Multi-scale Simulation Of Protein Conformational Changes And Exploration Of The Underlying Mechanisms

Proteins are the major functional macromolecules in organisms. Since the organism is always in dynamic non-equilibrium states, various external environments like temperature, pH, local ion concentration, macromolecular crowding and binding interaction from partners are all important factors which influence the protein structure and dynamics. Accurate description of the dynamics of proteins under different environments is the basis to understand the mechanisms of various physiological processes. Due to the limited spatio-temporal resolution, traditional experimental methods are difficult to study the dynamic changes of proteins directly. Computational simulation approach, especially molecular dynamics (MD) simulation, constitutes a valuable tool to study such problems on the atomic scale. In this work, previously developed MD simulation package is extended and optimized. On this basis, three environment induced protein conformational change problems are investigated. The compromise in competition between the underlying mechanisms and their effects on protein dynamic structures is analyzed. The main results and conclusions are as follows:In chapter 2, previously developed MD simulation package, GPU_MD-1.0.5, was extended and optimized. The more accurate and commonly used force-field, CHARMM27, was integrated into the package. In addition, a GPU+CPU heterogeneous PME algorithm was developed through putting the global communication steps on CPU in the PME algorithm. The new simulation package was named GPU_MD-2.0. Several systems were simulated to validate the accuracy and performance of the package. Compared with the GROMACS-4.5.5 running on CPU, GPU_MD-2.0 on a single GPU card could achieve about 6～15 times speed up for single thread GROMACS and about 1～2 times speed up for 8 threads GROMACS. Compared with the GPU accelerated GROMACS-4.6, the computing speed of GPU_MD-2.0 is slightly faster under the same hardware conditions, illustrating that GPU_MD-2.0 has achieved relatively high computing efficiency.In chapter 3, a flow induced protein conformational transition process, that is, the external flow induced loop to β-sheet conformational change in the β-switch region of glycoprotein Iba was investigated. Direct MD, flow MD and metadynamics were employed to investigate the mechanisms of this flow induced conformational transition process. Specifically, the free energy landscape of the whole transition process was calculated by metadynamics with the path collective variable approach. The results revealed that without external flow, the free energy tending to the minimum is the solely dominant mechanism and the β-switch adopts random coil conformations. When the external flow exists, the dynamics of β-switch is dominated jointly by two mechanisms, the free energy tending to the minimum and water molecules tending to flow through the protein with minimum resistance. Each of these mechanisms has an extreme tendency that corresponds to a possible characteristic state. The compromise in competition between these two mechanisms leads to alternate occurrence of different characteristic states, resulting in the dynamics structures of β-switch.In chapter 4, the coupled folding and binding process of intrinsically disordered protein (IDP), α-MoRE, to its partner protein XD was investigated. The isolated α-MoRE system was first simulated by the parallel tempering method. Then the coupled folding and binding process of α-MoRE to XD was simulated by a combined metadynamics and parallel tempering in explicit solvent. Starting from an unbound and partially folded state of α-MoRE, multiple folding and binding events were observed during the simulation and the energy landscape was well estimated. The results revealed that for isolated α-MoRE, the free energy tending to the minimum is the solely dominant mechanism, and α-MoRE adopts random coil or partially folded conformations. When the partner protein XD is present, the binding effect constitutes another dominant mechanism, that is, the binding energy tending to the minimum and the α-MoRE tends to adopt fully helical conformation. The compromise in competition between these two mechanisms jointly determines the coupled folding and binding process of α-MoRE to XD.In chapter 5, a more complicated IDP system, the C-terminal domain of tumor suppressor p53 (p53 CTD) was investigated. The isolated p53 CTD was first simulated by the parallel tempering method. The three regulatory binding complexes of p53 CTD with different partner proteins were simulated by direct MD to investigate the interaction between free energy and the binding effect. The results demonstrated that, for isolated p53 CTD system, the free energy tending to the minimum is the solely dominant mechanism, and p53 CTD mainly adopts random coil conformations with a limited extent to form helical structures. However, when the binding partners present, the binding interactions constitute another dominant mechanism, that is, the binding energy tending to the minimum and the p53 CTD tends to adopt the corresponding bound structures. The compromise in competition between these two mechanisms leads to alternate occurrence of different characteristic states, and the relative strength of the two mechanisms determines the sampling frequency of each state.With the continuous development of computer hardware and software, as well as further improvement of various advanced enhanced sampling methods, more protein conformational change problems induced by external environments could be modeled and investigated. MD simulations should also be more in line with the complex environment in living organisms. Based on these researches, the underlying mechanisms of protein structural changes and the stability conditions will be further investigated and elaborated.