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北京石油化工學(xué)院2026年研究生招生接收調(diào)劑公告
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cnlics

木蟲 (小有名氣)

[交流] 【分享】蛋白質(zhì)結(jié)構(gòu)預(yù)測(cè)流程 已有23人參與

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(shí)。

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(cè)一般流程見下圖：


內(nèi)容目錄：

•相關(guān)實(shí)驗(yàn)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(shí)別結(jié)構(gòu)域
•多序列比對(duì)
•比較或同源建模
•二級(jí)結(jié)構(gòu)預(yù)測(cè)
•折疊的識(shí)別
•折疊分析與二級(jí)結(jié)構(gòu)比對(duì)
•序列與結(jié)構(gòu)的比對(duì)

[ Last edited by cnlics on 2010-9-16 at 08:24 ]
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cnlics

木蟲 (小有名氣)
實(shí)驗(yàn)數(shù)據(jù)

許多實(shí)驗(yàn)數(shù)據(jù)可以輔助結(jié)構(gòu)預(yù)測(cè)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(jí)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(diǎn)的殘基
•蛋白酶切割位點(diǎn)，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(cè)時(shí)，必須清楚所有的數(shù)據(jù)。必須時(shí)刻考慮：預(yù)測(cè)與實(shí)驗(yàn)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
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cnlics

木蟲 (小有名氣)
蛋白序列數(shù)據(jù)

對(duì)蛋白序列的初步分析有一定價(jià)值。例如，如果蛋白是直接來自基因預(yù)測(cè)，就可能包含多個(gè)結(jié)構(gòu)域。更嚴(yán)重的是，可能會(huì)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(gè)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(cè)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(cè)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(gè)版本）

•蛋白包含低復(fù)雜性區(qū)域？蛋白經(jīng)常含有數(shù)個(gè)聚谷氨酸或聚絲氨酸區(qū)，這些地方不容易預(yù)測(cè)�？梢杂肧EG（GCG程序包里包含了一個(gè)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(gè)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
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cnlics

木蟲 (小有名氣)
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(jì)算機(jī)上完成。而且，有許多WEB服務(wù)器可以進(jìn)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。可以容易得到在本地運(yùn)行的版本（從 NCBI 或者 Washington University），也有許多的WEB頁面允許對(duì)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(gè)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(diǎn)

最近序列比較的重要進(jìn)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(gè)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(cè)進(jìn)化距離非常遠(yuǎn)的同源序列。很重要的一點(diǎn)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(duì)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(duì)于識(shí)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
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cnlics

木蟲 (小有名氣)
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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cnlics

木蟲 (小有名氣)
多序列比對(duì)

Regardless of the outcome of your searches, you will want a multiple sequence alignment containing your sequence and all the homologues you have found above.
Some sites for performing multiple alignment:
•        EBI (UK) Clustalw Server
•        IBCP (France) Multalin Server
•        IBCP (France) Clustalw Server
•        IBCP (France) Combined Multalin/Clustalw
•        MSA (USA) Server
•        BCM Multiple Sequence Alignment ClustalW Sever (USA)
If you are going to do a lot of alignments, then it is probably best to get your own copy of one of many programs, some FTP sites for some of these are:
•        HMMer (HMM method, Wash U)
•        SAM (HMM method, Santa Cruz)
•        ClustalW (EBI,UK)
•        ClustalW (USA)
•        MSA (USA)
•        AMPS (UK)
Note that PileUp is contained within the GCG commercial package. Most institutions with people doing this sort of work will have access to this software, so ask around if you want to use it.
Probably the most important advance since these pages first appeared are Hidden Markov Models for sequence alignment. Several methods are listed above.
Alignments can provide:
•        Information as to protein domain structure
•        The location of residues likely to be involved in protein function
•        Information of residues likely to be buried in the protein core or exposed to solvent
•        More information than a single sequence for applications like homology modelling and secondary structure prediction.
Some tips
•        Don't just take everything found in the searches and feed them directly into the alignment program. Searches will almost always return matches that do not indicate a significant sequence similarity. Look through the output carefully and throw things out if they don't appear to be a member of the sequence family. Inclusion of non-members in your alignment will confuse things and likely lead to errors later.
•        Remember that the programs for aligning sequences aren't perfect, and do not always provide the best alignment. This is particularly so for large families of proteins with low sequence identities. If you can see a better way of aligning the sequences, then by all means edit the alignment manually.
6樓2010-09-14 01:45:08
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cnlics

