25.04.23 - Georg Diez "Cooperative Effects in Protein Dynamics: Insights from Correlation Analysis"

Cooperative Effects in Protein Dynamics: Insights from Correlation Analysis

Molecular dynamics simulations are a powerful tool for studying protein dynamics, but the resulting high-dimensional feature space can make interpretation of the complex dynamics challenging. In order to shed light on the underlying mechanisms of processes, one typically models the dynamics using a few key internal coordinates (or features) that capture the most important conformational changes of the protein. However, selecting these key internal coordinates can be difficult and biased.

To address this challenge, we present a scalable and unbiased approach to simplify the study of protein dynamics by partitioning the feature space into subsets that describe collective motion. Our approach allows us to identify the key reaction coordinates, providing an effective dimensionality reduction scheme while discarding uncorrelated motion and noise, increasing the interpretability of post-simulation models such as Markov state models.

We demonstrate the effectiveness of our approach on several protein systems, including proteins undergoing an allosteric transition and proteins folding into their native structure. By comparing and using different similarity measures, we reveal cooperative effects in protein dynamics and gain insight into the underlying mechanisms. Our approach is a valuable tool for reducing the complexity of (non-)equilibrium protein dynamics and helps to enhance our understanding of these important biological processes.