The evaluation of reduction potentials of proteins by ab initio approaches presents a major challenge for computational chemistry. This is addressed in the present investigation by reporting detailed calculations of the reduction potentials of the blue copper proteins plastocyanin and rusticyanin using the QM/MM all-atom frozen density functional theory, FDFT, method. The relevant ab initio free energies are evaluated by using a classical reference potential. This approach appears to provide a general consistent and effective way for reproducing the configurational ensemble needed for consistent ab initio free energy calculations. The FDFT formulation allows us to treat a large part of the protein quantum mechanically by a consistently coupled QM/QM/MM embedding method while still retaining a proper configurational sampling. To establish the importance of proper configurational sampling and the need for a complete representation of the protein+solvent environment, we also consider several classical approaches. These include the semi-macroscopic PDLD/S-LRA method and classical all-atom simulations with and without a polarizable force field. The difference between the reduction potentials of the two blue copper proteins is reproduced in a reasonable way, and its origin is deduced from the different calculations. It is found that the protein permanent dipole tunes down the reduction potential for plastocyanin compared to the active site in regular water solvent, whereas in rusticyanin it is instead tuned up. This electrostatic environment, which is the major effect determining the reduction potential, is a property of the entire protein and solvent system and cannot be ascribed to any particular single interaction.
As a part of the intelligent video surveillance, off-position detection, which needs a real-time and precise algorithm, is used to detect whether the person on duty is absent from working position.This work is necessary for improving efficiency and reducing human resource consumption.Considering the excellent performance of convolutional neural network in image classification, we first propose a method for off-position detection using CNN in this paper and get good results.Furthermore, we introduce a new dataset for working position by generating crops from video frames.Then we randomly generate 224×224 crops from training images to fine-tune our deep neural network.
The empirical valence bond (EVB) model provides an extremely powerful way for modeling and analyzing chemical reactions in solutions and proteins. However, this model is based on the unverified assumption that the off diagonal elements of the EVB Hamiltonian do not change significantly upon transfer of the reacting system from one phase to another. This ad hoc assumption has been rationalized by its consistency with empirically observed linear free energy relationships, as well as by other qualitative considerations. Nevertheless, this assumption has not been rigorously established. The present work explores the validity of the above EVB key assumption by a rigorous numerical approach. This is done by exploiting the ability of the frozen density functional theory (FDFT) and the constrained density functional theory (CDFT) models to generate convenient diabatic states for QM/MM treatments, and thus to examine the relationship between the diabatic and adiabatic surfaces, as well as the corresponding effective off diagonal elements. It is found that, at least for the test case of SN2 reactions, the off diagonal element does not change significantly upon moving from the gas phase to solutions and thus the EVB assumption is valid and extremely useful.
In this work, to explain doping behavior of single-layer graphene upon HSSYWYAFNNKT (P1) and HSSAAAAFNNKT (P1–3A) adsorption in field-effect transistors (GFETs), we applied a combined computational approach, whereby peptide adsorption was modeled by molecular dynamics simulations, and the lowest energy configuration was confirmed by density functional theory calculations. On the basis of the resulting structures of the hybrid materials, electronic structure and transport calculations were investigated. We demonstrate that π–π stacking of the aromatic residues and proximate peptide backbone to the graphene surface in P1 have a role in the p-doping. These results are consistent with our experimental observation of the GFET's p-doping even after a 24-h annealing procedure. Upon substitution of three of the aromatic residues to Ala in (P1–3A), a considerable decrease from p-doping is observed experimentally, demonstrating n-doping as compared to the nonadsorbed device, yet not explained based on the atomistic MD simulation structures. To gain a qualitative understanding of P1–3A's adsorption over a longer simulation time, which may differ from aromatic amino acid residues' swift anchoring on the surface, we analyzed equilibrated coarse-grain simulations performed for 500 ns. Desorption of the Ala residues from the surface was shown computationally, which could in turn affect charge transfer, yet a full explanation of the mechanism of n-doping will require elucidation of differences between various aromatic residues as dependent on peptide composition, and inclusion of effects of the substrate and environment, to be considered in future work.
In this paper, a detection method of extra matters on the transmission lines is proposed. Our method can be divided into two steps: the detection of the transmission lines and the detection of the sky. To locate the lines, we design a set of simple and efficient filters to obtain the candidates of the lines. Compared with the previous work using the length of the lines to perform the transmission lines classification, we use the color and texture features to make it more robust to the variation of the background. To recognize the sky, we first over-segment the image. Then, we design the color and texture features for the detection of the sky. Finally, these features are used to train the classifier of the sky. After the transmission lines and the sky are detected, we confirm whether there is extra matter on the transmission lines. The experimental results indicate that our algorithm can recognize the extra matters on transmission lines fast and accurately.
In examining adsorption of a few selected single amino acids on Au and Pd cluster models by density functional theory calculations, we have shown that specific side-chain binding affinity to the surface may occur because of a combination of effects, including charge transfer. Larger binding was calculated at the Pd interface. In addition, the interplay between amino acid solvation and adsorption at the interface was considered from first principles. This analysis serves as the first step toward gaining a more accurate understanding of specific interactions at the interface of biological−metal nanostructures than has been attempted in the past.
Quantum mechanical calculations of activation free energies of chemical reactions in condensed phases present a major challenge for computational chemistry. On one hand, it is important to use high-level ab initio methods to obtain reliable results. On the other hand, it is essential to perform sufficient configurational sampling to obtain meaningful free energies. Although the advance of quantum mechanical/molecular mechanics (QM/MM) approaches has made this problem tractable, it still requires an enormous amount of computer time. The present work advances several strategies that allow one to perform practical ab initio QM/MM calculations of free energy profiles in solutions and proteins. The basic idea is the use of a simple reference potential for the ab initio calculations (e.g., Bentzien; et al. J. Phys. Chem. B 1998, 102, 2293). One version of this approach evaluates the free energy of transfer from the reference potential to the ab initio potential by a single step free energy perturbation (FEP) approach. A new version evaluates this free energy by the linear response approximation (LRA), which involves running trajectories on both the reference and the ab initio potentials. The performance of both approaches is examined by calculating the potential of mean force for the autodissociation reaction of water in solution. It is found that the LRA approach allows one to obtain reasonable results even in cases where the ab initio and reference potentials are significantly different. The present work also explores options for increasing the size of the quantum mechanical region. Here it is shown that the constrained DFT (CDFT) method provides a promising strategy. Finally, the general issue of modeling the autodissociation reaction by quantum mechanical approaches is briefly considered. It is pointed out that the use of the empirical valence bond (EVB) approach in the sampling process should provide a way for evaluating the elusive nonequilibrium solvation effect.
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