Mutations in a human kinase cause drug resistance by shifting conformational state distributions.

Abstract

In pharmacology, protein structures are frequently represented as static snapshots with mostly rigid conformations. This portrayal is likely myopic, as both theory and experiment have shown that biological macromolecules tend to occupy time-dependent conformational ensembles with thousands of states, each of which can adopt distinct functional and binding profiles. We believe that studying the often disregarded dynamics of these macromolecular structures should take center stage in the next generation of drug discovery efforts. This philosophy stems from the intuition that ensembles and their internal transitions multiply the number of potentially druggable targets. Recognizing this paradigm shift, we have deployed enhanced-sampling molecular dynamics (MD) simulations to map the conformational landscape of cancer-associated protein kinase domains. Through MD, we have successfully sampled the transition from the active state (known as DFG in) of wild-type and mutant forms of the Abelson tyrosine kinase (Abl1) to two distinct inactive states: DFG out and DFG out/collapsed A-loop. This transition spans an overall free energy barrier of over 80 kcal/mol from the active state to the second inactive state. Our work has revealed a rugged conformational landscape, with a collection of potentially metastable conformations that are virtually undetectable with traditional methods and could be used as targets for drug discovery. Importantly, we have also derived molecular mechanisms for the observed transitions between states, with key implications for biochemistry and protein engineering. Finally, we observed that two mutations that are known to cause inhibitor resistance in cancer patients lead to their phenotypes by significantly shifting the free energy barriers and pathways between states, locking the kinases in their active conformations. Ultimately, our work could lead to exciting opportunities for pharmacology, and further cement the importance of macromolecular conformational dynamics in biomedical discovery.