Distance-AF improves predicted protein structure models by AlphaFold2 with user-specified distance constraints

Abstract

The three-dimensional structure provides essential information for understanding biological functions of proteins. To aid structure determination, computational prediction has been extensively studied. Despite significant progress, challenges remain on difficult targets, such as those with multiple domains and proteins that fold into several conformations. Here we present Distance-AF, which aims to improve the performance of AlphaFold2 by incorporating distance constraints. Distance-AF reduced the root mean square deviation (RMSD) of structure models to native on average by 11.75 Å when compared to the models by AlphaFold2 on a test set of 25 targets. Distance-AF outperformed Rosetta and AlphaLink, which consider distance constraints. The average RMSD values for Distance-AF, Rosetta, and AlphaLink were 4.22 Å, 6.40 Å, and 14.29 Å, respectively. We further demonstrate its applications in various scenarios, including fitting structures into cryo-electron microscopy density maps, modeling active and inactive conformations, and generating conformational ensembles that satisfy Nuclear Magnetic Resonance data. Distance-AF has the potential to accelerate structural biology research, facilitate drug discovery, and provide a foundation for integrating experimental and computational approaches to study protein dynamics and interactions in complex biological systems.