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cnlics木蟲 (小有名氣)
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[交流]
【分享】蛋白質(zhì)結(jié)構(gòu)預(yù)測流程 已有23人參與
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我慢慢翻譯慢慢貼 這里貼的內(nèi)容是以前收集的，應(yīng)該是來自EMBL，我粗略瀏覽了下內(nèi)容，還沒有過時。 WORD文檔可以在這里下載： http://ifile.it/dwzy278 蛋白質(zhì)結(jié)構(gòu)預(yù)測一般流程見下圖：  內(nèi)容目錄： •相關(guān)實驗數(shù)據(jù) •序列數(shù)據(jù)和初步分析 •搜索序列數(shù)據(jù)庫 •識別結(jié)構(gòu)域 •多序列比對 •比較或同源建模 •二級結(jié)構(gòu)預(yù)測 •折疊的識別 •折疊分析與二級結(jié)構(gòu)比對 •序列與結(jié)構(gòu)的比對 [ Last edited by cnlics on 2010-9-16 at 08:24 ] |
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cnlics
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蛋白序列數(shù)據(jù) 對蛋白序列的初步分析有一定價值。例如，如果蛋白是直接來自基因預(yù)測，就可能包含多個結(jié)構(gòu)域。更嚴(yán)重的是，可能會包含不太可能是球形或可溶性的區(qū)域。此流程圖假設(shè)你的蛋白是可溶的，可能是一個結(jié)構(gòu)域并不包含非球形結(jié)構(gòu)域。 需要考慮以下方面： •是跨膜蛋白或者包含跨膜片段嗎？有許多方法預(yù)測這些片段，包括： o TMAP (EMBL) o PredictProtein (EMBL/Columbia) o TMHMM (CBS, Denmark) o TMpred (Baylor College) o DAS (Stockholm) •如果包含卷曲(coiled-coils)可以在COILS server 預(yù)測coiled coils 或者下載 COILS 程序（最近已經(jīng)重寫，注意GCG程序包里包含了COILS的一個版本） •蛋白包含低復(fù)雜性區(qū)域？蛋白經(jīng)常含有數(shù)個聚谷氨酸或聚絲氨酸區(qū)，這些地方不容易預(yù)測。可以用SEG（GCG程序包里包含了一個版本的SEG程序）檢查 。 如果出現(xiàn)以上一種情況，就應(yīng)該將序列打成碎片，或忽略序列中的特定區(qū)段，等等。這個問題與細(xì)胞定位結(jié)構(gòu)域相關(guān)。 [ Last edited by cnlics on 2010-9-16 at 08:25 ] |
3樓2010-09-14 01:41:58
cnlics
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實驗數(shù)據(jù) 許多實驗數(shù)據(jù)可以輔助結(jié)構(gòu)預(yù)測過程，包括： •二硫鍵，固定了半胱氨酸的空間位置 •光譜數(shù)據(jù)，可以提供蛋白的二級結(jié)構(gòu)內(nèi)容 •定位突變研究，可以發(fā)現(xiàn)活性或結(jié)合位點的殘基 •蛋白酶切割位點，翻譯后修飾如磷酸化或糖基化提示了殘基必須是暴露的 •其他 預(yù)測時，必須清楚所有的數(shù)據(jù)。必須時刻考慮：預(yù)測與實驗結(jié)果是否一致？如果不是，就有必要修改做法。 [ Last edited by cnlics on 2010-9-14 at 19:31 ] |
2樓2010-09-14 01:41:00
cnlics
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搜索序列數(shù)據(jù)庫 分析任何新序列的第一步顯然是搜索序列數(shù)據(jù)庫以發(fā)現(xiàn)同源序列。這樣的搜索可以在任何地方或者在任何計算機上完成。而且，有許多WEB服務(wù)器可以進行此類搜索，可以輸入或粘貼序列到服務(wù)器上并交互式地接收結(jié)果。 序列搜索也有許多方法，目前最有名的是BLAST程序�？梢匀菀椎玫皆诒镜剡\行的版本（從 NCBI 或者 Washington University），也有許多的WEB頁面允許對多基因或蛋白質(zhì)序列的數(shù)據(jù)庫比較蛋白質(zhì)或DNA序列，僅舉幾個例子： •National Center for Biotechnology Information (USA) Searches •European Bioinformatics Institute (UK) Searches •BLAST search through SBASE (domain database; ICGEB, Trieste) •還有更多的站點 最近序列比較的重要進展是發(fā)展了gapped BLAST 和PSI-BLAST (position specific interated BLAST)，二者均使BLAST更敏感，后者通過選取一條搜索結(jié)果，建立模式（profile），然后用再它搜索數(shù)據(jù)庫尋找其他同源序列（這個過程可以一直重復(fù)到發(fā)現(xiàn)不了新的序列為止），可以探測進化距離非常遠(yuǎn)的同源序列。很重要的一點是，在利用下面章節(jié)方法之前，通過PSI-BLAST把蛋白質(zhì)序列和數(shù)據(jù)庫比較，找尋是否有已知結(jié)構(gòu)。 將一條序列和數(shù)據(jù)庫比較的其他方法有： •FASTA軟件包 (William Pearson, University of Virginia, USA) •SCANPS (Geoff Barton, European Bioinformatics Institute, UK) •BLITZ (Compugen's fast Smith Waterman search) •其他方法. It is also possible to use multiple sequence information to perform more sensitive searches. Essentially this involves building a profile from some kind of multiple sequence alignment. A profile essentially gives a score for each type of amino acid at each position in the sequence, and generally makes searches more sentive. Tools for doing this include: •PSI-BLAST (NCBI, Washington) •ProfileScan Server (ISREC, Geneva) •HMMER 隱馬氏模型（Sean Eddy， Washington University） •Wise package （Ewan Birney， Sanger Centre；用于蛋白質(zhì)對DNA的比較） •其他方法. A different approach for incorporating multiple sequence information into a database search is to use a MOTIF. Instead of giving every amino acid some kind of score at every position in an alignment, a motif ignores all but the most invariant positions in an alignment, and just describes the key residues that are conserved and define the family. Sometimes this is called a "signature". For example, "H-[FW]-x-[LIVM]-x-G-x(5)-[LV]-H-x(3)-[DE]" describes a family of DNA binding proteins. It can be translated as "histidine, followed by either a phenylalanine or tryptophan, followed by an amino acid (x), followed by leucine, isoleucine, valine or methionine, followed by any amino acid (x), followed by glycine,... [etc.]". PROSITE (ExPASy Geneva) contains a huge number of such patterns, and several sites allow you to search these data: •ExPASy •EBI It is best to search a few different databases in order to find as many homologues as possible. A very important thing to do, and one which is sometimes overlooked, is to compare any new sequence to a database