Literature DB >> 30825370

AutoDock Bias: improving binding mode prediction and virtual screening using known protein-ligand interactions.

Juan Pablo Arcon1,2, Carlos P Modenutti1, Demian Avendaño1, Elias D Lopez1, Lucas A Defelipe1, Francesca Alessandra Ambrosio2,3, Adrian G Turjanski1, Stefano Forli2, Marcelo A Marti1.   

Abstract

SUMMARY: The performance of docking calculations can be improved by tuning parameters for the system of interest, e.g. biasing the results towards the formation of relevant protein-ligand interactions, such as known ligand pharmacophore or interaction sites derived from cosolvent molecular dynamics. AutoDock Bias is a straightforward and easy to use script-based method that allows the introduction of different types of user-defined biases for fine-tuning AutoDock4 docking calculations.
AVAILABILITY AND IMPLEMENTATION: AutoDock Bias is distributed with MGLTools (since version 1.5.7), and freely available on the web at http://ccsb.scripps.edu/mgltools/ or http://autodockbias.wordpress.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Year:  2019        PMID: 30825370      PMCID: PMC6761960          DOI: 10.1093/bioinformatics/btz152

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  12 in total

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Authors:  Richard A Friesner; Jay L Banks; Robert B Murphy; Thomas A Halgren; Jasna J Klicic; Daniel T Mainz; Matthew P Repasky; Eric H Knoll; Mee Shelley; Jason K Perry; David E Shaw; Perry Francis; Peter S Shenkin
Journal:  J Med Chem       Date:  2004-03-25       Impact factor: 7.446

2.  Development and validation of a genetic algorithm for flexible docking.

Authors:  G Jones; P Willett; R C Glen; A R Leach; R Taylor
Journal:  J Mol Biol       Date:  1997-04-04       Impact factor: 5.469

3.  Molecular Dynamics in Mixed Solvents Reveals Protein-Ligand Interactions, Improves Docking, and Allows Accurate Binding Free Energy Predictions.

Authors:  Juan Pablo Arcon; Lucas A Defelipe; Carlos P Modenutti; Elias D López; Daniel Alvarez-Garcia; Xavier Barril; Adrián G Turjanski; Marcelo A Martí
Journal:  J Chem Inf Model       Date:  2017-03-31       Impact factor: 4.956

Review 4.  Protein-ligand docking in the new millennium--a retrospective of 10 years in the field.

Authors:  S F Sousa; A J M Ribeiro; J T S Coimbra; R P P Neves; S A Martins; N S H N Moorthy; P A Fernandes; M J Ramos
Journal:  Curr Med Chem       Date:  2013       Impact factor: 4.530

5.  AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility.

Authors:  Garrett M Morris; Ruth Huey; William Lindstrom; Michel F Sanner; Richard K Belew; David S Goodsell; Arthur J Olson
Journal:  J Comput Chem       Date:  2009-12       Impact factor: 3.376

6.  Solvent structure improves docking prediction in lectin-carbohydrate complexes.

Authors:  Diego F Gauto; Ariel A Petruk; Carlos P Modenutti; Juan I Blanco; Santiago Di Lella; Marcelo A Martí
Journal:  Glycobiology       Date:  2012-10-22       Impact factor: 4.313

7.  Computational protein-ligand docking and virtual drug screening with the AutoDock suite.

Authors:  Stefano Forli; Ruth Huey; Michael E Pique; Michel F Sanner; David S Goodsell; Arthur J Olson
Journal:  Nat Protoc       Date:  2016-04-14       Impact factor: 13.491

8.  Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking.

Authors:  Michael M Mysinger; Michael Carchia; John J Irwin; Brian K Shoichet
Journal:  J Med Chem       Date:  2012-07-05       Impact factor: 7.446

9.  Knowledge-guided docking: accurate prospective prediction of bound configurations of novel ligands using Surflex-Dock.

Authors:  Ann E Cleves; Ajay N Jain
Journal:  J Comput Aided Mol Des       Date:  2015-05-05       Impact factor: 3.686

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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  10 in total

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Authors:  Juan Pablo Arcon; Adrián G Turjanski; Marcelo A Martí; Stefano Forli
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2.  The AutoDock suite at 30.

Authors:  David S Goodsell; Michel F Sanner; Arthur J Olson; Stefano Forli
Journal:  Protein Sci       Date:  2020-09-12       Impact factor: 6.725

3.  Accelerating AutoDock4 with GPUs and Gradient-Based Local Search.

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Review 4.  Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

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Authors:  Carlos P Modenutti; Juan I Blanco Capurro; Santiago Di Lella; Marcelo A Martí
Journal:  Front Chem       Date:  2019-12-03       Impact factor: 5.221

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Authors:  Robert Wawrzinek; Eike-Christian Wamhoff; Jonathan Lefebre; Mareike Rentzsch; Gunnar Bachem; Gary Domeniconi; Jessica Schulze; Felix F Fuchsberger; Hengxi Zhang; Carlos Modenutti; Lennart Schnirch; Marcelo A Marti; Oliver Schwardt; Maria Bräutigam; Mónica Guberman; Dirk Hauck; Peter H Seeberger; Oliver Seitz; Alexander Titz; Beat Ernst; Christoph Rademacher
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7.  Cosolvent Sites-Based Discovery of Mycobacterium Tuberculosis Protein Kinase G Inhibitors.

Authors:  Osvaldo Burastero; Lucas A Defelipe; Gabriel Gola; Nancy L Tateosian; Elias D Lopez; Camila Belen Martinena; Juan Pablo Arcon; Martín Dodes Traian; Diana E Wetzler; Isabel Bento; Xavier Barril; Javier Ramirez; Marcelo A Marti; Maria M Garcia-Alai; Adrián G Turjanski
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8.  Arylsulfonyl histamine derivatives as powerful and selective α-glucosidase inhibitors.

Authors:  M I Osella; M O Salazar; M D Gamarra; D M Moreno; F Lambertucci; D E Frances; R L E Furlan
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9.  2-Mercaptomethyl Thiazolidines (MMTZs) Inhibit All Metallo-β-Lactamase Classes by Maintaining a Conserved Binding Mode.

Authors:  Philip Hinchliffe; Diego M Moreno; Maria-Agustina Rossi; Maria F Mojica; Veronica Martinez; Valentina Villamil; Brad Spellberg; George L Drusano; Claudia Banchio; Graciela Mahler; Robert A Bonomo; Alejandro J Vila; James Spencer
Journal:  ACS Infect Dis       Date:  2021-08-06       Impact factor: 5.578

10.  Quantum simulations of SARS-CoV-2 main protease Mpro enable high-quality scoring of diverse ligands.

Authors:  Yuhang Wang; Sruthi Murlidaran; David A Pearlman
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  10 in total

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