posted on 2023-11-20, 08:13authored byBodhi
P. Vani, Akashnathan Aranganathan, Pratyush Tiwary
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
make specific and effective drug design difficult. In particular,
conformational variability due to the evolutionarily conserved Asp-Phe-Gly
(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 noticeably 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 learned order parameters
and potentials, thereby supplementing AlphaFold2 as a tool for exploration
of Boltzmann-weighted protein conformations (Meller, A.; Bhakat, S.;
Solieva, S.; Bowman, G. R. Accelerating Cryptic Pocket Discovery Using
AlphaFold. J. Chem. Theory Comput.2023, 19, 4355–4363).