木蟲 (小有名氣)
比較或同源建模

如果蛋白序列和已知三維結(jié)構(gòu)的其他蛋白有顯著的相似性，就可以通過同源建模的方法獲得這個(gè)蛋白相當(dāng)精確的3D結(jié)構(gòu)。It is also possible to build models if you have found a suitable fold via fold recognition and are happy with the alignment of sequence to structure (Note that the accuracy of models constructed in this manner has not been assessed properly, so treat with caution).
It is possible now to generate models automatically using the very useful SWISSMODEL server.
Some other sites useful for homology modelling include:
•        WHAT IF (G. Vriend, EMBL, Heidelberg)
•        MODELLER (A. Sali, Rockefeller University)
•        MODELLER Mirror FTP site
Sequence alignments, particularly those involving proteins having low percent sequence identities can be inacurrate. If this is the case, then a model built using the alignment will obvious be wrong in some places. I would suggest that you look over the alignment carefully before building a model.
Note that when using SWISSMODEL it is possible to send in a protein sequence only. I would only recommend doing this if the degree of sequence homology is high (50% or greater) for the above reasons. It is best, particularly if one has edited an alignment, to send an alignment directly to the server.
Once you have a three-dimensional model, it is useful to look at protein 3D structures. There are numerous free programs for doing this, including:
•        GRASP Anthony Nicholls, Columbia, USA.
•        MolMol Reto Koradi, ETH, Zurrich, C.H.
•        Prepi Suhail Islam, ICRF, U.K.
•        RasMol Roger Sayle, Glaxo, U.K.
Most places with groups studying structural biology also have commercial packages, such as Quanta, SYBL or Insight, which contain more features than the visualisation packages described above. Crystallographers also tend to use O and FRODO, though these require a lot of experience to use with ease.
7樓2010-09-14 01:47:00
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cnlics

