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Simultaneous prediction of binding free energy and specificity for PDZ domain–peptide interactions

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Abstract

Interactions between protein domains and linear peptides underlie many biological processes. Among these interactions, the recognition of C-terminal peptides by PDZ domains is one of the most ubiquitous. In this work, we present a mathematical model for PDZ domain–peptide interactions capable of predicting both affinity and specificity of binding based on X-ray crystal structures and comparative modeling with Rosetta. We developed our mathematical model using a large phage display dataset describing binding specificity for a wild type PDZ domain and 91 single mutants, as well as binding affinity data for a wild type PDZ domain binding to 28 different peptides. Structural refinement was carried out through several Rosetta protocols, the most accurate of which included flexible peptide docking and several iterations of side chain repacking and backbone minimization. Our findings emphasize the importance of backbone flexibility and the energetic contributions of side chain-side chain hydrogen bonds in accurately predicting interactions. We also determined that predicting PDZ domain–peptide interactions became increasingly challenging as the length of the peptide increased in the N-terminal direction. In the training dataset, predicted binding energies correlated with those derived through calorimetry and specificity switches introduced through single mutations at interface positions were recapitulated. In independent tests, our best performing protocol was capable of predicting dissociation constants well within one order of magnitude of the experimental values and specificity profiles at the level of accuracy of previous studies. To our knowledge, this approach represents the first integrated protocol for predicting both affinity and specificity for PDZ domain–peptide interactions.

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Acknowledgments

The authors thank Nils Woetzel for reconfiguring the BioChemistry Library Monte Carlo minimizer for this work and Jordan Willis and Sarel Fleishman for help with RosettaScripts protocol development. Additionally, we thank the entire Meiler Lab and Rosetta community for insightful feedback on this project. In particular, we are grateful to Tanja Kortemme, Colin Smith, Ora Schueler-Furman, and Nir London for useful discussion. Work in the Meiler Lab is supported through NIH (R01 GM080403, R01 MH090192, R01 GM099842) and NSF (Career 0742762). J.J.C. received support through the Beckman Scholars Program of the Arnold and Mabel Beckman Foundation.

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Author notes

  1. Joseph J. Crivelli

    Present address: Weill Cornell Medical College, New York, NY, USA

Authors and Affiliations

  1. Department of Chemistry, Vanderbilt University, Station B #351822, Nashville, TN, 37235, USA

    Joseph J. Crivelli, Gordon Lemmon, Kristian W. Kaufmann & Jens Meiler

  2. Department of Pharmacology, Vanderbilt University, Nashville, TN, USA

    Kristian W. Kaufmann & Jens Meiler

  3. Department of Biomedical Informatics, Center for Structural Biology, and Institute of Chemical Biology, Vanderbilt University, Nashville, TN, USA

    Jens Meiler

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  1. Joseph J. Crivelli
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Correspondence to Jens Meiler.

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Crivelli, J.J., Lemmon, G., Kaufmann, K.W. et al. Simultaneous prediction of binding free energy and specificity for PDZ domain–peptide interactions. J Comput Aided Mol Des 27, 1051–1065 (2013). https://doi.org/10.1007/s10822-013-9696-9

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  • DOI: https://doi.org/10.1007/s10822-013-9696-9

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