of sequences for which 3D structure information is available. Whether or not your sequence is homologous to a protein of known 3D structure is not obvious in the output from many searches of large sequence databases. Moreover, if the homology is weak, the similarity may not be apparent at all during the search through a larger database. One last thing to remember is that one can save a lot of time by making use of pre-prepared protein alignments. Many of these alignments are hand edited by experts on the particular protein families, and thus represent probably the best alignment one can get given the data they contain (i.e. they are not always as up to date as the most recent sequence databases). These databases include: •SMART (Oxford/EMBL) •PFAM (Sanger Centre/Wash-U/Karolinska Intitutet) •COGS (NCBI) •PRINTS (UCL/Manchester) •BLOCKS (Fred Hutchinson Cancer Research Centre, Seatle) •SBASE (ICGEB, Trieste) 通常把蛋白質(zhì)序列和數(shù)據(jù)比較都有很多的方法，這些對于識別結(jié)構(gòu)域非常有用。 [ Last edited by cnlics on 2010-9-14 at 19:54 ] |
4樓2010-09-14 01:42:52
cnlics
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確定結(jié)構(gòu)域 If you have a sequence of more than about 500 amino acids, you can be nearly certain that it will be divided into discrete functional domains. If possible, it is preferable to split such large proteins up and consider each domain separately. You can predict the locatation of domains in a few different ways. The methods below are given (approximately) from most to least confident. • If homology to other sequences occurs only over a portion of the probe sequence and the other sequences are whole (i.e. not partial sequences), then this provides the strongest evidence for domain structure. You can either do database searches yourself or make use of well-curated, pre-defined databases of protein domains. Searches of these databases (see links below) will often assign domains easily. o SMART (Oxford/EMBL) o PFAM (Sanger Centre/Wash-U/Karolinska Intitutet) o COGS (NCBI) o PRINTS (UCL/Manchester) o BLOCKS (Fred Hutchinson Cancer Research Centre, Seatle) o SBASE (ICGEB, Trieste) You can also find domain descriptions in the annotations in SWISSPROT. • Regions of low-complexity often separate domains in multidomain proteins. Long stretches of repeated residues, particularly Proline, Glutamine, Serine or Threonine often indicate linker sequences and are usually a good place to split proteins into domains. Low complexity regions can be defined using the program SEG which is generally available in most BLAST distributions or web servers (a version of SEG is also contained within the GCG suite of programs). • Transmembrane segments are also very good dividing points, since they can easily separate extracellular from intracellular domains. There are many methods for predicting these segments, including: o TMAP (EMBL) o PredictProtein (EMBL/Columbia) o TMHMM (CBS, Denmark) o TMpred (Baylor College) o DAS (Stockholm) • Something else to consider are the presence of coiled-coils. These unusual structural features sometimes (but not always) indicate where proteins can be divided into domains. You can predict coiled coils at the COILS server or you can download the COILS program (recently re-written by me of all people; a version of SEG is also contained within the GCG suite of programs). • Secondary structure prediction methods (see below) will often predict regions of proteins to have different protein structural classes. For example one region of sequence may be predicted to contain only lpha helices and another to contain only beta sheets. These can often, though not always, suggest likely domain structure (e.g. an all alpha domain and an all beta domain) If you have separated a sequence into domains, then it is very important to repeat all the database searches and alignments using the domains separately. Searches with sequences containing several domains may not find all sub-homologies, particularly if the domains are abundent in the database (e.g. kinases, SH2 domains, etc.). There may also be "hidden" domains. For example if there is a stretch of 80 amino acids with few homologues nested in between a kinase and an SH2 domain, then you may miss matches found when searching the whole sequence against a database. Anyway, here is my slide from the talk related to this subject: |
5樓2010-09-14 01:44:10
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