从序列化输入到蛋白质结构预测（RoseTTAFold&AlphaFold2）

目录

1、背景介绍

2、生成.fasta代码

3、批量化运行RoseTTAFold并输出相应的特征

（1）下载RoseTTAFold安装包

（2）创建conda环境

（3）下载预训练权重

（4）下载第三方安装包

（5）下载相应的数据库：或者直接使用迅雷下载

（6）测试

4、批量化运行RoseTTAFold

（1）修改predict_e2e.py

 （2）修改make_msa.sh

（3）修改run_e2e_ver.sh

（4）批量化运行并输出结果

 5、AlphaFold2原理介绍和安装

.fasta文件或者.fa文件(两个是一样的)，这里是只是输出下面这种形式，在RoseTTAFold和alphafold中的输入形式是一样的

>T1078 Tsp1, Trichoderma virens, 138 residues|
MAAPTPADKSMMAAVPEWTITNLKRVCNAGNTSCTWTFGVDTHLATATSCTYVVKANANASQASGGPVTCGPYTITSSWSGQFGPNNGFTTFAVTDFSKKLIVWPAYTDVQVQAGKVVSPNQSYAPANLPLEHHHHHH

input_file的格式：

"""
author:chukai
time:2022/04/25
content:输入一个sequence文件，输出对应的.fasta文件

usage:
    1、mkdir test
    2、python generate_fasta.py -i 'XXXXXX.csv' -o test/
    3、rm -rf test

"""

import pandas as pd
import argparse
def generate(path,output_path):
    data = pd.read_csv(path)
    for i in range(len(data)):
        fastafile = []
        MHC = data['HLA'][i].replace(':','')
        length = len(data['sequence'][i])
        fastafile.append('>' + MHC + "\t" + str(length))
        fastafile.append(data['sequence'][i])
        pd.DataFrame(fastafile).to_csv(output_path + MHC +'.fa', index=0, header=0)
        
        
if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='输入sequence获得.fasta文件')
    parser.add_argument('-i', help='please input an file eg xxx.csv',default='xxxx.csv')
    parser.add_argument('-o', help='the output of .fasta file',default='/home/chuk/xxxxx/xxxxx/')
    args = parser.parse_args()
    input = args.i
    output = args.o
    generate(input,output)

测试：在Linux中执行下面指令即可获得批量的.fasta文件

mkdir test

python generate_fasta.py -i 'xxxxxx.csv' -o test/

rm -rf test/

git clone https://github.com/RosettaCommons/RoseTTAFold.git
cd RoseTTAFold

# create conda environment for RoseTTAFold
#   If your NVIDIA driver compatible with cuda11
conda env create -f RoseTTAFold-linux.yml
#   If not (but compatible with cuda10)
conda env create -f RoseTTAFold-linux-cu101.yml

# create conda environment for pyRosetta folding & running DeepAccNet
conda env create -f folding-linux.yml

wget https://files.ipd.uw.edu/pub/RoseTTAFold/weights.tar.gz
tar xfz weights.tar.gz

./install_dependencies.sh

# uniref30 [46G]
wget http://wwwuser.gwdg.de/~compbiol/uniclust/2020_06/UniRef30_2020_06_hhsuite.tar.gz
mkdir -p UniRef30_2020_06
tar xfz UniRef30_2020_06_hhsuite.tar.gz -C ./UniRef30_2020_06

# BFD [272G]
wget https://bfd.mmseqs.com/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt.tar.gz
mkdir -p bfd
tar xfz bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt.tar.gz -C ./bfd

# structure templates (including *_a3m.ffdata, *_a3m.ffindex) [over 100G]
wget https://files.ipd.uw.edu/pub/RoseTTAFold/pdb100_2021Mar03.tar.gz
tar xfz pdb100_2021Mar03.tar.gz
# for CASP14 benchmarks, we used this one: https://files.ipd.uw.edu/pub/RoseTTAFold/pdb100_2020Mar11.tar.gz

# For monomer structure prediction
cd example
../run_[pyrosetta, e2e]_ver.sh input.fa .

# For complex modeling
# please see README file under example/complex_modeling/README for details.
python network/predict_complex.py -i paired.a3m -o complex -Ls 218 310 

