Identifying Interaction Sites in "Recalcitrant" Proteins: Predicted Protein and Rna Binding Sites in Rev Proteins of Hiv-1 and Eiav Agree with Experimental Data
<|reference_start|>Identifying Interaction Sites in "Recalcitrant" Proteins: Predicted Protein and Rna Binding Sites in Rev Proteins of Hiv-1 and Eiav Agree with Experimental Data: Protein-protein and protein nucleic acid interactions are vitally important for a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed machine learning approaches for predicting which amino acids of a protein participate in its interactions with other proteins and/or nucleic acids, using only the protein sequence as input. In this paper, we describe an application of classifiers trained on datasets of well-characterized protein-protein and protein-RNA complexes for which experimental structures are available. We apply these classifiers to the problem of predicting protein and RNA binding sites in the sequence of a clinically important protein for which the structure is not known: the regulatory protein Rev, essential for the replication of HIV-1 and other lentiviruses. We compare our predictions with published biochemical, genetic and partial structural information for HIV-1 and EIAV Rev and with our own published experimental mapping of RNA binding sites in EIAV Rev. The predicted and experimentally determined binding sites are in very good agreement. The ability to predict reliably the residues of a protein that directly contribute to specific binding events - without the requirement for structural information regarding either the protein or complexes in which it participates - can potentially generate new disease intervention strategies.<|reference_end|>
@article{terribilini2005identifying,
    title={Identifying Interaction Sites in "Recalcitrant" Proteins: Predicted
  Protein and Rna Binding Sites in Rev Proteins of Hiv-1 and Eiav Agree with
  Experimental Data},
    author={Michael Terribilini, Jae-Hyung Lee, Changhui Yan, Robert L. Jernigan,
  Susan Carpenter, Vasant Honavar, Drena Dobbs},
    journal={arXiv preprint arXiv:cs/0511075},
    year={2005},
    archivePrefix={arXiv},
    eprint={cs/0511075},
    primaryClass={cs.LG cs.AI}
}