Abstract: Protein-protein interactions (PPI) are ubiquitous in biological processes. Their study has significant implications for drug discovery as PPIs are important pharmacological targets for small molecules and important in various therapeutic modalities, such as targeted protein degradation and antibodies. Therefore, understanding the stability and dissociation of protein-protein complexes is of great fundamental and practical interest. In particular, the unbinding/residence times of ligands and peptide therapeutics from their target proteins (i.e., k_off, the dissociation rate constant) is a vital parameter for drug discovery and development as it is known that the dissociation kinetics are directly correlated to the pharmacological activity and efficacy of drug molecules [1]. Therefore, obtaining the dissociation kinetics from atomistic simulations has been a fundamental task in the atomistic modeling field [2]. Here, enhanced sampling methods are needed as the unbinding/dissociation event is a rare event on the simulation time scale [3]. While considerable effort has been made to obtain the unbinding/residence times in protein-ligand complexes, there have been much more limited applications towards the more challenging case of protein-protein dissociation kinetics [2].
In this talk, I will present my group’s research program that is focused on elucidating the dissociation kinetics of protein-protein complexes using atomistic simulations augmented with enhanced sampling methods, such as infrequent metadynamics [3]. I will present our results for various challenging protein-protein system, including MDM2-p53 and barnase-barstar. I will present how atomistic simulations can predict the effect of amino acid mutations on protein-protein unbinding kinetics. I will discuss how the protein model (i.e., force field) can affect the results. I will discuss the future perspective of our research program, including investigating how stabilizing molecules effect the stability and dissociation kinetics of protein-protein complexes, and the design of peptide binders using machine learning-based approaches.
References
[1] R. A. Copeland, “The drug–target residence time model: a 10-year retrospective”, Nat. Rev. Drug Discov., 15, 87–95 (2016).
[2] J. Wang, H. N. Do, K. Koirala, and Y. Miao, “Predicting Biomolecular Binding Kinetics: A Review”, J. Chem. Theory Comput., 19, 2135-2148 (2023).
[3] J. Hénin, T. Lelièvre, M. R. Shirts, O. Valsson, and L. Delemotte, “Enhanced Sampling Methods for Molecular Dynamics Simulation”, Living J. Comput. Mol. Sci. 4, (2022).