Computation of conformational transitions and activation of Src tyrosine kinase.

Abstract

A protein molecule can adapt different structural forms depending on solution conditions or ligation state. The stable forms can be defined experimentally, but the transition pathways and metastable states between them cannot. A computational method to explore equilibrium conformational transition pathways is the adaptive bias path optimization (ABPO) algorithm. ABPO, implemented in the CHARMM program, differs from other path methods by using enhanced sampling along a path between two known states without restraining the system to the reduced coordinates of the evolving path. ABPO converges to a physically reasonable path even in the case of a highly rugged energy landscape and is amenable to assessing the power of the reduced variables chosen to explore the transition. ABPO was applied to three cases where a conformational transition is localized to one part of the polypeptide chain. With proper selection of the reduced coordinate set, sampling without restraints converged to a physically reasonable path. We also applied ABPO to a more complex transition of Src tyrosine kinase catalytic domain that undergoes a more complex transition between enzymatically down‑regulated and activated conformations. The results using coarse-grained and all‑atom models identified a switched electrostatic network (SEN) that could confer an entropic advantage to reduce energetic barriers by limiting the search space of the activation loop. The richness in atomic detail provided by the ABPO path reveals how the SEN couples the dynamics of the key αC‑helix and the activation loop, which provided a new rationale for the highly conserved HRD sequence of kinases. Further, the results related to energetics of the rotation of the αC‑helix provide a rationale for the interactions observed in a number of kinase regulatory complexes, some for which the basis for regulation from inspection of the structure along is puzzling.