Traditional molecular modeling is performed at atomic resolution, which relies on X-ray and NMR experiments to provide structural information. When deal with biomolecular assemblies of millions of atoms, atomic description of molecular objects becomes very computational inefficient. We developed a method that uses map objects for molecular modeling to efficiently derive structural information from experimental maps, as well as conveniently manipulate map objects, perform conformational search directly using map objects. This development work has been implemented into CHARMM as the EMAP module. This implementation enables CHARMM to manipulate map objects, including map input, output, comparison, docking, etc. Other experiments, such as transition metal ion FRET (tmFRET, are becoming a useful way to obtain protein structure information. A new focus of our research is to combine efficient simulation technique with structural information from experiment to assist high throughput protein structure determination. Structure determination from low resolution EM maps We developed a map-restrained self-guided Langevin dynamics (MapSGLD) simulation method for efficient targeted conformational search. The targeted conformational search represents simulations under restraints defined by experimental observations and/or by user specified structural requirements. Through map-restraints, this method provides an efficient way to maintain substructures and to set structure targets during conformational searching. With an enhanced conformational searching ability of self-guided Langevin dynamics, this approach is suitable for simulating large-scale conformational changes, such as the formation of macromolecular assemblies and transitions between different conformational states. A direct application of this method is to determine macromolecular structures by flexible fitting of atomic structures into density maps derived from cryo-electron microscopy. Molecular basis of Chemotaxi In chemotaxic bacteria, transmembrane chemoreceptors, CheA and CheW form the core signaling complex of the chemotaxis sensory apparatus. These complexes are organized in extended arrays in the cytoplasmic membrane that allow bacteria to respond to changes in concentration of extracellular ligands via a cooperative, allosteric response that leads to substantial amplification of the signal induced by ligand binding. Here, we have combined cryo-electron tomography studies of the 3D spatial architecture of chemoreceptor arrays in intact E. coli cells with computational modeling to develop a predictive model for the cooperativity and sensitivity of the chemotaxis response. The predictions were tested experimentally using fluorescence resonance energy transfer (FRET) microscopy. Our results demonstrate that changes in lateral packing densities of the partially ordered, spatially extended chemoreceptor arrays can modulate the bacterial chemotaxis response, and that information about the molecular organization of the arrays derived by cryo-electron tomography of intact cells can be translated into testable, predictive computational models of the chemotaxis response. Accurate High-Throughput Structure Mapping and Prediction with Transition Metal Ion FRET Mapping the landscape of a protein's conformational space is essential to understanding its functions and regulation. The limitations of many structural methods have made this process challenging for most proteins. We collaborated with Dr. Justin Taraska of NHLBI to use transition metal ion FRET (tmFRET) in a rapid, highly parallel screen, to determine distances from multiple locations within a protein at extremely low concentrations. The distances generated through this screen for the protein maltose binding protein (MBP) match distances from the crystal structure to within a few angstroms. Applying SGLDfp simulations with FRET distance restrains, we can quickly determine the structures at corresponding states. Our results open the door to rapid, accurate mapping and prediction of protein structures at low concentrations, in large complex systems, and in living cells. Protein complex structures prediction Proteinprotein interactions, defined as specific physical contacts between protein pairs that occur by selective molecular docking in a particular biological context, are critical to many biological functions such as signal transduction and immune response and are therapeutic drug targets. Hence, a detailed understanding of the mechanisms of protein association is of wide interest and of importance for drug design. Knowledge of the 3-dimensional (3D) structure of the proteinprotein complex is prerequisite for understanding how proteins associate. However, experimental determination of these proteinprotein complex structures by X-ray, NMR, and cyroelectron microscopy is time-consuming and is limited by the size of the complex. Thus, in silico proteinprotein docking approaches, which can predict the complex structure from the coordinates of the unbound component proteins, complement experimentally determined protein complex structures. The EMAP method implemented in the CHARMM program provides an efficient tool to perform protein-protein docking. Further development of EMAP will focus on the energy function to more accurately recognize native complexes. A generalized self-guided Langevin dynamics simulation method Langevin dynamics (SGLD) was developed for enhanced conformational search. This method makes rare events that otherwise not accessible by regular dynamics simulation observable with current available computing resources. Typical applications include protein folding, signal transaction, water penetration, etc. A major challenge when applying SGMD/SGLD method in simulation studies is that how to quantitatively measure the ensemble deviation from regular dynamics simulation and how to correct SGLD results. By analyzing the characters of the guiding force and SGLD simulation behavior, we derived a thermodynamic relation between a regular simulation and a self-guided simulation. Recently, we collaborated with Prof. Eric Vanden-Eijnden of New York University to apply generalized Langevin equation into SGLD to sample the canonical ensemble exactly. A virtual mixture simulation approach to study multi-state equilibrium: Application to constant pH simulation in explicit water Chemical and thermodynamic equilibrium of multiple states is a common phenomenon in scientific research. Typical examples of multiple state equilibrium are proteins in multiple protonation states, in multiple ligand binding states, or in multiple phosphorylation states. In molecular simulation studies, due to the limitation of computing resources, multiple state equilibrium is often studied through simulations of individual states. Transition between states is often a complicated issue especially with explicit solvent molecules. This work presents a virtual mixture simulation approach to study the equilibrium of multiple states. Using a constant pH simulation in explicit water as an example, we illustrate this virtual mixture method can effectively and accurately address the equilibrium of multiple protonation states at any given pH value.

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