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SignalP+TMHMM预测微生物分泌蛋白
发表于:2021-10-14 | 分类: 生物信息
字数统计: 3k | 阅读时长: 14分钟 | 阅读量:

Subtitle: Predict Secretory Protein in microbes with SignalP and TMHMM

分泌蛋白Secretory Protein是指在细胞内合成后,分泌到细胞外起作用的蛋白质。分泌蛋白的 N 端有一般由 15~30 个氨基酸组成的信号肽。信号肽是引导新合成的蛋白质向分泌通路转移的短(长度 5-30 个氨基酸)肽链。常指新合成多肽链中用于指导蛋白质的跨膜转移(定位)的 N - 末端的氨基酸序列(有时不一定在 N 端)。使用 SignalP 注释蛋白序列是否含有信号肽结构,使用 TMHMM 注释蛋白序列是否含有跨膜结构,最终筛选出含有信号肽结构并且不含跨膜结构的蛋白为分泌蛋白

软件 Software

  • SignalP V6.0

  • SignalP 6.0预测来自古细菌、革兰氏阳性细菌、革兰氏阴性细菌和真核生物的蛋白质中存在的信号肽predicts signal peptides and the location of their cleavage sites in proteins from Archaea, Gram-positive Bacteria,及其切割位点的位置。Gram-negative Bacteria and Eukarya.在细菌和古细菌中,SignalP 6.0 可以区分五种类型的信号肽:In Bacteria and Archaea, SignalP 6.0 can discriminate between five types of signal peptides:

    • Sec/SPI:由 Sec 转座转运,并由信号肽酶 I (Lep) 切割的 “标准” 分泌信号肽;"Standard" secretory signal peptides transported by Sec translocon and cleaved by Signal Peptidase I (Lep).

    • Sec/SPII:由 Sec 转座子运输,并由信号肽酶 II (Lsp) 切割的脂蛋白信号肽;lipoprotein signal peptides transported by the Sec translocon and cleaved by Signal Peptidase II (Lsp).

    • Tat/SPI:由 Tat 转座子转运,并由信号肽酶 I (Lep) 切割的 Tat 信号肽;Tat signal peptides transported by the Tat translocon and cleaved by Signal Peptidase I (Lep).

    • Tat/SPII:由 Tat 转位子转运,并由信号肽酶 II (Lsp) 切割的 Tat 脂蛋白信号肽;Tat lipoprotein signal peptides transported by Tat translocon & cleaved by Signal Peptidase II (Lsp).

    • Sec/SPIII:由 Sec 转位子运输,并由信号肽酶 III (PilD/PibD) 切割的菌毛蛋白和菌毛蛋白样信号肽。Pilin & pilin-like signal peptides transported by Sec translocon & cleaved by Signal Peptidase III (PilD/PibD).

    • 此外,SignalP 6.0 预测信号肽的区域。Additionally, SignalP 6.0 predicts the regions of signal peptides.根据类型,预测 n、h 和 c 区域以及其他显着特征的位置。Depending on the type, the positions of n-, h- and c-regions as well as of other distinctive features are predicted.

  • TMHMM V2.0c

    • 用于预测蛋白质中的跨膜螺旋。
  • Python

SignalP 和 TMHMM 对于学术用户免费,但是需要填写相关信息和邮箱,以接收下载链接(4h 有效时间)。

软件安装 Installation of Softwares

安装 SignalP 6.0

  • 下载

    访问 SignalP V6.0 网站,找到 “Download”,填写相关信息,获取下载链接,下载得到 “signalp-6.0.fast.tar.gz”。有两个模式可以选择 ——“slow_sequential” 和 “fast"。前者 runs the full model sequentially, taking the same amount of RAM as fast but being 6 times slower;后者 uses a smaller model that approximates the performance of the full model, requiring a fraction of the resources and being significantly faste。本教程下载的是 fast 模式。

