Christophe Chipot

Abstract Enhanced-sampling molecular dynamics is indispensable for studying biomolecular processes beyond the reach of conventional simulation, yet its reliability in realistic biological settings remains poorly characterized.

Abstract Protein conformational energy landscapes are shaped not only by intramolecular interactions but also by their environment. In protein crystals and protein–protein complexes, intermolecular contacts alter this energy landscape, but the exact nature of this alteration is difficult to decipher. Understanding how the crystal lattice affects protein dynamics is crucial for crystallography-based studies of motion, yet its influence on collective motions remains unclear.

Abstract Click to copy section linkSection link copied! As computational chemistry methods evolve, dynamic effects have been increasingly recognized to govern chemical reaction pathways in both organic and inorganic systems. Here, we introduce a committor-based workflow that integrates a path-committor-consistent artificial neural network (PCCANN) with an iteratively trained hybrid-DFT-level message passing atomic convolutional encoder (MACE) potential.