| Prediction of mitochondrial proteins of malaria parasite using split amino acid composition and PSSM profile. | |
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MedLine Citation:
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PMID: 19908123 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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The rate of human death due to malaria is increasing day-by-day. Thus the malaria causing parasite Plasmodium falciparum (PF) remains the cause of concern. With the wealth of data now available, it is imperative to understand protein localization in order to gain deeper insight into their functional roles. In this manuscript, an attempt has been made to develop prediction method for the localization of mitochondrial proteins. In this study, we describe a method for predicting mitochondrial proteins of malaria parasite using machine-learning technique. All models were trained and tested on 175 proteins (40 mitochondrial and 135 non-mitochondrial proteins) and evaluated using five-fold cross validation. We developed a Support Vector Machine (SVM) model for predicting mitochondrial proteins of P. falciparum, using amino acids and dipeptides composition and achieved maximum MCC 0.38 and 0.51, respectively. In this study, split amino acid composition (SAAC) is used where composition of N-termini, C-termini, and rest of protein is computed separately. The performance of SVM model improved significantly from MCC 0.38 to 0.73 when SAAC instead of simple amino acid composition was used as input. In addition, SVM model has been developed using composition of PSSM profile with MCC 0.75 and accuracy 91.38%. We achieved maximum MCC 0.81 with accuracy 92% using a hybrid model, which combines PSSM profile and SAAC. When evaluated on an independent dataset our method performs better than existing methods. A web server PFMpred has been developed for predicting mitochondrial proteins of malaria parasites ( http://www.imtech.res.in/raghava/pfmpred/). |
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Authors:
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Ruchi Verma; Grish C Varshney; G P S Raghava |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't Date: 2009-11-12 |
Journal Detail:
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Title: Amino acids Volume: 39 ISSN: 1438-2199 ISO Abbreviation: Amino Acids Publication Date: 2010 Jun |
Date Detail:
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Created Date: 2010-06-09 Completed Date: 2010-09-14 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9200312 Medline TA: Amino Acids Country: Austria |
Other Details:
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Languages: eng Pagination: 101-10 Citation Subset: IM |
Affiliation:
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Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India. ruchi@imtech.res.in |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Amino Acids / analysis*, chemistry Artificial Intelligence* Databases, Protein Dipeptides / chemistry Mitochondrial Proteins / analysis*, chemistry*, metabolism Models, Molecular Plasmodium falciparum / chemistry* Protein Transport |
| Chemical | |
Reg. No./Substance:
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0/Amino Acids; 0/Dipeptides; 0/Mitochondrial Proteins |
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