Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics

Badaoui, Magd, Buigues, Pedro, Berta, Denes , Mandana, Guarav, Gu, Hankang, Földes, Tamás, Dickson, Callum, Hornak, Viktor, Kato, Mitsunori, Molteni, Carla, Parsons, Simon and Rosta, Edina (2022) Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics. Journal of Chemical Theory and Computation, 18 (4). pp. 2543-2555. ISSN 1549-9618

Full content URL: https://doi.org/10.1021/acs.jctc.1c00924

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Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics
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Abstract

The determination of drug residence times, which define the time an inhibitor is in complex with its
target, is a fundamental part of the drug discovery process. Synthesis and experimental
measurements of kinetic rate constants are, however, expensive, and time-consuming. In this work,
we aimed to obtain drug residence times computationally. Furthermore, we propose a novel
algorithm to identify molecular design objectives based on ligand unbinding kinetics. We designed
an enhanced sampling technique to accurately predict the free energy profiles of the ligand
unbinding process, focusing on the free energy barrier for unbinding. Our method first identifies
unbinding paths determining a corresponding set of internal coordinates (IC) that form contacts
between the protein and the ligand, it then iteratively updates these interactions during a series of
biased molecular-dynamics (MD) simulations to reveal the ICs that are important for the whole of
the unbinding process. Subsequently, we performed finite temperature string simulations to obtain
the free energy barrier for unbinding using the set of ICs as a complex reaction coordinate.
Importantly, we also aimed to enable further design of drugs focusing on improved residence
times. To this end, we developed a supervised machine learning (ML) approach with inputs from
unbiased “downhill” trajectories initiated near the transition state (TS) ensemble of the string
unbinding path. We demonstrate that our ML method can identify key ligand-protein interactions
driving the system through the TS. Some of the most important drugs for cancer treatment are
kinase inhibitors. One of these kinase targets is Cyclin Dependent Kinase 2 (CDK2), an appealing
target for anticancer drug development. Here, we tested our method using two different CDK2
inhibitors for potential further development of these compounds. We compared the free energy
barriers obtained from our calculations with those observed in available experimental data. We
highlighted important interactions at the distal ends of the ligands that can be targeted for
improved residence times. Our method provides a new tool to determine unbinding rates, and to
identify key structural features of the inhibitors that can be used as starting points for novel design
strategies in drug discovery.

Keywords:Ligand-protein unbinding, Molecular kinetics, Machine learning
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
F Physical Sciences > F100 Chemistry
Divisions:College of Science > School of Computer Science
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ID Code:49062
Deposited On:25 Apr 2022 08:53

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