", "O", "CB", "CG1", "CG2"],
}
# Bases
DNA_ATOMS = [
"C5",
"N9",
"N2",
"C8",
"O2",
"N4",
"N7",
"C7",
"N1",
"N6",
"C2",
"O4",
"C6",
"N3",
"C4",
"O6",
]
DNA_FULL_DICT = {
"DA": [
"P",
"O1P",
"O2P",
"O5'",
"C5'",
"C4'",
"O4'",
"C1'",
"N9",
"C4",
"N3",
"C2",
"N1",
"C6",
"N6",
"C5",
"N7",
"C8",
"C2'",
"C3'",
"O3'",
"N2",
"O2",
"N4",
"C7",
"O4",
"O6",
],
"DG": [
"P",
"O1P",
"O2P",
"O5'",
"C5'",
"C4'",
"O4'",
"C1'",
"N9",
"C4",
"N3",
"C2",
"N2",
"N1",
"C6",
"O6",
"C5",
"N7",
"C8",
"C2'",
"C3'",
"O3'",
"O2",
"N4",
"C7",
"N6",
"O4",
],
"DC": [
"P",
"O1P",
"O2P",
"O5'",
"C5'",
"C4'",
"O4'",
"C1'",
"N1",
"C6",
"C2",
"O2",
"N3",
"C4",
"N4",
"C5",
"C2'",
"C3'",
"O3'",
"N9",
"N2",
"C8",
"N7",
"C7",
"N6",
"O4",
"O6",
],
"DT": [
"P",
"O1P",
"O2P",
"O5'",
"C5'",
"C4'",
"O4'",
"C1'",
"N1",
"C6",
"C2",
"O2",
"N3",
"C4",
"O4",
"C5",
"C7",
"C2'",
"C3'",
"O3'",
"N9",
"N2",
"C8",
"N4",
"N7",
"N6",
"O6",
],
}
RNA_RES = ["A", "G", "C", "U"]
RNA_ATOMS = ["P", "O5'", "C5'", "C4'", "C3'", "O3'"]
RNA_FULL_DICT = {
"A": [
"P",
"OP1",
"OP2",
"O5'",
"C5'",
"C4'",
"O4'",
"C3'",
"O3'",
"C2'",
"O2'",
"C1'",
"N9",
"C8",
"N7",
"C5",
"C6",
"N6",
"N1",
"C2",
"N3",
"C4",
],
"G": [
"P",
"OP1",
"OP2",
"O5'",
"C5'",
"C4'",
"O4'",
"C3'",
"O3'",
"C2'",
"O2'",
"C1'",
"N9",
"C8",
"N7",
"C5",
"C6",
"O6",
"N1",
"C2",
"N2",
"N3",
"C4",
],
"C": [
"P",
"OP1",
"OP2",
"O5'",
"C5'",
"C4'",
"O4'",
"C3'",
"O3'",
"C2'",
"O2'",
"C1'",
"N9",
"C5",
"C6",
"O6",
"N1",
"C2",
"N2",
"N3",
"C4",
"N4",
],
"U": [
"P",
"OP1",
"OP2",
"O5'",
"C5'",
"C4'",
"O4'",
"C3'",
"O3'",
"C2'",
"O2'",
"C1'",
"N1",
"C2",
"O2",
"N3",
"C4",
"O4",
"C5",
"C6",
],
}
[docs]
class ALIGNError(Exception):
"""Raised when something goes wrong with the ALIGNMENT library."""
def __init__(self, msg: object = "") -> None:
self.msg = msg
super().__init__(self.msg)
[docs]
def calc_rmsd(V: NDFloat, W: NDFloat) -> float:
"""
Calculate the RMSD from two vectors.
Parameters
----------
V : np.array dtype=float, shape=(n_atoms,3)
W : np.array dtype=float, shape=(n_atoms,3)
Returns
-------
rmsd : float
"""
diff = np.array(V) - np.array(W)
N = len(V)
rmsd = np.sqrt((diff * diff).sum() / N)
return rmsd
[docs]
def kabsch(P: NDFloat, Q: NDFloat) -> NDFloat:
"""
Find the rotation matrix using Kabsch algorithm.