木蟲 (小有名氣)
二級(jí)結(jié)構(gòu)預(yù)測(cè)方法和鏈接

有許多做結(jié)構(gòu)預(yù)測(cè)的WEB服務(wù)器，下面是簡(jiǎn)單的總括：
•        PSI-pred (PSI-BLAST profiles used for prediction; David Jones, Warwick)
•        JPRED Consensus prediction (includes many of the methods given below; Cuff & Barton, EBI)
•        DSC King & Sternberg （本服務(wù)器）
•        PREDATORFrischman & Argos（EMBL）
•        PHD home page Rost & Sander，EMBL，Germany
•        ZPRED server Zvelebil et al.，Ludwig，U.K.
•        nnPredict Cohen et al，UCSF，USA.
•        BMERC PSA Server Boston University，USA
•        SSP (Nearest-neighbor) Solovyev and Salamov，Baylor College， USA.
With no homologue of known structure from which to make a 3D model, a logical next step is to predict secondary structure. Although they differ in method, the aim of secondary structure prediction is to provide the location of alpha helices, and beta strands within a protein or protein family.
單條序列的方法
二級(jí)結(jié)構(gòu)預(yù)測(cè)已經(jīng)存在約1/4世紀(jì)了，早期的方法受制于缺乏數(shù)據(jù)，僅對(duì)單條序列進(jìn)行預(yù)測(cè)，而不是對(duì)同源序列家族，而且能得到數(shù)據(jù)的已知3D結(jié)構(gòu)較少。早期最有名的方法是Chou & Fasman，Garnier，Osguthorbe & Robson (GOR)以及Lim。盡管作者開始聲稱準(zhǔn)確率很高（70-80 %），仔細(xì)檢查后，這些方法僅有56 到60%的準(zhǔn)確率（Kabsch & Sander，1984，見下）。早期預(yù)測(cè)二級(jí)結(jié)構(gòu)的一個(gè)問題是 An early problem in secondary structure prediction had been the inclusion of structures used to derive parameters in the set of structures used to assess the accuracy of the method.
關(guān)于主題的一些好的參考資料：
•        對(duì)單條序列的早期方法Early methods on single sequences
o        Chou, P.Y. & Fasman, G.D. (1974). Biochemistry, 13, 211-222.
o        Lim, V.I. (1974). Journal of Molecular Biology, 88, 857-872.
o        Garnier, J., Osguthorpe, D.~J. \& Robson, B. (1978).Journal of Molecular Biology, 120, 97-120.
o        Kabsch, W. & Sander, C. (1983). FEBS Letters, 155, 179-182. (An assessment of the above methods)
•        Later methods on single sequences
o        Deleage, G. & Roux, B. (1987). Protein Engineering , 1, 289-294 (DPM)
o        Presnell, S.R., Cohen, B.I. & Cohen, F.E. (1992). Biochemistry, 31, 983-993.
o        Holley, H.L. & Karplus, M. (1989). Proceedings of the National Academy of Science, 86, 152-156.
o        King, R. & Sternberg, M. J.E. (1990). Journal of Molecular Biology, 216, 441-457.
o        D. G. Kneller, F. E. Cohen & R. Langridge (1990) Improvements in Protein Secondary Structure Prediction by an Enhanced Neural Network, Journal of Molecular Biology, 214, 171-182. (NNPRED)
Recent improvments
The availability of large families of homologous sequences revolutionised secondary structure prediction. Traditional methods, when applied to a family of proteins rather than a single sequence proved much more accurate at identifying core secondary structure elements. The combination of sequence data with sophisticated computing techniques such as neural networks has lead to accuracies well in excess of 70 %. Though this seems a small percentage increase, these predictions are actually much more useful than those for single sequence, since they tend to predict the core accurately. Moreover, the limit of 70-80% may be a function of secondary structure variation within homologous proteins.
Automated methods
There are numerous automated methods for predicting secondary structure from multiply aligned protein sequences. Some good references on the subject include (the acronyms in parentheses given after each reference refer to the associated WWW servers, given below):
•        Zvelebil, M.J.J.M., Barton, G.J., Taylor, W.R. & Sternberg, M.J.E. (1987). Prediction of Protein Secondary Structure and Active Sites Using the Alignment of Homologous Sequences Journal of Molecular Biology, 195, 957-961. (ZPRED)
•        Rost, B. & Sander, C. (1993), Prediction of protein secondary structure at better than 70 % Accuracy, Journal of Molecular Biology, 232, 584-599. PHD)
•        Salamov A.A. & Solovyev V.V. (1995), Prediction of protein secondary sturcture by combining nearest-neighbor algorithms and multiply sequence alignments. Journal of Molecular Biology, 247,1 (NNSSP)
•        Geourjon, C. & Deleage, G. (1994), SOPM : a self optimised prediction method for protein secondary structure prediction. Protein Engineering, 7, 157-16. (SOPMA)
•        Solovyev V.V. & Salamov A.A. (1994) Predicting alpha-helix and beta-strand segments of globular proteins. (1994) Computer Applications in the Biosciences,10,661-669. (SSP)
•        Wako, H. & Blundell, T. L. (1994), Use of amino-acid environment-depdendent substitution tables and conformational propensities in structure prediction from aligned sequences of homologous proteins. 2. Secondary Structures, Journal of Molecular Biology, 238, 693-708.
•        Mehta, P., Heringa, J. & Argos, P. (1995), A simple and fast approach to prediction of protein secondary structure from multiple aligned sequences with accuracy above 70 %. Protein Science, 4, 2517-2525. (SSPRED)
•        King, R.D. & Sternberg, M.J.E. (1996) Identification and application of the concepts important for accurate and reliable protein secondary structure prediction. Protein Sci,5, 2298-2310. (DSC).
Nearly all of these now run via the world wide web. For individual details, see the papers for the individual methods, or click on the underlined acronyms given after most of the references given above (note that you can also run the methods by going to the approriate WWW site).
Manual intervention
It has long been recognised that patterns of residue conservation are indicative of particular secondary structure types. Alpha helices have a periodicity of 3.6, which means that for helices with one face buried in the protein core, and the other exposed to solvent, will have residues at positions i, i+3, i+4 & i+7 (where i is a residue in an a helix) will lie on one face of the helix. Many alpha helices in proteins are amphipathic, meaning that one face is pointing towards the hydrophobic core and the other towards the solvent. Thus patterns of hydrophobic residue conservation showing the i, i+3, i+4, i+7 pattern are highly indicative of an alpha helix.
For example, this helix in myoglobin has this classic pattern of hydrophobic and polar residue conservation (i = 1):