  • 安装 Installation

    • 安装依赖 Dependencies

      • Python

      • matplotlib>3.3.2

      • numpy>1.19.2

      • torch>1.7.0

        pip install torch
      • tqdm>4.46.1

    • 安装 SignalP 6.0

      # 解压缩安装文件
      tar zxvf signalp-6.0.fast.tar.gz
      
      # 进入解压后的软件目录,在终端运行
      python setup.py install
      
      # 测试安装
      signalp6 --help

安装 TMHMM V2.0c

  • 下载

    访问 TMHMM V2.0c 网站,找到 “Download”,填写相关信息,获取下载链接,下载得到 “tmhmm-2.0c.Linux.tar.gz”。

  • 安装

    # 解压缩
    tar zxvf tmhmm-2.0c.Linux.tar.gz
    
    # 进入解压后的目录
    cd tmhmm-2.0c
    
    # 获取当前路径,我的是“/home/liu/tools/tmhmm-2.0c/bin”
    pwd
    
    # 将该路径加入到系统的环境变量中,参考我之前的文章来(编辑~/.bashrc)https://liaochenlanruo.github.io/post/f6c9.html#%E6%B7%BB%E5%8A%A0%E7%8E%AF%E5%A2%83%E5%8F%98%E9%87%8F
    
    # 修改bin目录下的tmhmm和tmhmmformat.pl的首行为“#!/usr/bin/perl”
  • 运行错误

    运行软件时总报 Segmentation fault (core dumped) 错误,暂时无解。各位可以使用其在线版

软件用法 Usage

SignalP 6.0

预测 Prediction

A command takes the following form

signalp6 --fastafile /path/to/input.fasta --organism other --output_dir path/to/be/saved --format txt --mode fast
  • fastafile输入文件为 FASTA 格式的蛋白序列文件Specifies the fasta file with the sequences to be predicted.

  • organism is either other or Eukarya . Specifying Eukarya triggers post-processing of the SP predictions to prevent spurious results (only predicts type Sec/SPI).

  • format can take the values txt , png , eps , all . It defines what output files are created for individual sequences. txt produces a tabular .gff file with the per-position predictions for each sequence. png , eps , all additionally produce probability plots in the requested format. For larger prediction jobs, plotting will slow down the processing speed significantly.

  • mode is either fast , slow or slow-sequential . Default is fast , which uses a smaller model that approximates the performance of the full model, requiring a fraction of the resources and being significantly faster. slow runs the full model in parallel, which requires more than 14GB of RAM to be available. slow-sequential runs the full model sequentially, taking the same amount of RAM as fast but being 6 times slower. If the specified model is not installed, SignalP will abort with an error.

输出Outputs

  • output_dir/output.gff3:仅包含含有信号肽的序列信息;

    output.gff3

  • output_dir/prediction_results.txt:包含了输入文件中的所有序列(不重要);

  • output_dir/region_output.gff3:包含所有的信号肽区域信息。

    • n-region: The n-terminal region of the signal peptide. Reported for Sec/SPI, Sec/SPII, Tat/SPI and Tat/SPII. Labeled as N

    • h-region: The center hydrophobic region of the signal peptide. Reported for Sec/SPI, Sec/SPII, Tat/SPI and Tat/SPII. Labeled as H

    • c-region: The c-terminal region of the signal peptide, reported for Sec/SPI and Tat/SPI.

    • Cysteine: The conserved cysteine in +1 of the cleavage site of Lipoproteins that is used for Lipidation. Labeled as c.

    • Twin-arginine motif: The twin-arginine motif at the end of the n-region that is characteristic for Tat signal peptides. Labeled as R.

    • Sec/SPIII: These signal peptides have no known region structure.

