Sequence analysis

Sequence analysis

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Summary ...

 

Contents

Examples

Motifs

Disorder

Deng et al. (2012) A comprehensive overview of computational protein disorder prediction methods. Mol Biosyst 8:114-21. (pmid: 21874190)

[ PubMed ] [ DOI ] Over the past decade there has been a growing acknowledgement that a large proportion of proteins within most proteomes contain disordered regions. Disordered regions are segments of the protein chain which do not adopt a stable structure. Recognition of disordered regions in a protein is of great importance for protein structure prediction, protein structure determination and function annotation as these regions have a close relationship with protein expression and functionality. As a result, a great many protein disorder prediction methods have been developed so far. Here, we present an overview of current protein disorder prediction methods including an analysis of their advantages and shortcomings. In order to help users to select alternative tools under different circumstances, we also evaluate 23 disorder predictors on the benchmark data of the most recent round of the Critical Assessment of protein Structure Prediction (CASP) and assess their accuracy using several complementary measures.

Kozlowski & Bujnicki (2012) MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins. BMC Bioinformatics 13:111. (pmid: 22624656)

[ PubMed ] [ DOI ] BACKGROUND: Intrinsically unstructured proteins (IUPs) lack a well-defined three-dimensional structure. Some of them may assume a locally stable structure under specific conditions, e.g. upon interaction with another molecule, while others function in a permanently unstructured state. The discovery of IUPs challenged the traditional protein structure paradigm, which stated that a specific well-defined structure defines the function of the protein. As of December 2011, approximately 60 methods for computational prediction of protein disorder from sequence have been made publicly available. They are based on different approaches, such as utilizing evolutionary information, energy functions, and various statistical and machine learning methods. RESULTS: Given the diversity of existing intrinsic disorder prediction methods, we decided to test whether it is possible to combine them into a more accurate meta-prediction method. We developed a method based on arbitrarily chosen 13 disorder predictors, in which the final consensus was weighted by the accuracy of the methods. We have also developed a disorder predictor GSmetaDisorder3D that used no third-party disorder predictors, but alignments to known protein structures, reported by the protein fold-recognition methods, to infer the potentially structured and unstructured regions. Following the success of our disorder predictors in the CASP8 benchmark, we combined them into a meta-meta predictor called GSmetaDisorderMD, which was the top scoring method in the subsequent CASP9 benchmark. CONCLUSIONS: A series of disorder predictors described in this article is available as a MetaDisorder web server at http://iimcb.genesilico.pl/metadisorder/. Results are presented both in an easily interpretable, interactive mode and in a simple text format suitable for machine processing.

Signal peptides

Bendtsen et al. (2004) Improved prediction of signal peptides: SignalP 3.0. J Mol Biol 340:783-95. (pmid: 15223320)

[ PubMed ] [ DOI ] We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the cleavage site position and the amino acid composition of the signal peptide are correlated, new features have been included as input to the neural network. This addition, combined with a thorough error-correction of a new data set, have improved the performance of the predictor significantly over SignalP version 2. In version 3, correctness of the cleavage site predictions has increased notably for all three organism groups, eukaryotes, Gram-negative and Gram-positive bacteria. The accuracy of cleavage site prediction has increased in the range 6-17% over the previous version, whereas the signal peptide discrimination improvement is mainly due to the elimination of false-positive predictions, as well as the introduction of a new discrimination score for the neural network. The new method has been benchmarked against other available methods. Predictions can be made at the publicly available web server

Emanuelsson et al. (2007) Locating proteins in the cell using TargetP, SignalP and related tools. Nat Protoc 2:953-71. (pmid: 17446895)

[ PubMed ] [ DOI ] Determining the subcellular localization of a protein is an important first step toward understanding its function. Here, we describe the properties of three well-known N-terminal sequence motifs directing proteins to the secretory pathway, mitochondria and chloroplasts, and sketch a brief history of methods to predict subcellular localization based on these sorting signals and other sequence properties. We then outline how to use a number of internet-accessible tools to arrive at a reliable subcellular localization prediction for eukaryotic and prokaryotic proteins. In particular, we provide detailed step-by-step instructions for the coupled use of the amino-acid sequence-based predictors TargetP, SignalP, ChloroP and TMHMM, which are all hosted at the Center for Biological Sequence Analysis, Technical University of Denmark. In addition, we describe and provide web references to other useful subcellular localization predictors. Finally, we discuss predictive performance measures in general and the performance of TargetP and SignalP in particular.

Petersen et al. (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 8:785-6. (pmid: 21959131)

[ PubMed ] [ DOI ]

Secondary Structure

Pirovano & Heringa (2010) Protein secondary structure prediction. Methods Mol Biol 609:327-48. (pmid: 20221928)

[ PubMed ] [ DOI ] While the prediction of a native protein structure from sequence continues to remain a challenging problem, over the past decades computational methods have become quite successful in exploiting the mechanisms behind secondary structure formation. The great effort expended in this area has resulted in the development of a vast number of secondary structure prediction methods. Especially the combination of well-optimized/sensitive machine-learning algorithms and inclusion of homologous sequence information has led to increased prediction accuracies of up to 80%. In this chapter, we will first introduce some basic notions and provide a brief history of secondary structure prediction advances. Then a comprehensive overview of state-of-the-art prediction methods will be given. Finally, we will discuss open questions and challenges in this field and provide some practical recommendations for the user.