Parameters
----------
P : np.array dtype=float, shape=(n_atoms,3)
Q : np.array dtype=float, shape=(n_atoms,3)
Returns
-------
U : np.array dtype=float, shape=(3,3)
"""
# Covariance matrix
P = np.array(P)
Q = np.array(Q)
C = np.dot(np.transpose(P), Q)
# use SVD
V, S, W = np.linalg.svd(C)
d = (np.linalg.det(V) * np.linalg.det(W)) < 0.0
if d:
S[-1] = -S[-1]
V[:, -1] = -V[:, -1]
# Create Rotation matrix U
U = np.dot(V, W)
return U
[docs]
def centroid(X: NDFloat) -> NDFloat:
"""
Get the centroid.
Parameters
----------
X : np.array dtype=float, shape=(n_atoms,3)
Returns
-------
C : np.array dtype=float, shape=(3,)
"""
X = np.array(X)
C = X.mean(axis=0)
return C
[docs]
def load_coords(
pdb_f,
atoms,
filter_resdic=None,
numbering_dic=None,
model2ref_chain_dict=None,
add_resname=None,
keep_hetatm: bool = False,
):
"""Load coordinates from PDB.
Parameters
----------
pdb_f : PDBFile
atoms : dict
dictionary of atoms
filter_resdic : dict
dictionary of residues to be loaded (one list per chain)
numbering_dic : dict
dict of numbering dictionaries (one dictionary per chain)
add_resname : bool
use the residue name in the identifier
keep_hetatm : bool
Should HETATM lines be considered ?
Returns
-------
coord_dic : dict
dictionary of coordinates (one per chain)
chain_ranges: dict
dictionary of chain ranges
"""
coord_dic: CoordsDict = {}
chain_dic: ResDict = {}
idx: int = 0
# Set types of coordinates lines to extract
if keep_hetatm:
coordinates_line_to_extract = ("ATOM", "HETATM")
else:
coordinates_line_to_extract = ("ATOM", )
# Check filetype
if isinstance(pdb_f, PDBFile):
pdb_f = pdb_f.rel_path
# Read file
with open(pdb_f, "r") as fh:
for line in fh.readlines():
# Skip non ATOM records lines
if not line.startswith(coordinates_line_to_extract):
continue
# Extract PDB line data
atom_name = line[slc_name].strip()
resname = line[slc_resname].strip()
# Skip entries to be ignored
if resname in RES_TO_BE_IGNORED:
continue
else:
if atom_name not in atoms[resname]:
continue
# Continue parsing of the PDB line
chain = line[slc_chainid]
resnum = int(line[slc_resseq])
x = float(line[slc_x])
y = float(line[slc_y])
z = float(line[slc_z])
coords = np.asarray([x, y, z])
# Remap chain name
if model2ref_chain_dict:
# Skip chain matching if not present in reference structure
if chain not in model2ref_chain_dict.keys():
continue
chain = model2ref_chain_dict[chain]
if numbering_dic:
try:
resnum = numbering_dic[chain][resnum]
except KeyError:
# this residue is not matched, and so it should
# not be considered
# self.log(
# f"WARNING: {chain}.{resnum}.{atom_name}"
# " was not matched!"
# )
continue
# Create identifier tuple
if add_resname is True:
identifier = (chain, resnum, atom_name, resname)
else:
identifier = (chain, resnum, atom_name)
# Create empty chain entries
if chain not in chain_dic.keys():
if filter_resdic:
if chain in filter_resdic.keys():
chain_dic[chain] = []
else:
chain_dic[chain] = []
# Check if must eventually filter this entry
if filter_resdic:
# Only retrieve coordinates from the filter_resdic
if chain in filter_resdic.keys():
if resnum in filter_resdic[chain]:
coord_dic[identifier] = coords
chain_dic[chain].append(idx)
idx += 1
else:
# retrieve everything
coord_dic[identifier] = coords
chain_dic[chain].append(idx)
idx += 1
# Obtain chain ranges
chain_ranges: ChainsRange = {}
for chain, indice in chain_dic.items():
# Check if NONE residues indices were extracted (empty list)
if not indice:
continue
else:
min_idx = min(indice)
max_idx = max(indice)
chain_ranges[chain] = (min_idx, max_idx)
# Check that chain_ranges is not empty
# NOTE: this may happen when filter_resdic is defined on a different set
# of chains or residues
if chain_ranges == {}:
# Build error message
_err_msg = (
f"Chain matching error on {pdb_f}! "
f"Filtering scheme used: {filter_resdic}."