      region_output.gff3

批处理与结果优化

脚本名:run_SignalP.pl

#!/usr/bin/perl
use strict;
use warnings;
# Author: Liu Hualin
# Date: Oct 14, 2021


open IDNOSEQ, ">IDNOSEQ.txt" || die;
my @faa = glob("*.faa");
foreach  (@faa) {
	$_ =~ /(.+).faa/;
	my $str = $1;
	my $out = $1 . ".nodesc";
	my $sigseq = $1 . ".sigseq";
	my $outdir = $1 . "_signalp";
	open IN, $_ || die;
	open OUT, ">$out" || die;
	while (<IN>) {
		chomp;
		if (/^(>\S+)/) {
			print OUT $1 . "\n";
		}else {
			print OUT $_ . "\n";
		}
	}
	close IN;
	close OUT;
	my %hash = idseq($out);
	system("signalp6 --fastafile $out --organism other --output_dir $outdir --format txt --mode fast");
	my $gff = $outdir . "/output.gff3";
	if (! -z $gff) {
		open IN, "$gff" || die;
		<IN>;
		open OUT, ">$sigseq" || die;
		while (<IN>) {
			chomp;
			my @lines = split /\t/;
			if (exists $hash{$lines[0]}) {
				print OUT ">$lines[0]\n$hash{$lines[0]}\n";
			}else {
				print IDNOSEQ $str . "\t" . "$lines[0]\n";
			}
		}
		close IN;
		close OUT;
	}
	system("rm $out");
	system("mv $sigseq $outdir");
}
close IDNOSEQ;


sub idseq {
	my ($fasta) = @_;
	my %hash;
	local $/ = ">";
	open IN, $fasta || die;
	<IN>;
	while (<IN>) {
		chomp;
		my ($header, $seq) = split (/\n/, $_, 2);
		$header =~ /(\S+)/;
		my $id = $1;
		$hash{$id} = $seq;
	}
	close IN;
	return (%hash);
}

将 run_SignalP.pl 与后缀名为 “.faa” 的 FASTA 格式文件放在同一目录下,在终端中运行如下代码:

perl run_SignalP.pl

结果解读Output interpretation

* 代表输入文件的名字。

  • *_signalp/output.gff3:仅包含含有信号肽的序列信息;

  • *_signalp/prediction_results.txt:包含了输入文件中的所有序列(不重要);

  • *_signalp/region_output.gff3:包含所有的信号肽区域信息;

  • *_signalp/*.sigseq:存储所有信号肽的氨基酸序列文件,可用作 TMHMM 的输入文件。

TMHMM

预测

离线版总是报错,找不出原因,因此使用网页服务器进行,输入文件为上述生成的 “*_signalp/*.sigseq”,将其上传至网页版 TMHMM,提交任务,等待结果即可。

结果展示

TMHMM 可以输出多种格式的结果文件,具体请参考其官方说明

在TMHMM网站提交任务

  • Long output format

    • Length:蛋白序列的长度。The length of the protein sequence.

    • Number of predicted TMHs:预测到的跨膜螺旋的数量。The number of predicted transmembrane helices.

    • Exp number of AAs in TMHs:跨膜螺旋中氨基酸的预期数量。The expected number of amino acids intransmembrane helices.如果此数字大于 18,则很可能是跨膜蛋白(或具有信号肽)。If this number is larger than 18 it is very likely to be a transmembrane protein (OR have a signal peptide).

    • Exp number, first 60 AAs:在蛋白的前 60 个氨基酸中跨膜螺旋中氨基酸的预期数量。The expected number of amino acids in transmembrane helices in the first 60 amino acids of the protein.如果该数字超过几个,你应该被警告在 N 端预测的跨膜螺旋可能是一个信号肽。If it more than a few, you are warned that a predicted transmembrane helix in the N-term could be a signal peptide.

    • Total prob of N-in:N 端在膜的细胞质一侧的总概率。The total probability that the N-term is on the cytoplasmic side of the membrane.

    • POSSIBLE N-term signal sequence:当 “Exp number, first 60 AAs” 大于 10 时产生的警告。 A warning that is produced when "Exp number, first 60 AAs" is larger than 10.

  • 蛋白 F01_bin.1_00110 共计 436 个氨基酸,有 5 个跨膜螺旋结构。

    含有跨膜结构的蛋白

  • 蛋白 F01_bin.1_00142 共计 557 个氨基酸,所有序列均在膜外,即该序列编码的是分泌蛋白。

    不含跨膜结构的蛋白

  • Short output format

    • "len=":蛋白序列的长度。 The length of the protein sequence.

