Vincent Schnapka

The paper extends a framework to generate atomic resolution ensembles of intrinsically disordered proteins using six AI structure predictors and compares their performance against experimental data and simulations.

The paper measures electrostatic potentials of histone H3 tails using NMR and simulations to show how arginine-lysine balance affects their conformational behavior in nucleosome core particles.

This study uses integrative ensemble modeling to identify RNA conformations that can be targeted by small molecules, revealing ligandable sites not visible in static structures.

This paper introduces bAIes, a method that improves AlphaFold2 models using Bayesian inference to enhance virtual screening accuracy for drug discovery.

This paper introduces bAIes, a Bayesian framework for generating accurate atomic-resolution ensembles of intrinsically disordered proteins using AlphaFold2 and molecular mechanics, showing improved accuracy over existing methods.