Literature DB >> 17880924

Signal-3L: A 3-layer approach for predicting signal peptides.

Hong-Bin Shen1, Kuo-Chen Chou.   

Abstract

Functioning as an "address tag" that directs nascent proteins to their proper cellular and extracellular locations, signal peptides have become a crucial tool in finding new drugs or reprogramming cells for gene therapy. To effectively and timely use such a tool, however, the first important thing is to develop an automated method for rapidly and accurately identifying the signal peptide for a given nascent protein. With the avalanche of new protein sequences generated in the post-genomic era, the challenge has become even more urgent and critical. In this paper, we have developed a novel method for predicting signal peptide sequences and their cleavage sites in human, plant, animal, eukaryotic, Gram-positive, and Gram-negative protein sequences, respectively. The new predictor is called Signal-3L that consists of three prediction engines working, respectively, for the following three progressively deepening layers: (1) identifying a query protein as secretory or non-secretory by an ensemble classifier formed by fusing many individual OET-KNN (optimized evidence-theoretic K nearest neighbor) classifiers operated in various dimensions of PseAA (pseudo amino acid) composition spaces; (2) selecting a set of candidates for the possible signal peptide cleavage sites of a query secretory protein by a subsite-coupled discrimination algorithm; (3) determining the final cleavage site by fusing the global sequence alignment outcome for each of the aforementioned candidates through a voting system. Signal-3L is featured by high success prediction rates with short computational time, and hence is particularly useful for the analysis of large-scale datasets. Signal-3L is freely available as a web-server at http://chou.med.harvard.edu/bioinf/Signal-3L/ or http://202.120.37.186/bioinf/Signal-3L, where, to further support the demand of the related areas, the signal peptides identified by Signal-3L for all the protein entries in Swiss-Prot databank that do not have signal peptide annotations or are annotated with uncertain terms but are classified by Signal-3L as secretory proteins are provided in a downloadable file. The large-scale file is prepared with Microsoft Excel and named "Tab-Signal-3L.xls", and will be updated once a year to include new protein entries and reflect the continuous development of Signal-3L.

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Year:  2007        PMID: 17880924     DOI: 10.1016/j.bbrc.2007.08.140

Source DB:  PubMed          Journal:  Biochem Biophys Res Commun        ISSN: 0006-291X            Impact factor:   3.575


  67 in total

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Review 3.  Computational Prediction of Effector Proteins in Fungi: Opportunities and Challenges.

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4.  Study of peptide fingerprints of parasite proteins and drug-DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks.

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Journal:  Polymer (Guildf)       Date:  2009-06-03       Impact factor: 4.430

5.  Hemolymph proteins of Anopheles gambiae larvae infected by Escherichia coli.

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7.  Protein domain boundary predictions: a structural biology perspective.

Authors:  Svetlana Kirillova; Suresh Kumar; Oliviero Carugo
Journal:  Open Biochem J       Date:  2009-01-21

8.  Flanking signal and mature peptide residues influence signal peptide cleavage.

Authors:  Khar Heng Choo; Shoba Ranganathan
Journal:  BMC Bioinformatics       Date:  2008-12-12       Impact factor: 3.169

9.  Evaluation of signal peptide prediction algorithms for identification of mycobacterial signal peptides using sequence data from proteomic methods.

Authors:  Nils Anders Leversen; Gustavo A de Souza; Hiwa Målen; Swati Prasad; Inge Jonassen; Harald G Wiker
Journal:  Microbiology (Reading)       Date:  2009-04-23       Impact factor: 2.777

10.  A comprehensive assessment of N-terminal signal peptides prediction methods.

Authors:  Khar Heng Choo; Tin Wee Tan; Shoba Ranganathan
Journal:  BMC Bioinformatics       Date:  2009-12-03       Impact factor: 3.169

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