"\nPlease check the input file and queried filterings."
)
# Raise the error
raise ALIGNError(_err_msg)
return coord_dic, chain_ranges
[docs]
def get_atoms(pdb: PDBPath, full: bool = False) -> AtomsDict:
"""Identify what is the molecule type of each PDB.
Parameters
----------
pdb : PosixPath or :py:class:`haddock.libs.libontology.PDBFile`
PDB file to have its atoms identified
full : bool
Weather or not to take `full` atoms into consideration.
If False, only main-chain atoms retrieved.
If True, all heavy atoms retrieved.
Returns
-------
atom_dic : dict
dictionary of atoms
"""
atom_dic: AtomsDict = {}
atom_dic.update((r, PROT_ATOMS) for r in PROT_RES)
atom_dic.update((r, DNA_ATOMS) for r in DNA_RES)
atom_dic.update((r, RNA_ATOMS) for r in RNA_RES)
if full:
atom_dic.update(PROT_SIDE_CHAINS_DICT)
atom_dic.update(DNA_FULL_DICT)
atom_dic.update(RNA_FULL_DICT)
if isinstance(pdb, PDBFile):
pdb = pdb.rel_path
exists, msg = pdb_path_exists(pdb)
if not exists:
raise Exception(msg)
with open(pdb) as fh:
for line in fh.readlines():
if line.startswith(("ATOM", "HETATM")):
resname = line[slc_resname].strip()
atom_name = line[slc_name].strip()
element = line[slc_element].strip()
if all(
[
resname not in PROT_RES,
resname not in DNA_RES,
resname not in RNA_RES,
resname not in RES_TO_BE_IGNORED,
]
):
# its neither DNA/RNA nor protein, use the heavy atoms
# WARNING: Atoms that belong to unknown residues must
# be bound to a residue name;
# For example: residue NEP, also contains
# CB and CG atoms, if we do not bind it to the
# residue name, the next functions will include
# CG and CG atoms in the calculations for all
# other residue names
if element != "H":
if resname not in atom_dic:
atom_dic[resname] = []
if atom_name not in atom_dic[resname]:
atom_dic[resname].append(atom_name)
return atom_dic
ResCode = Literal[
"C",
"D",
"S",
"Q",
"K",
"I",
"P",
"T",
"F",
"N",
"G",
"H",
"L",
"R",
"W",
"A",
"V",
"E",
"Y",
"M",
"A",
"G",
"C",
"T",
"X",
]
"""
The single letter code of a residue.
Unrecognized residues' code is `X`.
"""
[docs]
def pdb2fastadic(pdb_f: PDBPath) -> dict[str, dict[int, str]]:
"""
Write the sequence as a fasta.
