Investigating biomolecular self-organization with simulation intelligence

Abstract:
Biomolecules are highly dynamic systems. They reorganize between a network of conformations connected by rare structural intermediates, which is referred to as their conformational ensemble. The ensemble, including the rare intermediate structures, determines biomolecular function in the cell. However, mapping biomolecular conformational ensembles is still challenging in computational and experimental approaches. Computer simulations are the modern manifestation of scientific theories and, merged with machine learning, can help us overcome challenges in the biomolecular sciences.
In the first part of my talk, I will illustrate our work to integrate path sampling with machine learning to empower our ability to simulate rare conformational transitions. Our algorithm provides efficient sampling, mechanism, thermodynamic, and kinetic information of rare molecular events at a moderate computational cost.
In the second part, I will discuss using simulation-based inference to identify biomolecular conformations in cryo-electron microscopy (cryo-EM) data. cryoEM is a powerful paradigm for characterizing protein conformational ensembles. However, even though the frozen sample contains information on the entire ensemble, accurately identifying rare or disordered molecular conformations depicted in a single cryoEM image is still challenging. We integrated physics-based simulations, Bayesian inference, and deep learning to develop the cryoEM simulation-based inference (cryoSBI) framework for inferring molecular conformations and their uncertainties from individual cryoEM images. We validated cryoSBI on synthetic and experimental data. Our approach paves the way to characterizing entire conformational ensembles from experimental data.

Language: English