Browsing by Author "Yu, Linglin"

Now showing 1 - 3 of 3
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    A Novel Statistical Potential for Protein Beta-Sheets Prediction
    (2014-04-25) Yu, Linglin; Ma, Jianpeng; Nordlander, Peter J.; Raphael, Robert M.
    One of the most long-term challenging problems in biophysics studies for both computational scientists and experimentalists is protein structure prediction, whose goal is to obtain three-dimensional native protein structure from one-dimensional sequence. In protein structure prediction problems, a fundamental problem is Beta-sheets structure prediction. Though more than 85% of experimentally solved proteins contain Beta-sheet structures, limited methods have been found to rapidly and accurately predict the folded conformations. In this study, we proposed a novel statistical potential, named NP-Beta, to predict the protein Beta-sheet structure only based on the sequence information. We included three kinds of potential terms in NP-Beta, i.e. the self-packing term, the pair interacting term and the lattice term. The number of hydrogen bonds in Beta-sheets is also considered as a potential component, corresponding to a global penalty of the potential function. Computational tests show that the new statistical potential has an outstanding performance on native structure recognition from decoys comparing to the Beta-sheet specific potentials in literature. We will apply the potential to improve the prediction of Beta-strand arrangement and registration for beta proteins.
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    Gene Network Modeling of Cancer Metabolism
    (2016-04-20) Yu, Linglin; Ma, Jianpeng
    Metabolism plays a crucial role in cellular behaviors and activities. The abnormal metabolism has been proposed to be one of the hallmarks of cancer. Unlike normal cells, cancer cells largely depend on glycolysis to produce energy even in the presence of oxygen, which is referred as the Warburg effect. Recent evidences, however, suggest that oxidative phosphorylation is also required for cancer progression. Yet, the underlying regulatory mechanism of these metabolic modes in cancer cells is still poorly understood. Here we use the computational systems biology approach to establish a theoretical framework for modeling genetic regulation of cancer metabolism. According to experimental evidences, we built a network of both regulatory proteins and metabolites. The network was first coarse-grained to a three-component regulatory circuit composed of HIF-1, AMPK and ROS. Thereafter, we further explored the interplay between the circuit and the metabolic pathways, including glucose oxidation, glycolysis and fatty acid oxidation. By exploring the dynamics of the metabolic circuits, we show that, while normal cells have two stable steady states – an oxidative state (O: low HIF-1, high AMPK) and a Warburg state (W: high HIF-1, low AMPK), cancer cells open an additional hybrid state (W/O: high HIF-1, high AMPK) due to higher mitochondrial ROS production and lower HIF-1 degradation rate. The ‘W/O’ hybrid phenotype contributes to cancer metabolic heterogeneity and plasticity, thus allowing cancer cells to adapt to the changes in tumor microenvironment and to promote cell proliferation and metastasis. Based on the model, we investigated the effectiveness of possible cancer therapies targeting metabolism in reducing the metabolic plasticity and circumventing the hybrid state during the course of treatment. We also discuss the connection of the metabolic hybrid state to EMT and stemness of cancer cells.
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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.