Prediction of signal peptides in protein sequences by neural networks

• Autorzy: Plewczynski D. , Slabinski L. , Ginalski K. , Rychlewski L.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

PL

neural network; protein database; protein sequence; signal peptide; prediction

• Wydawca: -
• Czasopismo: Acta Biochimica Polonica
• Rocznik: 2008
• Tom: 55
• Numer: 2
• Opis fizyczny: p.261-267,fig.,ref.

Twórcy

autor

Plewczynski D.

• Warsaw University, A.Pawinskiego 5a, 02-106 Warsaw, Poland

autor

Slabinski L.

autor

Ginalski K.

autor

Rychlewski L.

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