Novel type of virtual ligand screening on the basis of quantum-chemical calculations for protein-ligand complexes and extended clustering techniques

被引:18
|
作者
Kurauchi, Ryo [1 ]
Watanabe, Chiduru [2 ]
Fukuzawa, Kaori [3 ]
Tanaka, Shigenori [1 ]
机构
[1] Kobe Univ, Dept Computat Sci, Grad Sch Syst Informat, Nada Ku, Kobe, Hyogo 6578501, Japan
[2] Univ Tokyo, Inst Ind Sci, Meguro Ku, Tokyo 1538505, Japan
[3] Nihon Univ, Sch Dent Matsudo, Matsudo, Chiba 2718587, Japan
关键词
Virtual ligand screening (VLS); Self-organizing map (SOM); Multi-dimensional scaling (MDS); Fragment molecular orbital (FMO) method; MOLECULAR-ORBITAL METHOD; DRUG DISCOVERY; GENETIC ALGORITHM; RECEPTOR; BINDING; DOCKING; PREDICTION; MODULATORS; AFFINITY; POTENCY;
D O I
10.1016/j.comptc.2015.02.016
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
We propose a novel method for virtual ligand screening to explore drug candidates binding to target proteins. Employing both information on the ligand-residue interactions calculated by the fragment molecular orbital method and the molecular properties represented by the MDL MACCS keys, a couple of advanced clustering analyses on the basis of the self-organizing map and the multi-dimensional scaling are carried out. In comparison to earlier, similar approaches, the present method can provide higher-dimensional, wider viewpoints to look for better inhibitors and to improve them with reducing the possibilities of false-positives and false-negatives for hit or lead compounds, thus accelerating drug designs. The feasibility and usefulness of the proposed methodology are then demonstrated through the application to the complex systems of estrogen receptor and its ligand molecules, in which a molecular modification to improve the binding property of drug candidates is also suggested. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:12 / 22
页数:11
相关论文
共 50 条
  • [31] Efficient screening of protein-ligand complexes in lipid bilayers using LoCoMock score
    Morita, Rikuri
    Shigeta, Yasuteru
    Harada, Ryuhei
    JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, 2023, 37 (04) : 217 - 225
  • [32] Efficient screening of protein-ligand complexes in lipid bilayers using LoCoMock score
    Rikuri Morita
    Yasuteru Shigeta
    Ryuhei Harada
    Journal of Computer-Aided Molecular Design, 2023, 37 : 217 - 225
  • [33] Fast and accurate approach for binding free energy calculations for protein-ligand complexes: A Movable Type sampling method
    Zhong, Haizhen
    Zheng, Zheng
    Merz, Kenneth
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2015, 250
  • [34] Distilling the essential features of a protein surface for improving protein-ligand docking, scoring, and virtual screening
    Zavodszky, MI
    Sanschagrin, PC
    Korde, RS
    Kuhn, LA
    JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, 2002, 16 (12) : 883 - 902
  • [35] Quantum-chemical simulation of alkyl ligand transformations in β-diketiminato nickel(II) complexes
    Petrusha, Yury V.
    Shestakov, Alexander F.
    MENDELEEV COMMUNICATIONS, 2006, 16 (02) : 72 - 73
  • [36] Distilling the essential features of a protein surface for improving protein-ligand docking, scoring, and virtual screening
    Maria I. Zavodszky
    Paul C. Sanschagrin
    Leslie A. Kuhn
    Rajesh S. Korde
    Journal of Computer-Aided Molecular Design, 2002, 16 : 883 - 902
  • [37] Robust Scoring Functions for Protein-Ligand Interactions with Quantum Chemical Charge Models
    Wang, Jui-Chih
    Lin, Jung-Hsin
    Chen, Chung-Ming
    Perryman, Alex L.
    Olson, Arthur J.
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2011, 51 (10) : 2528 - 2537
  • [38] Comparison of Ligand-, Target Structure-, and Protein-Ligand Interaction Fingerprint-based Virtual Screening Methods
    Huang Qi
    Kang Hong
    Zhang Duanfeng
    Sheng Zhen
    Liu Qi
    Zhu Ruixin
    Cao Zhiwei
    ACTA CHIMICA SINICA, 2011, 69 (05) : 515 - 522
  • [39] A quantum chemical interaction energy dataset for accurately modeling protein-ligand interactions
    Spronk, Steven A.
    Glick, Zachary L.
    Metcalf, Derek P.
    Sherrill, C. David
    Cheney, Daniel L.
    SCIENTIFIC DATA, 2023, 10 (01)
  • [40] A quantum chemical interaction energy dataset for accurately modeling protein-ligand interactions
    Steven A. Spronk
    Zachary L. Glick
    Derek P. Metcalf
    C. David Sherrill
    Daniel L. Cheney
    Scientific Data, 10