Parameters
----------
pdb_f : PosixPath or :py:class:`haddock.libs.libontology.PDBFile`
Returns
-------
seq_dic : dict
dict of fasta sequences (one per chain)
"""
res_codes = dict(
[
("CYS", "C"),
("ASP", "D"),
("SER", "S"),
("GLN", "Q"),
("LYS", "K"),
("ILE", "I"),
("PRO", "P"),
("THR", "T"),
("PHE", "F"),
("ASN", "N"),
("GLY", "G"),
("HIS", "H"),
("LEU", "L"),
("ARG", "R"),
("TRP", "W"),
("ALA", "A"),
("VAL", "V"),
("GLU", "E"),
("TYR", "Y"),
("MET", "M"),
("DA", "A"),
("DG", "G"),
("DC", "C"),
(
"DT",
"T",
), # 9/8/2023: adding non-standard amino-acids (src/haddock/cns/toppar/protein-allhdg5-4.top) # noqa: E501
("ALY", "K"),
("ASH", "D"),
("CFE", "C"),
("CSP", "C"),
("CYC", "C"),
("CYF", "C"),
("CYM", "C"),
("DDZ", "A"),
("GLH", "E"),
("HLY", "P"),
("HY3", "P"),
("HYP", "P"),
("M3L", "K"),
("MLY", "K"),
("MLZ", "K"),
("MSE", "M"),
("NEP", "H"),
("PNS", "S"),
("PTR", "Y"),
("SEP", "S"),
("TOP", "T"),
("TYP", "Y"),
("TYS", "Y"),
]
)
seq_dic: dict[str, dict[int, str]] = {}
if isinstance(pdb_f, PDBFile):
pdb_f = pdb_f.rel_path
with open(pdb_f) as fh:
for line in fh.readlines():
if line.startswith("ATOM"):
res_num = int(line[slc_resseq])
res_name = line[slc_resname].strip()
chain = line[slc_chainid]
if res_name in RES_TO_BE_IGNORED:
continue
try:
one_letter = res_codes[res_name]
except KeyError:
one_letter = "X"
if chain not in seq_dic:
seq_dic[chain] = {}
seq_dic[chain][res_num] = one_letter
return seq_dic
[docs]
def get_align(
method: str, lovoalign_exec: FilePath
) -> partial[dict[str, dict[int, int]]]:
"""
Get the alignment function.
Parameters
----------
method : str
Available options: ``sequence`` and ``structure``.
lovoalign_exec : str
Path to the lovoalign executable.
Returns
-------
align_func : functools.partial
desired alignment function
"""
if method == "structure":
align_func = partial(align_strct, lovoalign_exec=lovoalign_exec)
elif method == "sequence":
align_func = partial(align_seq)
else:
available_alns = ("sequence", "structure")
raise ValueError(
f"Alignment method {method!r} not recognized. "
f"Available options are {', '.join(available_alns)}"
)
return align_func
[docs]
def align_strct(
reference: PDBFile,
model: PDBFile,
output_path: FilePath,
lovoalign_exec: Optional[FilePath] = None,
) -> dict[str, dict[int, int]]:
"""
Structuraly align and get numbering relationship.
Parameters
----------
reference : :py:class:`haddock.libs.libontology.PDBFile`
model : :py:class:`haddock.libs.libontology.PDBFile`
output_path : Path
lovoalign_exec : Path
lovoalign executable
Returns
-------
numbering_dic : dict
dict of numbering dictionaries (one dictionary per chain)
"""
if lovoalign_exec is None:
log.error(
"Structural alignment needs LovoAlign " "get it at github.com/m3g/lovoalign"
)
raise ALIGNError("Path to LovoAlign executable required.")
if not lovoalign_exec:
raise ALIGNError("lovoalign_exec parameter not defined ")
if not os.access(lovoalign_exec, os.X_OK):
raise ALIGNError(f"{lovoalign_exec!r} for LovoAlign is not executable")
numbering_dic: dict[str, dict[int, int]] = {}
protein_a_dic = {
e.stem.split("_")[-1]: e for e in split_by_chain(reference.rel_path)
}
protein_b_dic = {e.stem.split("_")[-1]: e for e in split_by_chain(model.rel_path)}
# check if chain ids match
if protein_a_dic.keys() != protein_b_dic.keys():
# TODO: Make this a clearer raise
return numbering_dic
for chain in protein_a_dic.keys():
pa_seqdic = pdb2fastadic(protein_a_dic[chain])
pb_seqdic = pdb2fastadic(protein_b_dic[chain])
# logging.debug(f"Structurally aligning chain {chain}")
numbering_dic[chain] = {}
cmd = (
f"{lovoalign_exec} -p1 {protein_a_dic[chain]} "
f"-p2 {protein_b_dic[chain]} "
f"-c1 {chain} -c2 {chain}"
)
# logging.debug(f"Command is: {cmd}")
p = subprocess.run(shlex.split(cmd), capture_output=True, text=True)
lovoalign_out = p.stdout.split(os.linesep)
# we don"t need the splitted proteins anymore
protein_a_dic[chain].unlink()
protein_b_dic[chain].unlink()
# find out where the alignment starts and ends
alignment_pass = True
for i, line in enumerate(lovoalign_out):
if "SEQUENCE ALIGNMENT" in line:
# there are 2 extra white lines after this header
alignment_start_index = i + 2
elif "FINAL" in line:
# there are 2 extra white lines after this header
alignment_end_index = i - 2
elif "ERROR" in line:
failed_pdb = line.split()[-1]
_msg = f"LovoAlign could not read {failed_pdb} " "is it a ligand?"