    • "ExpAA=":跨膜螺旋中氨基酸的预期数量。The expected number of amino acids intransmembrane helices.如果此数字大于 18,则很可能是跨膜蛋白(或具有信号肽)。If this number is larger than 18 it is very likely to be a transmembrane protein (OR have a signal peptide).

    • "First60=":在蛋白的前 60 个氨基酸中跨膜螺旋中氨基酸的预期数量。The expected number of amino acids in transmembrane helices in the first 60 amino acids of the protein.如果该数字超过几个,你应该被警告在 N 端预测的跨膜螺旋可能是一个信号肽。If it more than a few, you are warned that a predicted transmembrane helix in the N-term could be a signal peptide.

    • "PredHel=":预测到的跨膜螺旋的数量。 The number of predicted transmembrane helices by N-best.

    • "Topology=":N-best 预测的拓扑结构。The topology predicted by N-best.拓扑是由跨膜螺旋的位置给出的,如果螺旋在内部,则由 “i” 分隔,如果螺旋在外部,则由 “o” 分隔。'i7-29o44-66i87-109o' 意味着它从膜内开始,在位置 7 到 29 有一个预测的 TMH,30-43 在膜外,然后是位置 44-66 的 TMH。

    Short output format

结果汇总

通过网页版预测我们仅得到了一个列表文件(Short output format),该文件需要自己复制网页内容粘贴到新文件中,我将其命名为*_TMHMM_SHORT.txt,并将其存放在*_signalp目录中,该目录是由 run_SignalP.pl 生成的。下面我将会统计各个基因组中信号肽蛋白的总数量、分泌蛋白数量和跨膜蛋白数量到文件 Statistics.txt 中,并分别提取每个基因组的分泌蛋白序列到*_signalp/*.secretory.faa文件中,提取跨膜蛋白序列到*_signalp/*.membrane.faa文件中。该过程将通过 tmhmm_parser.pl 完成。

#!/usr/bin/perl
use strict;
use warnings;
# Author: Liu Hualin
# Date: Oct 15, 2021

open OUT, ">Statistics.txt" || die;
print OUT "Strain name\tSignal peptide numbers\tSecretory protein numbers\tMembrane protein numbers\n";
my @sig = glob("*_signalp");
foreach my $sig (@sig) {
	$sig=~/(.+)_signalp/;
	my $str = $1;
	my $tmhmm = $sig . "/$str" . "_TMHMM_SHORT.txt";
	my $fasta = $sig . "/$str" . ".sigseq";
	my $secretory = $str . ".secretory.faa";
	my $membrane = $str . ".membrane.faa";
	open SEC, ">$secretory" || die;
	open MEM, ">$membrane" || die;
	my $out = 0;
	my $on = 0;
	my %hash = idseq($fasta);
	open IN, $tmhmm || die;
	while (<IN>) {
		chomp;
		$_=~s/[\r\n]+//g;
#		print $_ . "\n";
		my @lines = split /\t/;
		if ($lines[5] eq "Topology=o") {
			$out++;
			print SEC ">$lines[0]\n$hash{$lines[0]}\n";
		}else {
			$on++;
			print MEM ">$lines[0]\n$hash{$lines[0]}\n";
		}
	}
	close IN;
	close SEC;
	close MEM;
	system("mv $secretory $membrane $sig");
	my $total = $out + $on;
	print OUT "$str\t$total\t$out\t$on\n";
}

close OUT;

sub idseq {
	my ($fasta) = @_;
	my %hash;
	local $/ = ">";
	open IN, $fasta || die;
	<IN>;
	while (<IN>) {
		chomp;
		my ($header, $seq) = split (/\n/, $_, 2);
		$header =~ /(\S+)/;
		my $id = $1;
		$hash{$id} = $seq;
	}
	close IN;
	return (%hash);
}

运行方法:将tmhmm_parser.pl放在*_signalp的上一级目录下,*_signalp目录中必须包含*_TMHMM_SHORT.txt文件和*.sigseq文件。在终端运行如下代码:

perl tmhmm_parser.pl

脚本获取

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