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638ea38 | 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 | import itertools
import numpy as np
import pandas as pd
from Bio.PDB import PDBParser, PPBuilder
from common import RES, EXT, abc
from structure_utils import alphafold_model, correspondence, kabsch, tm_score
AF = EXT / "alphafold"
A = abc().set_index("gene")
def ca_chain(gene):
s = PDBParser(QUIET=True).get_structure(gene, alphafold_model(A.uniprot[gene], AF))
res = [r for r in s[0].get_residues() if "CA" in r]
seq = "".join(str(pp.get_sequence()) for pp in PPBuilder().build_peptides(s[0]))
return seq, np.array([r["CA"].coord for r in res], dtype=float)
chains = {g: ca_chain(g) for g in A.index}
rows = []
for g1, g2 in itertools.combinations(A.index, 2):
(s1, x1), (s2, x2) = chains[g1], chains[g2]
i1, i2 = correspondence(s1, s2)
L = min(len(s1), len(s2))
d0 = 1.24 * (L - 15) ** (1 / 3) - 1.8
R, pc, qc = kabsch(x1[i1], x2[i2])
d = np.linalg.norm((x1[i1] - pc) @ R + qc - x2[i2], axis=1)
tm_single = np.sum(1 / (1 + (d / d0) ** 2)) / L
tm_opt, rmsd = tm_score(x1[i1], x2[i2], L)
rows.append((g1, g2, A.subfamily[g1] == A.subfamily[g2], round(tm_single, 4), round(tm_opt, 4), round(rmsd, 2), len(i1), L))
out = pd.DataFrame(rows, columns=["gene1", "gene2", "same_subfamily", "tmscore_single_superposition", "tmscore", "rmsd_core",
"aligned", "L"])
out.to_csv(RES / "abc_struct_sim.csv", index=False)
par = pd.read_csv(RES / "abc_paralog_pairs.csv") if (RES / "abc_paralog_pairs.csv").exists() else None
pd.set_option("display.width", 200)
if par is not None:
keys = {frozenset(p) for p in par[["gene1", "gene2"]].itertuples(index=False)}
out["paralog"] = [frozenset((a, b)) in keys for a, b in zip(out.gene1, out.gene2)]
out.to_csv(RES / "abc_struct_sim.csv", index=False)
from scipy import stats
I = pd.read_csv(RES / "abc_identity.csv", index_col=0)
genes = list(A.index)
n = len(genes)
T = pd.DataFrame(np.nan, index=genes, columns=genes)
for g1, g2, tm in zip(out.gene1, out.gene2, out.tmscore):
T.loc[g1, g2] = T.loc[g2, g1] = tm
iu = np.triu_indices(n, 1)
Iv, Tv = I.loc[genes, genes].values, T.values
obs = stats.spearmanr(Iv[iu], Tv[iu]).statistic
rng = np.random.default_rng(0)
B = 10000
null = np.array([stats.spearmanr(Iv[iu], Tv[np.ix_(p, p)][iu]).statistic for p in (rng.permutation(n) for _ in range(B))])
p_mantel = (np.sum(np.abs(null) >= abs(obs)) + 1) / (B + 1)
pd.DataFrame([("identity vs TM-score, Mantel", obs, p_mantel, len(iu[0]), B)],
columns=["test", "rho", "p_gene_label_permutation", "pairs", "permutations"]).to_csv(
RES / "abc_identity_tm_mantel.csv", index=False)
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