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Research in Data Science pp 177–197Cite as

Feature Design for Protein Interface Hotspots Using KFC2 and Rosetta

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Part of the book series: Association for Women in Mathematics Series ((AWMS,volume 17))

Abstract

Protein–protein interactions regulate many essential biological processes and play an important role in health and disease. The process of experimentally characterizing protein residues that contribute the most to protein–protein interaction affinity and specificity is laborious. Thus, developing models that accurately characterize hotspots at protein–protein interfaces provides important information about how to inhibit therapeutically relevant protein–protein interactions. During the course of the ICERM WiSDM workshop 2017, we combined the KFC2a protein–protein interaction hotspot prediction features with Rosetta scoring function terms and interface filter metrics. A two-way and three-way forward selection strategy was employed to train support vector machine classifiers, as was a reverse feature elimination strategy. From these results, we identified subsets of KFC2a and Rosetta combined features that show improved performance over KFC2a features alone.

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Acknowledgements

The feature table and feature selection code are available by email to the corresponding author. We thank the Association for Women in Mathematics (AWM) and the Brown University Institute for Computational and Experimental Research in Mathematics (ICERM) for hosting the Women in Data Science and Mathematics (WiSDM) workshop. The Brown University Center for Computation and Visualization (CCV) and the Institute for Protein Design at the University of Washington provided computational resources used for this project. Participation by JM was sponsored by the National Science Foundation [NSF DMS 1160360]. The AWM Advance Program supported participation by FS, AL, YC, TW, and HC. Participation by TW was also supported by DIMACS. FS is generously funded by the Washington Research Foundation Institute for Protein Design Postdoctoral Innovation Fellowship.

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Authors and Affiliations

  1. University of Washington, Institute for Protein Design, Seattle, WA, USA

    Franziska Seeger

  2. Michigan State University, East Lansing, MI, USA

    Anna Little

  3. University of Michigan, Ann Arbor, MI, USA

    Yang Chen

  4. Jet Propulsion Laboratory, Pasadena, CA, USA

    Tina Woolf

  5. Willamette University, Salem, OR, USA

    Haiyan Cheng

  6. Oak Ridge National Laboratory, Knoxville, TN, USA

    Julie C. Mitchell

  7. University of Wisconsin - Madison, Madison, WI, USA

    Julie C. Mitchell

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  1. Franziska Seeger
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  2. Anna Little
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  3. Yang Chen
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  4. Tina Woolf
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  5. Haiyan Cheng
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  6. Julie C. Mitchell
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Correspondence to Julie C. Mitchell .

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Editors and Affiliations

  1. Department of Mathematics, Union College, Schenectady, NY, USA

    Ellen Gasparovic

  2. Department of Computer Science, George Mason University, Fairfax, VA, USA

    Carlotta Domeniconi

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Seeger, F., Little, A., Chen, Y., Woolf, T., Cheng, H., Mitchell, J.C. (2019). Feature Design for Protein Interface Hotspots Using KFC2 and Rosetta. In: Gasparovic, E., Domeniconi, C. (eds) Research in Data Science. Association for Women in Mathematics Series, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-030-11566-1_8

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