2025/10/11 by Advaith Maddipatla, Nadav Bojan Sellam, Meital Bojan +5 · 1 voice · 1 citation
Biochemistry, Genetics and Molecular Biology · Materials Science · #Protein Structure and Dynamics #Machine Learning in Bioinformatics #Enzyme Structure and Function
paper · pdf · doi:10.1101/2025.10.11.681796
Abstract AlphaFold3 predicts highly accurate protein structures from sequence, but tends to collapse to a single dominant conformation, even when the underlying structure is inherently heterogeneous. Moreover, its predictions are oblivious to experimental conditions that can alter local sequence conformation. In this work we show that AlphaFold3 can be guided to match data obtained by NMR spectroscopy, X-ray crystallography and cryo-EM experiments, and combinations thereof. Our approach can also incorporate data that explicitly report on dynamics, such as site-resolved order parameters. We demonstrate that this methodology can generate ensembles of conformations having less distance restraint violations than traditionally resolved NMR structures and uncover unmodelled alternate conformations detectable in electron density. This methodology paves the way for the development of experimentally aware predictive models that capture the ensemble nature of protein structures.