网站建立软件,芯片联盟最新消息,做网站的软件dw,长沙市网站制作hhsearch 是 HMM-HMM#xff08;Hidden Markov Model to Hidden Markov Model#xff09;比对方法的一部分#xff0c;属于 HMMER 软件套件。它用于进行蛋白质序列的高效比对#xff0c;特别适用于检测远缘同源性。
以下是 hhsearch 的一些主要特点和用途#xff1a; HMM…hhsearch 是 HMM-HMMHidden Markov Model to Hidden Markov Model比对方法的一部分属于 HMMER 软件套件。它用于进行蛋白质序列的高效比对特别适用于检测远缘同源性。
以下是 hhsearch 的一些主要特点和用途 HMM-HMM比对 hhsearch 使用隐藏马尔可夫模型HMM来表示蛋白质家族的模型。与传统的序列-序列比对方法不同HMM-HMM比对考虑了氨基酸残基的多序列信息使得在比对中能够更好地捕捉蛋白质家族的模式和结构。 检测远缘同源性 hhsearch 的一个主要优势是其能够检测到相对远离的同源关系。它在比对中引入了更多的信息从而提高了对远缘同源蛋白的发现能力。 灵敏度和特异性 hhsearch 的设计旨在在维持高灵敏度的同时减少假阳性的比对。这使得它在寻找结构和功能相似性时更为可靠。 数据库搜索 用户可以使用 hhsearch 在大型蛋白质数据库中搜索与给定蛋白质序列相似的蛋白质。
Library to run HHsearch from Python.import glob
import os
import subprocess
from typing import Sequence, Optional, List, Iterable
from absl import logging
import contextlib
import tempfile
import dataclasses
import contextlib
import time
import shutil
import recontextlib.contextmanager
def timing(msg: str):logging.info(Started %s, msg)tic time.time()yieldtoc time.time()logging.info(Finished %s in %.3f seconds, msg, toc - tic)dataclasses.dataclass(frozenTrue)
class TemplateHit:Class representing a template hit.index: intname: straligned_cols: intsum_probs: Optional[float]query: strhit_sequence: strindices_query: List[int]indices_hit: List[int]contextlib.contextmanager
def tmpdir_manager(base_dir: Optional[str] None):Context manager that deletes a temporary directory on exit.tmpdir tempfile.mkdtemp(dirbase_dir)try:yield tmpdirfinally:shutil.rmtree(tmpdir, ignore_errorsTrue)def parse_hhr(hhr_string: str) - Sequence[TemplateHit]:Parses the content of an entire HHR file.lines hhr_string.splitlines()# Each .hhr file starts with a results table, then has a sequence of hit# paragraphs, each paragraph starting with a line No hit number. We# iterate through each paragraph to parse each hit.block_starts [i for i, line in enumerate(lines) if line.startswith(No )]hits []if block_starts:block_starts.append(len(lines)) # Add the end of the final block.for i in range(len(block_starts) - 1):hits.append(_parse_hhr_hit(lines[block_starts[i]:block_starts[i 1]]))return hitsdef _parse_hhr_hit(detailed_lines: Sequence[str]) - TemplateHit:Parses the detailed HMM HMM comparison section for a single Hit.This works on .hhr files generated from both HHBlits and HHSearch.Args:detailed_lines: A list of lines from a single comparison section between 2sequences (which each have their own HMMs)Returns:A dictionary with the information from that detailed comparison sectionRaises:RuntimeError: If a certain line cannot be processed# Parse first 2 lines.number_of_hit int(detailed_lines[0].split()[-1])name_hit detailed_lines[1][1:]# Parse the summary line.pattern (Probab(.*)[\t ]*E-value(.*)[\t ]*Score(.*)[\t ]*Aligned_cols(.*)[\t ]*Identities(.*)%[\t ]*Similarity(.*)[\t ]*Sum_probs(.*)[\t ]*Template_Neff(.*))match re.match(pattern, detailed_lines[2])if match is None:raise RuntimeError(Could not parse section: %s. Expected this: \n%s to contain summary. %(detailed_lines, detailed_lines[2]))(_, _, _, aligned_cols, _, _, sum_probs, _) [float(x)for x in match.groups()]# The next section reads the detailed comparisons. These are in a human# readable format which has a fixed length. The strategy employed is to# assume that each block starts with the query sequence line, and to parse# that with a regexp in order to deduce the fixed length used for that block.query hit_sequence indices_query []indices_hit []length_block Nonefor line in detailed_lines[3:]:# Parse the query sequence lineif (line.startswith(Q ) and not line.startswith(Q ss_dssp) andnot line.startswith(Q ss_pred) andnot line.startswith(Q Consensus)):# Thus the first 17 characters must be Q query_name , and we can parse# everything after that.