Predicting the Conformational Variability of Abl Tyrosine Kinase using Molecular Dynamics Simulations and Markov State Models

Understanding protein conformational
variability remains a challenge
in drug discovery. The issue arises in protein kinases, whose multiple
conformational states can affect the binding of small-molecule inhibitors.
To overcome this challenge, we propose a comprehensive computational
framework based on Markov state models (MSMs). Our framework integrates
the information from explicit-solvent molecular dynamics simulations
to accurately rank-order the accessible conformational variants of
a target protein. We tested the methodology using Abl kinase with
a reference and blind-test set. Only half of the Abl conformational
variants discovered by our approach are present in the disclosed X-ray
structures. The approach successfully identified a protein conformational
state not previously observed in public structures but evident in
a retrospective analysis of Lilly in-house structures: the X-ray structure
of Abl with WHI-P154. Using a MSM-derived model, the free energy landscape
and kinetic profile of Abl was analyzed in detail highlighting opportunities
for targeting the unique metastable states.