Abstract
BackgroundMalaria parasite secretes various proteins in infected RBC for its growth and survival. Thus identification of these secretory proteins is important for developing vaccine/drug against malaria. The existing motif-based methods have got limited success due to lack of universal motif in all secretory proteins of malaria parasite.ResultsIn this study a systematic attempt has been made to develop a general method for predicting secretory proteins of malaria parasite. All models were trained and tested on a non-redundant dataset of 252 secretory and 252 non-secretory proteins. We developed SVM models and achieved maximum MCC 0.72 with 85.65% accuracy and MCC 0.74 with 86.45% accuracy using amino acid and dipeptide composition respectively. SVM models were developed using split-amino acid and split-dipeptide composition and achieved maximum MCC 0.74 with 86.40% accuracy and MCC 0.77 with accuracy 88.22% respectively. In this study, for the first time PSSM profiles obtained from PSI-BLAST, have been used for predicting secretory proteins. We achieved maximum MCC 0.86 with 92.66% accuracy using PSSM based SVM model. All models developed in this study were evaluated using 5-fold cross-validation technique.ConclusionThis study demonstrates that secretory proteins have different residue composition than non-secretory proteins. Thus, it is possible to predict secretory proteins from its residue composition-using machine learning technique. The multiple sequence alignment provides more information than sequence itself. Thus performance of method based on PSSM profile is more accurate than method based on sequence composition. A web server PSEApred has been developed for predicting secretory proteins of malaria parasites,the URL can be found in the Availability and requirements section.
Highlights
Malaria parasite secretes various proteins in infected RBC for its growth and survival
The frequency of occurrence of amino acid alanine, cysteine, isoleucine, lysine, glutamine and threonine are higher in secretory proteins than non-secretory proteins, while composition of aspartic acid, phenylalanine, glycine are higher in non-secretory proteins than secretory proteins
In order to utilize the compositional biasness in terminus of secretory and non-secretory proteins, we developed Support Vector Machine (SVM) models using split amino acid and dipeptides composition
Summary
Malaria parasite secretes various proteins in infected RBC for its growth and survival Identification of these secretory proteins is important for developing vaccine/drug against malaria. The human malaria caused by Plasmodium falciparum has been one of the major infectious diseases in the world causing illness in 300 to 600 million people leading to 2 to 3 million deaths annually [1]. It is putting huge economic burden on affected countries (page number not for citation purposes). Parasite secretes an array of proteins within the host erythrocyte and beyond to facilitate its own survival within the host cell and for immunomodulation These proteins secreted by parasite can serve as potential drug/vaccine targets. The identification of secretory proteins of Plasmodium falciparum has got limited success, since experimental identification of these proteins is rather difficult due to complex nature of parasite
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