Accurate nuclear quantum statistics on machine-learned classical effective potentials.

The contribution of nuclear quantum effects (NQEs) to the properties of various hydrogen-bound systems, including biomolecules, is increasingly recognized. Despite the development of many acceleration techniques, the computational overhead of incorporating NQEs in complex systems is sizable, particularly at low temperatures. In this work, we leverage deep learning and multiscale coarse-graining techniques to mitigate the computational burden of path integral molecular dynamics (PIMD).

Structure prediction of protein-ligand complexes from sequence information with Umol.

Protein-ligand docking is an established tool in drug discovery and development to narrow down potential therapeutics for experimental testing. However, a high-quality protein structure is required and often the protein is treated as fully or partially rigid. Here we develop an AI system that can predict the fully flexible all-atom structure of protein-ligand complexes directly from sequence information.

The role of an amphiphilic helix and transmembrane region in the efficient acylation of the M2 protein from influenza virus.

Protein palmitoylation, a cellular process occurring at the membrane-cytosol interface, is orchestrated by members of the DHHC enzyme family and plays a pivotal role in regulating various cellular functions. The M2 protein of the influenza virus, which is acylated at a membrane-near amphiphilic helix serves as a model for studying the intricate signals governing acylation and its interaction with the cognate enzyme, DHHC20. We investigate it here using both experimental and computational assays.

Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics.

Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution. However, accurately learning a CG force field remains a challenge. In this work, we leverage connections between score-based generative models, force fields, and molecular dynamics to learn a CG force field without requiring any force inputs during training.