Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective

Katya Ahmad; Andrea Rizzi; Riccardo Capelli; Davide Mandelli; Wenping Lyu; Paolo Carloni

doi:10.3389/fmolb.2022.899805

Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective

Front Mol Biosci. 2022 Jun 8:9:899805. doi: 10.3389/fmolb.2022.899805. eCollection 2022.

Authors

Katya Ahmad¹, Andrea Rizzi^{1

2}, Riccardo Capelli³, Davide Mandelli¹, Wenping Lyu^{4

5}, Paolo Carloni^{1

6}

Affiliations

¹ Computational Biomedicine (IAS-5/INM-9), Forschungszentrum Jülich, Jülich, Germany.
² Atomistic Simulations, Istituto Italiano di Tecnologia, Genova, Italy.
³ Department of Applied Science and Technology (DISAT), Politecnico di Torino, Torino, Italy.
⁴ Warshel Institute for Computational Biology, School of Life and Health Sciences, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China.
⁵ School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, China.
⁶ Molecular Neuroscience and Neuroimaging (INM-11), Forschungszentrum Jülich, Jülich, Germany.

Abstract

The dissociation rate (k _off) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k _off. Next, we discuss the impact of the potential energy function models on the accuracy of calculated k _off values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.

Keywords: QM/MM; drug discovery; enhanced sampling; kinetics; machine learning; molecular dynamics; parallel computing.

Publication types

Review