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Kinases compose one of the largest fractions of the human proteome, and theirmisfunction is implicated in many diseases, in particular cancers. Theubiquitousness and structural similarities of kinases makes specific andeffective drug design difficult. In particular, conformational variability dueto the evolutionarily conserved DFG motif adopting in and out conformations andthe relative stabilities thereof are key in structure-based drug design for ATPcompetitive drugs. These relative conformational stabilities are extremelysensitive to small changes in sequence, and provide an important problem forsampling method development. Since the invention of AlphaFold2, the world ofstructure-based drug design has noticably changed. In spite of it being limitedto crystal-like structure prediction, several methods have also leveraged itsunderlying architecture to improve dynamics and enhanced sampling ofconformational ensembles, including AlphaFold2-RAVE. Here, we extendAlphaFold2-RAVE and apply it to a set of kinases: the wild type DDR1 sequenceand three mutants with single point mutations that are known to behavedrastically differently. We show that AlphaFold2-RAVE is able to efficientlyrecover the changes in relative stability using transferable learnt orderparameters and potentials, thereby supplementing AlphaFold2 as a tool forexploration of Boltzmann-weighted protein conformations.

Exploring kinase DFG loop conformational stability with AlphaFold2-RAVE