Pilar Cossio, Research Scientist and Project Leader for the Center for Computational Mathematics (CCB) and the Structural and Molecular Biophysics (SMB), respectively, at the Flatiron Institute, will deliver a seminar entitled, "Extracting free-energy profiles and transition rates from biophysical experiments and simulations." Hosted by Glen Hocky.
This seminar will take place in-person in room 540 Waverly with the NYU Daily Screener,
and on zoom at
For more information about Pilar Cossio, click here.
Abstract: Understanding the molecular mechanisms of biomolecules involves characterizing at atomic detail conformational states, extracting probability distributions of the conformations and elucidating the time to transition between states, among others. Unfortunately, there is no single experimental technique that can provide all these observables. Because of this, complementary techniques that integrate theoretical and computational methods with the available experimental data are required. In this talk, I will present two complementary methods to bridge this gap.
The first method is related to cryo-electron microscopy (cryo-EM), an experimental technique that measures single-particle projections of biomolecules. Although single-particle cryo-EM is widely used for 3D reconstruction, it has the potential to provide information about a biomolecule’s conformational variability, which leads to the underlying free-energy landscape of the system. However, cryo-EM images are challenging to analyze due to their low signal-to-noise ratio. To address these issues, we developed the cryo-BIFE method. This method uses a path collective variable together with a Bayesian approach to infer free-energy profiles and their uncertainties from cryo-EM raw images. We apply the method over a diverse set of synthetic and real systems, finding that the signal-to-noise ratio and pose estimate as key determinants to extracting accurate profiles.
The second method is developed to extract transition rates (inverse of the transition time) to cross a barrier using biased molecular simulations. It is based on Kramers' theory for calculating the barrier-crossing rate when a time-dependent bias is added to the system, even if a non-ideal collective variable is used. We assess the quality of the bias parameter by measuring how efficiently the bias accelerates the transitions compared to ideal behavior. We present approximate analytical expressions of the survival probability that accurately reproduce the barrier-crossing time statistics, and enable the extraction of the unbiased transition rate even for challenging cases, where previous methods fail.
This seminar is sponsored by the Simons Center for Computational Physical Chemistry at NYU.