Information Theory in Prediction of Cleavage Sites of Signal Peptides

Liu, L.; Li, J.; Tian, Xueli; Ren, Da; Lin, Jianqing

doi:10.2174/0929866053765644

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…A number of methods that are unavailable for testing are omitted from this study. They include several neural network-based approaches [39,40]; SVMs-based approaches [41][42][43][44]; a profile HMM-based method called CJ-SPHMM [45]; matrix-based approach that uses the concept of information theory [46]; a BLOMAP-encoding scheme to transform input sequences [47]; a hybrid approach that uses bio-basis function NNs and decision trees [48]; a global alignment approach based on the Needleman-Wunsch algorithm [49,50] and several earlier prediction tools [51,52]. Other tools such as those for the prediction of subcellular localizations (e.g.…”

Section: Omission Of Prediction Toolsmentioning

confidence: 99%

A comprehensive assessment of N-terminal signal peptides prediction methods

2009

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BackgroundAmino-terminal signal peptides (SPs) are short regions that guide the targeting of secretory proteins to the correct subcellular compartments in the cell. They are cleaved off upon the passenger protein reaching its destination. The explosive growth in sequencing technologies has led to the deposition of vast numbers of protein sequences necessitating rapid functional annotation techniques, with subcellular localization being a key feature. Of the myriad software prediction tools developed to automate the task of assigning the SP cleavage site of these new sequences, we review here, the performance and reliability of commonly used SP prediction tools.ResultsThe available signal peptide data has been manually curated and organized into three datasets representing eukaryotes, Gram-positive and Gram-negative bacteria. These datasets are used to evaluate thirteen prediction tools that are publicly available. SignalP (both the HMM and ANN versions) maintains consistency and achieves the best overall accuracy in all three benchmarking experiments, ranging from 0.872 to 0.914 although other prediction tools are narrowing the performance gap.ConclusionThe majority of the tools evaluated in this study encounter no difficulty in discriminating between secretory and non-secretory proteins. The challenge clearly remains with pinpointing the correct SP cleavage site. The composite scoring schemes employed by SignalP may help to explain its accuracy. Prediction task is divided into a number of separate steps, thus allowing each score to tackle a particular aspect of the prediction.

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Section: Omission Of Prediction Toolsmentioning

confidence: 99%

A comprehensive assessment of N-terminal signal peptides prediction methods

2009

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“…For instance, the residues at positions -3 and -1 relative to the cleavage site are usually small and neutral. The regular expression search and weight matrix algorithms are now replaced by more sophisticated methods that include various types of machine learning methods such as neural networks (Nielsen et al, 1997), support vector machines (Vert, 2002), hidden Markov models (Nielsen et al, 1999) and many others (Ladunga et al, 1991;Talmud et al, 1996;Nielsen et al, 1997a;1997b;Nielsen & Krogh, 1998;Nielsen et al, 1999;Bendtsen et al, 2004a;Menne et al, 2000;Chou 2001;Lao et al, 2002a;2002b;Vert, 2002;Juncker et al, 2003;Hiller et al, 2004;Kall et al, 2004;Zhang & Henzel, 2004;Liu et al, 2005;Sidhu & Yang, 2006). However, most of these methods classify proteins as secretory or non-secretory but do not provide cleavage site assignment.…”

Section: Introductionmentioning

confidence: 99%

Prediction of signal peptides in protein sequences by neural networks.

Plewczyński¹,

Slabinski²,

Ginalski³

et al. 2008

Acta Biochim Pol

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We present here a neural network-based method for detection of signal peptides (abbreviation used: SP) in proteins. The method is trained on sequences of known signal peptides extracted from the Swiss-Prot protein database and is able to work separately on prokaryotic and eukaryotic proteins. A query protein is dissected into overlapping short sequence fragments, and then each fragment is analyzed with respect to the probability of it being a signal peptide and containing a cleavage site. While the accuracy of the method is comparable to that of other existing prediction tools, it provides a significantly higher speed and portability. The accuracy of cleavage site prediction reaches 73% on heterogeneous source data that contains both prokaryotic and eukaryotic sequences while the accuracy of discrimination between signal peptides and non-signal peptides is above 93% for any source dataset. As a consequence, the method can be easily applied to genome-wide datasets. The software can be downloaded freely from http://rpsp.bioinfo.pl/RPSP.tar.gz.

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