Similarly, the geometry of beta strands means that adjacent residues have their side chains pointing in oppposite directions. Beta strands that are half buried in the protein core will tend to have hydrophobic residues at positions i, i+2, i+4, i+8 etc, and polar residues at positions i+1, i+3, i+5, etc.
For example, this beta strand in CD8 shows this classic pattern:

Beta strands that are completely buried (as is often the case in proteins containing both alpha helices and beta strands) usually contain a run of hydrophobic residues, since both faces are buried in the protein core.
This strand from Chemotaxis protein CheY is a good example:

The principle behind most manual secondary structure predictions is to look for patterns of residue conservation that are indicative of secondary structures like those shown above. It has been shown in numerous successful examples that this strategy often leads to nearly perfect predictions. The work of Barton et al, Nierman & Krischner, Bazan and Benner & co-workers provide good starting points for getting doing this sort of work oneself. Some useful references are:
•        Recent reviews on the subject (and on secondary structure prediction generally) See also references therein
o        Rost, B., Schneider, R. & Sander, C. (1993), Trends in Biochemical Sciences, 18, 120-123.
o        Benner, S. A., Gerloff, D. L. & Jenny, T. F. (1994), Science, 265, 1642-1644.
o        Barton, G. J. (1995), Protein Secondary Structure Prediction, Current Opinion in Structural Biology,5, 372-376.
o        Russell, R. B. & Sternberg, M. J. E. (1995), Protein Structure Prediction: How Good Are We?, Current Biology, 5, 488-490.
•        Some guides for predicting structure:
o        Benner, S. A. (1989), Patterns of divergence in homolgous proteins as indicators of tertiary and quaternary structure, Advances in Enzyme Regulation, 31, 219-236.
o        Benner, S. A. (1992), Predicting de novo the folded structure of proteins, Current Opinion in Structural Biology, 2, 402-412.
•        Some particular examples of protein secondary structure predictions:
o        Crawford, I. P., Niermann, T. & Kirschner, K. (1987), Predictions of secondary structure by evolutionary comparison: Application to the alpha subunit of tryptophan synthase, PROTEINS: Structure, Function and Genetics, 1, 118-129.
o        Bazan, J. F. (1990), Structural Design and Molecular Evolution of a Cytokine Receptor Superfamily,Proceedings of the National Academy of Science, 87, 6934-6938.
o        Benner, S. A. & Gerloff, D. (1990), Patterns of Divergence in Homologous Proteins and tertiary structure. A prediction of the structure of the catalytic domain of protein kinases, Advances in Enzyme Regulation, 31, 121-181.
o        Jenny, T. F. & Benner, S. A. (1994) A prediction of the secondary structure of the pleckstrin homology domain, A prediction of the secondary structure of the pleckstrin homology domain, PROTEINS: Structure, Function and Genetics, 20, 1-3.
o        Benner, S. A., Badcoe, I., Cohen, M. A. and Gerloff, D. L. (1993) Predicted secondary structure for the src homology 3 domain, Journal of Molecular Biology, 229, 295-305.
o        Gerloff, D. L., Jenny, T. F., Knecht, L. J., Gonnet, G.H. & Benner, S. A. (1993), The nitrogenase MoFe protein. A secondary structure prediction. FEBS Letters, 318, 118-124.
o        Gerloff, D. L., Chelvanayagam, G. & Benner, S. A. (1995), A predicted consensus structure for the protein-kinase c2 homology (c2h) domain, the repeating unit of synaptotagmin, PROTEINS: Structure, Function and Genetics, 22, 299-310.
o        Barton, G. J., Newman, R. H., Freemont, P. F. & Crumpton, M. J. (1991), Amino acid sequence analysis of the annexin super-gene family of proteins, European Journal of Biochemistry, 198, 749-760.
o        Russell, R. B., Breed, J. & Barton, G. J., (1992) Conservation analysis and secondary structure prediction of the SH2 family of phosphotyrosine binding domains, FEBS Letters, 304, 15-20.
o        Livingstone, C. D. & Barton, G. J. (1994), Secondary structure prediction from multiple sequence data: Blood clotting factor XII and Yersinia protein tyrosine phosphatase, International Journal of Peptide and Protein Research
o        Barton, G. J., Barford, D. A. & Cohen, P. T. (1994), European Journal of Biochemsitry, 220, 225-237.
o        Perkins, S. J., Smith K. F., Williams, S. C., Haris, P. I., Chapman, D. & Sim, R. B. (1994), The secondary structure of the von Willebrand Factor Type A Domain in Factor B of Human Complement by Fourier Transform Infrared Spectroscopy, Journal of Molecular Biology, 238, 104-119.
o        Edwards, Y. J. K. & Perkins, S. J., (1995) The protein fold of the von Willebrand factor type A is predicted to be similar to the open twisted beta-sheet flanked by alpha-helices found in human ras-p21, 358, 283-286.
o        Lupas, A., Koster, A. J., Walz, J. & Baumeister, W. (1994) Predicted secondary structure of the 20S proteasome and model structure of the putative peptide channel, FEBS Letters, 354, 45-49.
A strategy for secondary structure prediction
In practice, I recommend getting as many state-of-the-art prediction approaches as possible and combining this with some human insight to give a consensus prediction for the family. If you then align all of your predictions (including ideas you have based on residue conservation) with your multiple sequence alignment you can get a consensus picture of the structure. For example, here is part of an alignment of a family of proteins I looked at recently:

