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CBE PhD Dissertation Defense: Keshav Patil

November 4, 2022 at 10:00 AM - 12:00 PM

Keshav Patil

Ph.D. Candidate, Department of Chemical and Biomolecular Engineering, University of Pennsylvania

Title: “Protein Dynamics in Mutated Kinases – A computational perspective”
Kinases are one of the most prominent oncology targets for drug therapy as they play essential roles in cell signaling, differentiation, proliferation, and metabolism. Still, mutations in them frustrate the development of successful drugs, often making clinical decisions challenging. Large-scale sequencing efforts have been led to create publicly available databases that store sequenced mutations to the order of several thousand, with impending questions on their impact on protein activity and providing scope for computational techniques to help address this issue. Computational methodologies (both correlative and mechanistic) are constantly researched to bridge the gap between molecular mutational data and the clinical decision pipeline.
This thesis investigates the mechanistic backdrop for the molecular origins of mutational activity in ALK, MEK, and EGFR kinases through statistical mechanics-based techniques like molecular dynamics and enhanced sampling to map the conformational landscapes of mutated kinase systems that are indicative of kinase activity. A modified Boltzmann weighted dynamical cross-correlation (DCC) is proposed, implemented, and tested to utilize the free energy landscapes to bridge the gap between protein dynamics data obtained through metadynamics-based molecular simulations and the experimental timescale protein dynamics quantities like percentage exchange profiles obtained from Hydrogen Deuterium Exchange Experiments (HDX), a prominent experimental tool to study protein dynamics. Our analysis underscores the impact of mutations in altering protein dynamics in a stick-shift pattern and provides a dynamics-based clustering metric for classifying the mutations. Protein dynamics also have direct implications for drug sensitivity. The Exon-19 deletion mutants in EGFR and their HDX exchange data provided a good case since deletion of 3-4-5 residues at specific locations had a significant alteration in the dynamics of EGFR, causing a disorder in ATP binding site, leading to altered ATP binding affinity and hence variational drug sensitivity. Our work analyzed the contribution to protein dynamics by intra-protein and protein solvent interactions in mutated EGFR using long-time molecular simulations. A machine learning-based model is developed to predict the HDX exchanges and estimate the contributions of the molecular origins behind the observed protein dynamics using SHAP values. Finally, this thesis discusses addressing the tradeoff of complexity vs. scalability, where features from molecular simulations are obtained to be incorporated into the machine learning models for predicting mutational effects of mutations in larger datasets, wherein we observed hydrogen bond network in the alpha C helix and the activation loop of the kinases as an effective fingerprint in indicating the preferred kinase configuration in mutated systems and thereby reflecting its activity.
Primary Advisor: Ravi Radhakrishnan
Committee Members: Dr. Dennis Discher, Dr. Tobias Baumgart, Dr. John Crocker


November 4, 2022
10:00 AM - 12:00 PM
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Room 534 A Wing, 3401 Walnut
3401 Walnut Street
Philadelphia, PA 19104 United States
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