Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Small molecule kinase inhibitors are critical in the modern treatment of cancers, evidenced by the existence of over 80 FDA-approved small-molecule kinase inhibitors. Unfortunately, intrinsic or acquired resistance, often causing therapy discontinuation, is frequently caused by mutations in the kinase therapeutic target. The advent of clinical tumor sequencing has opened additional opportunities for precision oncology to improve patient outcomes by pairing optimal therapies with tumor mutation profiles. However, modern precision oncology efforts are hindered by lack of sufficient biochemical or clinical evidence to classify each mutation as resistant or sensitive to existing inhibitors. Structure-based methods show promising accuracy in retrospective benchmarks at predicting whether a kinase mutation will perturb inhibitor binding, but comparisons are made by pooling disparate experimental measurements across different conditions. We present the first prospective benchmark of structure-based approaches on a blinded dataset of in-cell kinase inhibitor affinities to Abl kinase mutants using a NanoBRET reporter assay. We compare NanoBRET results to structure-based methods and their ability to estimate the impact of mutations on inhibitor binding (measured as ΔΔG). Comparing physics-based simulations, Rosetta, and previous machine learning models, we find that structure-based methods accurately classify kinase mutations as inhibitor-resistant or inhibitor-sensitizing, and each approach has a similar degree of accuracy. We show that physics-based simulations are best suited to estimate ΔΔG of mutations that are distal to the kinase active site. To probe modes of failure, we retrospectively investigate two clinically significant mutations poorly predicted by our methods, T315A and L298F, and find that starting configurations and protonation states significantly alter the accuracy of our predictions. Our experimental and computational measurements provide a benchmark for estimating the impact of mutations on inhibitor binding affinity for future methods and structure-based models. These structure-based methods have potential utility in identifying optimal therapies for tumor-specific mutations, predicting resistance mutations in the absence of clinical data, and identifying potential sensitizing mutations to established inhibitors.

Authors 16

1. Sukrit Singh corresponding

   Memorial Sloan Kettering Cancer Center

   Affiliation as printed

   Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA

   Memorial Sloan Kettering Cancer Center

2. Vytautas Gapsys

   Janssen (Belgium)

   Affiliation as printed

   Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse 2340, Belgium

   Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse 340, Belgium

3. Matteo Aldeghi

   Max Planck Institute for Multidisciplinary Sciences

   Affiliation as printed

   Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for multidisciplinary sciences, D-37077 Göttingen, Germany

   Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for multidisciplinary sciences, D-7077 Göttingen, Germany

4. David Schaller

   Memorial Sloan Kettering Cancer Center · Humboldt-Universität zu Berlin · Charité - Universitätsmedizin Berlin

   Affiliation as printed

   Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA

   In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany

   In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité, Universitätsmedizin Berlin, corporate member of Freie Universitat Berlin and Humboldt-Universitat zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany

5. Aziz M. Rangwala

   Stony Brook School · Stony Brook University

   Affiliation as printed

   Department of Pharmacological Sciences, Stony Brook University Medical School, Stony Brook, NY 11794, United States

6. Jessica B. White

   Cornell University · Weill Cornell Medicine

   Affiliation as printed

   Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY 10065, United States

   Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY 1005, United States

7. Joseph P. Bluck

   Bayer (Germany)

   Affiliation as printed

   Structural Biology & Computational Design, Research and Development, Pharmaceuticals, Bayer AG, 13342 Berlin, Germany

8. Jenke Scheen

   Open Molecular Software Foundation

   Affiliation as printed

   Open Molecular Software Foundation, Davis, CA 95618, USA

   Open Molecular Software Foundation, Davis, CA 9561, USA

9. William G. Glass

   Memorial Sloan Kettering Cancer Center

   Affiliation as printed

   Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA

   Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

10. Jiaye Guo

    Memorial Sloan Kettering Cancer Center

    Affiliation as printed

    Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA

    Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

11. Sikander Hayat Aachen Department of Medicine II

    Universitätsklinikum Aachen

    Affiliation as printed

    Department of medicine II, University Hospital Aachen, Pauwelsstraße 30, 52074 Aachen, Germany

    Department of medicine II, University Hospital Aachen, Aachen, Germany

12. Bert L. de Groot

    Max Planck Institute for Multidisciplinary Sciences

    Affiliation as printed

    Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for multidisciplinary sciences, D-37077 Göttingen, Germany

    Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for multidisciplinary sciences, D-7077 Göttingen, Germany

13. Andrea Volkamer

    Humboldt-Universität zu Berlin · Charité - Universitätsmedizin Berlin · Saarland University

    Affiliation as printed

    Data Driven Drug Design, Faculty of Mathematics and Computer Sciences, Saarland University, 66123 Saarbrücken, Germany

    In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany

    Data Driven Drug Design, Faculty of Mathematics and Computer Sciences, Saarland University, Saarbrucken, Germany

    In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charite, Augustenburger Platz 1, 13353 Berlin, Germany

14. Clara D. Christ

    Bayer (Germany)

    Affiliation as printed

    Structural Biology & Computational Design, Research and Development, Pharmaceuticals, Bayer AG, 13342 Berlin, Germany

15. Markus A. Seeliger

    Stony Brook School · Stony Brook University

    Affiliation as printed

    Department of Pharmacological Sciences, Stony Brook University Medical School, Stony Brook, NY 11794, United States

16. John D. Chodera

    Memorial Sloan Kettering Cancer Center

    Affiliation as printed

    Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA

    Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA