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The charge transfer effect is an important component in the physical description of realistic proteins. In the hybrid quantum mechanical-molecular mechanical (QM/MM) simulations, the significant charge transfer between the QM/MM boundaries could lead to the slow convergence problem with very large QM regions. In this work, we will discuss how community structure in complex network (electronic structure topology associated domain) can be used to measure QM/MM boundary effects and how it can provide a different perspective on well established concepts in available QM/MM simulations. The graph theory is employed to provide an useful solution to distinguish the significant charge transfer regions between the core active site and the surrounding protein environment in the QM/MM simulations. According to community detection algorithm for complex network, the charge transfer topology associated domain (ctTAD) is suggested to provide an alternative tool for systematically minimizing the charge transfer effects of QM/MM boundary with minor computational costs., Comment: 32 pages, 11 figures

The charge transfer and polarization effects are important components in the molecular mechanism description of bio-molecules. Classical force field with fixed point charge cannot take into the account of the non-negligible correlation between atomic charge and structure changes. In this work, high throughput ab initio calculations for the pentapeptide Met-enkephalin (MetEnk) reveal that geometric dependent charge transfer among residues is significant among tens of thousands of conformations. And we suggest a data driven model with machine learning algorithms to solve the geometric dependent charge fluctuations problem. This data driven model can directly provide ab initio level atomic charges of any structure for MetEnk, and avoids self-consistent iteration in polarizable force field. Molecular dynamics simulations demonstrated that the data driven model provides a possible choice to describe the explicit charge flux with minor modification of available classical force fields. This work provides us an alternative molecular mechanism model for future dynamics simulation of oligopeptides., Comment: 24 pages, 11 figures

The anisotropic feature of charge transfer reactions in realistic proteins cannot be ignored, due to the highly complex chemical structure of bio-molecules. In this work, we have performed the first large-scale quantitative assessment of charge transfer preference in protein complexes by calculating the charge transfer couplings in all 20*20 possible amino acid side chain combinations, which are extracted from available high-quality structures of thousands of protein complexes. The charge transfer database quantitatively shows distinct features of charge transfer couplings among millions of amino acid side-chains combinations. The knowledge graph of charge transfer couplings reveals that only one average or representative structure cannot be regarded as the typical charge transfer preference in realistic proteins. This data driven model provides us an alternative route to comprehensively understand the pairwise charge transfer coupling parameters based structural similarity, without any require of the knowledge of chemical intuition about the chemical interactions., Comment: 19 pages, 5 figures

Due to the highly complex chemical structure of biomolecules, the extensive understanding of the electronic information for proteomics can be challenging. Here, we constructed a charge transfer database at residue level derived from millions of electronic structure calculations among 20x20 possible amino acid side-chains combinations, which were extracted from available high-quality structures of thousands of protein complexes. Then, the data driven network (D2Net) analysis was proposed to quickly identify the critical residue or residue groups for any possible protein complex. As an initial evaluation, we applied this model to scrutinize the charge transfer networks for two randomly selected proteins, which highlighted the most critical residues with large node degrees as network hubs. This work may provide us a promising computional protocol for topologically understanding the electronic structure information in the growing number of high-quality experimental proteins structures, with minor computational costs., Comment: 36 pages, 9 figures

The excited states of polyatomic systems are rather complex, and often exhibit meta-stable dynamical behaviors. Static analysis of reaction pathway often fails to sufficiently characterize excited state motions due to their highly non-equilibrium nature. Here, we proposed a time series guided clustering algorithm to generate most relevant meta-stable patterns directly from ab initio dynamic trajectories. Based on the knowledge of these meta-stable patterns, we suggested an interpolation scheme with only a concrete and finite set of known patterns to accurately predict the ground and excited state properties of the entire dynamics trajectories. As illustrated with the example of sinapic acids, the estimation error for both ground and excited state is very close, which indicates one could predict the ground and excited state molecular properties with similar accuracy. These results may provide us some insights to construct an excited state force field with compatible energy terms as traditional ones., Comment: 25 pages, 7 figures