# For PPI screening using faster 2-track version (example input and output are at example/complex_2track)
python network_2track/predict_msa.py -msa [paired MSA file in a3m format] -npz [output npz file name] -L1 [Length of first chain]
e.g. python network_2track/predict_msa.py -msa input.a3m -npz complex.npz -L1 218

cd RoseTTAFold-main/network

vim predict_e2e.py

import sys, os
# os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
import time
import numpy as np
import torch
import torch.nn as nn
from torch.utils import data
from parsers import parse_a3m, read_templates
from RoseTTAFoldModel  import RoseTTAFoldModule_e2e
import util
from collections import namedtuple
from ffindex import *
from kinematics import xyz_to_c6d, c6d_to_bins2, xyz_to_t2d
from trFold import TRFold

script_dir = '/'.join(os.path.dirname(os.path.realpath(__file__)).split('/')[:-1])

NBIN = [37, 37, 37, 19]

MODEL_PARAM ={
        "n_module"     : 8,
        "n_module_str" : 4,
        "n_module_ref" : 4,
        "n_layer"      : 1,
        "d_msa"        : 384 ,
        "d_pair"       : 288,
        "d_templ"      : 64,
        "n_head_msa"   : 12,
        "n_head_pair"  : 8,
        "n_head_templ" : 4,
        "d_hidden"     : 64,
        "r_ff"         : 4,
        "n_resblock"   : 1,
        "p_drop"       : 0.0,
        "use_templ"    : True,
        "performer_N_opts": {"nb_features": 64},
        "performer_L_opts": {"nb_features": 64}
        }

SE3_param = {
        "num_layers"    : 2,
        "num_channels"  : 16,
        "num_degrees"   : 2,
        "l0_in_features": 32,
        "l0_out_features": 8,
        "l1_in_features": 3,
        "l1_out_features": 3,
        "num_edge_features": 32,
        "div": 2,
        "n_heads": 4
        }

REF_param = {
        "num_layers"    : 3,
        "num_channels"  : 32,
        "num_degrees"   : 3,
        "l0_in_features": 32,
        "l0_out_features": 8,
        "l1_in_features": 3,
        "l1_out_features": 3,
        "num_edge_features": 32,
        "div": 4,
        "n_heads": 4
        }
MODEL_PARAM['SE3_param'] = SE3_param
MODEL_PARAM['REF_param'] = REF_param

# params for the folding protocol
fold_params = {
    "SG7"     : np.array([[[-2,3,6,7,6,3,-2]]])/21,
    "SG9"     : np.array([[[-21,14,39,54,59,54,39,14,-21]]])/231,
    "DCUT"    : 19.5,
    "ALPHA"   : 1.57,
    
    # TODO: add Cb to the motif
    "NCAC"    : np.array([[-0.676, -1.294,  0.   ],
                          [ 0.   ,  0.   ,  0.   ],
                          [ 1.5  , -0.174,  0.   ]], dtype=np.float32),
    "CLASH"   : 2.0,
    "PCUT"    : 0.5,
    "DSTEP"   : 0.5,
    "ASTEP"   : np.deg2rad(10.0),
    "XYZRAD"  : 7.5,
    "WANG"    : 0.1,
    "WCST"    : 0.1
}

fold_params["SG"] = fold_params["SG9"]

class Predictor():
    def __init__(self, model_dir=None, use_cpu=False):
        if model_dir == None:
            self.model_dir = "%s/models"%(os.path.dirname(os.path.realpath(__file__)))
        else:
            self.model_dir = model_dir
        #
        # define model name
        self.model_name = "RoseTTAFold"
        # if torch.cuda.is_available() and (not use_cpu):
        #     self.device = torch.device("cuda")
        # else:
        #     self.device = torch.device("cpu")
        self.device = torch.device("cuda:0")
        self.active_fn = nn.Softmax(dim=1)

