SMBp Group Meeting: Roberto Covino

Thursday Jul 28, 2022, 2:00 PM → 3:00 PM America/New_York

3rd Floor Classroom/3-Flatiron Institute (162 5th Avenue)

Speaker: Roberto Covino 

Topic: Investigating mechanisms of biomolecular self-organization by integrating physics-
based simulations and AI

Abstract: Molecular self-organization driven by concerted many-body interactions produces the ordered structures that define both inanimate and living matter. Understanding the physical
mechanisms that govern the formation of molecular complexes is key to controlling the
assembly of nanomachines and new materials.
Molecular dynamics simulations and single-molecule experiments offer the unprecedented
possibility to reveal mechanisms of molecular self-organization in high resolution. However,
outstanding challenges hinder their power. Machine learning and artificial intelligence
promise to empower both approaches to overcome fundamental challenges.
In the first part of my talk, I will present an autonomous AI that learns molecular mechanisms
from computer simulations. The AI agent simulates infrequent and stochastic molecular
reorganizations and progressively learns how to predict their outcome. By using symbolic
regression, we distill simplified quantitative models that reveal mechanistic insight in a
human-understandable form. Our innovative AI enables the sampling of rare events by
autonomously driving many parallel simulations with minimal human intervention and aids
their mechanistic interpretation. I will present applications on nucleation processes and the
assembly of membrane proteins in lipid bilayers.
In the second part of my talk, I will discuss how the integration of physical modeling and AI
helps extracting mechanistic understanding from single-molecule force spectroscopy. While
these experiments offer the possibility of measuring fundamental quantities like free
energies, these measurements are often incomplete and indirect. In practice, we measure a
few order parameters that are the outcome of the coupled dynamics of the molecule and the
mesoscopic experimental apparatus, which could lead to estimation artifacts. I will discuss
this problem as a Bayesian inference and illustrate how simulation-based inference provides
a powerful solution. Coupling a simulator that encodes the physics of the measuring process
with density estimation using neural networks leads to accurate estimates of molecular free
energies.
In conclusion, the integration of physics-based models and AI provides a general and
powerful way to extract accurate quantitative information from simulations and biophysical
experiments.