Literature DB >> 26958710

GPCR-Bench: A Benchmarking Set and Practitioners' Guide for G Protein-Coupled Receptor Docking.

Dahlia R Weiss1, Andrea Bortolato1, Benjamin Tehan1, Jonathan S Mason1.   

Abstract

Virtual screening is routinely used to discover new ligands and in particular new ligand chemotypes for G protein-coupled receptors (GPCRs). To prepare for a virtual screen, we often tailor a docking protocol that will enable us to select the best candidates for further screening. To aid this, we created GPCR-Bench, a publically available docking benchmarking set in the spirit of the DUD and DUD-E reference data sets for validation studies, containing 25 nonredundant high-resolution GPCR costructures with an accompanying set of diverse ligands and computational decoy molecules for each target. Benchmarking sets are often used to compare docking protocols; however, it is important to evaluate docking methods not by "retrospective" hit rates but by the actual likelihood that they will produce novel prospective hits. Therefore, docking protocols must not only rank active molecules highly but also produce good poses that a chemist will select for purchase and screening. Currently, no simple objective machine-scriptable function exists that can do this; instead, docking hit lists must be subjectively examined in a consistent way to compare between docking methods. We present here a case study highlighting considerations we feel are of importance when evaluating a method, intended to be useful as a practitioners' guide.

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Year:  2016        PMID: 26958710     DOI: 10.1021/acs.jcim.5b00660

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  9 in total

1.  Influence of the Structural Accuracy of Homology Models on Their Applicability to Docking-Based Virtual Screening: The β2 Adrenergic Receptor as a Case Study.

Authors:  Stefano Costanzi; Austin Cohen; Abigail Danfora; Marjan Dolatmoradi
Journal:  J Chem Inf Model       Date:  2019-07-01       Impact factor: 4.956

2.  Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking.

Authors:  Jeffrey R Wagner; Christopher P Churas; Shuai Liu; Robert V Swift; Michael Chiu; Chenghua Shao; Victoria A Feher; Stephen K Burley; Michael K Gilson; Rommie E Amaro
Journal:  Structure       Date:  2019-06-27       Impact factor: 5.006

3.  Fine tuning for success in structure-based virtual screening.

Authors:  Emilie Pihan; Martin Kotev; Obdulia Rabal; Claudia Beato; Constantino Diaz Gonzalez
Journal:  J Comput Aided Mol Des       Date:  2021-11-20       Impact factor: 3.686

4.  Consensus scoring evaluated using the GPCR-Bench dataset: Reconsidering the role of MM/GBSA.

Authors:  Mei Qian Yau; Jason S E Loo
Journal:  J Comput Aided Mol Des       Date:  2022-05-18       Impact factor: 4.179

5.  Integrated In Silico Fragment-Based Drug Design: Case Study with Allosteric Modulators on Metabotropic Glutamate Receptor 5.

Authors:  Yuemin Bian; Zhiwei Feng; Peng Yang; Xiang-Qun Xie
Journal:  AAPS J       Date:  2017-05-30       Impact factor: 4.009

6.  Virtual Screening of Human Class-A GPCRs Using Ligand Profiles Built on Multiple Ligand-Receptor Interactions.

Authors:  Wallace K B Chan; Yang Zhang
Journal:  J Mol Biol       Date:  2020-07-09       Impact factor: 5.469

7.  Property-Unmatched Decoys in Docking Benchmarks.

Authors:  Reed M Stein; Ying Yang; Trent E Balius; Matt J O'Meara; Jiankun Lyu; Jennifer Young; Khanh Tang; Brian K Shoichet; John J Irwin
Journal:  J Chem Inf Model       Date:  2021-01-25       Impact factor: 4.956

Review 8.  Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors.

Authors:  Damian Bartuzi; Agnieszka A Kaczor; Katarzyna M Targowska-Duda; Dariusz Matosiuk
Journal:  Molecules       Date:  2017-02-22       Impact factor: 4.411

9.  Sequential ligand- and structure-based virtual screening approach for the identification of potential G protein-coupled estrogen receptor-1 (GPER-1) modulators.

Authors:  Shafi Ullah Khan; Nafees Ahemad; Lay-Hong Chuah; Rakesh Naidu; Thet Thet Htar
Journal:  RSC Adv       Date:  2019-01-21       Impact factor: 4.036

  9 in total

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