Literature DB >> 31257108

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

Jeffrey R Wagner1, Christopher P Churas1, Shuai Liu1, Robert V Swift1, Michael Chiu1, Chenghua Shao2, Victoria A Feher1, Stephen K Burley3, Michael K Gilson4, Rommie E Amaro5.   

Abstract

Docking calculations can accelerate drug discovery by predicting the bound poses of ligands for a targeted protein. However, it is not clear which docking methods work best. Furthermore, predicting poses requires steps outside the docking algorithm itself, such as preparation of the protein and ligand, and it is not known which components are most in need of improvement. The Continuous Evaluation of Ligand Protein Predictions (CELPP) is a blinded prediction challenge designed to address these issues. Participants create a workflow to predict protein-ligand binding poses, which is then tasked with predicting 10-100 new protein-ligand crystal structures each week. CELPP evaluates the accuracy of each workflow's predictions and posts the scores online. The results can be used to identify the strengths and weaknesses of current approaches, help map docking problems to the algorithms most likely to overcome them, and illuminate areas of unmet need in structure-guided drug design.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  CELPP; D3R; RCSB PDB; community resource; drug design data resource; drug docking; pose prediction

Mesh:

Substances:

Year:  2019        PMID: 31257108      PMCID: PMC6685748          DOI: 10.1016/j.str.2019.05.012

Source DB:  PubMed          Journal:  Structure        ISSN: 0969-2126            Impact factor:   5.006


  102 in total

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Authors:  Demetri T Moustakas; P Therese Lang; Scott Pegg; Eric Pettersen; Irwin D Kuntz; Natasja Brooijmans; Robert C Rizzo
Journal:  J Comput Aided Mol Des       Date:  2006-12-06       Impact factor: 3.686

Review 3.  Community benchmarks for virtual screening.

Authors:  John J Irwin
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Review 4.  Discovery and development of DNA methyltransferase inhibitors using in silico approaches.

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Review 5.  A Critical Review of Validation, Blind Testing, and Real- World Use of Alchemical Protein-Ligand Binding Free Energy Calculations.

Authors:  Robert Abel; Lingle Wang; David L Mobley; Richard A Friesner
Journal:  Curr Top Med Chem       Date:  2017       Impact factor: 3.295

6.  Blind prediction of host-guest binding affinities: a new SAMPL3 challenge.

Authors:  Hari S Muddana; C Daniel Varnado; Christopher W Bielawski; Adam R Urbach; Lyle Isaacs; Matthew T Geballe; Michael K Gilson
Journal:  J Comput Aided Mol Des       Date:  2012-02-25       Impact factor: 3.686

7.  CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma.

Authors:  Heather A Carlson; Richard D Smith; Kelly L Damm-Ganamet; Jeanne A Stuckey; Aqeel Ahmed; Maire A Convery; Donald O Somers; Michael Kranz; Patricia A Elkins; Guanglei Cui; Catherine E Peishoff; Millard H Lambert; James B Dunbar
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8.  Docking and scoring with ICM: the benchmarking results and strategies for improvement.

Authors:  Marco A C Neves; Maxim Totrov; Ruben Abagyan
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9.  An improved relaxed complex scheme for receptor flexibility in computer-aided drug design.

Authors:  Rommie E Amaro; Riccardo Baron; J Andrew McCammon
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10.  rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids.

Authors:  Sergio Ruiz-Carmona; Daniel Alvarez-Garcia; Nicolas Foloppe; A Beatriz Garmendia-Doval; Szilveszter Juhos; Peter Schmidtke; Xavier Barril; Roderick E Hubbard; S David Morley
Journal:  PLoS Comput Biol       Date:  2014-04-10       Impact factor: 4.475

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  12 in total

1.  The Protein Data Bank Archive.

Authors:  Sameer Velankar; Stephen K Burley; Genji Kurisu; Jeffrey C Hoch; John L Markley
Journal:  Methods Mol Biol       Date:  2021

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.  Development of an Automatic Pipeline for Participation in the CELPP Challenge.

Authors:  Marina Miñarro-Lleonar; Sergio Ruiz-Carmona; Daniel Alvarez-Garcia; Peter Schmidtke; Xavier Barril
Journal:  Int J Mol Sci       Date:  2022-04-26       Impact factor: 6.208

4.  Assessing the binding properties of CASP14 targets and models.

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Journal:  Proteins       Date:  2021-08-19

5.  Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts.

Authors:  Mostafa Karimi; Di Wu; Zhangyang Wang; Yang Shen
Journal:  J Chem Inf Model       Date:  2020-12-21       Impact factor: 4.956

Review 6.  Open-access data: A cornerstone for artificial intelligence approaches to protein structure prediction.

Authors:  Stephen K Burley; Helen M Berman
Journal:  Structure       Date:  2021-05-12       Impact factor: 5.871

7.  Automation of absolute protein-ligand binding free energy calculations for docking refinement and compound evaluation.

Authors:  Germano Heinzelmann; Michael K Gilson
Journal:  Sci Rep       Date:  2021-01-13       Impact factor: 4.379

8.  Dissimilar Ligands Bind in a Similar Fashion: A Guide to Ligand Binding-Mode Prediction with Application to CELPP Studies.

Authors:  Xianjin Xu; Xiaoqin Zou
Journal:  Int J Mol Sci       Date:  2021-11-15       Impact factor: 5.923

9.  Designing Novel Compounds for the Treatment and Management of RET-Positive Non-Small Cell Lung Cancer-Fragment Based Drug Design Strategy.

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10.  Hybrids of Imatinib with Quinoline: Synthesis, Antimyeloproliferative Activity Evaluation, and Molecular Docking.

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