Secretory Protein是指在細胞內分解后，分泌到細胞外起作用的蛋白質。分泌蛋白的N 端有普通由15～30 個氨基酸組成的信號肽。信號肽是引導新分解的蛋白質向分泌通路轉移的短（長度5-30個氨基酸）肽鏈。常指新分解多肽鏈中用于指點蛋白質的跨膜轉移（定位）的N-末端的氨基酸序列（有時不一定在N端）。運用SignalP 注釋蛋白序列能否含有信號肽結構，運用TMHMM注釋蛋白序列能否含有跨膜結構，*終挑選出含有信號肽結構并且不含跨膜結構的蛋白為分泌蛋白。

軟件Software

• SignalP V6.0
• SignalP 6.0 預測來自古細菌、革蘭氏陽性細菌、革蘭氏陰性細菌和真核生物的蛋白質中存在的信號肽predicts signal peptides and the location of their cleavage sites in proteins from Archaea, Gram-positive Bacteria,及其切割位點的位置。Gram-negative Bacteria and Eukarya.在細菌和古細菌中，SignalP 6.0 可以區分五種類型的信號肽：In Bacteria and Archaea, SignalP 6.0 can discriminate between five types of signal peptides:
• Sec/SPI：由 Sec 轉座轉運，并由信號肽酶 I (Lep) 切割的“規范”分泌信號肽；"Standard" secretory signal peptides transported by Sec translocon and cleaved by Signal Peptidase I (Lep).
• Sec/SPII：由 Sec 轉座子運輸，并由信號肽酶 II (Lsp) 切割的脂蛋白信號肽；lipoprotein signal peptides transported by the Sec translocon and cleaved by Signal Peptidase II (Lsp).
• Tat/SPI：由 Tat 轉座子轉運，并由信號肽酶 I (Lep) 切割的 Tat 信號肽；Tat signal peptides transported by the Tat translocon and cleaved by Signal Peptidase I (Lep).
• Tat/SPII：由 Tat 轉位子轉運，并由信號肽酶 II (Lsp) 切割的 Tat 脂蛋白信號肽；Tat lipoprotein signal peptides transported by Tat translocon & cleaved by Signal Peptidase II (Lsp).
• Sec/SPIII：由 Sec 轉位子運輸，并由信號肽酶 III (PilD/PibD) 切割的菌毛蛋白和菌毛蛋白樣信號肽。Pilin & pilin-like signal peptides transported by Sec translocon & cleaved by Signal Peptidase III (PilD/PibD).
• 此外，SignalP 6.0 預測信號肽的區域。Additionally, SignalP 6.0 predicts the regions of signal peptides.依據類型，預測 n、h 和 c 區域以及其他顯著特征的位置。Depending on the type, the positions of n-, h- and c-regions as well as of other distinctive features are predicted.

• TMHMM V2.0c
• 用于預測蛋白質中的跨膜螺旋。

• Python

SignalP和TMHMM關于學術用戶收費，但是需求填寫相關信息和郵箱，以接納下載鏈接（4h有效時間）。

軟件裝置Installation of Softwares

裝置SignalP 6.0

• 下載 訪問SignalP V6.0網站，找到“Download”，填寫相關信息，獲取下載鏈接，下載失掉“signalp-6.0.fast.tar.gz”。有兩個形式可以選擇——“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。本教程下載的是fast形式。
• 裝置Installation
• 裝置依賴Dependencies
• Python
• matplotlib>3.3.2
• numpy>1.19.2
• torch>1.7.0 pip install torch
• tqdm>4.46.1

• 裝置SignalP 6.0 # 解緊縮裝置文件 tar zxvf signalp-6.0.fast.tar.gz # 進入解壓后的軟件目錄，在終端運轉 python setup.py install # 測試裝置 signalp6 --help

裝置TMHMM V2.0c

• 下載 訪問TMHMM V2.0c網站，找到“Download”，填寫相關信息，獲取下載鏈接，下載失掉“tmhmm-2.0c.Linux.tar.gz”。
• 裝置 # 解緊縮 tar zxvf tmhmm-2.0c.Linux.tar.gz # 進入解壓后的目錄 cd tmhmm-2.0c # 獲取以后途徑，我的是“/home/liu/tools/tmhmm-2.0c/bin” pwd # 將該途徑參與到系統的環境變量中，參考我之前的文章來（編輯~/.bashrc）http://liaochenlanruo.github.io/post/f6c9.html#%E6%B7%BB%E5%8A%A0%E7%8E%AF%E5%A2%83%E5%8F%98%E9%87%8F # 修正bin目錄下的tmhmm和tmhmmformat.pl的首行為“#!/usr/bin/perl”
• 運轉錯誤 運轉軟件時總報Segmentation fault (core dumped)錯誤，暫時無解。各位可以運用其在線版。

軟件用法Usage

SignalP 6.0

預測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 輸入文件為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. txtproduces 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.

輸入Outputs

• output_dir/output.gff3：僅包括含有信號肽的序列信息；

• output_dir/prediction_results.txt：包括了輸入文件中的一切序列（不重要）；
• output_dir/region_output.gff3：包括一切的信號肽區域信息。
• 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.

