Journal of Computer Chemistry, Japan
Online ISSN : 1347-3824
Print ISSN : 1347-1767
ISSN-L : 1347-1767
Volume 14, Issue 5
Displaying 1-6 of 6 articles from this issue
Foreword
Highlight
  • Takahiro KUDOU
    2015 Volume 14 Issue 5 Pages A34-A37
    Published: 2015
    Released on J-STAGE: December 18, 2015
    JOURNAL FREE ACCESS FULL-TEXT HTML
    Some molecular viewers that can visualize molecular structural data have been introduced. Those viewers can be generally classified into some categories, such as Java based, JavaScript based, WebGL based, for mobile and for PC. The Java based software that requires the Java Runtime Environment (JRE) is widely used both for a local application and an embedding one in web pages. The JavaScript based software only requires a web browser without JRE but the response speed is lower than the Java based one. The WebGL based softwares work more lightly than the formers, but I think so far there is no software with an advanced display controller such as a command interface. For mobile devices, some molecular viewers are provided from application distribution sites, such as Google Play and App Store. Many viewers for PC environment have some advanced controllers for changing expressions of molecular modeling images and drawing them in detail. As an example, a series of operating procedure scripts for creating a molecular image using a stand-alone application, called "jV", was introduced here.
  • Sumio TOKITA
    2015 Volume 14 Issue 5 Pages A38-A41
    Published: 2015
    Released on J-STAGE: December 18, 2015
    JOURNAL FREE ACCESS FULL-TEXT HTML
    Supplementary material
    Probability density distribution in the 3-dimensional representation of hydrogen several p orbitals (Figure 2), d orbitals (Figure 3), or f orbitals (Figure 5) was developed [6, 7]. It was compared with 3-D isosurface model such as Figure 1 (c) or Figure 4. Isosurface models can hardly show the entire region where an electron can be found. On the other hand, in the diagram of probability density distribution models, an electron is found everywhere around the nucleus. The density of the dots sculptured in the glass block (Figures 2, 3, 5) shows the probability density of finding an electron, so the nodal plane is well described as spherical shell(s) or planar and conical node(s) symmetrical about z axis (Figure 6) where the dots cannot be found. In the diagram of probability density distribution model, we can compare the size of the orbitals from their distribution regions. Number of these nodes together with planar nodes including z axis in hydrogen atomic orbitals is summarized in Table 1 in terms of n: principal quantum number, l: azimuthal quantum number, and m: magnetic quantum number.
General Paper
  • Masataka SAKAGUCHI, Yuji MOCHIZUKI, Chiduru WATANABE, Kaori FUKUZAWA
    2015 Volume 14 Issue 5 Pages 155-163
    Published: 2015
    Released on J-STAGE: December 18, 2015
    Advance online publication: December 03, 2015
    JOURNAL FREE ACCESS FULL-TEXT HTML
    DsRed is a sort of red fluorescent protein (RFP) isolated from Discosoma coral. Previously, we successfully calculated the excitation and emission energies of DsRed, based on the CIS (D) type excited state method in conjunction with the multilayer version of fragment molecular orbital (MFMO) scheme [Mochizuki et al., Chem. Phys. Lett., 433, 360 (2007) & Taguchi et al., J. Phys. Chem. B, 113, 1153 (2009) ]. However, there have been no reports on the effects from water molecules, configurations of amino acid residues surrounding the DsRed chromophore. In the present work, we made a series of examinations on these effects on the excitation energy of DsRed. As a result, the following three factors were found to be important in obtaining quantitative correspondence to the experimental value; (1) two water molecules having hydrogen-bonds with the crucial pigment part, (2) orientation of the OH group of Ser69 to make a hydrogen-bond with CRQ66, and (3) configurational relaxation of neighboring amino acid residues (in particular, positively charged Lys163 as well as negatively charged Glu215). The present work could justify the reliability of protein modeling in our previous two papers.
  • Suguru UEMURA, George OCHIAI, Katsuyuki KAWAMURA, Shuichiro HIRAI
    2015 Volume 14 Issue 5 Pages 164-171
    Published: 2015
    Released on J-STAGE: December 18, 2015
    JOURNAL FREE ACCESS FULL-TEXT HTML
    The aqueous lithium-air battery is receiving considerable research attention because of its high theoretical energy density. However, high-power discharge has not been achieved. Because ion transport phenomena in the battery determine current density, molecular interpretation of the electrolyte is required to improve the battery performance. In order to clarify the fundamental mechanism, the present study investigated LiCl electrolyte by using molecular dynamics simulation. The results showed that hydrated structures of Cl- and Li+ were reproduced successfully. Although the LiCl solution is categorized as strong electrolyte, Cl- and Li+ existed with forming ion-pair. Formation of cluster structure was also suggested under higher concentration condition. As a result, the reduction of self-diffusion coefficient caused by the increasing of hydration radius was investigated.
Technical Paper
  • Yuri YAMADA, Yosuke KATAOKA
    2015 Volume 14 Issue 5 Pages 172-176
    Published: 2015
    Released on J-STAGE: December 18, 2015
    Advance online publication: December 03, 2015
    JOURNAL FREE ACCESS FULL-TEXT HTML
    Supplementary material
    A molecular dynamics (MD) simulation program is developed using Wolfram Mathematica [1] as a learning guide for beginners. One can simulate particles interacting under the Lennard-Jones 12–6 potential function in a cubic cell with three-dimensional periodic boundary conditions. Microcanonical (NEV) ensemble and canonical (NVT) ensemble are available on the program (see Figure 2). The MD conditions, number of particles, number density, temperature, number of MD steps, and others, are set in a Mathematica notebook file [8]. Thermodynamic properties, particle trajectories, configurations of the system, pair correlation function, velocity autocorrelation function, mean square displacement, and self-diffusion coefficient are output as a result of the MD simulation (see Figures 3–7).
feedback
Top