Exploring kinase DFG loop conformational stability with AlphaFold2-RAVE

TL;DR

This paper extends AlphaFold2-RAVE to study kinase DFG loop conformational stability, demonstrating its ability to predict stability changes in kinases and mutants, aiding structure-based drug design.

Contribution

The work advances AlphaFold2-RAVE by applying it to kinases, showing it can predict conformational stability changes due to mutations, enhancing sampling of protein conformations.

Findings

AlphaFold2-RAVE accurately predicts stability differences in kinase mutants.

Transferable order parameters enable efficient exploration of conformational ensembles.

Method enhances structure-based drug design by providing Boltzmann-weighted conformations.

Abstract

Kinases compose one of the largest fractions of the human proteome, and their misfunction is implicated in many diseases, in particular cancers. The ubiquitousness and structural similarities of kinases makes specific and effective drug design difficult. In particular, conformational variability due to the evolutionarily conserved DFG motif adopting in and out conformations and the relative stabilities thereof are key in structure-based drug design for ATP competitive drugs. These relative conformational stabilities are extremely sensitive to small changes in sequence, and provide an important problem for sampling method development. Since the invention of AlphaFold2, the world of structure-based drug design has noticably changed. In spite of it being limited to crystal-like structure prediction, several methods have also leveraged its underlying architecture to improve dynamics and enhanced sampling of conformational ensembles, including AlphaFold2-RAVE. Here, we extend AlphaFold2-RAVE and apply it to a set of kinases: the wild type DDR1 sequence and three mutants with single point mutations that are known to behave drastically differently. We show that AlphaFold2-RAVE is able to efficiently recover the changes in relative stability using transferable learnt order parameters and potentials, thereby supplementing AlphaFold2 as a tool for exploration of Boltzmann-weighted protein conformations.