Prediction of Signal Peptides in Proteins from Malaria Parasites

Michał Burdukiewicz , Piotr Sobczyk , Jarosław Chilimoniuk , Przemysław Gagat , Paweł Mackiewicz


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.
Author Michał Burdukiewicz (FMIS / DCSDCAM)
Michał Burdukiewicz,,
- Department of CAD/CAM Systems Design and Computer-Aided Medicine
, Piotr Sobczyk - [Wrocław University of Science and Technology]
Piotr Sobczyk,,
, Jarosław Chilimoniuk - [University of Wroclaw]
Jarosław Chilimoniuk,,
, Przemysław Gagat - [University of Wroclaw]
Przemysław Gagat,,
, Paweł Mackiewicz - [University of Wroclaw]
Paweł Mackiewicz,,
Journal seriesInternational Journal of Molecular Sciences, ISSN 1422-0067, (A 30 pkt)
Issue year2018
Publication size in sheets0.3
Keywords in Polishuczenie maszynowe
Keywords in Englishmachine learning, apicomplexa, plasmodium, malaria, HSMM, hidden semi-Markov model, signal peptides
ASJC Classification1604 Inorganic Chemistry; 1605 Organic Chemistry; 1606 Physical and Theoretical Chemistry; 1706 Computer Science Applications; 1607 Spectroscopy; 1312 Molecular Biology; 2700 General Medicine; 1503 Catalysis
Abstract in PolishPraca przedstawia nowy algorytm przewidujący peptydy sygnałowe i pokazuje jego zastosowanie w przewidywania peptydów sygnałowych u białek produkowanych przez zarodżce malarii.
Languageen angielski
Score (nominal)30
ScoreMinisterial score = 30.0, 23-09-2019, ArticleFromJournal
Publication indicators Scopus Citations = 0; WoS Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2016 = 1.147; WoS Impact Factor: 2017 = 3.687 (2) - 2017=3.878 (5)
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