        # define model & load model
        self.model = RoseTTAFoldModule_e2e(**MODEL_PARAM).to(self.device)

    def load_model(self, model_name, suffix='e2e'):
        chk_fn = "%s/%s_%s.pt"%(self.model_dir, model_name, suffix)
        if not os.path.exists(chk_fn):
            return False
        checkpoint = torch.load(chk_fn, map_location=self.device)
        self.model.load_state_dict(checkpoint['model_state_dict'], strict=True)
        return True
    
    # 修改位置1
    def predict(self, a3m_fn, out_prefix,inputt1d,inputt2d,count1d,count2d,temp1d,temp2d,hhr_fn=None, atab_fn=None,window=150, shift=75):
        msa = parse_a3m(a3m_fn)
        N, L = msa.shape
        #
        if hhr_fn != None:
            xyz_t, t1d, t0d = read_templates(L, ffdb, hhr_fn, atab_fn, n_templ=10)
        else:
            xyz_t = torch.full((1, L, 3, 3), np.nan).float()
            t1d = torch.zeros((1, L, 3)).float()
            t0d = torch.zeros((1,3)).float()
        #
        msa = torch.tensor(msa).long().view(1, -1, L)
        idx_pdb = torch.arange(L).long().view(1, L)
        seq = msa[:,0]
        #
        # template features
        xyz_t = xyz_t.float().unsqueeze(0)
        t1d = t1d.float().unsqueeze(0)
        t0d = t0d.float().unsqueeze(0)
        t2d = xyz_to_t2d(xyz_t, t0d)
       
        could_load = self.load_model(self.model_name, suffix="e2e")
        if not could_load:
            print ("ERROR: failed to load model")
            sys.exit()
        self.model.eval()
        with torch.no_grad():
            # do cropped prediction if protein is too big
            if L > window*2:
                prob_s = [np.zeros((L,L,NBIN[i]), dtype=np.float32) for  i in range(4)]
                count_1d = np.zeros((L,), dtype=np.float32)
                count_2d = np.zeros((L,L), dtype=np.float32)
                node_s = np.zeros((L,MODEL_PARAM['d_msa']), dtype=np.float32)
                #
                grids = np.arange(0, L-window+shift, shift)
                ngrids = grids.shape[0]
                print("ngrid:     ", ngrids)
                print("grids:     ", grids)
                print("windows:   ", window)

                for i in range(ngrids):
                    for j in range(i, ngrids):
                        start_1 = grids[i]
                        end_1 = min(grids[i]+window, L)
                        start_2 = grids[j]
                        end_2 = min(grids[j]+window, L)
                        sel = np.zeros((L)).astype(np.bool)
                        sel[start_1:end_1] = True
                        sel[start_2:end_2] = True
                       
                        input_msa = msa[:,:,sel]
                        mask = torch.sum(input_msa==20, dim=-1) < 0.5*sel.sum() # remove too gappy sequences
                        input_msa = input_msa[mask].unsqueeze(0)
                        input_msa = input_msa[:,:1000].to(self.device)
                        input_idx = idx_pdb[:,sel].to(self.device)
                        input_seq = input_msa[:,0].to(self.device)
                        #
                        # Select template
                        input_t1d = t1d[:,:,sel].to(self.device) # (B, T, L, 3)
                        input_t2d = t2d[:,:,sel][:,:,:,sel].to(self.device)
                        np.savez_compressed("%s.npz"%(inputt1d),input_t1d.cpu())
                        np.savez_compressed("%s.npz"%(inputt2d),input_t2d.cpu())
                        
                        #
                        print ("running crop: %d-%d/%d-%d"%(start_1, end_1, start_2, end_2), input_msa.shape)
                        with torch.cuda.amp.autocast():
                            logit_s, node, init_crds, pred_lddt = self.model(input_msa, input_seq, input_idx, t1d=input_t1d, t2d=input_t2d, return_raw=True)
                        #
                        # Not sure How can we merge init_crds.....
                        sub_idx = input_idx[0].cpu()
                        sub_idx_2d = np.ix_(sub_idx, sub_idx)
                        count_2d[sub_idx_2d] += 1.0
                        count_1d[sub_idx] += 1.0
                        np.savez_compressed("%s.npz"%(count2d),count_2d)
                        np.savez_compressed("%s.npz"%(count1d),count_1d)
                        
