High-Throughput Virtual Screening of Proteins Using GRID Molecular Interaction Fields

被引:64
|
作者
Sciabola, Simone [1 ]
Stanton, Robert V. [1 ]
Mills, James E. [2 ]
Flocco, Maria M. [2 ]
Baroni, Massimo [3 ]
Cruciani, Gabriele [4 ]
Perruccio, Francesca [5 ]
Mason, Jonathan S. [6 ]
机构
[1] Pfizer Res Technol Ctr, Cambridge, MA 02139 USA
[2] Pfizer Global Res & Dev, Sandwich CT13 9NJ, Kent, England
[3] Mol Discovery Ltd, Pinner HA5 5NE, Middx, England
[4] Univ Perugia, Lab Chemometr, I-60123 Perugia, Italy
[5] Syngenta, CH-4332 Stein Ag, Switzerland
[6] Lundbeck AS, DK-25000 Copenhagen, Denmark
关键词
RESOLUTION CRYSTAL-STRUCTURE; FAVORABLE BINDING-SITES; COUPLED RECEPTOR; EFFICIENT DETECTION; KINASE INHIBITORS; FUNCTIONAL SITES; LIGAND COMPLEXES; DRUG DESIGN; DATA-BANK; SEQUENCE;
D O I
10.1021/ci9003317
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
A new computational algorithm for protein binding sites characterization and comparison has been developed, which uses a common reference framework of the projected ligand-space four-point pharmacophore fingerprints, includes cavity shape. and can be used with diverse proteins as no structural alignment is required. Protein binding sites are first described using GRID molecular interaction fields (GRID-MIFs), and the FLAP (fingerprints for ligands and proteins) method is then used to encode and compare this information. The discriminating power of the algorithm and its applicability for large-scale protein analysis was validated by analyzing various scenarios: clustering of kinase protein families in a relevant manner, predicting ligand activity across related targets, and protein-protein Virtual screening. In all cases the results showed the effectiveness of the GRID-FLAP method and its potential use in applications Such as identifying selectivity targets and tools/hits for new targets via the identification of other proteins with pharmacophorically similar binding sites.
引用
收藏
页码:155 / 169
页数:15
相关论文
共 50 条
  • [21] Accessible High-Throughput Virtual Screening Molecular Docking Software for Students and Educators
    Jacob, Reed B.
    Andersen, Tim
    McDougal, Owen M.
    PLOS COMPUTATIONAL BIOLOGY, 2012, 8 (05)
  • [22] Molecular docking-based computational platform for high-throughput virtual screening
    Baohua Zhang
    Hui Li
    Kunqian Yu
    Zhong Jin
    CCF Transactions on High Performance Computing, 2022, 4 : 63 - 74
  • [23] High-throughput screening of soluble recombinant proteins
    Shih, YP
    Kung, WM
    Chen, JC
    Yeh, CH
    Wang, AHJ
    Wang, TF
    PROTEIN SCIENCE, 2002, 11 (07) : 1714 - 1719
  • [24] High-throughput virtual molecular docking with AutoDockCloud
    Ellingson, Sally R.
    Baudry, Jerome
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2014, 26 (04): : 907 - 916
  • [25] High-Throughput Screening for Transglutaminase Activities Using Recombinant Fluorescent Proteins
    Lee, Jae-Hun
    Song, Eunjung
    Lee, Sun-Gu
    Kim, Byung-Gee
    BIOTECHNOLOGY AND BIOENGINEERING, 2013, 110 (11) : 2865 - 2873
  • [26] Theory for High-Throughput Genetic Interaction Screening
    McCarthy, Madeline E.
    Dodd, William B.
    Lu, Xiaoming
    Pritko, Daniel J.
    Patel, Nishi D.
    Haskell, Charlotte V.
    Sanabria, Hugo
    Blenner, Mark A.
    Birtwistle, Marc R.
    ACS SYNTHETIC BIOLOGY, 2023, 12 (08): : 2290 - 2300
  • [27] Grid-enabled high throughput virtual screening
    Jacq, Nicolas
    Breton, Vincent
    Chen, Hsin-Yen
    Ho, Li-Yung
    Hoftmann, Martin
    Lee, Hurng-Chun
    Legre, Yannick
    Lin, Simon C.
    Maass, Astrid
    Medernach, Emmanuel
    Merelli, Ivan
    Milanesi, Luciano
    Rastelli, Giulio
    Reichstadt, Matthieu
    Salzemann, Jean
    Schwichtenberg, Horst
    Sridhar, Mahendrakar
    Kasam, Vinod
    Wu, Ying-Ta
    Zimmermann, Marc
    DISTRIBUTED, HIGH-PERFORMANCE AND GRID COMPUTING IN COMPUTATIONAL BIOLOGY, PROCEEDINGS, 2007, 4360 : 45 - +
  • [28] Group A streptococcus inhibitors by high-throughput virtual screening
    Hu, Haipeng
    Mao, Shuli
    Bugrysheva, Julia V.
    Pruett, Sarah
    Liotta, Dennis C.
    Scott, June R.
    Snyder, James P.
    EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY, 2014, 82 : 120 - 126
  • [29] High-throughput virtual screening for drug discovery in parallel
    Toledo-Sherman, LM
    Chen, DQ
    CURRENT OPINION IN DRUG DISCOVERY & DEVELOPMENT, 2002, 5 (03) : 414 - 421
  • [30] Congestion Game Scheduling Implementation for High-Throughput Virtual Drug Screening Using BOINC-Based Desktop Grid
    Nikitina, Natalia
    Ivashko, Evgeny
    Tchernykh, Andrei
    PARALLEL COMPUTING TECHNOLOGIES (PACT 2017), 2017, 10421 : 480 - 491