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Sequence- and Interactome-Based Prediction of Viral Protein Hotspots Targeting Host Proteins: A Case Study for HIV Nef

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  • Mahdi Sarmady
  • William Dampier
  • Aydin Tozeren

Abstract

Virus proteins alter protein pathways of the host toward the synthesis of viral particles by breaking and making edges via binding to host proteins. In this study, we developed a computational approach to predict viral sequence hotspots for binding to host proteins based on sequences of viral and host proteins and literature-curated virus-host protein interactome data. We use a motif discovery algorithm repeatedly on collections of sequences of viral proteins and immediate binding partners of their host targets and choose only those motifs that are conserved on viral sequences and highly statistically enriched among binding partners of virus protein targeted host proteins. Our results match experimental data on binding sites of Nef to host proteins such as MAPK1, VAV1, LCK, HCK, HLA-A, CD4, FYN, and GNB2L1 with high statistical significance but is a poor predictor of Nef binding sites on highly flexible, hoop-like regions. Predicted hotspots recapture CD8 cell epitopes of HIV Nef highlighting their importance in modulating virus-host interactions. Host proteins potentially targeted or outcompeted by Nef appear crowding the T cell receptor, natural killer cell mediated cytotoxicity, and neurotrophin signaling pathways. Scanning of HIV Nef motifs on multiple alignments of hepatitis C protein NS5A produces results consistent with literature, indicating the potential value of the hotspot discovery in advancing our understanding of virus-host crosstalk.

Suggested Citation

  • Mahdi Sarmady & William Dampier & Aydin Tozeren, 2011. "Sequence- and Interactome-Based Prediction of Viral Protein Hotspots Targeting Host Proteins: A Case Study for HIV Nef," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-11, June.
  • Handle: RePEc:plo:pone00:0020735
    DOI: 10.1371/journal.pone.0020735
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    References listed on IDEAS

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    1. Evangelia Petsalaki & Alexander Stark & Eduardo García-Urdiales & Robert B Russell, 2009. "Accurate Prediction of Peptide Binding Sites on Protein Surfaces," PLOS Computational Biology, Public Library of Science, vol. 5(3), pages 1-10, March.
    2. Richard J Edwards & Norman E Davey & Denis C Shields, 2007. "SLiMFinder: A Probabilistic Method for Identifying Over-Represented, Convergently Evolved, Short Linear Motifs in Proteins," PLOS ONE, Public Library of Science, vol. 2(10), pages 1-11, October.
    3. Yanay Ofran & Burkhard Rost, 2007. "Protein–Protein Interaction Hotspots Carved into Sequences," PLOS Computational Biology, Public Library of Science, vol. 3(7), pages 1-8, July.
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