                        node_s[sub_idx] += node[0].cpu().numpy()
                        for i_logit, logit in enumerate(logit_s):
                            prob = self.active_fn(logit.float()) # calculate distogram
                            prob = prob.squeeze(0).permute(1,2,0).cpu().numpy()
                            prob_s[i_logit][sub_idx_2d] += prob
                        del logit_s, node
                #
                # combine all crops
                for i in range(4):
                    prob_s[i] = prob_s[i] / count_2d[:,:,None]
                prob_in = np.concatenate(prob_s, axis=-1)
                node_s = node_s / count_1d[:, None]
                #
                # Do iterative refinement using SE(3)-Transformers
                # clear cache memory
                torch.cuda.empty_cache()
                #
                node_s = torch.tensor(node_s).to(self.device).unsqueeze(0)
                seq = msa[:,0].to(self.device)
                idx_pdb = idx_pdb.to(self.device)
                prob_in = torch.tensor(prob_in).to(self.device).unsqueeze(0)
                with torch.cuda.amp.autocast():
                    xyz, lddt = self.model(node_s, seq, idx_pdb, prob_s=prob_in, refine_only=True)
            else:
                msa = msa[:,:1000].to(self.device)
                seq = msa[:,0]
                idx_pdb = idx_pdb.to(self.device)
                t1d = t1d[:,:10].to(self.device)
                t2d = t2d[:,:10].to(self.device)
                # 修改位置2
                np.savez_compressed("%s.npz"%(temp1d),t1d.cpu())
                np.savez_compressed("%s.npz"%(temp2d),t2d.cpu())
                
                with torch.cuda.amp.autocast():
                    logit_s, _, xyz, lddt = self.model(msa, seq, idx_pdb, t1d=t1d, t2d=t2d)
                prob_s = list()
                for logit in logit_s:
                    prob = self.active_fn(logit.float()) # distogram
                    prob = prob.reshape(-1, L, L).permute(1,2,0).cpu().numpy()
                    prob_s.append(prob)
        
        np.savez_compressed("%s.npz"%(out_prefix), dist=prob_s[0].astype(np.float16), \
                            omega=prob_s[1].astype(np.float16),\
                            theta=prob_s[2].astype(np.float16),\
                            phi=prob_s[3].astype(np.float16))
        
        self.write_pdb(seq[0], xyz[0], idx_pdb[0], Bfacts=lddt[0], prefix="%s_init"%(out_prefix))
        
        # run TRFold
        prob_trF = list()
        for prob in prob_s:
            prob = torch.tensor(prob).permute(2,0,1).to(self.device)
            prob += 1e-8
            prob = prob / torch.sum(prob, dim=0)[None]
            prob_trF.append(prob)
        xyz = xyz[0, :, 1]
        TRF = TRFold(prob_trF, fold_params)
        xyz = TRF.fold(xyz, batch=15, lr=0.1, nsteps=200)
        xyz = xyz.detach().cpu().numpy()
        # add O and Cb
        N = xyz[:,0,:]
        CA = xyz[:,1,:]
        C = xyz[:,2,:]
        O = self.extend(np.roll(N, -1, axis=0), CA, C, 1.231, 2.108, -3.142)
        xyz = np.concatenate((xyz, O[:,None,:]), axis=1)
        self.write_pdb(seq[0], xyz, idx_pdb[0], Bfacts=lddt[0], prefix=out_prefix)

    def extend(self, a,b,c, L,A,D):
        '''
        input:  3 coords (a,b,c), (L)ength, (A)ngle, and (D)ihedral
        output: 4th coord
        '''
        N = lambda x: x/np.sqrt(np.square(x).sum(-1,keepdims=True) + 1e-8)
        bc = N(b-c)
        n = N(np.cross(b-a, bc))
        m = [bc,np.cross(n,bc),n]
        d = [L*np.cos(A), L*np.sin(A)*np.cos(D), -L*np.sin(A)*np.sin(D)]
        return c + sum([m*d for m,d in zip(m,d)])

