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d766458 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | import os,sys
from colabdesign.mpnn import mk_mpnn_model
from colabdesign.af import mk_af_model
from colabdesign.shared.protein import pdb_to_string
from colabdesign.shared.parse_args import parse_args
import pandas as pd
import numpy as np
from string import ascii_uppercase, ascii_lowercase
alphabet_list = list(ascii_uppercase+ascii_lowercase)
def get_info(contig):
F = []
free_chain = False
fixed_chain = False
sub_contigs = [x.split("-") for x in contig.split("/")]
for n,(a,b) in enumerate(sub_contigs):
if a[0].isalpha():
L = int(b)-int(a[1:]) + 1
F += [1] * L
fixed_chain = True
else:
L = int(b)
F += [0] * L
free_chain = True
return F,[fixed_chain,free_chain]
def main(argv):
ag = parse_args()
ag.txt("-------------------------------------------------------------------------------------")
ag.txt("Designability Test")
ag.txt("-------------------------------------------------------------------------------------")
ag.txt("REQUIRED")
ag.txt("-------------------------------------------------------------------------------------")
ag.add(["pdb=" ], None, str, ["input pdb"])
ag.add(["loc=" ], None, str, ["location to save results"])
ag.add(["contigs=" ], None, str, ["contig definition"])
ag.txt("-------------------------------------------------------------------------------------")
ag.txt("OPTIONAL")
ag.txt("-------------------------------------------------------------------------------------")
ag.add(["copies=" ], 1, int, ["number of repeating copies"])
ag.add(["num_seqs=" ], 8, int, ["number of mpnn designs to evaluate"])
ag.add(["initial_guess" ], False, None, ["initialize previous coordinates"])
ag.add(["use_multimer" ], False, None, ["use alphafold_multimer_v3"])
ag.add(["use_soluble" ], False, None, ["use solubleMPNN"])
ag.add(["num_recycles=" ], 3, int, ["number of recycles"])
ag.add(["rm_aa="], "C", str, ["disable specific amino acids from being sampled"])
ag.add(["num_designs=" ], 1, int, ["number of designs to evaluate"])
ag.add(["mpnn_sampling_temp=" ], 0.1, float, ["sampling temperature used by proteinMPNN"])
ag.txt("-------------------------------------------------------------------------------------")
o = ag.parse(argv)
if None in [o.pdb, o.loc, o.contigs]:
ag.usage("Missing Required Arguments")
if o.rm_aa == "":
o.rm_aa = None
# filter contig input
contigs = []
for contig_str in o.contigs.replace(" ",":").replace(",",":").split(":"):
if len(contig_str) > 0:
contig = []
for x in contig_str.split("/"):
if x != "0": contig.append(x)
contigs.append("/".join(contig))
chains = alphabet_list[:len(contigs)]
info = [get_info(x) for x in contigs]
fixed_pos = []
fixed_chains = []
free_chains = []
both_chains = []
for pos,(fixed_chain,free_chain) in info:
fixed_pos += pos
fixed_chains += [fixed_chain and not free_chain]
free_chains += [free_chain and not fixed_chain]
both_chains += [fixed_chain and free_chain]
flags = {"initial_guess":o.initial_guess,
"best_metric":"rmsd",
"use_multimer":o.use_multimer,
"model_names":["model_1_multimer_v3" if o.use_multimer else "model_1_ptm"]}
if sum(both_chains) == 0 and sum(fixed_chains) > 0 and sum(free_chains) > 0:
protocol = "binder"
print("protocol=binder")
target_chains = []
binder_chains = []
for n,x in enumerate(fixed_chains):
if x: target_chains.append(chains[n])
else: binder_chains.append(chains[n])
af_model = mk_af_model(protocol="binder",**flags)
prep_flags = {"target_chain":",".join(target_chains),
"binder_chain":",".join(binder_chains),
"rm_aa":o.rm_aa}
opt_extra = {}
elif sum(fixed_pos) > 0:
protocol = "partial"
print("protocol=partial")
af_model = mk_af_model(protocol="fixbb",
use_templates=True,
**flags)
rm_template = np.array(fixed_pos) == 0
prep_flags = {"chain":",".join(chains),
"rm_template":rm_template,
"rm_template_seq":rm_template,
"copies":o.copies,
"homooligomer":o.copies>1,
"rm_aa":o.rm_aa}
else:
protocol = "fixbb"
print("protocol=fixbb")
af_model = mk_af_model(protocol="fixbb",**flags)
prep_flags = {"chain":",".join(chains),
"copies":o.copies,
"homooligomer":o.copies>1,
"rm_aa":o.rm_aa}
batch_size = 8
if o.num_seqs < batch_size:
batch_size = o.num_seqs
print("running proteinMPNN...")
