Division/Committee: [COMP] Division of Computers in Chemistry

It is tempting to picture a protein or nucleic acid structure as static, occupying a single canonical conformation which has been determined through careful experimentation. And yet, we know that under physiological conditions, biomolecules sample a complex free energy landscape. The collection of structures sampled by a biomolecule is termed its conformational ensemble, and different structures within the ensemble can play important roles in biological function, enabling processes such as self-assembly and allosteric regulation, and determining ligand affinity. Despite their critical importance to these and many other biomolecular phenomena, conformational ensembles have been difficult to quantify for anything other than the simplest systems. Recent advances in computational power (GPUs, anton, etc), computational approaches (machine learning, enhanced sampling, MSMs, etc), and connection to experiment (cryoEM and NMR) provide the tools necessary to quantify biomolecular ensembles for a broad range of systems. In this symposium, we will hear from experts driving the development and application of these tools, as well as how they are being applied in connection with experiments.

Martin McCullagh

Colorado State University

Appearances