Literature DB >> 16860582

eHiTS: a new fast, exhaustive flexible ligand docking system.

Zsolt Zsoldos1, Darryl Reid, Aniko Simon, Sayyed Bashir Sadjad, A Peter Johnson.   

Abstract

The flexible ligand docking problem is divided into two subproblems: pose/conformation search and scoring function. For successful virtual screening the search algorithm must be fast and able to find the optimal binding pose and conformation of the ligand. Statistical analysis of experimental data of bound ligand conformations is presented with conclusions about the sampling requirements for docking algorithms. eHiTS is an exhaustive flexible-docking method that systematically covers the part of the conformational and positional search space that avoids severe steric clashes, producing highly accurate docking poses at a speed practical for virtual high-throughput screening. The customizable scoring function of eHiTS combines novel terms (based on local surface point contact evaluation) with traditional empirical and statistical approaches. Validation results of eHiTS are presented and compared to three other docking software on a set of 91 PDB structures that are common to the validation sets published for the other programs.

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Year:  2006        PMID: 16860582     DOI: 10.1016/j.jmgm.2006.06.002

Source DB:  PubMed          Journal:  J Mol Graph Model        ISSN: 1093-3263            Impact factor:   2.518


  68 in total

1.  Improving molecular docking through eHiTS' tunable scoring function.

Authors:  Orr Ravitz; Zsolt Zsoldos; Aniko Simon
Journal:  J Comput Aided Mol Des       Date:  2011-11-11       Impact factor: 3.686

2.  Targeting the binding function 3 (BF3) site of the human androgen receptor through virtual screening.

Authors:  Nathan A Lack; Peter Axerio-Cilies; Peyman Tavassoli; Frank Q Han; Ka Hong Chan; Clementine Feau; Eric LeBlanc; Emma Tomlinson Guns; R Kiplin Guy; Paul S Rennie; Artem Cherkasov
Journal:  J Med Chem       Date:  2011-11-18       Impact factor: 7.446

3.  Scoring and lessons learned with the CSAR benchmark using an improved iterative knowledge-based scoring function.

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  J Chem Inf Model       Date:  2011-08-31       Impact factor: 4.956

4.  Ultrafast protein structure-based virtual screening with Panther.

Authors:  Sanna P Niinivehmas; Kari Salokas; Sakari Lätti; Hannu Raunio; Olli T Pentikäinen
Journal:  J Comput Aided Mol Des       Date:  2015-09-25       Impact factor: 3.686

5.  Do crystal structures obviate the need for theoretical models of GPCRs for structure-based virtual screening?

Authors:  Hao Tang; Xiang Simon Wang; Jui-Hua Hsieh; Alexander Tropsha
Journal:  Proteins       Date:  2012-03-13

6.  LASSO-ligand activity by surface similarity order: a new tool for ligand based virtual screening.

Authors:  Darryl Reid; Bashir S Sadjad; Zsolt Zsoldos; Aniko Simon
Journal:  J Comput Aided Mol Des       Date:  2008-01-18       Impact factor: 3.686

7.  Generation of a 3D model for human GABA transporter hGAT-1 using molecular modeling and investigation of the binding of GABA.

Authors:  Thomas Wein; Klaus T Wanner
Journal:  J Mol Model       Date:  2009-06-09       Impact factor: 1.810

8.  Further analysis and comparative study of intermolecular interactions using dimers from the S22 database.

Authors:  Laszlo Fusti Molnar; Xiao He; Bing Wang; Kenneth M Merz
Journal:  J Chem Phys       Date:  2009-08-14       Impact factor: 3.488

9.  Cyclic dinucleotides bind the C-linker of HCN4 to control channel cAMP responsiveness.

Authors:  Marco Lolicato; Annalisa Bucchi; Cristina Arrigoni; Stefano Zucca; Marco Nardini; Indra Schroeder; Katie Simmons; Marco Aquila; Dario DiFrancesco; Martino Bolognesi; Frank Schwede; Dmitry Kashin; Colin W G Fishwick; A Peter Johnson; Gerhard Thiel; Anna Moroni
Journal:  Nat Chem Biol       Date:  2014-04-28       Impact factor: 15.040

10.  Benchmarking methods and data sets for ligand enrichment assessment in virtual screening.

Authors:  Jie Xia; Ermias Lemma Tilahun; Terry-Elinor Reid; Liangren Zhang; Xiang Simon Wang
Journal:  Methods       Date:  2014-12-03       Impact factor: 3.608

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