Transmembrane Helices

BENCHMARK OF MEMBRANE HELIX PREDICTIONS FROM SEQUENCE

Location

Repeats

Heger & Holm (2000) Rapid automatic detection and alignment of repeats in protein sequences. Proteins 41:224-37. (pmid: 10966575)

[ PubMed ] [ <224::aid-prot70>3.0.co;2-z DOI ] Many large proteins have evolved by internal duplication and many internal sequence repeats correspond to functional and structural units. We have developed an automatic algorithm, RADAR, for segmenting a query sequence into repeats. The segmentation procedure has three steps: (i) repeat length is determined by the spacing between suboptimal self-alignment traces; (ii) repeat borders are optimized to yield a maximal integer number of repeats, and (iii) distant repeats are validated by iterative profile alignment. The method identifies short composition biased as well as gapped approximate repeats and complex repeat architectures involving many different types of repeats in the query sequence. No manual intervention and no prior assumptions on the number and length of repeats are required. Comparison to the Pfam-A database indicates good coverage, accurate alignments, and reasonable repeat borders. Screening the Swissprot database revealed 3,000 repeats not annotated in existing domain databases. A number of these repeats had been described in the literature but most were novel. This illustrates how in times when curated databases grapple with ever increasing backlogs, automatic (re)analysis of sequences provides an efficient way to capture this important information.

http://www.ebi.ac.uk/Tools/pfa/radar/

Pellegrini et al. (2012) Ab initio detection of fuzzy amino acid tandem repeats in protein sequences. BMC Bioinformatics 13 Suppl 3:S8. (pmid: 22536906)

[ PubMed ] [ DOI ] BACKGROUND: Tandem repetitions within protein amino acid sequences often correspond to regular secondary structures and form multi-repeat 3D assemblies of varied size and function. Developing internal repetitions is one of the evolutionary mechanisms that proteins employ to adapt their structure and function under evolutionary pressure. While there is keen interest in understanding such phenomena, detection of repeating structures based only on sequence analysis is considered an arduous task, since structure and function is often preserved even under considerable sequence divergence (fuzzy tandem repeats). RESULTS: In this paper we present PTRStalker, a new algorithm for ab-initio detection of fuzzy tandem repeats in protein amino acid sequences. In the reported results we show that by feeding PTRStalker with amino acid sequences from the UniProtKB/Swiss-Prot database we detect novel tandemly repeated structures not captured by other state-of-the-art tools. Experiments with membrane proteins indicate that PTRStalker can detect global symmetries in the primary structure which are then reflected in the tertiary structure. CONCLUSIONS: PTRStalker is able to detect fuzzy tandem repeating structures in protein sequences, with performance beyond the current state-of-the art. Such a tool may be a valuable support to investigating protein structural properties when tertiary X-ray data is not available.

Biegert & Söding (2008) De novo identification of highly diverged protein repeats by probabilistic consistency. Bioinformatics 24:807-14. (pmid: 18245125)

[ PubMed ] [ DOI ] MOTIVATION: An estimated 25% of all eukaryotic proteins contain repeats, which underlines the importance of duplication for evolving new protein functions. Internal repeats often correspond to structural or functional units in proteins. Methods capable of identifying diverged repeated segments or domains at the sequence level can therefore assist in predicting domain structures, inferring hypotheses about function and mechanism, and investigating the evolution of proteins from smaller fragments. RESULTS: We present HHrepID, a method for the de novo identification of repeats in protein sequences. It is able to detect the sequence signature of structural repeats in many proteins that have not yet been known to possess internal sequence symmetry, such as outer membrane beta-barrels. HHrepID uses HMM-HMM comparison to exploit evolutionary information in the form of multiple sequence alignments of homologs. In contrast to a previous method, the new method (1) generates a multiple alignment of repeats; (2) utilizes the transitive nature of homology through a novel merging procedure with fully probabilistic treatment of alignments; (3) improves alignment quality through an algorithm that maximizes the expected accuracy; (4) is able to identify different kinds of repeats within complex architectures by a probabilistic domain boundary detection method and (5) improves sensitivity through a new approach to assess statistical significance. AVAILABILITY: Server: http://toolkit.tuebingen.mpg.de/hhrepid; Executables: ftp://ftp.tuebingen.mpg.de/pub/protevo/HHrepID

http://toolkit.lmb.uni-muenchen.de/hhrepid

Integrated tools

Ooi et al. (2009) ANNIE: integrated de novo protein sequence annotation. Nucleic Acids Res 37:W435-40. (pmid: 19389726)

[ PubMed ] [ DOI ] Function prediction of proteins with computational sequence analysis requires the use of dozens of prediction tools with a bewildering range of input and output formats. Each of these tools focuses on a narrow aspect and researchers are having difficulty obtaining an integrated picture. ANNIE is the result of years of close interaction between computational biologists and computer scientists and automates an essential part of this sequence analytic process. It brings together over 20 function prediction algorithms that have proven sufficiently reliable and indispensable in daily sequence analytic work and are meant to give scientists a quick overview of possible functional assignments of sequence segments in the query proteins. The results are displayed in an integrated manner using an innovative AJAX-based sequence viewer. ANNIE is available online at: http://annie.bii.a-star.edu.sg. This website is free and open to all users and there is no login requirement.

http://annie.bii.a-star.edu.sg

   

Further reading and resources