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Genetica

, Volume 136, Issue 1, pp 189–209 | Cite as

Improved prediction of malaria degradomes by supervised learning with SVM and profile kernel

  • Rui Kuang
  • Jianying Gu
  • Hong Cai
  • Yufeng Wang
Article

Abstract

The spread of drug resistance through malaria parasite populations calls for the development of new therapeutic strategies. However, the seemingly promising genomics-driven target identification paradigm is hampered by the weak annotation coverage. To identify potentially important yet uncharacterized proteins, we apply support vector machines using profile kernels, a supervised discriminative machine learning technique for remote homology detection, as a complement to the traditional alignment based algorithms. In this study, we focus on the prediction of proteases, which have long been considered attractive drug targets because of their indispensable roles in parasite development and infection. Our analysis demonstrates that an abundant and complex repertoire is conserved in five Plasmodium parasite species. Several putative proteases may be important components in networks that mediate cellular processes, including hemoglobin digestion, invasion, trafficking, cell cycle fate, and signal transduction. This catalog of proteases provides a short list of targets for functional characterization and rational inhibitor design.

Keywords

Malaria Protease Parasite Plasmodium SVM 

Abbreviations

EGFR

Epidermal growth factor receptor

ER

Endoplasmic reticulum

PF-SVM

Support vector machine using profile kernels

ORF

Open reading frame

RIP

Regulated intramembrane proteolysis

SBDD

Structured based drug design

SERA

Serine-repeat antigen

SPP

Signal peptide peptidase

SVM

Support vector machine

Notes

Acknowledgments

We thank the anonymous reviewers for their constructive comments. We thank PlasmoDB for providing an all-in-one portal for malaria genomic data. The project described is supported by grants 1SC1GM081068, 8SC1AI080579, and R21AI067543 from the National Institute of General Medical Sciences and National Institute of Allergy and Infectious Diseases to Y. Wang. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases or the National Institutes of Health. YW is also supported by NIH grant G12RR013646, and San Antonio Area Foundation Biomedical Research Funds. RK is supported by Grant-in-Aid of Research, Artistry and Scholarship at University of Minnesota, and the Biomedical Informatics and Computational Biology Seed Grant for UM-Mayo-IBM Collaboration. JG is supported by PSC-CUNY 37 Research Award and Summer Research Award for faculty at College of Staten Island/CUNY.

Supplementary material

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© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of MinnesotaTwin Cities, MinneapolisUSA
  2. 2.Department of BiologyCollege of Staten Island/City University of New YorkStaten IslandUSA
  3. 3.Department of BiologyUniversity of Texas at San AntonioSan AntonioUSA

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