Marco de Vivo

Laboratory of Molecular Modelling & Drug Discovery, Istituto Italiano di Tecnologia, Via Morego 30, 16163, Genoa, Italy

Decoding Biochemical Complexity with Simulations and AI-Enhanced Sampling

Abstract

In my contribution, I will illustrate our recent efforts to understand complex chemical processes in biochemistry through molecular simulations. I will present and discuss our work on dissecting the molecular mechanisms by which enzymes perform their complex functions. Examples will include the mechanism of DNA translocation by polymerase (Pol) enzymes, a critical step in Pol-mediated nucleic acid polymerization. The goal is to understand how complex chemical processes often involve critical conformational changes that are difficult to capture with equilibrium molecular dynamics or simple collective variables for enhanced-sampling simulations. I will explain how these problems were tackled through advanced simulations and AI-guided enhanced sampling, decoding molecular mechanisms that align well with the experimental evidence. Our efforts demonstrate how such approaches can address (bio)chemical questions of increasing complexity, where identifying the proper collective variable is inherently challenging.

References

[1] A. Visigalli, E. Trizio, Enrico, L. Bonati, P. Vidossich, M. Parrinello, M. De Vivo. “Coordinated residue motions at the enzyme-substrate interface promote DNA translocation in polymerases” J. Am. Chem. Soc., 2025