In this figure, three automated secondary structure predictions (PHD, SOPMA and SSPRED) appear below the alignment of 12 glutamyl tRNA reductase sequences. Positions within the alignment showing a conservation of hydrophobic side-chain character are shown in yellow, and those showing near total conservation of non-hydrophobic residues (often indicative of active sites) are coloured green.
Predictions of accessibility performed by PHD (PHD Acc. Pred.) are also shown (b = buried, e = exposed), as is a prediction I performed by looking for patterns indicative of the three secondary structure types shown above. For example, positions (within the alignment) 38-45 exhibit the classical amphipathic helix pattern of hydrophobic residue conservation, with positions i, i+3, i+4 and i+7 showing a conservation of hydrophobicity, with intervening positions being mostly polar. Positions 13-16 comprise a short stretch of conserved hydrophobic residues, indicative of a beta-strand, similar to the example from CheY protein shown above.
By looking for these patterns I built up a prediction of the secondary structure for most regions of the protein. Note that most methods - automated and manual - agree for many regions of the alignment.
Given the results of several methods of predicting secondary structure, one can build up a consensus picture of the secondary structure, such as that shown at the bottom of the alignment above.
Note that you can get predictions like the above (i.e. consensus predictions) from the very useful JPRED server.
8樓2010-09-14 01:52:29
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cnlics

木蟲 (小有名氣)
折疊識(shí)別方法及其鏈接
一些折疊識(shí)別方法的鏈接（僅列名稱）：

•通過web網(wǎng)運(yùn)行的方法：
    o 3D-pssm (本站)
    o TOPITS (EMBL)
    o UCLA-DOE Structre Prediction Server (UCLA)
    o 123D
    o UCSC HMM (UCSC)
    o FAS (Burnham Institute)
•有可執(zhí)行程序或代碼的方法：
    o THREADER(Warwick)
    o ProFIT CAME (Salzburg)
•其他相關(guān)鏈接：
    o Protein Structure Prediction Centre (US)
    o CASP1
    o CASP2
    o CASP3
    o UCLA-DOE Fold-Recognition Benchmark Home Page
即使不存在已知3D結(jié)構(gòu)的同源蛋白，仍然可能通過折疊識(shí)別方法，從已知的3D結(jié)構(gòu)中找到未知蛋白最接近的折疊。

