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Secreted proteins play an important part in the pathogenicity of Mycobacterium tuberculosis, and are the primary source of vaccine and diagnostic candidates. A majority of these proteins are exported via the signal peptidase I-dependent pathway, and have a signal peptide that is cleaved off during the secretion process. Sequence similarities within signal peptides have spurred the development of several algorithms for predicting their presence as well as the respective cleavage sites. For proteins exported via this pathway, algorithms exist for eukaryotes, and for Gram-negative and Gram-positive bacteria. However, the unique structure of the mycobacterial membrane raises the question of whether the existing algorithms are suitable for predicting signal peptides within mycobacterial proteins. In this work, we have evaluated the performance of nine signal peptide prediction algorithms on a positive validation set, consisting of 57 proteins with a verified signal peptide and cleavage site, and a negative set, consisting of 61 proteins that have an N-terminal sequence that confirms the annotated translational start site. We found the hidden Markov model of SignalP v3.0 to be the best-performing algorithm for predicting the presence of a signal peptide in mycobacterial proteins. It predicted no false positives or false negatives, and predicted a correct cleavage site for 45 of the 57 proteins in the positive set. Based on these results, we used the hidden Markov model of SignalP v3.0 to analyse the 10 available annotated proteomes of mycobacterial species, including annotations of M. tuberculosis H37Rv from the Wellcome Trust Sanger Institute and the J. Craig Venter Institute (JCVI). When excluding proteins with transmembrane regions among the proteins predicted to harbour a signal peptide, we found between 7.8 and 10.5% of the proteins in the proteomes to be putative secreted proteins. Interestingly, we observed a consistent difference in the percentage of predicted proteins between the Sanger Institute and JCVI. We have determined the most valuable algorithm for predicting signal peptidase I-processed proteins of M. tuberculosis, and used this algorithm to estimate the number of mycobacterial proteins with the potential to be exported via this pathway.
Pubmed ID: 19389770 RIS Download
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The Center for Biological Sequence Analysis of the Technical University of Denmark conducts basic research in the field of bioinformatics and systems biology and directs its research primarily towards topics related to the elucidation of the functional aspects of complex biological mechanisms. A large number of computational methods have been produced, which are offered to others via WWW servers. Several data sets are also available. The center also has experimental efforts in gene expression analysis using DNA chips and data generation in relation to the physical and structural properties of DNA. The on-line prediction services at CBS are available as interactive input forms. Most of the servers are also available as stand-alone software packages with the same functionality. In addition, for some servers, programmatic access is provided in the form of SOAP-based Web Services. The center also educates engineering students in biotechnology and systems biology and offers a wide range of courses in bioinformatics, systems biology, human health, microbiology and nutrigenomics.
View all literature mentionsSoftware analysis package for molecular biology community. Automatically copes with data in variety of formats and allows transparent retrieval of sequence data from web. Libraries are provided with package. Provides toolkit for creating bioinformatics applications or workflows. Provides set of sequence analysis programs. Provided programs cover areas such as sequence alignment, rapid database searching with sequence patterns, protein motif identification, nucleotide sequence pattern analysis, codon usage analysis for small genomes, rapid identification of sequence patterns in large scale sequence sets, and presentation tools for publication.
View all literature mentionsWeb application for prediction of the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.
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