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OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design

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Computational Protein Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1529))

Abstract

Drug resistance in protein targets is an increasingly common phenomenon that reduces the efficacy of both existing and new antibiotics. However, knowledge of future resistance mutations during pre-clinical phases of drug development would enable the design of novel antibiotics that are robust against not only known resistant mutants, but also against those that have not yet been clinically observed. Computational structure-based protein design (CSPD) is a transformative field that enables the prediction of protein sequences with desired biochemical properties such as binding affinity and specificity to a target. The use of CSPD to predict previously unseen resistance mutations represents one of the frontiers of computational protein design. In a recent study (Reeve et al. Proc Natl Acad Sci U S A 112(3):749–754, 2015), we used our osprey (Open Source Protein REdesign for You) suite of CSPD algorithms to prospectively predict resistance mutations that arise in the active site of the dihydrofolate reductase enzyme from methicillin-resistant Staphylococcus aureus (SaDHFR) in response to selective pressure from an experimental competitive inhibitor. We demonstrated that our top predicted candidates are indeed viable resistant mutants. Since that study, we have significantly enhanced the capabilities of osprey with not only improved modeling of backbone flexibility, but also efficient multi-state design, fast sparse approximations, partitioned continuous rotamers for more accurate energy bounds, and a computationally efficient representation of molecular-mechanics and quantum-mechanical energy functions. Here, using SaDHFR as an example, we present a protocol for resistance prediction using the latest version of osprey. Specifically, we show how to use a combination of positive and negative design to predict active site escape mutations that maintain the enzyme’s catalytic function but selectively ablate binding of an inhibitor.

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Acknowledgements

The authors would like to thank Drs. Mark Hallen and Kyle Roberts for thoughtful suggestions and technical assistance. This work was supported by NIH grant R01 GM-78031 to B.R.D., R01 AI-111957 to A.C.A., and A.O. was supported in part by NSF Graduate Research Fellowships Program Award 1106401.

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Author notes

  1. Ivelin Georgiev

    Present address: Department of Computer Science, Duke University, Durham, NC, 27708, USA

Authors and Affiliations

  1. Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, 27708, USA

    Adegoke Ojewole & Anna Lowegard

  2. Department of Computer Science, Duke University, Durham, NC, 27708, USA

    Pablo Gainza & Bruce R. Donald

  3. Department of Biochemistry, Duke University, Durham, NC, 27708, USA

    Bruce R. Donald

  4. Department of Chemistry, Duke University, Durham, NC, 27708, USA

    Bruce R. Donald

  5. Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT, 06269, USA

    Stephanie M. Reeve & Amy C. Anderson

  6. Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD, 20892, USA

    Ivelin Georgiev

Authors
  1. Adegoke Ojewole
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  2. Anna Lowegard
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  3. Pablo Gainza
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  4. Stephanie M. Reeve
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  5. Ivelin Georgiev
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  6. Amy C. Anderson
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  7. Bruce R. Donald
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Corresponding author

Correspondence to Bruce R. Donald .

Editor information

Editors and Affiliations

  1. Department of Plants and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel

    Ilan Samish

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Ojewole, A. et al. (2017). OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design. In: Samish, I. (eds) Computational Protein Design. Methods in Molecular Biology, vol 1529. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6637-0_15

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  • DOI: https://doi.org/10.1007/978-1-4939-6637-0_15

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6635-6

  • Online ISBN: 978-1-4939-6637-0

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