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

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.
AuthorsRui Kuang, Jianying Gu, Hong Cai, Yufeng Wang
JournalGenetica (Genetica) Vol. 136 Issue 1 Pg. 189-209 (May 2009) ISSN: 1573-6857 [Electronic] Netherlands
PMID19057851 (Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)
Chemical References
  • Protozoan Proteins
  • Peptide Hydrolases
Topics
  • Animals
  • Artificial Intelligence
  • Databases, Protein
  • Models, Statistical
  • Pattern Recognition, Automated (methods)
  • Peptide Hydrolases (chemistry, classification)
  • Phylogeny
  • Plasmodium (enzymology)
  • Protozoan Proteins (chemistry, classification)

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