Abstract:
The seemingly random fluctuations that proteins exhibit in their native states actually harbor major contributions from the global/cooperative motions they undergo during functional transitions. Computationally efficient, robust approaches like elastic network models (ENM) that exploit the intrinsic dynamics of proteins are of great value in building up hybrid approaches bridging experimental and computational studies and extending each application's range. ENM global modes show functional significance and compares well with functional conformational changes. The purpose of this study is to build upon currently available computational techniques that generate transition pathways connecting end states. In this thesis, a practical hybrid approach, anisotropic network model (ANM) guided Langevin dynamics (LD) simulations (ANM-LD) has been developed, where the dominant ANM low-frequency modes are utilized to drive LD simulations. In ANM-LD, the initial conformation is moved along the selected ANM mode at each cycle, taking advantage of the evaluation of the interactions and energetics of the system via short cycles of all-atom implicit LD simulations. A detailed assessment of the method, in terms of creating physically meaningful pathways and approaching the final state, are carried out for a set of proteins. As exemplary cases, the analysis and results for adenylate kinase and maltose transporter is presented. ANM-LD was also applied to heat shock protein 90 (Hsp90) in apo, ATP-bound and Geldanamycin (GDM)-bound states, the latter combined with AFM experiments, to study the effect of temperature on the Hsp90-GDM binding dynamics. Lastly, conventional molecular dynamics simulations, ENM and ANM-LD are utilized to investigate CRP dynamics.
