Interpretable Atomistic Prediction and Functional Analysis of Conformational Ensembles and Allosteric States in Protein Kinases Using AlphaFold2 Adaptation with Randomized Sequence Scanning and Local Frustration Profiling

Abstract

AbstractThe groundbreaking achievements of AlphaFold2 (AF2) approaches in protein structure modeling marked a transformative era in structural biology. Despite the success of AF2 tools in predicting single protein structures, these methods showed intrinsic limitations in predicting multiple functional conformations of allosteric proteins and fold-switching systems. The recent NMR-based structural determination of the unbound ABL kinase in the active state and two inactive low-populated functional conformations that are unique for ABL kinase presents an ideal challenge for AF2 approaches. In the current study we employ several implementations of AF2 methods to predict protein conformational ensembles and allosteric states of the ABL kinase including (a) multiple sequence alignments (MSA) subsampling approach; (b) SPEACH_AF approach in which alanine scanning is performed on generated MSAs; and (c) introduced in this study randomized full sequence mutational scanning for manipulation of sequence variations combined with the MSA subsampling. We show that the proposed AF2 adaptation combined with local frustration mapping of conformational states enable accurate prediction of the ABL active and intermediate structures and conformational ensembles, also offering a robust approach for interpretable characterization of the AF2 predictions and limitations in detecting hidden allosteric states. We found that the large high frustration residue clusters are uniquely characteristic of the low-populated, fully inactive ABL form and can define energetically frustrated cracking sites of conformational transitions, presenting difficult targets for AF2 methods. This study uncovered previously unappreciated, fundamental connections between distinct patterns of local frustration in functional kinase states and AF2 successes/limitations in detecting low-populated frustrated conformations, providing a better understanding of benefits and limitations of current AF2-based adaptations in modeling of conformational ensembles.