BIEN Seminar Series: Chia-en Chang, UC Riverside

Beyond AlphaFold3: From Physical Chemistry Principles to Machine-Learned Physics in Molecular Design The presentation will discuss our recent advances in protein variants and cyclic-peptide design in the AI era. While powerful tools such as AlphaFold3 and ProteinMPNN are widely used, what can we do when these data-driven AI methods based on statistical correlations fail? Here we will introduce 1) using residue correlation networks to design high-affinity protein-based binders; 2) teaching AI to learn physics to understand molecular motions and assist cyclic-peptide design. Using all-atom molecular dynamics (MD) simulations, we identify the essential network of residue interactions and dihedral angle correlations critical in protein–protein recognition and guide residue substitution to enhance protein-protein binding. Using ubiquitin (Ub), a central player in multiple cellular functions, and MERS coronaviral papain-like protease (PLpro), an antiviral drug target, our designed ubiquitin variant (UbV) hosting 3 mutated residues achieved a ~3,500-fold increase in functional inhibition relative to wild-type Ub, demonstrating the effectiveness of this physical-chemistry-based approach to design high-affinity protein binders for cell biology research and future therapeutics. We will then introduce our Internal Coordinate Net (ICoN), an autoencoder-based deep learning model that utilizes atomistic bond-angle-torsion coordinates as features and force-field based lost function to learn the physical principles of conformational changes from MD simulation data and to sample novel conformations in the latent space. The talk will highlight how coordinated torsion rotations drive conformational transition pathways in macro-cyclic peptides -- systems with highly concerted atomic motions and complex energy landscapes that challenge traditional conformational sampling and design strategies. We will show how ICoN improves accuracy, interpretability, and computational efficiency of deep learning models to bring scientific insights and assist macrocycle design. Biography: Prof. Chia-en Chang is a Professor and Vice Chair of Chemistry at the University of California, Riverside. She received her Ph.D. in Chemistry from the University of Maryland, College Park (Gilson lab) and conducted postdoctoral research at UC San Diego (McCammon group) before joining UCR in 2008. The Chang group develops and applies computational methods and theoretical models to address medically and chemically important problems, including cancer drug development, protein engineering, protein degrader design, and the use of deep learning to sample molecular conformations. Her work has been recognized by the Distinguished Lectureship Award from the Japanese Society for Molecular Science and the NSF CAREER Award, with research supported by the NIH and NSF.

Where

Winston Chung Hall, 205/206

Host

Seminars