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ProRNA3D-single.py
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#!/usr/bin/python
# ProRNA3D-single
#
# Copyright (C) Bhattacharya Laboratory 2024
#
# ProRNA3D-single is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# ProRNA3D-single is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ProRNA3D-single. If not, see <http://www.gnu.org/licenses/>.
#
############################################################################
from ProRNA3D_net import *
import argparse
import os
import sys
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
from dgl.dataloading import GraphDataLoader
import operator
import dgl
import math
import numpy as np
import torch
from dataloader import buildGraph
from torch import nn, optim
from torch.nn import functional as F
from torch.utils.data import DataLoader
def to_np(x):
return x.cpu().detach().numpy()
def test_epoch(epoch, model, dataloader, PARS):
model.eval()
rloss = 0
for i, (data_feats) in enumerate(dataloader):
(tgtname, nodeFeats_prot, nodeFeats_rna, edges_prot, edges_rna, edge_att_prot, edge_att_rna, xyz_prot, xyz_rna) = data_feats
print(tgtname[0])
name = tgtname[0]
#print(edges[0].shape)
n_nodes_prot = len(nodeFeats_prot[0])
n_nodes_rna = len(nodeFeats_rna[0])
n_e_prot = len(edges_prot[0])
n_e_rna = len(edges_rna[0])
nodeFeats_prot = nodeFeats_prot.to(PARS.device)
nodeFeats_rna = nodeFeats_rna.to(PARS.device)
#print('node feat', nodeFeats_rna.shape)
xyz_prot = xyz_prot.to(PARS.device)
xyz_rna = xyz_rna.to(PARS.device)
edges_prot[0] = edges_prot[0].to(PARS.device)
edges_prot[1] = edges_prot[1].to(PARS.device)
edges_rna[0] = edges_rna[0].to(PARS.device)
edges_rna[1] = edges_rna[1].to(PARS.device)
edge_att_prot = edge_att_prot.to(PARS.device)
edge_att_rna = edge_att_rna.to(PARS.device)
nodeFeats_prot = nodeFeats_prot.squeeze()
nodeFeats_rna = nodeFeats_rna.squeeze()
xyz_prot = xyz_prot.squeeze()
xyz_rna = xyz_rna.squeeze()
edges_prot[0] = edges_prot[0].squeeze()
edges_prot[1] = edges_prot[1].squeeze()
edges_rna[0] = edges_rna[0].squeeze()
edges_rna[1] = edges_rna[1].squeeze()
edge_att_prot = edge_att_prot.squeeze()
edge_att_prot = edge_att_prot.unsqueeze(dim=1)
edge_att_rna = edge_att_rna.squeeze()
edge_att_rna = edge_att_rna.unsqueeze(dim=1)
pred = model(nodeFeats_prot, xyz_prot, edges_prot, edge_att_prot, nodeFeats_rna, xyz_rna, edges_rna, edge_att_rna)
pred = torch.nn.Softmax(1)(pred)
#pred = pred[:, :, :prot_feat2d.shape[-1]:, prot_feat2d.shape[-1]:]
pred = pred.detach().numpy()
#print(pred.shape)
frr = open(PARS.outdir + '/' + str(name) + '.10bins.out', 'w')
rrList = []
for protres in range(pred.shape[-2]):
for rnares in range(pred.shape[-1]):
#print(pred[0, : , protres, rnares])
rrList.append([str(protres+1), str(rnares+1), sum(pred[0, 0:1,protres,rnares]), sum(pred[0, :4, protres, rnares]), sum(pred[0, :8, protres, rnares]), sum(pred[0, :12, protres, rnares]), sum(pred[0, :16, protres, rnares]), sum(pred[0, :20, protres, rnares]), sum(pred[0, :24, protres, rnares]), sum(pred[0, :28, protres, rnares]), sum(pred[0, :32, protres, rnares]), sum(pred[0, :-1, protres, rnares])])
#fo.write(str(protres+1) + ' ' + str(rnares+1) + ' ' + str(pred[0,:,protres,rnares][-1]) + '\n')
dict1 = {}
for x in rrList:
dict1[(x[0], x[1], x[2], x[3], x[4], x[5], x[6], x[7], x[8], x[9], x[10])] = x[11]
sorted_rr = sorted(dict1.items(), key=operator.itemgetter(1))
sorted_rr.reverse()
count = 0
for sr in sorted_rr:
(i, j, p1, p2, p3, p4, p5, p6, p7, p8, p9), p10 = sr[0], sr[1]
frr.write(str(i))
frr.write(' ')
frr.write(str(j))
frr.write(' ' + str(p1) + ' ' + str(p2) + ' ' + str(p3) + ' ' + str(p4) + ' ' + str(p5) + ' ' + str(p6) + ' ' + str(p7) + ' ' + str(p8) + ' ' + str(p9) + ' ' + str(p10))
frr.write('\n')
count += 1
frr.close()
print('done!')
def print_usage():
print("\nUsage: ProRNA3D-single [options]\n")
print("Options:")
print(" -h, --help show this help message and exit")
print(" --model_state_dict MODEL_STATE_DICT")
print(" Saved model")
print(" --indir INDIR Path to input data containing distance maps and input features (default 'datasets/DNA_test_129_Preprocessing_AlphaFold2/')")
print(" --outdir OUTDIR Prediction output directory")
print(" --num_workers NUM_WORKERS")
print(" Number of data loader workers")
def main(PARS):
dataset = buildGraph()
inference_loader = GraphDataLoader(dataset, batch_size=1, shuffle=False)
# Get Model
model = EGNN_TriangularAttnNet(prot=1280,prot_egnn_hidden=128, prot_e=1, prot_egnn_layers=4, rna=640, rna_egnn_hidden=128, rna_e=1, rna_egnn_layers=4, mid_c=256, out_c=37, device=PARS.device, attention=True)
if not PARS.model_state_dict:
print("PARS.model_state_dict file must be set")
model.load_state_dict(torch.load(PARS.model_state_dict))
model.to(PARS.device)
test_epoch(0, model, inference_loader, PARS)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--model_state_dict', type=str, default=None,
help="Saved model")
parser.add_argument('--outdir', type=str, default='test/',
help="Prediction output directory")
parser.add_argument('--num_workers', type=int, default=4,
help="Number of data loader workers")
PARS, _ = parser.parse_known_args()
#basic input check
if not PARS.model_state_dict:
print('Error! Trained model must be provided. Exiting ...')
print_usage()
sys.exit()
if (PARS.outdir == ''):
print('Error! Path to the output directory must be provided. Exiting ...')
print_usage()
sys.exit()
#existance check
if not os.path.exists(PARS.model_state_dict):
print('Error! No such trained model exists. Exiting ...')
print_usage()
sys.exit()
if not os.path.exists(PARS.outdir):
print('Error! No such output directory exists. Exiting ...')
print_usage()
sys.exit()
#header
print("\n********************************************************************************")
print("* ProRNA3D-single *")
print("* Single-sequence protein-RNA complex structure prediction *")
print("* by geometric triangle-aware pairing of language models *")
print("* For comments, please email to dbhattacharya@vt.edu *")
print("********************************************************************************\n")
print('Inter protein-RNA hybrid interaction predictions for each target is being saved at ' + PARS.outdir + '/\n')
seed = 1992
torch.manual_seed(seed)
np.random.seed(seed)
PARS.device = torch.device('cpu') #cuda can be used if that is available
main(PARS)