3D結(jié)構(gòu)的相似性：
目前（真正 意義上的）從頭預(yù)測(cè)蛋白質(zhì)3D結(jié)構(gòu)仍然是不可能的，在較短的將來也不可能找到識(shí)別折疊的一般性方法。但是，長(zhǎng)期以來人們就意識(shí)到，即使沒有顯著的序列或功能上的相似性，蛋白質(zhì)常常采取相似的折疊，
Ab initio prediction of protein 3D structures is not possible at present, and a general solution to the protein folding problem is not likely to be found in the near future. However, it has long been recognised that proteins often adopt similar folds despite no significant sequence or functional similarity and that nature is apparently restricted to a limited number of protein folds.
There are numerous protein structure classifications now available via the WWW:
•        SCOP (MRC Cambridge)
•        CATH (University College, London)
•        FSSP (EBI, Cambridge)
•        3 Dee (EBI, Cambridge)
•        HOMSTRAD (Biochemistry, Cambridge)
•        VAST (NCBI, USA)
Thus for many proteins (~ 70%) there will be a suitable structure in the database from which to build a 3D model. Unfortuantely, the lack of sequence similarity will mean that many of these go undetected until after 3D structure determination.
The goal of fold recognition
Methods of protein fold recognition attempt to detect similarities between protein 3D structure that are not accompanied by any significant sequence similarity. There are many approaches, but the unifying theme is to try and find folds that are compatable with a particular sequence. Unlike sequence-only comparison, these methods take advantage of the extra information made available by 3D structure information. In effect, the turn the protein folding problem on it's head: rather than predicting how a sequence will fold, they predict how well a fold will fit a sequence.
部分相關(guān)文章（略）

The structure was correctly predicted to adopt a ras-p21 type fold
The realities of fold recognition
Despite initially promising results, methods of fold recognition are not always accurate. Guides to the accuracy of protein fold recognition can be found in the proceedings of the Critical Assessment of Structure Predictions (CASP) conferences. At the first meeting in 1994 (CASP1) the methods were found to be about 50 % accurate at best with respect to their ability to place a correct fold at the top of a ranked list. Though many methods failed to detect the correct fold at the top of a ranked list, a correct fold was often found in the top 10 scoring folds. Even when the methods were successful, alignments of sequence on to protein 3D structure were usually incorrect, meaning that comparative modelling performed using such models would be inaccurate.
The CASP2 meeting held in December 1996, showed that many of the methods had improved, though it is difficult to compare the results of the two assessments (i.e. CASP1 & CASP2) since very different criteria were used to assess correct answers. It would be foolish and over-ambitious for me to present a detailed assessment of the results here. However, and important thing to note, was that Murzin & Bateman managed to attain near 100% success by the use of careful human insight, a knowledge of known structures, secondary structure predictions and thoughts about the function of the target sequences. Their results strongly support the arguments given below that human insight can be a powerful aid during fold recognition. A summary of the results from this meeting can be found in the PROTEINS issue dedicated to the meeting (PROTEINS, Suppl 1, 1997).
The CASP3 meeting was held in December 1998. It showed some progress in the ability of fold recognition methods to detect correct protein folds and in the quality of alignments obtained. A detailed summary of the results will appear towards the end of 1999 in the PROTEINS supplement.
For my talk, I did a crude assessment of 5 methods of fold recognition. I took 12 proteins of known structure (3 from each folding class) an ran each of the five methods using default parameters. I then asked how often was a correct fold (not allowing trival sequence detectable folds) found in the first rank, or in the top 10 scoring folds. I also asked how often the method found the correct folding class in the first rank. The results are summarised in here in a PostScript file.
Perhaps the worst result from this study is shown below:

