Molecular modeling and theoretical characterization of protein dynamics and ligand binding

Molecular modeling and theoretical characterization of protein dynamics and ligand binding Molecular recognition plays a crucial role in biology, chemistry and medicine. To answer key biological questions and enhance drug development, it is essential to understand protein-ligand binding at atomic detail. While multiple factors (i.e. pharmacokinetics) influence drug efficacy, protein-drug recognition is essential for achieving effective therapeutic action. While recent advances in molecular modeling show great success in computing ligand-protein binding affinity (i.e., binding free energy ΔG), challenges remain, especially in protein systems with shallow ligand binging pocket and conformation changes induced by a binding partner(s). Moreover, drug candidates showing good in vitro binding affinity to their target protein are not always effective in cells. In some cases, binding kinetics are the major determinant of drugs’ in vivo efficacy. Yet, the nature of binding kinetics (kon and koff), especial drug binding residence time (~1/koff), is not well understood, preventing drug design for desired kinetic properties. In cells, many proteins are not a standalone protein and have binding partners, which can affect a target protein’s conformations/dynamics. Considering the protein complex can lead better drug design and open new opportunities to design “molecular glues” to degrade the target protein or strengthen its functional inhibition. Molecular dynamics (MD) simulation techniques are powerful tools to explore the dynamics of the molecular complexes. Still, there is incomplete knowledge of how/why the proteins fluctuate between multiple conformations in the local-protein complex environment and how to efficiently glue proteins with a small molecule. Our long-term goal is to bridge all these knowledge gaps. Here we continue developing and applying modeling tools to compute and understand drug-protein binding affinity and residence time. This research will also involve the ongoing development of artificial intelligence (AI) modeling to analyze the complex interactions and molecular motions to learn the physics that governs protein conformational transitions and ligand-protein binding. Using the findings from our deep learning (DL) models, we will use DL to further sample protein conformations and assist drug design. The proposed work expands our classical view of molecular recognition - knowledge of the free and final bound states of a ligand and a protein - to examining the hidden states unseen in experiments for computing drug binding ΔG and residence time to better translate in vitro binding studies to drug’s in vivo efficacy. We also consider target protein’s local-protein complex environment, allowing detailed molecular glue and PROTAC design and/or improved drug design. Three main overarching themes are: (1) efficiently compute and understand non-covalent binding residence time and affinity, (2) utilize protein’s binding partners to design highly specific drugs and develop molecular glues and PROTACs, (3) integrate physicochemical and AI modeling to understand governing physics for sampling protein conformations and drug design. Project Number: 1R35GM164210-01 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of General Medical Sciences (NIGMS) | Principal Investigator: Chia-en Chang | Institution: UNIVERSITY OF CALIFORNIA RIVERSIDE, RIVERSIDE, CA | Award Amount: $399,391 | Activity Code: R35 | Study Section: Special Emphasis Panel[ZRG1 MCST-G (56)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11331712