import os,torch from pyuul.sources.globalVariables import * from pyuul.sources import hashings import numpy as np import random def setup_seed(seed): torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) random.seed(seed) torch.backends.cudnn.deterministic = True setup_seed(100) def parseSDF(SDFFile): """ function to parse pdb files. It can be used to parse a single file or all the pdb files in a folder. In case a folder is given, the coordinates are gonna be padded Parameters ---------- SDFFile : str path of the PDB file or of the folder containing multiple PDB files Returns ------- coords : torch.Tensor coordinates of the atoms in the pdb file(s). Shape ( batch, numberOfAtoms, 3) atomNames : list a list of the atom identifier. It encodes atom type, residue type, residue position and chain """ if not os.path.isdir(SDFFile): fil = SDFFile totcoords=[] totaname=[] coords = [] atomNames = [] for line in open(fil).readlines(): a=line.strip().split() if len(a)==16: ## atom element = a[3] x = float(a[0]) y = float(a[1]) z = float(a[2]) coords += [[x,y,z]] #aname = line[17:20].strip()+"_"+str(resnum)+"_"+line[12:16].strip()+"_"+line[21] aname = "MOL"+"_"+"0"+"_"+element+"_"+"A" atomNames += [aname] elif "$$$$" in line: totcoords+=[torch.tensor(coords)] totaname += [atomNames] coords=[] atomNames=[] return torch.torch.nn.utils.rnn.pad_sequence(totcoords, batch_first=True, padding_value=PADDING_INDEX),totaname else: totcoords = [] totaname = [] for fil in sorted(os.listdir(SDFFile)): coords = [] atomNames = [] for line in open(SDFFile+fil).readlines(): a = line.strip().split() if len(a) == 16: ## atom element = a[3] x = float(a[0]) y = float(a[1]) z = float(a[2]) coords += [[x, y, z]] aname = "MOL"+"_"+"0"+"_"+element+"_"+"A" atomNames += [aname] elif "$$$$" in line: totcoords += [torch.tensor(coords)] totaname += [atomNames] coords = [] atomNames = [] return torch.torch.nn.utils.rnn.pad_sequence(totcoords, batch_first=True, padding_value=PADDING_INDEX),totaname def parsePDB(PDBFile,keep_only_chains=None,keep_hetatm=True,bb_only=False): """ function to parse pdb files. It can be used to parse a single file or all the pdb files in a folder. In case a folder is given, the coordinates are gonna be padded Parameters ---------- PDBFile : str path of the PDB file or of the folder containing multiple PDB files bb_only : bool if True ignores all the atoms but backbone N, C and CA keep_only_chains : str or None ignores all the chain but the one given. If None it keeps all chains keep_hetatm : bool if False it ignores heteroatoms Returns ------- coords : torch.Tensor coordinates of the atoms in the pdb file(s). Shape ( batch, numberOfAtoms, 3) atomNames : list a list of the atom identifier. It encodes atom type, residue type, residue position and chain """ bbatoms = ["N", "CA", "C"] if not os.path.isdir(PDBFile): fil = PDBFile coords = [] atomNames = [] cont = -1 oldres=-999 for line in open(fil).readlines(): if line[:4] == "ATOM": if keep_only_chains is not None and (not line[21] in keep_only_chains): continue if bb_only and not line[12:16].strip() in bbatoms: continue if oldres != int(line[22:26]): cont+=1 oldres=int(line[22:26]) resnum = int(line[22:26]) atomNames += [line[17:20].strip()+"_"+str(resnum)+"_"+line[12:16].strip()+"_"+line[21]] x = float(line[30:38]) y = float(line[38:46]) z = float(line[47:54]) coords+=[[x,y,z]] elif line[:6] == "HETATM" and keep_hetatm: resname_het = line[17:20].strip() resnum = int(line[22:26]) x = float(line[30:38]) y = float(line[38:46]) z = float(line[47:54]) coords += [[x, y, z]] atnameHet = line[12:16].strip() atomNames += [resname_het+"_"+str(resnum)+"_"+atnameHet+"_"+line[21]] return torch.tensor(coords).unsqueeze(0), [atomNames] else: coords = [] atomNames = [] pdbname = [] pdb_num = 0 for fil in sorted(os.listdir(PDBFile)): # print(pdb_num) pdb_num +=1 pdbname.append(fil) atomNamesTMP = [] coordsTMP = [] cont = -1 oldres=-999 for line in open(PDBFile+"/"+fil).readlines(): if line[:4] == "ATOM": if keep_only_chains is not None and (not line[21] in keep_only_chains): continue if bb_only and not line[12:16].strip() in bbatoms: continue if oldres != int(line[22:26]): cont += 1 oldres = int(line[22:26]) resnum = int(line[22:26]) atomNamesTMP += [line[17:20].strip()+"_"+str(resnum)+"_"+line[12:16].strip()+"_"+line[21]] x = float(line[30:38]) y = float(line[38:46]) z = float(line[47:54]) coordsTMP+=[[x,y,z]] elif line[:6] == "HETATM" and keep_hetatm: if line[17:20].strip()!="GTP": continue x = float(line[30:38]) y = float(line[38:46]) z = float(line[47:54]) resnum = int(line[22:26]) coordsTMP += [[x, y, z]] atnameHet = line[12:16].strip() atomNamesTMP += ["HET_"+str(resnum)+"_"+atnameHet+"_"+line[21]] coords+=[torch.tensor(coordsTMP)] atomNames += [atomNamesTMP] return torch.torch.nn.utils.rnn.pad_sequence(coords, batch_first=True, padding_value=PADDING_INDEX), atomNames, pdbname, pdb_num def atomlistToChannels(atomNames, hashing="Element_Hashing", device="cpu"): """ function to get channels from atom names (obtained parsing the pdb files with the parsePDB function) Parameters ---------- atomNames : list atom names obtained parsing the pdb files with the parsePDB function hashing : "TPL_Hashing" or "Element_Hashing" or dict define which atoms are grouped together. You can use two default hashings or build your own hashing: TPL_Hashing: uses the hashing of torch protein library (https://github.com/lupoglaz/TorchProteinLibrary) Element_Hashing: groups atoms in accordnce with the element only: C -> 0, N -> 1, O ->2, P ->3, S- >4, H ->5, everything else ->6 Alternatively, if you are not happy with the default hashings, you can build a dictionary of dictionaries that defines the channel of every atom type in the pdb. the first dictionary has the residue tag (three letters amino acid code) as key (3 letters compound name for hetero atoms, as written in the PDB file) every residue key is associated to a dictionary, which the atom tags (as written in the PDB files) as keys and the channel (int) as value for example, you can define the channels just based on the atom element as following: { 'CYS': {'N': 1, 'O': 2, 'C': 0, 'SG': 3, 'CB': 0, 'CA': 0}, # channels for cysteine atoms 'GLY': {'N': 1, 'O': 2, 'C': 0, 'CA': 0}, # channels for glycine atom ... 'GOL': {'O1':2,'O2':2,'O3':2,'C1':0,'C2':0,'C3':0}, # channels for glycerol atom ... } The default encoding is the one that assigns a different channel to each element other encodings can be found in sources/hashings.py device : torch.device The device on which the model should run. E.g. torch.device("cuda") or torch.device("cpu:0") Returns ------- coords : torch.Tensor coordinates of the atoms in the pdb file(s). Shape ( batch, numberOfAtoms, 3) channels : torch.tensor the channel of every atom. Shape (batch,numberOfAtoms) """ if hashing == "TPL_Hashing": hashing = hashings.TPLatom_hash elif hashing == "Element_Hashing": hashing = hashings.elements_hash else: assert type(hashing) is dict if type(hashing[list(hashing.keys())[0]]) == dict: useResName = True else: useResName = False assert type(hashing[list(hashing.keys())[0]]) == int channels = [] for singleAtomList in atomNames: haTMP = [] for i in singleAtomList: resname = i.split("_")[0] atName = i.split("_")[2] # if resname=="HET": # atName="HET" if useResName: if resname in hashing and atName in hashing[resname]: haTMP += [hashing[resname][atName]] else: haTMP += [PADDING_INDEX] print("missing ", resname, atName) else: if atName in hashing: haTMP += [hashing[atName]] elif atName[0] in hashing: haTMP += [hashing[atName[0]]] elif hashing == "Element_Hashing": haTMP += [6] else: haTMP += [PADDING_INDEX] print("missing ", resname, atName) channels += [torch.tensor(haTMP, dtype=torch.float, device=device)] channels = torch.torch.nn.utils.rnn.pad_sequence(channels, batch_first=True, padding_value=PADDING_INDEX) return channels def atomlistToRadius(atomList, hashing="FoldX_radius", device="cpu"): """ function to get radius from atom names (obtained parsing the pdb files with the parsePDB function) Parameters ---------- atomNames : list atom names obtained parsing the pdb files with the parsePDB function hashing : FoldX_radius or dict "FoldX_radius" provides the radius used by the FoldX force field Alternatively, if you are not happy with the foldX radius, you can build a dictionary of dictionaries that defines the radius of every atom type in the pdb. The first dictionary has the residue tag (three letters amino acid code) as key (3 letters compound name for hetero atoms, as written in the PDB file) every residue key is associated to a dictionary, which the atom tags (as written in the PDB files) as keys and the radius (float) as value for example, you can define the radius as following: { 'CYS': {'N': 1.45, 'O': 1.37, 'C': 1.7, 'SG': 1.7, 'CB': 1.7, 'CA': 1.7}, # radius for cysteine atoms 'GLY': {'N': 1.45, 'O': 1.37, 'C': 1.7, 'CA': 1.7}, # radius for glycine atoms ... 'GOL': {'O1':1.37,'O2':1.37,'O3':1.37,'C1':1.7,'C2':1.7,'C3':1.7}, # radius for glycerol atoms ... } The default radius are the ones defined in FoldX Radius default dictionary can be found in sources/hashings.py device : torch.device The device on which the model should run. E.g. torch.device("cuda") or torch.device("cpu:0") Returns ------- coords : torch.Tensor coordinates of the atoms in the pdb file(s). Shape ( batch, numberOfAtoms, 3) radius : torch.tensor The radius of every atom. Shape (batch,numberOfAtoms) """ if hashing == "FoldX_radius": hashing = hashings.radius hahsingSomgleAtom = hashings.radiusSingleAtom else: assert type(hashing) is dict radius = [] for singleAtomList in atomList: haTMP = [] for i in singleAtomList: resname = i.split("_")[0] atName = i.split("_")[2] if resname in hashing and atName in hashing[resname]: haTMP += [hashing[resname][atName]] elif atName[0] in hahsingSomgleAtom: haTMP += [hahsingSomgleAtom[atName[0]]] else: haTMP += [1.0] print("missing ", resname, atName) radius += [torch.tensor(haTMP, dtype=torch.float, device=device)] radius = torch.torch.nn.utils.rnn.pad_sequence(radius, batch_first=True, padding_value=PADDING_INDEX) return radius ''' def write_pdb(batchedCoords, atomNames , name=None, output_folder="outpdb/"): #I need to add the chain id if name is None: name = range(len(batchedCoords)) for struct in range(len(name)): f = open(output_folder + str(name[struct]) + ".pdb", "w") coords=batchedCoords[struct].data.numpy() atname=atomNames[struct] for i in range(len(coords)): rnName = atname[i].split("_")[0]#hashings.resi_hash_inverse[resi_list[i]] atName = atname[i].split("_")[2]#hashings.atom_hash_inverse[resi_list[i]][atom_list[i]] pos = atname[i].split("_")[1] chain = "A" num = " " * (5 - len(str(i))) + str(i) a_name = atName + " " * (4 - len(atName)) numres = " " * (4 - len(str(pos))) + str(pos) x = round(float(coords[i][0]), 3) sx = str(x) while len(sx.split(".")[1]) < 3: sx += "0" x = " " * (8 - len(sx)) + sx y = round(float(coords[i][1]), 3) sy = str(y) while len(sy.split(".")[1]) < 3: sy += "0" y = " " * (8 - len(sy)) + sy z = round(float(coords[i][2]), 3) sz = str(z) while len(sz.split(".")[1]) < 3: sz += "0" z = " " * (8 - len(sz)) + sz chain = " " * (2 - len(chain)) + chain if rnName !="HET": f.write("ATOM " + num + " " + a_name + "" + rnName + chain + numres + " " + x + y + z + " 1.00 64.10 " + atName[0] + "\n") else: f.write("HETATM" + num + " " + a_name + "" + rnName + chain + numres + " " + x + y + z + " 1.00 64.10 " + atName[0] + "\n") '''