Prediction of Signal Peptides in Proteins from Malaria Parasites
Michał Burdukiewicz , Piotr Sobczyk , Jarosław Chilimoniuk , Przemysław Gagat , Paweł Mackiewicz
AbstractSignal 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.
|Journal series||International Journal of Molecular Sciences, ISSN 1422-0067, (A 30 pkt)|
|Publication size in sheets||0.3|
|Keywords in Polish||uczenie maszynowe|
|Keywords in English||machine learning, apicomplexa, plasmodium, malaria, HSMM, hidden semi-Markov model, signal peptides|
|ASJC Classification||; ; ; ; ; ; ;|
|Abstract in Polish||Praca przedstawia nowy algorytm przewidujący peptydy sygnałowe i pokazuje jego zastosowanie w przewidywania peptydów sygnałowych u białek produkowanych przez zarodżce malarii.|
|Score|| = 30.0, 26-04-2019, ArticleFromJournal|
= 30.0, 11-03-2019, ArticleFromJournal
|Publication indicators||= 0; = 0; : 2016 = 1.147; : 2017 = 3.687 (2) - 2017=3.878 (5)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.