Speaker: Ravi Radhakrishnan, Ph.D., University of Pennsylvania
Topic: Altered protein dynamics and transition kinetics delineate oncogenic potential in mutated kinases
Kinases are a class of proteins that play essential roles in cell signaling, differentiation, and proliferation. They are frequently mutated in cancer and are the second-largest therapy target of specific inhibitors in clinical research. The activation status of mutated kinases in cancer can impact phenotypic outcomes not limited to tumor progression and drug sensitivity. Many approaches are implemented to quantify these phenotypic outcomes by controlling cell signaling through introducing mutations in kinase systems. These methods have been 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 mutated kinases systems. However, quantifying the degree of alterations caused by mutated systems to protein dynamics and transition kinetics to infer the resulting relative severity between wild-type and mutated kinases 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 and transition kinetics. Additionally, our Boltzmann weighted correlation approach efficiently quantifies the alteration in protein dynamics obtained through the free energy landscapes sampling the transition in kinase systems from inactive to active kinase states. 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 Exchange experiments which are established techniques to study protein dynamics to validate our model. Our enhanced atomistic simulations are a significant improvement over unbiased molecular dynamics techniques to study protein dynamics and transition kinetics and provide experimental timescale molecule-level insights into the role of mutational activity in kinases.