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: 3.1k | 阅读时长: 15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

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        pip install torch

      • tqdm>4.46.1

    • 安装SignalP 6.0

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      # 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

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    # 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 

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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

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#!/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：

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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。

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#!/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：

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perl tmhmm_parser.pl

脚本获取

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参考

Functional genomics SY179 WGS 生信软件
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