# start sequence end total_sequence_lengthpatt r[\t ]*([0-9]*) ([A-Z-]*)[\t ]*([0-9]*) \([0-9]*\)groups _get_hhr_line_regex_groups(patt, line[17:])# Get the length of the parsed block using the start and finish indices,# and ensure it is the same as the actual block length.start int(groups[0]) - 1 # Make index zero based.delta_query groups[1]end int(groups[2])num_insertions len([x for x in delta_query if x -])length_block end - start num_insertionsassert length_block len(delta_query)# Update the query sequence and indices list.query delta_query_update_hhr_residue_indices_list(delta_query, start, indices_query)elif line.startswith(T ):# Parse the hit sequence.if (not line.startswith(T ss_dssp) andnot line.startswith(T ss_pred) andnot line.startswith(T Consensus)):# Thus the first 17 characters must be T hit_name , and we can# parse everything after that.# start sequence end total_sequence_lengthpatt r[\t ]*([0-9]*) ([A-Z-]*)[\t ]*[0-9]* \([0-9]*\)groups _get_hhr_line_regex_groups(patt, line[17:])start int(groups[0]) - 1 # Make index zero based.delta_hit_sequence groups[1]assert length_block len(delta_hit_sequence)# Update the hit sequence and indices list.hit_sequence delta_hit_sequence_update_hhr_residue_indices_list(delta_hit_sequence, start, indices_hit)return TemplateHit(indexnumber_of_hit,namename_hit,aligned_colsint(aligned_cols),sum_probssum_probs,queryquery,hit_sequencehit_sequence,indices_queryindices_query,indices_hitindices_hit,)def _get_hhr_line_regex_groups(regex_pattern: str, line: str) - Sequence[Optional[str]]:match re.match(regex_pattern, line)if match is None:raise RuntimeError(fCould not parse query line {line})return match.groups()def _update_hhr_residue_indices_list(sequence: str, start_index: int, indices_list: List[int]):Computes the relative indices for each residue with respect to the original sequence.counter start_indexfor symbol in sequence:if symbol -:indices_list.append(-1)else:indices_list.append(counter)counter 1class HHSearch:Python wrapper of the HHsearch binary.def __init__(self,*,binary_path: str,databases: Sequence[str],maxseq: int 1_000_000):Initializes the Python HHsearch wrapper.Args:binary_path: The path to the HHsearch executable.databases: A sequence of HHsearch database paths. This should be thecommon prefix for the database files (i.e. up to but not including_hhm.ffindex etc.)maxseq: The maximum number of rows in an input alignment. Note that thisparameter is only supported in HHBlits version 3.1 and higher.Raises:RuntimeError: If HHsearch binary not found within the path.self.binary_path binary_pathself.databases databasesself.maxseq maxseq#for database_path in self.databases:# if not glob.glob(database_path _*):# logging.error(Could not find HHsearch database %s, database_path)# raise ValueError(fCould not find HHsearch database {database_path})propertydef output_format(self) - str:return hhrpropertydef input_format(self) - str:return a3mdef query(self, a3m: str) - str:Queries the database using HHsearch using a given a3m.with tmpdir_manager() as query_tmp_dir:input_path os.path.join(query_tmp_dir, query.a3m)hhr_path os.path.join(query_tmp_dir, output.hhr)with open(input_path, w) as f:f.write(a3m)db_cmd []for db_path in self.databases:db_cmd.append(-d)db_cmd.append(db_path)cmd [self.binary_path,-i, input_path,-o, hhr_path,-maxseq, str(self.maxseq)] db_cmdprint(cmd:,cmd)logging.info(Launching subprocess %s, .join(cmd))process subprocess.Popen(cmd, stdoutsubprocess.PIPE, stderrsubprocess.PIPE)with timing(HHsearch query):stdout, stderr process.communicate()retcode process.wait()if retcode:# Stderr is truncated to prevent