High-Throughput parallel blind Virtual Screening using BINDSURF

被引:154
|
作者
Sanchez-Linares, Irene [1 ]
Perez-Sanchez, Horacio [1 ]
Cecilia, Jose M. [1 ]
Garcia, Jose M. [1 ]
机构
[1] Univ Murcia, Sch Comp Sci, Dept Comp Engn, E-30001 Murcia, Spain
来源
BMC BIOINFORMATICS | 2012年 / 13卷
关键词
PROTEIN-LIGAND DOCKING; MOLECULAR DOCKING; AUTOMATED DOCKING; BINDING-SITES; DRUG-BINDING; ACCURACY; ARCHITECTURES; CHALLENGES;
D O I
10.1186/1471-2105-13-S14-S13
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Background: Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, usually derived from the interpretation of the protein crystal structure. However, it has been demonstrated that in many cases, diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. Results: We present BINDSURF, a novel VS methodology that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. Conclusions: BINDSURF is an efficient and fast blind methodology for the determination of protein binding sites depending on the ligand, that uses the massively parallel architecture of GPUs for fast pre-screening of large ligand databases. Its results can also guide posterior application of more detailed VS methods in concrete binding sites of proteins, and its utilization can aid in drug discovery, design, repurposing and therefore help considerably in clinical research.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] High-Throughput parallel blind Virtual Screening using BINDSURF
    Irene Sánchez-Linares
    Horacio Pérez-Sánchez
    José M Cecilia
    José M García
    [J]. BMC Bioinformatics, 13
  • [2] High-throughput virtual screening for drug discovery in parallel
    Toledo-Sherman, LM
    Chen, DQ
    [J]. CURRENT OPINION IN DRUG DISCOVERY & DEVELOPMENT, 2002, 5 (03) : 414 - 421
  • [3] High-throughput virtual screening
    Hirata, Shuzo
    Shizu, Katsuyuki
    [J]. NATURE MATERIALS, 2016, 15 (10) : 1056 - 1057
  • [4] High-throughput virtual screening
    Shuzo Hirata
    Katsuyuki Shizu
    [J]. Nature Materials, 2016, 15 : 1056 - 1057
  • [5] Integration of virtual and high-throughput screening
    Bajorath, F
    [J]. NATURE REVIEWS DRUG DISCOVERY, 2002, 1 (11) : 882 - 894
  • [6] Integration of virtual and high-throughput screening
    Jürgen Bajorath
    [J]. Nature Reviews Drug Discovery, 2002, 1 : 882 - 894
  • [7] DOVIS: an implementation for high-throughput virtual screening using AutoDock
    Shuxing Zhang
    Kamal Kumar
    Xiaohui Jiang
    Anders Wallqvist
    Jaques Reifman
    [J]. BMC Bioinformatics, 9
  • [8] DOVIS: an implementation for high-throughput virtual screening using AutoDock
    Zhang, Shuxing
    Kumar, Kamal
    Jiang, Xiaohui
    Wallqvist, Anders
    Reifman, Jaques
    [J]. BMC BIOINFORMATICS, 2008, 9 (1)
  • [9] Virtual high-throughput screening using a genetic algorithm.
    Brady, GP
    Stouten, PFW
    [J]. ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 1998, 216 : U688 - U688
  • [10] Virtual screening: a real screening complement to high-throughput screening
    Mestres, J
    [J]. BIOCHEMICAL SOCIETY TRANSACTIONS, 2002, 30 : 797 - 799