log.warning(_msg)
alignment_pass = False
for elem in [k for k in pa_seqdic[chain]]:
numbering_dic[chain][elem] = elem
if not alignment_pass:
# This alignment failed, move on to the next
log.warning(
f"Skipping alignment of chain {chain}, " "used sequential matching"
)
continue
aln_l = lovoalign_out[alignment_start_index:alignment_end_index]
# dump this alignment to a file
aln_fname = Path(output_path, f"lovoalign_{chain}.aln")
with open(aln_fname, "w") as fh:
fh.write(os.linesep.join(aln_l))
# remove the line between the alignment segments
alignment = [aln_l[i : i + 3][:2] for i in range(0, len(aln_l), 3)]
# 100% (5 identical nucleotides / min(length(A),length(B))).
len_seq_a = len(pa_seqdic[chain])
len_seq_b = len(pb_seqdic[chain])
identity = (
(len_seq_a - sum([e[0].count("-") for e in alignment]))
/ min(len_seq_a, len_seq_b)
* 100
)
if identity <= 40.0:
log.warning(
f'"Structural" identity of chain {chain} is {identity:.2f}%'
", please check the results carefully"
)
else:
log.info(f'"Structural" identity of chain {chain} is {identity:.2f}%')
# logging.debug("Reading alignment and matching numbering")
for element in alignment:
line_a, line_b = element
resnum_a, seq_a, _ = line_a.split()
resnum_b, seq_b, _ = line_b.split()
resnum_a = int(resnum_a) - 1 # type: ignore
resnum_b = int(resnum_b) - 1 # type: ignore
for resname_a, resname_b in zip(seq_a, seq_b):
if resname_a != "-":
resnum_a += 1 # type: ignore
if resname_b != "-":
resnum_b += 1 # type: ignore
if resname_a != "-" and resname_b != "-":
numbering_dic[chain][resnum_b] = resnum_a # type: ignore
izone_fname = Path(output_path, "lovoalign.izone")
dump_as_izone(izone_fname, numbering_dic)
return numbering_dic
[docs]
def write_alignment(top_aln, output_path, ref_ch):
"""
Write the alignment to a file.
Parameters
----------
top_aln : Bio.Align.PairwiseAlignments
alignment object
ref_ch : str
reference chain
"""
aln_fname = Path(output_path, f"blosum62_{ref_ch}.aln")
with open(aln_fname, "w") as fh:
fh.write(str(top_aln))
return aln_fname
[docs]
def sequence_alignment(seq_ref, seq_model):
"""
Perform a sequence alignment.
Parameters
----------
seq_ref : str
reference sequence
seq_model : str
model sequence
Returns
-------
identity : float
sequence identity
top_aln : Bio.Align.PairwiseAlignments
alignment object
aln_ref_seg : tuple
aligned reference segment
aln_mod_seg : tuple
aligned model segment
"""
aligner = Align.PairwiseAligner()
aligner.substitution_matrix = substitution_matrices.load("BLOSUM62")
alns = aligner.align(seq_ref, seq_model)
top_aln = alns[0]
aln_denom = min(len(seq_ref), len(seq_model))
identity = (str(top_aln).count("|") / float(aln_denom)) * 100
aln_ref_seg, aln_mod_seg = top_aln.aligned
# check alignment length
len_ref = len(aln_ref_seg)
len_mod = len(aln_mod_seg)
if len_ref != len_mod:
raise ALIGNError(f"Alignment length mismatch ({len_ref} != {len_mod})")
return identity, top_aln, aln_ref_seg, aln_mod_seg
[docs]
class SeqAlign:
"""SeqAlign class."""