批處置與結果優化

腳本名：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 () {

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;

;

open OUT, ">$sigseq" || die;

while () {

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;

;

while () {

chomp;

my ($header, $seq) = split (/\n/, $_, 2);

$header =~ /(\S+)/;

my $id = $1;

$hash{$id} = $seq;

}

close IN;

return (%hash);

}

將run_SignalP.pl與后綴名為“.faa”的FASTA格式文件放在同一目錄下，在終端中運轉如下代碼：

perl run_SignalP.pl

結果解讀Output interpretation

*代表輸入文件的名字。

• *_signalp/output.gff3：僅包括含有信號肽的序列信息；
• *_signalp/prediction_results.txt：包括了輸入文件中的一切序列（不重要）；
• *_signalp/region_output.gff3：包括一切的信號肽區域信息;
• *_signalp/*.sigseq：存儲一切信號肽的氨基酸序列文件，可用作TMHMM的輸入文件。

TMHMM

預測

離線版總是報錯，找不出緣由，因此運用網頁效勞器停止，輸入文件為上述生成的“*_signalp/*.sigseq”，將其上傳至網頁版TMHMM，提交義務，等候結果即可。

結果展現

TMHMM可以輸入多種格式的結果文件，詳細請參考其官方說明。

在TMHMM網站提交義務

• Long output format
• Length： 蛋白序列的長度。The length of the protein sequence.
• Number of predicted TMHs：預測到的跨膜螺旋的數量。The number of predicted transmembrane helices.
• Exp number of AAs in TMHs：跨膜螺旋中氨基酸的預期數量。The expected number of amino acids intransmembrane helices. 假設此數字大于 18，則很能夠是跨膜蛋白（或具有信號肽）。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個氨基酸中跨膜螺旋中氨基酸的預期數量。The expected number of amino acids in transmembrane helices in the first 60 amino acids of the protein.假設該數字超越幾個，你應該被正告在 N 端預測的跨膜螺旋能夠是一個信號肽。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端在膜的細胞質一側的總概率。The total probability that the N-term is on the cytoplasmic side of the membrane.
• POSSIBLE N-term signal sequence：當“Exp number, first 60 AAs”大于 10 時發生的正告。A warning that is produced when "Exp number, first 60 AAs" is larger than 10.

• 蛋白F01_bin.1_00110合計436個氨基酸，有5個跨膜螺旋結構。

• 蛋白F01_bin.1_00142合計557個氨基酸，一切序列均在膜外，即該序列編碼的是分泌蛋白。

• Short output format
• "len="： 蛋白序列的長度。The length of the protein sequence.
• "ExpAA="：跨膜螺旋中氨基酸的預期數量。The expected number of amino acids intransmembrane helices.假設此數字大于 18，則很能夠是跨膜蛋白（或具有信號肽）。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個氨基酸中跨膜螺旋中氨基酸的預期數量。The expected number of amino acids in transmembrane helices in the first 60 amino acids of the protein.假設該數字超越幾個，你應該被正告在 N 端預測的跨膜螺旋能夠是一個信號肽。If it more than a few, you are warned that a predicted transmembrane helix in the N-term could be a signal peptide.
• "PredHel="：預測到的跨膜螺旋的數量。The number of predicted transmembrane helices by N-best.
• "Topology="：N-best 預測的拓撲結構。The topology predicted by N-best.拓撲是由跨膜螺旋的位置給出的，假設螺旋在外部，則由“i”分隔，假設螺旋在外部，則由“o”分隔。'i7-29o44-66i87-109o'意味著它從膜內末尾，在位置7到29有一個預測的TMH，30-43在膜外，然后是位置44-66的TMH。

結果匯總

經過網頁版預測我們僅失掉了一個列表文件（Short output format），該文件需求自己復制網頁內容粘貼到新文件中，我將其命名為*_TMHMM_SHORT.txt，并將其寄存在*_signalp目錄中，該目錄是由run_SignalP.pl生成的。下面我將會統計各個基因組中信號肽蛋白的總數量、分泌蛋白數量和跨膜蛋白數量到文件Statistics.txt中，并區分提取每個基因組的分泌蛋白序列到*_signalp/*.secretory.faa文件中，提取跨膜蛋白序列到*_signalp/*.membrane.faa文件中。該進程將經過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 () { 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, $fasta || die; ; while () { chomp; my ($header, $seq) = split (/\n/, $_, 2); $header =~ /(\S+)/; my $id = $1; $hash{$id} = $seq; } close IN; return (%hash); }

運轉方法：將tmhmm_parser.pl放在*_signalp的上一級目錄下，*_signalp目錄中必需包括*_TMHMM_SHORT.txt文件和*.sigseq文件。在終端運轉如下代碼：

perl tmhmm_parser.pl

腳本獲取

本文腳本見GitHub。

敬告：運用文中腳本請援用本文網址，請尊重自己的休息效果，謝謝！Notice: When you use the scripts in this article, please cite the link of this webpage. Thank you!

參考

• SignalP V6.0
• TMHMM

原文鏈接：SignalP+TMHMM預測微生物分泌蛋白 | liaochenlanruo

轉載請注明出處！

保證產出水質的潔凈是純真水設備消費的關鍵，但是有時分也會出現純真水細菌繁殖的狀況，那么純真水設備如何檢測能否有細菌繁殖呢？罕見的有三種方法：

　　一、經典微生物培育法：微生物培育法的要素包括：培育基的類型、培育溫度和培育時間。培育方法包括：燒注皿培育法、鋪平皿法、膜過濾法。

　　二、儀器法主要有：顯微鏡直接計數法、放射法、阻抗法以及多種生化方法。

1、優點是精度好，準確度高，可以在較短時間內取得檢測結果， 有利于停止及時控制。

2、缺陷是需人工處置樣品，任務量大，樣品處置量小，易受儀器等其他方面的制約，并且儀器法對微生物是破壞性的，它無法對污染菌作進一步的分別和鑒別。

　　三、慣例方法：微生物的鑒別是一項專業性很強的任務，需少量任務閱歷及專業知識。

　　掌握純真水設備細菌檢測方法，足以可以看出各種不利于設備產水規范的現象，檢測出危機產水質量的污染細菌種類，保證用戶可以及時處置效果，結合純真水設備運轉條件保證系統產水動搖、牢靠。