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Open AccessArticle

Prediction of Signal Peptides in Proteins from Malaria Parasites

1
Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-661 Warszawa, Poland
2
Department of Mathematics, Wrocław University of Technology, 50-370 Wrocław, Poland
3
Department of Genomics, University of Wrocław, 50-383 Wrocław, Poland
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2018, 19(12), 3709; https://doi.org/10.3390/ijms19123709
Received: 23 October 2018 / Revised: 15 November 2018 / Accepted: 17 November 2018 / Published: 22 November 2018
(This article belongs to the Special Issue Special Protein or RNA Molecules Computational Identification 2018)
Signal peptides are N-terminal presequences responsible for targeting proteins to the endomembrane system, and subsequent subcellular or extracellular compartments, and consequently condition their proper function. The significance of signal peptides stimulates development of new computational methods for their detection. These methods employ learning systems trained on datasets comprising signal peptides from different types of proteins and taxonomic groups. As a result, the accuracy of predictions are high in the case of signal peptides that are well-represented in databases, but might be low in other, atypical cases. Such atypical signal peptides are present in proteins found in apicomplexan parasites, causative agents of malaria and toxoplasmosis. Apicomplexan proteins have a unique amino acid composition due to their AT-biased genomes. Therefore, we designed a new, more flexible and universal probabilistic model for recognition of atypical eukaryotic signal peptides. Our approach called signalHsmm includes knowledge about the structure of signal peptides and physicochemical properties of amino acids. It is able to recognize signal peptides from the malaria parasites and related species more accurately than popular programs. Moreover, it is still universal enough to provide prediction of other signal peptides on par with the best preforming predictors. View Full-Text
Keywords: apicomplexa; plasmodium; malaria; HSMM; hidden semi-Markov model; signal peptides apicomplexa; plasmodium; malaria; HSMM; hidden semi-Markov model; signal peptides
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Burdukiewicz, M.; Sobczyk, P.; Chilimoniuk, J.; Gagat, P.; Mackiewicz, P. Prediction of Signal Peptides in Proteins from Malaria Parasites. Int. J. Mol. Sci. 2018, 19, 3709.

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