def __init__(self):
"""Initialize the class."""
self.align_dic = {} # the alignment dictionary, important for CAPRI
self.model2ref_chain_dict = {} # model to reference chain dictionary
self.ref2model_chain_dict = {} # reference to model chain dictionary
self.seqdic_ref = None # reference sequence dictionary
self.seqdic_model = None # model sequence dictionary
self.seqs_ref = {} # reference sequences
self.seqs_model = {} # model sequences
self.identities = [] # list of identity values
self.aln_model_segs = [] # list of aligned model segments
self.aln_ref_segs = [] # list of aligned reference segments
self.top_alns = [] # list of alignment objects
[docs]
def postprocess_alignment(self, ref_ch, mod_ch, align_id):
"""
Postprocess the alignment.
Parameters
----------
ref_ch : str
reference chain
mod_ch : str
model chain
align_id : int
alignment id (index of the alignment)
"""
self.align_dic[ref_ch] = {}
if not np.any(self.top_alns[align_id].aligned):
# No alignment!
log.warning(
f"No alignment for chain {ref_ch} is it protein/dna-rna? "
"Matching sequentially"
)
if all("X" in s for s in self.seqs_ref[ref_ch]) and all(
"X" in s for s in self.seqs_model[mod_ch]
):
# this sequence contains only ligands, do it manually
if len(self.seqs_ref[ref_ch]) != len(self.seqs_model[mod_ch]):
# we cannot handle this
# FIXME: This should raise a proper exception instead
raise f"Cannot align chain {mod_ch}" # noqa: B016
for ref_res, model_res in zip(
self.seqdic_ref[ref_ch], self.seqdic_model[mod_ch]
):
self.align_dic[ref_ch].update({model_res: ref_res})
else:
identity = self.identities[align_id]
if identity <= 40.0:
# Identity is very low
log.warning(
f"Sequence identity of chain {ref_ch} is "
f"{identity:.2f}%, please check the results carefully"
)
else:
log.debug(
f"Sequence identity between chain {ref_ch} "
f" of reference and {mod_ch} of model is "
f"{identity:.2f}%"
)
for ref_segment, model_segment in zip(
self.aln_ref_segs[align_id], self.aln_model_segs[align_id]
):
start_ref_segment, end_ref_segment = ref_segment
start_model_segment, end_model_segment = model_segment
reslist_ref = list(self.seqdic_ref[ref_ch].keys())[
start_ref_segment:end_ref_segment
]
reslist_model = list(self.seqdic_model[mod_ch].keys())[
start_model_segment:end_model_segment
]
for _ref_res, _model_res in zip(reslist_ref, reslist_model):
self.align_dic[ref_ch].update({_model_res: _ref_res})
[docs]
def align_seq(reference, model, output_path):
"""
Sequence align and get the numbering relationship.
Parameters
----------
reference : PosixPath or :py:class:`haddock.libs.libontology.PDBFile`
model : PosixPath or :py:class:`haddock.libs.libontology.PDBFile`
output_path : Path
Returns
-------
align_dic : dict
dictionary of sequence alignments (one per chain)
"""
SeqAln = SeqAlign()
SeqAln.seqdic_ref = pdb2fastadic(reference)
SeqAln.seqdic_model = pdb2fastadic(model)
# assign sequences
for ref_ch in SeqAln.seqdic_ref.keys():
ref_seq = Seq("".join(SeqAln.seqdic_ref[ref_ch].values()))
SeqAln.seqs_ref[ref_ch] = ref_seq
for mod_ch in SeqAln.seqdic_model.keys():
mod_seq = Seq("".join(SeqAln.seqdic_model[mod_ch].values()))
SeqAln.seqs_model[mod_ch] = mod_seq
# check if chain ids match
if SeqAln.seqdic_ref.keys() != SeqAln.seqdic_model.keys():
