Soutenance de thèse de Karen Palacio Rodriguez

Karen Palacio Rodriguez, doctorante dans l'équipe Physique des systèmes simples en conditions extrêmes (PHYSIX) soutient sa thèse le vendredi 23 septembre 2022 à 14 h.

Sorbonne Université - IMPMC - 4 place Jussieu - 75005 Paris - Barre 22-23 - 4e étage, salle 401

DEVELOPMENT OF PREDICTIVE APPROACHES FOR BIOMOLECULAR ASSOCIATION KINETICS

Abstract

Atomistic computer simulations of rare events have three paramount goals: predicting de-
tailed mechanisms, free energy landscapes, and kinetic rates of transformation processes like
phase transitions, chemical reactions, biomolecular folding or association. In real-life appli-
cations, all of these tasks are cumbersome and require intensive human and computer effort,
especially the calculation of rates. The difficulty resides in the gap between the long time scale
associated to such processes, also known as rare events, and the short time scale that is ac-
cessible by molecular dynamics simulations. Enhanced sampling techniques can accelerate the
exploration of high-free energy regions, adding external forces to the system to pull it out of
free energy basins or focusing sampling on the transition region and efficiently exploring transi-
tion paths. These techniques allow to reconstruct mechanisms and free energy landscapes for a
wide range of activated processes in physics, chemistry and biology. Methods aimed at accurate
kinetic rates are at present less mature and still require large computational effort and/or rely
on ideal collective variables.
We developed two efficient methodologies for the prediction of transition rates from molecular
dynamics simulations in combination with enhanced sampling techniques. Both strategies only
require sets of short simulations, which allows exploiting the parallel capabilities of current
supercomputers. On one side, we use metadynamics, a widely used enhanced sampling technique
that adds a time-dependent bias potential to the system, disrupting its dynamics. We overcome
this limitation by developing a method based on Kramers’ theory for calculating the barrier-
crossing rate when a time-dependent bias is added to the system. We tested this method in a
benchmark system and apply it to complex all-atoms simulations, showing that we are able to
extract the rate and measure at the same time the quality of the collective variables for processes
where Kramers’ theory holds. On the other side, transition path sampling trajectories are
the golden standard to access mechanistic information: we demonstrate that they also encode
accurate thermodynamic and kinetic information, that can be extracted by training a data-
driven overdamped Langevin model of the dynamics projected on a collective variable. We also
tested this method over benchmark systems to establish a validation criteria for the accurate
time resolutions that yields Markovian behavior and apply it to complex all-atoms simulations
to recover free energies, position-dependent diffusion coefficients, and rates. Overall, these new
theoretical tools that can be freely downloaded from GitHub make efficient use of computing
resources providing simple procedures to accurately predict kinetic rates and could be suitable
for applications far beyond the field of biomolecular association.

Jury

• Edina Rosta -  University College of London - Rapportrice
• Jérôme Hénin - Institut de Biologie Physico-Chimique - Rapporteur 
• Isabelle Callebaut - Sorbonne Université - Examinatrice
• Rodolphe Vuilleumier - École Normale Supérieure - Examinateur
• Fabio Pietrucci - Sorbonne Université - Directeur de thèse
• Alessandro Barducci - Centre de Biologie Structurale - Co-Directeur de thèse

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