Browsing by Author "Zhang, Chong"

Now showing 1 - 4 of 4
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    Advanced Computational Methods for Macromolecular Modeling and Structure Determination
    (2013-12-05) Zhang, Chong; Ma, Jianpeng; Nordlander, Peter J.; Kiang, Ching-Hwa; Raphael, Robert M.
    As volume and complexity of macromolecules increase, theories and algorithms that deal with structure determination at low X-ray resolution are of particular importance. With limited diffraction data in hand, experimentalists rely on advanced computational tools to extract and utilize useful information, seeking to determinate a three dimensional model that best fits the experiment data. Success of further studies on the property and function of a specific molecule - the key to practical applications - is therefore heavily dependent on the validity and accuracy of the solved structure. In this thesis I propose Deformable Complex Network (DCN) and introduce Normal Mode Analysis (NMA), which are designed to model the average coordinates of atoms and associated fluctuations, respectively. Their applications on structure determination target two major branches ? the positional refinement and temperature factor refinement. I demonstrate their remarkable performance in structure improvements based on several criteria, such as the free R value, overfitting effect and Ramachandran Statistics, with tests carried out across a broad range of real systems for generality and consistency.
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    Deformable complex network for refining low-resolution X-ray structures
    (International Union of Crystallography, 2015) Zhang, Chong; Wang, Qinghua; Ma, Jianpeng; Applied Physics Program
    In macromolecular X-ray crystallography, building more accurate atomic models based on lower resolution experimental diffraction data remains a great challenge. Previous studies have used a deformable elastic network (DEN) model to aid in low-resolution structural refinement. In this study, the development of a new refinement algorithm called the deformable complex network (DCN) is reported that combines a novel angular network-based restraint with the DEN model in the target function. Testing of DCN on a wide range of low-resolution structures demonstrated that it constantly leads to significantly improved structural models as judged by multiple refinement criteria, thus representing a new effective refinement tool for low-resolution structural determination.
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    Improved Biomolecular Crystallography at Low Resolution with the Deformable Complex Network Approach
    (2013-07-24) Zhang, Chong; Ma, Jianpeng; Huang, Huey W.; Raphael, Robert M.
    It is often a challenge to atomically determine the structure of large macromolecular assemblies, even if successfully crystallized, due to their weak diffraction of X-rays. Refinement algorithms that work with low-resolution diffraction data are necessary for researchers to obtain a picture of the structure from limited experimental information. Relationship between the structure and function of proteins implies that a refinement approach delivering accurate structures could considerably facilitate further research on their function and other related applications such as drug design. Here a refinement algorithm called the Deformable Complex Network is presented. Computation results revealed that, significant improvement was observed over the conventional refinement and DEN refinement, across a wide range of test systems from the Protein Data Bank, indicated by multiple criteria, including the free R value, the Ramachandran Statistics, the GDT (<1Å) score, TM-score as well as associated electron density map.
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    Parallel continuous simulated tempering and its applications in large-scale molecular simulations
    (AIP Publishing, 2014) Zang, Tianwu; Yu, Linglin; Zhang, Chong; Ma, Jianpeng
    In this paper, we introduce a parallel continuous simulated tempering (PCST) method for enhanced sampling in studying large complex systems. It mainly inherits the continuous simulated tempering (CST) method in our previous studies [C. Zhang and J. Ma, J. Chem. Phys. 130, 194112 (2009); C. Zhang and J. Ma, J. Chem. Phys. 132, 244101 (2010)], while adopts the spirit of parallel tempering (PT), or replica exchange method, by employing multiple copies with different temperature distributions. Differing from conventional PT methods, despite the large stride of total temperature range, the PCST method requires very few copies of simulations, typically 2–3 copies, yet it is still capable of maintaining a high rate of exchange between neighboring copies. Furthermore, in PCST method, the size of the system does not dramatically affect the number of copy needed because the exchange rate is independent of total potential energy, thus providing an enormous advantage over conventional PT methods in studying very large systems. The sampling efficiency of PCST was tested in two-dimensional Ising model, Lennard-Jones liquid and all-atom folding simulation of a small globular protein trp-cage in explicit solvent. The results demonstrate that the PCST method significantly improves sampling efficiency compared with other methods and it is particularly effective in simulating systems with long relaxation time or correlation time. We expect the PCST method to be a good alternative to parallel tempering methods in simulating large systems such as phase transition and dynamics of macromolecules in explicit solvent.