    def write_pdb(self, seq, atoms, idx, Bfacts=None, prefix=None):
        L = len(seq)
        filename = "%s.pdb"%prefix
        ctr = 1
        with open(filename, 'wt') as f:
            if Bfacts == None:
                Bfacts = np.zeros(L)
            else:
                Bfacts = torch.clamp( Bfacts, 0, 1)
            
            for i,s in enumerate(seq):
                if (len(atoms.shape)==2):
                    f.write ("%-6s%5s %4s %3s %s%4d    %8.3f%8.3f%8.3f%6.2f%6.2f\n"%(
                            "ATOM", ctr, " CA ", util.num2aa[s], 
                            "A", idx[i]+1, atoms[i,0], atoms[i,1], atoms[i,2],
                            1.0, Bfacts[i] ) )
                    ctr += 1

                elif atoms.shape[1]==3:
                    for j,atm_j in enumerate((" N  "," CA "," C  ")):
                        f.write ("%-6s%5s %4s %3s %s%4d    %8.3f%8.3f%8.3f%6.2f%6.2f\n"%(
                                "ATOM", ctr, atm_j, util.num2aa[s], 
                                "A", idx[i]+1, atoms[i,j,0], atoms[i,j,1], atoms[i,j,2],
                                1.0, Bfacts[i] ) )
                        ctr += 1                
                
                elif atoms.shape[1]==4:
                    for j,atm_j in enumerate((" N  "," CA "," C  ", " O  ")):
                        f.write ("%-6s%5s %4s %3s %s%4d    %8.3f%8.3f%8.3f%6.2f%6.2f\n"%(
                                "ATOM", ctr, atm_j, util.num2aa[s], 
                                "A", idx[i]+1, atoms[i,j,0], atoms[i,j,1], atoms[i,j,2],
                                1.0, Bfacts[i] ) )
                        ctr += 1                
        
# 修改位置3
def get_args():
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument("-m", dest="model_dir", default="%s/weights"%(script_dir),
                        help="Path to pre-trained network weights [%s/weights]"%script_dir)
    parser.add_argument("-i", dest="a3m_fn", required=True,
                        help="Input multiple sequence alignments (in a3m format)")
    parser.add_argument("-o", dest="out_prefix", required=True,
                        help="Prefix for output file. The output files will be [out_prefix].npz and [out_prefix].pdb")
    parser.add_argument("--db", default="/NewWorkSpace/rosettafold/pdb100_2021Mar03/pdb100_2021Mar03",
                        help="Path to template database [%s/pdb100_2021Mar03]")
    parser.add_argument("--cpu", dest='use_cpu', default=False, action='store_true')
    parser.add_argument("--input_t1d",dest='inputt1d',required=True,
                        help="save the input_t1d of input sequence after model iteration")
    parser.add_argument("--input_t2d",dest='inputt2d',required=True,
                        help="save the input_t2d of input sequence after model iteration")
    parser.add_argument("--count_1d",dest='count1d',required=True,
                        help="save the count_1d of input sequence after model iteration")
    parser.add_argument("--count_2d",dest='count2d',required=True,
                        help="save the count_2d of input sequence after model iteration")
    parser.add_argument("--t1d",dest='temp1d',required=True,
                        help="save the t1d of input sequence after model iteration")
    parser.add_argument("--t2d",dest='temp2d',required=True,
                        help="save the t2d of input sequence after model iteration")
    parser.add_argument("--hhr", default=None,
                        help="HHsearch output file (hhr file). If not provided, zero matrices will be given as templates")
    parser.add_argument("--atab", default=None,
                        help="HHsearch output file (atab file)")

    args = parser.parse_args()
    return args

if __name__ == "__main__":
    args = get_args()
    FFDB=args.db
    FFindexDB = namedtuple("FFindexDB", "index, data")
    ffdb = FFindexDB(read_index(FFDB+'_pdb.ffindex'),
                     read_data(FFDB+'_pdb.ffdata'))

    if not os.path.exists("%s.npz"%args.out_prefix):
        pred = Predictor(model_dir=args.model_dir, use_cpu=args.use_cpu)
        # add new args for inter_feature
        # 修改位置4
        pred.predict(args.a3m_fn, args.out_prefix,args.inputt1d,args.inputt2d,args.count1d,args.count2d,args.temp1d,args.temp2d,args.hhr, args.atab)

