mirror of
https://github.com/baker-laboratory/RoseTTAFold-All-Atom.git
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46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
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import numpy as np
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import torch
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from rf2aa.data.parsers import parse_mixed_fasta, parse_multichain_fasta
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from rf2aa.data.data_loader_utils import merge_a3m_hetero, merge_a3m_homo, blank_template
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from rf2aa.data.data_loader import RawInputData
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from rf2aa.util import get_protein_bond_feats
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def load_nucleic_acid(fasta_fn, input_type, model_runner):
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if input_type not in ["dna", "rna"]:
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raise ValueError("Only DNA and RNA inputs allowed for nucleic acids")
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if input_type == "dna":
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dna_alphabet = True
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rna_alphabet = False
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elif input_type == "rna":
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dna_alphabet = False
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rna_alphabet = True
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loader_params = model_runner.config.loader_params
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msa, ins, L = parse_multichain_fasta(fasta_fn, rna_alphabet=rna_alphabet, dna_alphabet=dna_alphabet)
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if (msa.shape[0] > loader_params["MAXSEQ"]):
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idxs_tokeep = np.random.permutation(msa.shape[0])[:loader_params["MAXSEQ"]]
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idxs_tokeep[0] = 0
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msa = msa[idxs_tokeep]
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ins = ins[idxs_tokeep]
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if len(L) > 1:
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raise ValueError("Please provide separate fasta files for each nucleic acid chain")
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L = L[0]
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xyz_t, t1d, mask_t, _ = blank_template(loader_params["n_templ"], L)
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bond_feats = get_protein_bond_feats(L)
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chirals = torch.zeros(0, 5)
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atom_frames = torch.zeros(0, 3, 2)
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return RawInputData(
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torch.from_numpy(msa),
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torch.from_numpy(ins),
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bond_feats,
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xyz_t,
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mask_t,
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t1d,
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chirals,
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atom_frames,
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taxids=None,
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)
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