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Amino Acids

, Volume 39, Issue 1, pp 101–110 | Cite as

Prediction of mitochondrial proteins of malaria parasite using split amino acid composition and PSSM profile

  • Ruchi Verma
  • Grish C. Varshney
  • G. P. S. Raghava
Original Article

Abstract

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/).

Keywords

Plasmodium falciparum Mitochondria Support vector machine Position specific scoring matrix Online server 

Notes

Acknowledgments

The authors gratefully acknowledged the financial support provided by the Council of Science and Industrial Research (CSIR) and Department of Biotechnology (DBT), Government of India. This paper has IMTECH communication number 048/2007.

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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Ruchi Verma
    • 1
  • Grish C. Varshney
    • 2
  • G. P. S. Raghava
    • 1
  1. 1.Bioinformatics CentreInstitute of Microbial TechnologyChandigarhIndia
  2. 2.Cell biology and ImmunologyInstitute of Microbial TechnologyChandigarhIndia

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