Badaoui, Magd;
Buigues, Pedro J;
Berta, Dénes;
Mandana, Gaurav M;
Gu, Hankang;
Földes, Tamás;
Dickson, Callum J;
... Rosta, Edina; + view all
(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.
10.1021/acs.jctc.1c00924.
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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 (ICs) 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 the 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 the 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.
| Type: | Article |
|---|---|
| Title: | Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1021/acs.jctc.1c00924 |
| Publisher version: | https://doi.org/10.1021/acs.jctc.1c00924 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Ligand-protein unbinding, molecular kinetics, CDK2, free energy calculations, machine Learning, Collective Variable selection, CV identification, feature selection methods, Transition State Analysis, MLTSA |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10146650 |
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