Journal of Computer Chemistry, Japan
Online ISSN : 1347-3824
Print ISSN : 1347-1767
ISSN-L : 1347-1767
Volume 6, Issue 5
Displaying 1-5 of 5 articles from this issue
General Papers
  • Wankee KIM, Takahiro ITO, Ichiro YAMATO, Tadashi ANDO
    2007 Volume 6 Issue 5 Pages 253-262
    Published: December 15, 2007
    Released on J-STAGE: December 28, 2007
    JOURNAL FREE ACCESS
    We have developed an implicit membrane model to simulate membrane proteins based on the Brownian dynamics algorithm. In this model, the membrane environment was described by a solvent-accessible surface area model used in the calculation of solvation energy, where solvation parameters for atoms vary according to water and lipid phases (Figures 1, 2 and Table 1). By using the membrane model, BD simulations of three model peptides were performed for 100 ns: the polyalanine with 30 residues (A30), the papillomavirus E5 membrane protein (E5), and the amphiphilic bee venom melittin (MLT). A30 and E5 stayed in the membrane region as did the initial states throughout the simulation (Figures 4, 7, 8), which was in agreement with the experimental results and prediction based on the hydropathy index of those peptides. Amphiphilic MLT stayed stably in the interface region between water and lipid layers with helical conformation (Figures 5, 6), which was also consistent with the experimental results and other simulation results. These results indicate that the BD method with our implicit membrane model is useful for simulation of membrane proteins.
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  • Tomoo AOYAMA, Junko KAMBE, Umpei NAGASHIMA, Hidenori UMENO
    2007 Volume 6 Issue 5 Pages 263-274
    Published: December 15, 2007
    Released on J-STAGE: December 28, 2007
    JOURNAL FREE ACCESS
    Multi-layer neural networks are used for the multi regression analysis of many kinds of phenomena whose expressions are unknown. The application fields are medicine designs and environmental problems. We often find incomplete parts in descriptors, which make the precision of the analysis lower. Moreover, the incomplete parts make the linked parts of other descriptors invalid. We often cannot calculate the multi regression analysis, therefore, we wish to eliminate the erroneous effects. In the paper, we discuss some approaches to eliminate such effects, and derive a method based on neural networks, which compensates defect descriptors. We call the method the compensation quantitative structure-activity relationships method (CQSAR).
    The first step of CQSAR is to interpolate defective parts of descriptors. There are various interpolation methods. The selection depends on the number of data and characters of the data set. We introduce 3 kinds of methods; these are, variable absolute-function fitting, parametric observed-vector method, and interpolation by using a 3-layer neural network and an arithmetic progression. The first 2 methods are for small data set. The neural network approach is for general purpose, which requires over 7 data. If we can get over 11 data, the approach also gives partial derivative coefficients also. We confirm the effects in numerical calculations.
    The second step of CQSAR is to calculate the multi regression analysis based on the non-linear fitting functions of neural networks. We evaluate propagations of error caused by interpolations in the first step, and show that the error does not increase. In the evaluation, we give a new reconstruction learning, and show the effectiveness of the CQSAR method under a defect ratio of 50%.
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  • Kyouhei TSUCHIYA, Hiroyuki TERAMAE, Toshio WATANABE, Takayoshi ISHIMOT ...
    2007 Volume 6 Issue 5 Pages 275-282
    Published: December 15, 2007
    Released on J-STAGE: December 28, 2007
    JOURNAL FREE ACCESS
    In order to execute molecular dynamics (MD) simulation for the holding process and native structure of proteins efficiently, Hamiltonian algorithm (HA) was equipped to an MD program: PEACH. The difference between the conventional method and HA is evaluated using Leu-Enkephalin and Met-Enkephalin. HA was efficient for sampling a wide area of geometrical space because many low energy conformations were observed along trajectories of HA.
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  • Daisuke TOKUNAGA, Tetsuro SHIMO, Hiromitsu HASHIMOTO, Tomoko OOTO, Ken ...
    2007 Volume 6 Issue 5 Pages 283-294
    Published: December 15, 2007
    Released on J-STAGE: December 28, 2007
    JOURNAL FREE ACCESS
    A Frontier Molecular Orbital (FMO) analysis is not so effective for the singlet peri-, site-, regio- and stereoselectivities of singlet photodimerization (anti-3,6-[4+4] cycloaddition for 3) of 2-pyridone (1) and for the cross additions (mainly endo-3,4-ht-[2+2] cycloaddition for 4) with acrylates (2). Their Origins were analysed by transition state (TS) analysis of whole reaction (TS1, TS2, TS3) using MOPAC-PM5 and UCIS/6-31+G(d)//PM5 levels of calculation, and the major factors of the selectivities were inferred quantitatively. The singlet reactions may be concerted, controlled by TS1, and may be competed by orbital overlapping and ionic factors. The Origin for a novel adduct, azocinone (5) between excited singlet 1 and 2 was also estimated by PM5 simulation. The triplet and regioselective cross additions (mainly exo-5,6-hh-[2+2] cycloaddition for 7) between 1 and 2 were inferred to be two-step reactions, and to pass through twisted biradicals, with the first-step transition TS1 energy smaller than the second-step one. The energy of TS2 (ring- closure) was lower than that of TS3 (bond- cleavage).
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