Title: Navigating protein landscapes with a machine-learned transferable coarse-grained model
Abstract:
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics, but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar prediction performance has been a long-standing challenge. By combining recent deep-learning methods with a large and diverse training set of all-atom protein simulations, we have developed a bottom–up CG force field with chemical transferability, which can be used for extrapolative molecular dynamics on new sequences not used during model parameterization. The model successfully predicts metastable states of folded, unfolded and intermediate structures, the fluctuations of intrinsically disordered proteins and relative folding free energies of protein mutants, while being several orders of magnitude faster than an all-atom model. This showcases the feasibility of a universal and computationally efficient machine-learned CG model for proteins.
Bio:
Cecilia Clementi is Einstein Professor of Physics at Freie Universität (FU)
Berlin, Germany. She joined the faculty of FU in June 2020 after 19 years
as a Professor of Chemistry at Rice University in Houston, Texas.
Cecilia obtained her Ph.D. in Physics at SISSA and was a postdoctoral
fellow at the University of California, San Diego, where she was part of the
La Jolla Interfaces in Science program.
Her research focuses on the development and application of methods
for the modeling of complex biophysical processes, by means of molecular
dynamics, statistical mechanics, coarse-grained models, experimental data,
and machine learning.
Cecilia’s research has been recognized by a National Science Foundation
CAREER Award (2004), and the Robert A. Welch Foundation Norman
Hackerman Award in Chemical Research (2009).
Since 2016 she is also a co-Director of the National Science Foundation
Molecular Sciences Software Institute.
Keywords: molecular dynamics, coarse-graining, machine-learning, proteins
Host: Prof. Xuhui Huang and Prof. Kyle Cranmer
Live Stream Link: http://128.104.155.144/ClassroomStreams/chemistry1435_stream.html