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Learning Sequence Determinants of Protein: Protein Interaction Specificity with Sparse Graphical Models

  • Conference paper
Research in Computational Molecular Biology (RECOMB 2014)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8394))

Abstract

In studying the strength and specificity of interaction between members of two protein families, key questions center on which pairs of possible partners actually interact, how well they interact, and why they interact while others do not. The advent of large-scale experimental studies of interactions between members of a target family and a diverse set of possible interaction partners offers the opportunity to address these questions. We develop here a method, DgSpi (Data-driven Graphical models of Specificity in Protein:protein Interactions), for learning and using graphical models that explicitly represent the amino acid basis for interaction specificity (why) and extend earlier classification-oriented approaches (which) to predict the \(\varDelta{G}\) of binding (how well). We demonstrate the effectiveness of our approach in analyzing and predicting interactions between a set of 82 PDZ recognition modules, against a panel of 217 possible peptide partners, based on data from MacBeath and colleagues. Our predicted \(\varDelta{G}\) values are highly predictive of the experimentally measured ones, reaching correlation coefficients of 0.69 in 10-fold cross-validation and 0.63 in leave-one-PDZ-out cross-validation. Furthermore, the model serves as a compact representation of amino acid constraints underlying the interactions, enabling protein-level \(\varDelta{G}\) predictions to be naturally understood in terms of residue-level constraints. Finally, the model, DgSpi readily enables the design of new interacting partners, and we demonstrate that designed ligands are novel and diverse.

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Author information

Authors and Affiliations

  1. Department of Biochemistry, University of Washington, USA

    Hetunandan Kamisetty

  2. Department of Computer Science, Dartmouth, USA

    Bornika Ghosh & Chris Bailey-Kellogg

  3. School of Computer Science, Carnegie Mellon University, USA

    Christopher James Langmead

Authors
  1. Hetunandan Kamisetty
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  2. Bornika Ghosh
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  3. Christopher James Langmead
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  4. Chris Bailey-Kellogg
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Editor information

Editors and Affiliations

  1. School of Computer Science, Tel Aviv University, 69978, Tel Aviv, Israel

    Roded Sharan

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Kamisetty, H., Ghosh, B., Langmead, C.J., Bailey-Kellogg, C. (2014). Learning Sequence Determinants of Protein: Protein Interaction Specificity with Sparse Graphical Models. In: Sharan, R. (eds) Research in Computational Molecular Biology. RECOMB 2014. Lecture Notes in Computer Science(), vol 8394. Springer, Cham. https://doi.org/10.1007/978-3-319-05269-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-05269-4_10

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05268-7

  • Online ISBN: 978-3-319-05269-4

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