proto size errors in Beam.raise RuntimeError(HHSearch failed:\nstdout:\n%s\n\nstderr:\n%s\n % (stdout.decode(utf-8), stderr[:100_000].decode(utf-8)))with open(hhr_path) as f:hhr f.read()return hhrdef get_template_hits(self,output_string: str,input_sequence: str) - Sequence[TemplateHit]:Gets parsed template hits from the raw string output by the tool.del input_sequence # Used by hmmseach but not needed for hhsearch.return parse_hhr(output_string)def convert_stockholm_to_a3m (stockholm_format: str,max_sequences: Optional[int] None,remove_first_row_gaps: bool True) - str:Converts MSA in Stockholm format to the A3M format.descriptions {}sequences {}reached_max_sequences Falsefor line in stockholm_format.splitlines():reached_max_sequences max_sequences and len(sequences) max_sequencesif line.strip() and not line.startswith((#, //)):# Ignore blank lines, markup and end symbols - remainder are alignment# sequence parts.seqname, aligned_seq line.split(maxsplit1)if seqname not in sequences:if reached_max_sequences:continuesequences[seqname] sequences[seqname] aligned_seqfor line in stockholm_format.splitlines():if line[:4] #GS:# Description row - example format is:# #GS UniRef90_Q9H5Z4/4-78 DE [subseq from] cDNA: FLJ22755 ...columns line.split(maxsplit3)seqname, feature columns[1:3]value columns[3] if len(columns) 4 else if feature ! DE:continueif reached_max_sequences and seqname not in sequences:continuedescriptions[seqname] valueif len(descriptions) len(sequences):break# Convert sto format to a3m line by linea3m_sequences {}if remove_first_row_gaps:# query_sequence is assumed to be the first sequencequery_sequence next(iter(sequences.values()))query_non_gaps [res ! - for res in query_sequence]for seqname, sto_sequence in sequences.items():# Dots are optional in a3m format and are commonly removed.out_sequence sto_sequence.replace(., )if remove_first_row_gaps:out_sequence .join(_convert_sto_seq_to_a3m(query_non_gaps, out_sequence))a3m_sequences[seqname] out_sequencefasta_chunks (f{k} {descriptions.get(k, )}\n{a3m_sequences[k]}for k in a3m_sequences)return \n.join(fasta_chunks) \n # Include terminating newlinedef _convert_sto_seq_to_a3m(query_non_gaps: Sequence[bool], sto_seq: str) - Iterable[str]:for is_query_res_non_gap, sequence_res in zip(query_non_gaps, sto_seq):if is_query_res_non_gap:yield sequence_reselif sequence_res ! -:yield sequence_res.lower()if __name__ __main__:### 1. 准备输入数据## 输入序列先通过Jackhmmer多次迭代从uniref90MGnify数据库搜索同源序列输出的多序列比对文件如globins4.sto转化为a3m格式后再通过hhsearch从pdb数据库中找到同源序列input_fasta_file /home/zheng/test/Q94K49.fasta## input_sequencewith open(input_fasta_file) as f:input_sequence f.read()test_templates_sto_file /home/zheng/test/Q94K49_aln.stowith open(test_templates_sto_file) as f:test_templates_sto f.read()## sto格式转a3m格式test_templates_a3m convert_stockholm_to_a3m(test_templates_sto)hhsearch_binary_path /home/zheng/software/hhsuite-3.3.0-SSE2-Linux/bin/hhsearch### 2.类实例化# scop70_1.75文件名前缀scop70_database_path /home/zheng/database/scop70_1.75_hhsuite3/scop70_1.75pdb70_database_path /home/zheng/database/pdb70_from_mmcif_latest/pdb70#hhsuite数据库下载地址https://wwwuser.gwdg.de/~compbiol/data/hhsuite/databases/hhsuite_dbs/ ## 单一数据库#template_searcher HHSearch(binary_path hhsearch_binary_path,# databases [scop70_database_path])## 多个数据库database_lst [scop70_database_path, pdb70_database_path]template_searcher HHSearch(binary_path hhsearch_binary_path,databases database_lst) ### 3. 同源序列搜索## 搜索结果返回.hhr文件字符串templates_result template_searcher.query(test_templates_a3m)print(templates_result)## pdb序列信息列表template_hits template_searcher.get_template_hits(output_stringtemplates_result, input_sequenceinput_sequence)print(template_hits)