# they do not match, we need to do chain matching
n_partners = min(len(SeqAln.seqdic_ref.keys()), len(SeqAln.seqdic_model.keys()))
# unique combinations of chains
combs = []
for ref_key in SeqAln.seqdic_ref.keys():
for mod_key in SeqAln.seqdic_model.keys():
combs.append((ref_key, mod_key))
identities = []
aln_model_segs = []
aln_ref_segs = []
top_alns = []
# loop over combinations
for ref_ch, mod_ch in combs:
# align
identity, top_aln, aln_ref_seg, aln_mod_seg = sequence_alignment(
SeqAln.seqs_ref[ref_ch], SeqAln.seqs_model[mod_ch]
)
# append values to lists
identities.append(identity)
aln_model_segs.append(aln_mod_seg)
aln_ref_segs.append(aln_ref_seg)
top_alns.append(top_aln)
# chain matching
matches = 0 # counter for matched chains
while matches < n_partners:
# get the best alignment
max_identity = max(identities)
max_idx = identities.index(max_identity)
# assigning chains
ref_ch, mod_ch = combs[max_idx]
SeqAln.model2ref_chain_dict[mod_ch] = ref_ch
SeqAln.ref2model_chain_dict[ref_ch] = mod_ch
SeqAln.aln_ref_segs.append(aln_ref_segs[max_idx])
SeqAln.aln_model_segs.append(aln_model_segs[max_idx])
SeqAln.identities.append(identities[max_idx])
SeqAln.top_alns.append(top_alns[max_idx])
# writing the alignment
write_alignment(top_alns[max_idx], output_path, ref_ch)
# postprocess alignment
SeqAln.postprocess_alignment(ref_ch, mod_ch, matches)
# update identities to avoid double matches
identities = [
identities[n] if combs[n][0] != ref_ch and combs[n][1] != mod_ch else -1
for n in range(len(combs))
]
matches += 1
log.info(f"model2ref chain matching is {SeqAln.model2ref_chain_dict}")
else:
# chains do match. no need to do chain matching. Chains are the same.
# of course if the structures do not correspond the output will be a
# mess
matches = 0
for ref_ch in SeqAln.seqdic_ref.keys():
SeqAln.model2ref_chain_dict[ref_ch] = ref_ch
SeqAln.ref2model_chain_dict[ref_ch] = ref_ch
# extract sequences
seq_ref = SeqAln.seqs_ref[ref_ch]
seq_model = SeqAln.seqs_model[ref_ch]
# sequence alignment
identity, top_aln, aln_ref_seg, aln_mod_seg = sequence_alignment(
seq_ref, seq_model
)
# update quantities
SeqAln.identities.append(identity)
SeqAln.aln_model_segs.append(aln_mod_seg)
SeqAln.aln_ref_segs.append(aln_ref_seg)
SeqAln.top_alns.append(top_aln)
# write alignment
write_alignment(top_aln, output_path, ref_ch)
# postprocess alignment
SeqAln.postprocess_alignment(ref_ch, ref_ch, matches)
matches += 1
# dump the .izone file
izone_fname = Path(output_path, "blosum62.izone")
dump_as_izone(izone_fname, SeqAln.align_dic, SeqAln.ref2model_chain_dict)
return SeqAln.align_dic, SeqAln.model2ref_chain_dict
[docs]
def make_range(
chain_range_dic: dict[str, list[int]],
) -> dict[str, tuple[int, int]]:
"""
Expand a chain dictionary into ranges.
Parameters
----------
chain_range_dic : dict
dictionary of chain indexes (one list per chain)
Returns
-------
chain_ranges : dict
dictionary of chain ranges (one tuple per chain)
"""
chain_ranges: dict[str, tuple[int, int]] = {}
for chain in chain_range_dic:
min_idx = min(chain_range_dic[chain])
max_idx = max(chain_range_dic[chain])
chain_ranges[chain] = (min_idx, max_idx)
return chain_ranges
[docs]
def dump_as_izone(fname, numbering_dic, model2ref_chain_dict=None):
"""
Dump the numbering dictionary as .izone.
