Bend and Snap: Computationally Exploring the Helix Bias for Classifying Abl Kinase Allosteric Modulators

Abstract

Allosteric modulation of Abl kinase via its myristoyl binding pocket is a promising strategy for drug discovery, either aiming at inhibition to treat Chronic Myeloid Leukemia, or activation, under investigation for breast cancer therapy. Although activators and inhibitors can be differentiated by the induced α-helix-I conformation, in silico classification has been proven challenging. Our study distinguishes these classes effectively by integrating multiple computational approaches that account for the conformational plasticity of the regulatory α-helix-I. By evaluating traditional molecular docking, co-folding, and molecular dynamics simulations with methodology benchmarking, we use these methods to uncover mechanistic principles of allosteric modulation. Docking highlighted potentially stabilizing C–F interactions with deep-pocket residues, while co-folding correctly predicted helix bending for inhibitors and outperformed docking in virtual screening. Molecular dynamics revealed that the α-helix-I samples multiple possible conformations and uncovered motions consistent with dynamic coupling between helix bending and long-range restraint of the activation loop, a nuanced mechanism that static models could not elucidate. This study provides a validated framework that combines efficient classification with mechanistic analysis by combining molecular docking, co-folding, and molecular dynamics. Our approach aids in identifying myristoyl pocket ligands, differentiating inhibitors from activators, and suggesting allosteric principles that govern their function, paving the way for more rational design of next-generation, function-specific Abl modulators.