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 objects16. 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. Particularly, we implemented the core-weighted correlation functions to effectively recognize correct fit of component maps in complex maps, and the grid-threading Monte Carlo search algorithm to efficiently construct complex structures from electron density maps. Using EMAP, we are conducting a series collaboration studies. Below is a list of the projects during the past year. 1 Conformational states of KIT extracellular domain in complex with stem cell factor Collaborated with Prof. Savvas Savvides and Dr. Jonathan Elegheert at Ghent University, we studied Conformational states of KIT extracellular domain in complex with stem cell factor based on cryo-EM and SAX images. Constrained molecular dynamic simulations are applied to identify conformation states and ensembles. 2. Tubline formation Collabratoed with Prof. Ruxandra Dima at University of Cincinnati, the formation of a nanotube by tubline dimer was studied. Contacts between intra dimer and inter dimmers are identified, which are important for the geometry of the nanotube. Mutation effect on nanotube properties are studied. 3. Conformational study of Thermosom Collaborated with Prof. George Stan at University of Cincinnati, the conformational states of Thermosome is studied with the map constrained simulation method. The open state conformation was obtained through self-guided molecular dynamics simulation combined with the map constrained simulation method. The simulation results provide insight to the functional pathway of theromsome. It also demonstrate the powerful capability of the map constrained simulation method in bridging the experimental map information to structural and dynamic studies. 4. Molecular modeling and simulation of the gp140/sugar system Colaborated with Dr. Sriram Subramaniam at NCI we performed molecular modeling and simulation study of gp140/sugar system. GP140 is homology modeled mainly based PDB structure, 3jwd. The v1v2 loop region was modeled based on a remote homologeous PDB structure 1ciy. V3 loop was modeled based on PDB structure, 2b4c. Glycan molecules are docked on the gp140 surface using the EMAP module1 of CHARMM2. The gp140-sugar system was fit into the EM map determined from their lab with the EMAP module1 of CHARMM2 to produce the trimer system. The N- and C- terminal motifs of the trimer are fixed by assuming they binding to gp41. The rest part of the trimer is simulated using the self-guided langevin dynamics (SGLD) simulation method to promote conformational changes. In a 1000 ps SGLD simulation, we observed the conformation changed from initial closed state to a open state that is similar to the structure, 3DNO. 5. Molecular basis of Chemotaxi In chemotactic bacteria, transmembrane chemoreceptors, CheA and CheW form the core signalling 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 tomographic studies of the 3D spatial architecture of chemoreceptor arrays in intact E. coli cells with computational modelling 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. 6. Targeted conformational search with map-constrained SGLD simulation. Targeted conformational search (TCS) represents efforts to derive structures based on experiment information or knowledge. In addition to X-ray diffraction and NMR NOEs, many new information becomes available due to the progress in experimental technology. Electron microscopy and tomography have emerged as a powerful approach to obtain structure information of molecular assemblies. They provide structural information of molecular assemblies in the form of images. Due to its low resolution, the image is often indifferent to individual atoms. To cooperate the image information into conformational search in molecular simulations, correlation based constraint potential to induce conformation changing toward the image defined shapes. To facilite large scale conformational change, the self-guided Langevin dynamics (SGLD) method is employed to accelerate the convergence in conformational searching. This map-constrained SGLD simulation method can be used for a general purosed targeted conformational search by converting any target conformation into constraint maps. Application of this method in several macromolecular systems demonstrates the convenient and efficient of this method.

Project Start
Project End
Budget Start
Budget End
Support Year
30
Fiscal Year
2012
Total Cost
$611,398
Indirect Cost
Name
National Heart, Lung, and Blood Institute
Department
Type
DUNS #
City
State
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Bartesaghi, Alberto; Aguerrebere, Cecilia; Falconieri, Veronica et al. (2018) Atomic Resolution Cryo-EM Structure of ?-Galactosidase. Structure 26:848-856.e3
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