We constructed the four-body statistical pseudopotential proposed by Krishnamoorthy and Tropsha, which is a type of coarse-grained potential previously used for protein-peptide docking, using different data sets. The first data set is composed of crystal structures of proteins that satisfy the conditions specified using PISCES, a protein sequence culling server. The second data set consists of crystal structures of protein-protein complexes obtained from the PDBbind-CN database. The four-body potential has 44275 patterns of scores, depending on the type of amino acid in the quadruplet of residues and the continuity of amino acid in the sequence of protein or peptide. While the PISCES-based data set covers almost all patterns, docking simulations revealed that both potentials provided comparable accuracy in reproducing experimentally determined peptide binding poses.

The fragment molecular orbital (FMO) method is an efficient quantum chemical method suitable for calculating the electronic structures of large molecular systems. FMO can be accelerated by several approximations, an important one being the approximation to the environmental electrostatic potential (ESP) exerted on the fragment monomers or dimers. The environmental ESP is often approximated using the Mulliken atomic orbital charge (AOC) for proximal fragment dimers (ESP-AOC approximation) and the Mulliken point charge (PTC) for distant dimers (ESP-PTC approximation). Recently, another approximation method based on Cholesky decomposition with adaptive metric has been proposed for environmental ESP (ESP-CDAM, Okiyama et al., Bull. Chem. Soc. Jpn., 2021, 94, 91). In the current article, the energy gradient is derived under the ESP-CDAM approximation and implemented in FMO-based molecular dynamics (FMO-MD). Several test FMO-MD simulations are performed to compare the accuracy of the ESP-CDAM approximation with that of the conventional methods. The results show that ESP-CDAM is more accurate than ESP-AOC.

Plants can produce anthocyanins to survive their given environments. A model plant Arabidopsis thaliana (Arabidopsis) accumulates the 11 anthocyanins (A1–A11) with various substitutions to their basic structure. However, it is difficult to detect all 11 anthocyanins in every analysis using conventional analytical techniques. In this study, we predicted ultraviolet–visible (UV-vis) absorption spectra of A1–A11 by an ab initio calculation based on density functional theory. We could identify substituents attached to the basic structure of anthocyanins from the predicted absorption peaks. We found that glucose substitution significantly reduced the solvation free energy due to five hydroxyl groups, whereas sinapoyl substitution increased the solvent effect due to the methyl group in a sinapoyl moiety. Our findings may explain the antioxidant capacity of each anthocyanin by comparing the predicted UV-vis absorption spectra with the measured UV-vis spectra. These outcomes can provide clues to uncover anthocyanin biosynthesis in different stress conditions/tissues.

Ammonia is considered as a viable candidate for a hydrogen carrier in a hydrogen-based society. Currently, real-time analysis of products in the development of highly active ammonia electrolytic synthesis catalysts is limited to the extremely expensive technique of electrochemical mass spectrometry. In this study, we aimed to develop a real-time simultaneous quantification method for hydrogen and ammonia concentrations using the rotating ring-disk electrode technique. We adopted deep neural network-based machine learning technology using the data from cyclic voltammogram (CV) measurements on a Pt ring electrode. The sum of squared residuals decreased to 1/0.645 at hydrogen partial pressure and 1/92.8 at ammonia concentration compared to the conventional combination of nonlinear least-squares method and solving simultaneous equations. Moreover, classifying concentrations through image recognition based on the obtained CV images resulted in successful concentration determination with an accuracy of 78.9%.

Deep learning in combination with fluorescence excitation-emission spectroscopy was studied to quantitatively analyze vitamin A (retinol) in cattle blood. The neural network model being obtained with the deep learning predicted the vitamin-A levels with a coefficient of determination (R²) of 0.93 with respect to the experimental values. The combination of the deep learning and fluorescence excitation-emission spectroscopy has a potential to predict the vitamin-A level in the cattle blood accurately, rapidly and inexpensively and to improve production of marbled beef with maintaining cattle health. It could also be applied to quantitative vitamin-A assays of various biological tissues, foods and so on as well as to those of blood samples besides cattle.