修改数据库的地址：path

#!/bin/bash

# make the script stop when error (non-true exit code) is occured
set -e

# inputs
in_fasta="$1"
out_dir="$2"

# resource
CPU="$3"
MEM="$4"

path=/NewWorkSpace/rosettafold
# sequence databases
declare -a DATABASES=( \
    "$path/UniRef30_2020_06/UniRef30_2020_06" \
    "$path/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt")

# setup hhblits command
HHBLITS="hhblits -o /dev/null -mact 0.35 -maxfilt 100000000 -neffmax 20 -cov 25 -cpu $CPU -nodiff -realign_max 100000000 -maxseq 1000000 -maxmem $MEM -n 4"
echo $HHBLITS

tmp_dir="$out_dir/hhblits"
mkdir -p $tmp_dir
out_prefix="$out_dir/t000_"

# perform iterative searches
prev_a3m="$in_fasta"
for (( i=0; i<${#DATABASES[@]}; i++ ))
do
    for e in 1e-30 1e-10 1e-6 1e-3
    do
        echo db:$i e:$e
        $HHBLITS -i $prev_a3m -oa3m $tmp_dir/t000_.db$i.$e.a3m -e $e -v 0 -d ${DATABASES[$i]}

        hhfilter -id 90 -cov 75 -i $tmp_dir/t000_.db$i.$e.a3m -o $tmp_dir/t000_.db$i.$e.id90cov75.a3m
        prev_a3m="$tmp_dir/t000_.db$i.$e.id90cov50.a3m"
        n75=`grep -c "^>" $tmp_dir/t000_.db$i.$e.id90cov75.a3m`
        echo " -- cov75 results: $n75"
        if (($n75>2000))
        then
            cp $tmp_dir/t000_.db$i.$e.id90cov75.a3m ${out_prefix}.msa0.a3m
            break 2
        fi

        hhfilter -id 90 -cov 50 -i $tmp_dir/t000_.db$i.$e.a3m -o $tmp_dir/t000_.db$i.$e.id90cov50.a3m
        n50=`grep -c "^>" $tmp_dir/t000_.db$i.$e.id90cov50.a3m`
        echo " -- cov50 results: $n50"
        if (($n50>4000))
        then
            cp $tmp_dir/t000_.db$i.$e.id90cov50.a3m ${out_prefix}.msa0.a3m
            break 2
        fi
    done
done

if [ ! -s ${out_prefix}.msa0.a3m ]
then
    cp $prev_a3m ${out_prefix}.msa0.a3m
fi

配置数据库地址和输出文件

#!/bin/bash

# make the script stop when error (non-true exit code) is occured
set -e

############################################################
# >>> conda initialize >>>
# !! Contents within this block are managed by 'conda init' !!
__conda_setup="$('conda' 'shell.bash' 'hook' 2> /dev/null)"
eval "$__conda_setup"
unset __conda_setup
# <<< conda initialize <<<
############################################################

SCRIPT=`realpath -s `
export PIPEDIR=`dirname $SCRIPT`

CPU="8"  # number of CPUs to use
MEM="64" # max memory (in GB)

# Inputs:
#####---------------------------------------------------------
#
#   后续调整的思路：
#   dir=/WorkSpace/chuk/pdb100_2021Mar03
#
#   for file in $dir/*; do
#       echo $file # 输出目录下的每个文件
#   done
#   
#####---------------------------------------------------------
IN=""                # input.fasta
WDIR=`realpath -s `  # working folder


LEN=`tail -n1 $IN | wc -m`

mkdir -p $WDIR/log

conda activate RoseTTAFold
############################################################
# 1. generate MSAs
############################################################
if [ ! -s $WDIR/t000_.msa0.a3m ]
then
    echo "Running HHblits"
    $PIPEDIR/input_prep/make_msa.sh $IN $WDIR $CPU $MEM > $WDIR/log/make_msa.stdout 2> $WDIR/log/make_msa.stderr
fi


