Kinases are a class of proteins that play essential roles in cell signaling, differentiation, and proliferation. They are frequently mutated in cancer and are one of the largest therapy targets of specific inhibitors in clinical research. The activation status of mutated kinases in cancer can profoundly impact phenotypic outcomes not limited to tumor progression and drug sensitivity. To quantify these phenotypic outcomes through mutated kinase activities, optical approaches implemented to control cell signaling pathways by photoswitchable kinases have proved to be transformational by relying on specific gain-of-function mutations. To better understand this at the molecular level, the role of mutations in intrinsic kinase activity needs to be quantified. Free energy calculations obtained through enhanced sampling techniques of statistical mechanics have facilitated the understanding of structural stabilization of kinases and their mutated systems and provided a detailed view of the underlying structure-activity relationship. However, a quantification of the degree of alterations caused by the mutated systems to this structure-activity relationship and the resulting proximity between wild type and mutated kinase systems is not well studied. We implement a computational suite combining enhanced sampling techniques of Metadynamics and INDUS to investigate the role of mutations in altering protein dynamics.
Additionally, our Boltzmann weighted correlation approach efficiently quantifies these alterations in protein dynamics obtained through the free energy landscapes by sampling the conformational transition in kinase systems. Moreover, the suite also investigates the long timescale role of solvent water molecules in protein dynamics. Finally, we analyze our results with log P profiles obtained from Hydrogen Deuterium Exchange (HDX), an experimental technique to study protein dynamics to validate our model. Our method is a significant improvement over unbiased molecular dynamics techniques to study protein dynamics over experimental timescales and provides a molecular-level understanding of the role of mutations in conferring an oncogenic potential.
We acknowledge financial support from the NIH and computational support from the Flatiron Institute.