One method suggested that the sequence for the Probe (left) (a four helix bundle) would best fit onto the structure shown on the right (an OB fold, comprising a six stranded barrel).
The results suggest that one should use caution when using these methods. In spite of this, the methods remain very useful.
A practical approach:
Although they are not 100 % accurate, the methods are still very useful. To use the methods I would suggest the following:
•        Run as many methods as you can, and run each method on as many sequences (from your homologous protein family) as you can. The methods almost always give somewhat different answers with the same sequences. I have also found that a single method will often give different results for sets of homologous sequences, so I would also suggest running each method on as many homologoues as possible. After all of these runs, one can build up a consensus picture of the likely fold in a manner similar to that used for secondary structure prediction above.
•        Remember the expected accuracy of the methods, and don't use them as black-boxes. Remember that a correct fold may not be at the top of the list, but that it is likely to be in the top 10 scoring folds.
•        Think about the function of your protein, and look into the function of the proteins that have been found by the various methods. If you see a functional similarity, then you may have detected a weak sequence homologue, or remote homologue. At CASP2, as said above, Murzin & Bateman managed to obtain remarkably accurate predictions by identification of remote homologues. Their paper appeard in the PROTEINS supplement for the CASP2 experiment:
Murzin AG, Bateman A (1997) Distant homology recognition using structural classification of proteins Proteins, Suppl 1, 105-112.
and provides some key insights into protein fold recognition using humans rather than computers.
•        Don't trust the alignments that are output by the programs. They can be used as a starting point, but the best alignment of sequence on to tertiary structure is still likely to come from careful human intervention. One strategy for doing this is discussed in the next section

[ Last edited by cnlics on 2010-9-19 at 16:59 ]
9樓2010-09-14 01:55:58
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cnlics

木蟲 (小有名氣)
Analysis of protein folds and alignment of secondary structure elements
________________________________________
If you have predicted that your protein will adopt a particular fold within the database, then an important thing to consider to which fold your protein belongs, and other proteins that adopt a similar fold. To find out, look at one of the following databases:
•        SCOP (MRC Cambridge)
•        CATH (University College, London)
•        FSSP (EBI, Cambridge)
•        3 Dee (EBI, Cambridge)
•        HOMSTRAD (Biochemistry, Cambridge)
•        VAST (NCBI, USA)
(Note that these databases don't always agree as to what constitutes a similar fold, so I would recommend looking at as many of them as possible).
If your predicted fold has many "relatives", then have a look at what they are. Ask:
•        Do any of members show functional similarity to your protein? If there is any functional similarity between your protein and any members of the fold, then you may be able to back up your prediction of fold (possibly by the conservation of active site residues, or the approximate location of active site residues, etc.)
•        Is this fold a superfold? If so, does this superfold contain a supersite? Certain folds show a tendancy to bind ligands in a common location, even in the absense of any functional or clear evolutionary relationships. For an explanation of this, please see our work on supersites.
•        Are there core secondary structure elements that should really be present in any member of the fold?
•        Are there non-core secondary structure elements that might not be present in all members of the fold?
Core secondary structure elements, such as those comprising a beta-barrel, should really be present in a fold. If your predicted secondary structures can't be made to match up with what you think is the core of the protein fold, then your prediction of fold may be wrong (but be careful, since your secondary structure prediction may contain errors). You can also use your prediction together with the core secondary structure elements to derive an alignment of of predicted and observed secondary structures.
For example, we predicted that the glutamyl tRNA reductases (hemA family) would adopt an alpha-beta barrel fold using a combination of fold recognition and secondary structure prediction methods. We aligned the secondary structures of diverse members of the alpha-beta barrel fold using a structural alignment program, and aligned the secondary structures to the core (boxed below) secondary structure elements.

In the alignment above, each alpha and beta character refers to an entire secondary structure element. Those that are boxed are core secondary structure elements found in most members of the fold. The alignment of predicted secondary structures to the core elements appears at the bottom of the figure. Note that I have had to delete several alpha helices and beta strands from our prediction to allow for alignment. This is not surprising, because insertions or deletions of secondary structure elements are common across the diverse set of proteins that adopt this fold.
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