############################################################
# 2. predict secondary structure for HHsearch run
############################################################
if [ ! -s $WDIR/t000_.ss2 ]
then
    echo "Running PSIPRED"
    $PIPEDIR/input_prep/make_ss.sh $WDIR/t000_.msa0.a3m $WDIR/t000_.ss2 > $WDIR/log/make_ss.stdout 2> $WDIR/log/make_ss.stderr
fi


############################################################
# 3. search for templates
############################################################
DB=/NewWorkSpace/rosettafold/pdb100_2021Mar03/pdb100_2021Mar03
if [ ! -s $WDIR/t000_.hhr ]
then
    echo "Running hhsearch"
    HH="hhsearch -b 50 -B 500 -z 50 -Z 500 -mact 0.05 -cpu $CPU -maxmem $MEM -aliw 100000 -e 100 -p 5.0 -d $DB"
    cat $WDIR/t000_.ss2 $WDIR/t000_.msa0.a3m > $WDIR/t000_.msa0.ss2.a3m
    $HH -i $WDIR/t000_.msa0.ss2.a3m -o $WDIR/t000_.hhr -atab $WDIR/t000_.atab -v 0 > $WDIR/log/hhsearch.stdout 2> $WDIR/log/hhsearch.stderr
fi


############################################################
# 4. end-to-end prediction
############################################################
if [ ! -s $WDIR/t000_.3track.npz ]
then
    echo "Running end-to-end prediction"
    python $PIPEDIR/network/predict_e2e.py \
        -m $PIPEDIR/weights \
        -i $WDIR/t000_.msa0.a3m \
        -o $WDIR/t000_.e2e \
        --hhr $WDIR/t000_.hhr \
        --atab $WDIR/t000_.atab \
        --input_t1d $WDIR/input_t1d \
        --input_t2d $WDIR/input_t2d \
        --count_1d $WDIR/count_1d \
        --count_2d $WDIR/count_2d \
        --t1d $WDIR/t1d \
        --t2d $WDIR/t2d \
        --db $DB 1> $WDIR/log/network.stdout 2> $WDIR/log/network.stderr
fi
echo "Done"

 
（4）批量化运行并输出结果 

 
#! /bin/bash  
#说明
#echo 1 > /proc/sys/vm/drop_caches:表示清除pagecache，当前产链服务器缓存主要在这里。
#echo 2 > /proc/sys/vm/drop_caches:表示清除回收slab分配器中的对象（包括目录项缓存和inode缓存）。slab分配器是内核中管理内存的一种机制，其中很多缓存数据实现都是用的pagecache。
#echo 3 > /proc/sys/vm/drop_caches:表示清除pagecache和slab分配器中的缓存对象。

used=`free -m | awk 'NR==2' | awk '{print $3}'`  
free=`free -m | awk 'NR==2' | awk '{print $4}'`  
echo "===========================" >> /root/mem.log  
date >> /root/mem.log  
echo "Memory usage before | [Use：${used}MB][Free：${free}MB]" >> /root/mem.log  
if [ $free -le 10000 ] ; then  
            sync && echo 1 > /proc/sys/vm/drop_caches  
            sync && echo 2 > /proc/sys/vm/drop_caches  
            sync && echo 3 > /proc/sys/vm/drop_caches  
            used_ok=`free -m | awk 'NR==2' | awk '{print $3}'`  
            free_ok=`free -m | awk 'NR==2' | awk '{print $4}'`  
            echo "Memory usage after | [Use：${used_ok}MB][Free：${free_ok}MB]" >> /root/mem.log    
            echo "OK" >>  /root/mem.log   
else  
            echo "Not required" >> /root/mem.log   
fi  
exit 1  

 
# 单显卡
device = torch.device("cuda:0")

# 并行
model = nn.DataParallel(model)

# 多显卡
os.environ["CUDA_VISIBLE_DEVICES"] = "1,2,3,4"

model = Model(input_size, output_size)
if torch.cuda.device_count() > 1:
    # 如果不用os.environ的话，GPU的可见数量仍然是包括所有GPU的数量
    # 但是使用的还是只用指定的device_ids的GPU

    print("Let's use", torch.cuda.device_count(), "GPUs!")
    # dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
    model = nn.DataParallel(model, device_ids=[1, 2, 3])