Browsing by Author "Zheng, Wenjun"

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High-Resolution Modeling of Protein Structures Based on Flexible Fitting of Low-Resolution Structural Data
(Elsevier Academic Press inc, 2014) Zheng, Wenjun; Tekpinar, Mustafa
To circumvent the difficulty of directly solving high-resolution biomolecular structures, low-resolution structural data from Cryo-electron microscopy (EM) and small angle solution X-ray scattering (SAXS) are increasingly used to explore multiple conformational states of biomolecular assemblies. One promising venue to obtain high-resolution structural models from low-resolution data is via data-constrained flexible fitting. To this end, we have developed a new method based on a coarse-grained Cu-only protein representation, and a modified form of the elastic network model (ENM) that allows large-scale conformational changes while maintaining the integrity of local structures including pseudo-bonds and secondary structures. Our method minimizes a pseudo-energy which linearly combines various terms of the modified ENM energy with an EM/SAXS-fitting score and a collision energy that penalizes steric collisions. Unlike some previous flexible fitting efforts using the lowest few normal modes, our method effectively utilizes all normal modes so that both global and local structural changes can be fully modeled with accuracy. This method is also highly efficient in computing time. We have demonstrated our method using adenylate kinase as a test case which undergoes a large open-to-close conformational change. The EM-fitting method is available at a web server (htt://enm.lobos.nih.gov), and the SAXS-fitting method is available as a pre-compiled executable upon request.
Unzipping of Neuronal Snare Protein With Steered Molecular Dynamics Occurs in Three Steps
(Springer, 2014) Tekpinar, Mustafa; Zheng, Wenjun
Soluble NSF-attachment protein receptors (SNAREs) play a crucial role in membrane fusion. Neuronal SNAREs, a four-helix bundle, help synaptic vesicles fuse with plasma membranes. We applied constant velocity pulling forces in silico to C terminal of synaptobrevin, one of the helices in the bundle, to understand unzipping mechanism of neuronal SNAREs. We observed unzipping of snaptobrevin from the other helices in three steps: linker domain unzipping, C terminal unzipping and N terminal unzipping. Our results have good qualitative agreement with a recent optical tweezer experiment that observes this stepwise unzipping. Since we performed 14 different simulations for two large spring force constants, our results are robust and they reveal atomistic details of these distinct unzipping steps.