Title: Constant pH molecular dynamics and targeted covalent drug design
Bio: Link to Bio
Abstract:
Proton-coupled conformational dynamics mediate many biological and pharmaceutical processes, for example, enzyme catalysis and drug transport; however, the detailed mechanisms are poorly understood, due in part to the difficulty in experimentally resolving the proton locations. In this seminar, I will discuss the development of the continuous constant pH molecular dynamics simulations [1] and highlight some novel applications, e.g., to understand kinase conformational plasticity [2], drug partitioning in the membrane (ongoing work), and to inform cysteine and lysine reactivities for targeted covalent drug design [3]. Finally, I will discuss how the latter studies have inspired the development of machine learning models for the proteome ide assessment of covalent ligand abilities [4] to accelerate the current efforts in expanding the druggable proteome space.
References:
1. (a) Harris, JA, Liu, R, Martins de Oliveira, V, Vazquez Montelongo, E, Henderson, JA, and Shen, J*. GPU-Accelerated All-atom Particle-Mesh Ewald Continuous Constant pH Molecular Dynamics in Amber. J Chem Theory Comput 18: 7510–7527, 2022. (b) de Oliveira VM, Liu R, Shen J*. Constant pH Molecular Dynamics Simulations: Current Status and Recent Applications. Curr Opin Struct Biol 77: 102498, 2022.
2. Tsai CC, Yue Z, and Shen J*. How electrostatic coupling enables conformational plasticity in a tyrosine kinase. J Am Chem Soc 141: 15092-15101, 2019.
3. (a) Liu R, Yue Z, Tsai CC, and Shen J*. Assessing lysine and cysteine reactivities for designing targeted covalent kinase inhibitors. J Am Chem Soc 141: 6553-6560, 2019. (b) Romany E, Liu R, Zhan S, Clayton J, and Shen J*. Analysis of the ERK Pathway Cysteinome for Targeted Covalent Inhibition of RAF and MEK Kinases. J Chem Inf Model 63: 2483–2494, 2023.
4. Ruibin Liu, Joseph Clayton, Mingzhe Shen, and Shen J*. Machine Learning Models for Interrogating Proteome-wide Cysteine Ligandabilities. bioRxiv, 2023.
Host: Prof. Xuhui Huang