Parameters
----------
fname : str
output filename
numbering_dic : dict
dict of numbering dictionaries (one dictionary per chain)
"""
# FIXME: Collapse the izones so its faster to load in profit
with open(fname, "w") as fh:
for chain in numbering_dic:
for bound_res in numbering_dic[chain]:
unbound_res = numbering_dic[chain][bound_res]
unb_chain = chain
if model2ref_chain_dict:
unb_chain = model2ref_chain_dict[chain]
#
izone_str = (
"ZONE "
f"{chain}{bound_res}:{unb_chain}{unbound_res}"
f"{os.linesep}"
)
fh.write(izone_str)
[docs]
def rearrange_xyz_files(output_name: FilePath, path: FilePath, ncores: int) -> None:
"""Combine different xyz outputs in a single file.
Parameters
----------
output_name : FilePath
output name
path : FilePath
path to the output files
ncores : int
number of cores
"""
output_fname = Path(path, output_name)
# take the name without the xyz extension
output_fname_str = output_fname.stem
log.info(f"rearranging xyz files into {output_fname}")
# Combine files
with open(output_fname, "w") as out_file:
for core in range(ncores):
tmp_file = Path(path, output_fname_str + "_" + str(core) + ".xyz")
with open(tmp_file) as infile:
out_file.write(infile.read())
log.debug(f"File number {core} written")
tmp_file.unlink()
log.info("Completed reconstruction of xyz files.")
log.info(f"{output_fname} created.")
[docs]
def check_common_atoms(models, filter_resdic, allatoms, atom_similarity):
"""
Check if the models share the same atoms.
Parameters
----------
models : list
list of models
filter_resdic : dict
dictionary of residues to be loaded (one list per chain)
allatoms : bool
use all the heavy atoms
atom_similarity : float
minimum atom similarity required between models
Returns
-------
n_atoms : int
number of common atoms
common_keys : list
list of common atom keys
"""
# checking the common keys
common_keys: list[str] = []
coord_keys_lengths = []
for mod in models:
atoms: AtomsDict = get_atoms(mod, allatoms)
ref_coord_dic, _ = load_coords(mod, atoms, filter_resdic)
coord_keys_lengths.append(len(ref_coord_dic.keys()))
if common_keys != []:
common_keys = set(ref_coord_dic.keys()).intersection(common_keys)
else:
common_keys = ref_coord_dic.keys()
# checking the common atoms
n_atoms = len(common_keys) # common atoms
max_n_atoms = max(coord_keys_lengths)
perc = (n_atoms / max_n_atoms) * 100
if perc == 100.0:
log.info("All the models share the same atoms.")
elif perc > atom_similarity and perc < 100.0:
# if it's between 0.9 and 1, it's likely that the models share the same atoms
# but still the user may want to see a warning
log.warning(
"Not all the atoms are common to all the models."
f" Common atoms ({n_atoms}) != max_n_atoms {max_n_atoms}. Similarity ({perc:.2f}%) higher than allowed ({atom_similarity:.2f}%)."
)
else:
# common keys are less than 90% of the previous keys
# something is likely wrong
_err_msg = (
"Input atoms are not the same for all the models."
f" Common atoms ({n_atoms}) != max_n_atoms {max_n_atoms}. Similarity ({perc:.2f}%) lower than allowed ({atom_similarity:.2f}%)."
" Please check the input ensemble."
)
raise ALIGNError(_err_msg)
return n_atoms, list(common_keys)
# TODO: Add type signature
[docs]
def check_chains(obs_chains, inp_r_chain, inp_l_chains):
"""Check observed chains against the expected ones.
Logic: if at least one of inp_l_chains is among the observed chains and is
not selected as the receptor chain, then ligand_chains is equal to this
interesection. Otherwise, ligand_chains becomes equal to all the other
chains (once receptor chain is removed).
Parameters
----------
obs_chains : list
List of observed chains.
inp_r_chain : str
Receptor chain.
inp_l_chains : list
List of ligand chains.
"""
# Find receptor chain
r_chain = inp_r_chain if inp_r_chain in obs_chains else obs_chains[0]
# Remove it so cannot be selected as ligand chain
obs_chains.remove(r_chain)
# Define ligand chain(s)
l_chains = [el for el in inp_l_chains if el in obs_chains]
# If empty, select all remaining chains
if not l_chains:
l_chains = [el for el in obs_chains]
return r_chain, l_chains