Hujun Shen
Guizhou Normal University
46 Papers
182 Citations
Hujun Shen is an academic researcher from Guizhou Normal University. The author has contributed to research in topics: Chemistry & Molecular dynamics. The author has an hindex of 15, co-authored 37 publications. Previous affiliations of Hujun Shen include Cornell University & Johns Hopkins University.
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Papers
Atom-Pair Catalysts Supported by N-Doped Graphene for the Nitrogen Reduction Reaction: d-Band Center-Based Descriptor.
Ting Deng,Cen Chao,Cen Chao,Hujun Shen,Shuyi Wang,Shuyi Wang,Jingdong Guo,Shaohong Cai,Mingsen Deng,Mingsen Deng +9 more
TL;DR: The computations revealed that enzymatic pathway is the most suitable reaction pathway for the TM APCs, and the intrinsic activity trend of these APCs can be determined by the d-band center-based descriptor, which has a simple linear correlation with the bonding/antibonding orbital population.
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Fast and Accurate Computation Schemes for Evaluating Vibrational Entropy of Proteins
TL;DR: The authors' coarse‐grained NMA computation schemes can repeat correctly and efficiently the results of standard NMA for large proteins, and can be achieved by rescaling coarse-grained results with a specific factor that is derived on the basis of the linear correlation of protein vibrational entropy betweenstandard NMA and coarse‐ grained N MA.
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Coarse-Grained Modeling of Nucleic Acids Using Anisotropic Gay-Berne and Electric Multipole Potentials.
TL;DR: This work attempts to apply a coarse-grained (CG) model, which is based on anisotropic Gay-Berne and electric multipole (EMP) potentials, to the modeling of nucleic acids, and shows a promising ability to predict the melting temperatures of DNA duplexes with different lengths.
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Anisotropic Coarse-Grained Model for Proteins Based On Gay–Berne and Electric Multipole Potentials
TL;DR: It is demonstrated that the anisotropic coarse-grained model, namely GBEMP model, is able to reproduce many key features observed from experimental protein structures, as well as from atomistic force field simulations, while saving the computational cost by a factor of about 10–200 depending on specific cases and atomistic models.
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