Protein aimlessly fluctuates in its surrounding. In order for energy to be effectively channeled through the complex interaction network and so accurately activate essential transitions, often hundreds of microseconds, to milliseconds, even to tens of seconds of dynamics are required. Several decades? biophysical studies suggest that proteins likely possess characteristic energy landscapes that encode specific functions. Although theoretical and computational studies have greatly improved our understanding on protein energy landscape, the existing knowledge is still very limited. Dominant concepts, such as conformation selection model and hierarchical energy landscape (conformational slaving) model, have not been adequately understood at the atomistic level. This is largely due to lack of robust ?predictive? molecular dynamics sampling technique that can enable adequate exploration of long-timescale protein conformational changes. The orthogonal space sampling (OSS) scheme, particularly its high order generalization, allows for systematic acceleration of energy flow as required for thorough sampling enhancement. Preliminary studies suggest that orders of magnitude of sampling enhancement are plausible. However a major challenge for OSS has been lack of rigorous algorithmic solution to ensure sampling robustness. Our recent innovation in the adaptive dynamic reporting (ADR) method development sheds light on this challenge. In this project, we will systematically develop and improve this novel ?predictive? sampling strategy in the context of protein long-timescale dynamics and employ to-be-developed methods to quantitatively explore protein large-scale conformational dynamics and decipher biophysical principles underlying protein functional dynamics. This study includes three specific goals: (1) Developing high order orthogonal space tempering (HOOST) method based on the adaptive dynamic reporting (ADR) kernel to enable robust ?predictive? free energy sampling of biomolecular long-timescale dynamics; (2) Understanding roles of solvation fluctuation in protein dynamics; (3) Understanding the mechanistic basis of human Glucokinase (hGK) regulation.
?Predictive? free energy sampling of protein energy landscapes at the all-atom level, particularly via commonly tractable computing power, has been a long-pursued challenge in computational biophysics. In this project, we will develop mathematically rigorous solution to high-order orthogonal space tempering generalization to achieve this goal and apply to-be- developed methods to decipher underlying features of protein functional landscape. Understanding long-timescale protein dynamics is not only pivotal to deepening our knowledge on protein functions but also important to drug discovery process.