sampling_temp = o.mpnn_sampling_temp
mpnn_model = mk_mpnn_model(weights="soluble" if o.use_soluble else "original")
outs = []
pdbs = []
for m in range(o.num_designs):
if o.num_designs == 0:
pdb_filename = o.pdb
else:
pdb_filename = o.pdb.replace("_0.pdb",f"_{m}.pdb")
pdbs.append(pdb_filename)
af_model.prep_inputs(pdb_filename, **prep_flags)
if protocol == "partial":
p = np.where(fixed_pos)[0]
af_model.opt["fix_pos"] = p[p < af_model._len]
mpnn_model.get_af_inputs(af_model)
outs.append(mpnn_model.sample(num=o.num_seqs//batch_size, batch=batch_size, temperature=sampling_temp))
if protocol == "binder":
af_terms = ["plddt","i_ptm","i_pae","rmsd"]
elif o.copies > 1:
af_terms = ["plddt","ptm","i_ptm","pae","i_pae","rmsd"]
else:
af_terms = ["plddt","ptm","pae","rmsd"]
labels = ["design","n","score"] + af_terms + ["seq"]
data = []
best = {"rmsd":np.inf,"design":0,"n":0}
print("running AlphaFold...")
os.system(f"mkdir -p {o.loc}/all_pdb")
with open(f"{o.loc}/design.fasta","w") as fasta:
for m,(out,pdb_filename) in enumerate(zip(outs,pdbs)):
out["design"] = []
out["n"] = []
af_model.prep_inputs(pdb_filename, **prep_flags)
for k in af_terms: out[k] = []
for n in range(o.num_seqs):
out["design"].append(m)
out["n"].append(n)
sub_seq = out["seq"][n].replace("/","")[-af_model._len:]
af_model.predict(seq=sub_seq, num_recycles=o.num_recycles, verbose=False)
for t in af_terms: out[t].append(af_model.aux["log"][t])
if "i_pae" in out:
out["i_pae"][-1] = out["i_pae"][-1] * 31
if "pae" in out:
out["pae"][-1] = out["pae"][-1] * 31
rmsd = out["rmsd"][-1]
if rmsd < best["rmsd"]:
best = {"design":m,"n":n,"rmsd":rmsd}
af_model.save_current_pdb(f"{o.loc}/all_pdb/design{m}_n{n}.pdb")
af_model._save_results(save_best=True, verbose=False)
af_model._k += 1
score_line = [f'design:{m} n:{n}',f'mpnn:{out["score"][n]:.3f}']
for t in af_terms:
score_line.append(f'{t}:{out[t][n]:.3f}')
print(" ".join(score_line)+" "+out["seq"][n])
line = f'>{"|".join(score_line)}\n{out["seq"][n]}'
fasta.write(line+"\n")
data += [[out[k][n] for k in labels] for n in range(o.num_seqs)]
af_model.save_pdb(f"{o.loc}/best_design{m}.pdb")
# save best
with open(f"{o.loc}/best.pdb", "w") as handle:
remark_text = f"design {best['design']} N {best['n']} RMSD {best['rmsd']:.3f}"
handle.write(f"REMARK 001 {remark_text}\n")
handle.write(open(f"{o.loc}/best_design{best['design']}.pdb", "r").read())
labels[2] = "mpnn"
df = pd.DataFrame(data, columns=labels)
df.to_csv(f'{o.loc}/mpnn_results.csv')
if __name__ == "__main__":
main(sys.argv[1:])
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