The Experts below are selected from a list of 9126 Experts worldwide ranked by ideXlab platform
Arthur J. Olson - One of the best experts on this subject based on the ideXlab platform.
-
The AutoDock suite at 30
Protein Science, 2020Co-Authors: David S Goodsell, Arthur J. Olson, Michel F. Sanner, Stefano ForliAbstract:The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third-party developers. This article is protected by copyright. All rights reserved.
-
covalent docking using AutoDock two point attractor and flexible side chain methods
Protein Science, 2016Co-Authors: Giulia Bianco, David S Goodsell, Stefano Forli, Arthur J. OlsonAbstract:We describe two methods of automated covalent docking using AutoDock4: the two-point attractor method and the flexible side chain method. Both methods were applied to a training set of 20 diverse protein–ligand covalent complexes, evaluating their reliability in predicting the crystallographic pose of the ligands. The flexible side chain method performed best, recovering the pose in 75% of cases, with failures for the largest inhibitors tested. Both methods are freely available at the AutoDock website (http://AutoDock.scripps.edu).
-
Covalent docking using AutoDock: Two‐point attractor and flexible side chain methods
Protein Science, 2015Co-Authors: Giulia Bianco, David S Goodsell, Stefano Forli, Arthur J. OlsonAbstract:We describe two methods of automated covalent docking using AutoDock4: the two-point attractor method and the flexible side chain method. Both methods were applied to a training set of 20 diverse protein–ligand covalent complexes, evaluating their reliability in predicting the crystallographic pose of the ligands. The flexible side chain method performed best, recovering the pose in 75% of cases, with failures for the largest inhibitors tested. Both methods are freely available at the AutoDock website (http://AutoDock.scripps.edu).
-
AutoDock4(Zn): an improved AutoDock force field for small-molecule docking to zinc metalloproteins.
Journal of Chemical Information and Modeling, 2014Co-Authors: Diogo Santos-martins, Stefano Forli, Maria J. Ramos, Arthur J. OlsonAbstract:Zinc is present in a wide variety of proteins and is important in the metabolism of most organisms. Zinc metalloenzymes are therapeutically relevant targets in diseases such as cancer, heart disease, bacterial infection, and Alzheimer’s disease. In most cases a drug molecule targeting such enzymes establishes an interaction that coordinates with the zinc ion. Thus, accurate prediction of the interaction of ligands with zinc is an important aspect of computational docking and virtual screening against zinc containing proteins. We have extended the AutoDock force field to include a specialized potential describing the interactions of zinc-coordinating ligands. This potential describes both the energetic and geometric components of the interaction. The new force field, named AutoDock4Zn, was calibrated on a data set of 292 crystal complexes containing zinc. Redocking experiments show that the force field provides significant improvement in performance in both free energy of binding estimation as well as in r...
-
Virtual Screening with AutoDock: Theory and Practice.
Expert Opinion on Drug Discovery, 2010Co-Authors: Sandro Cosconati, David S Goodsell, Stefano Forli, Alexander L. Perryman, Rodney Harris, Arthur J. OlsonAbstract:Importance of the field: Virtual screening is a computer-based technique for identifying promising compounds to bind to a target molecule of known structure. Given the rapidly increasing number of protein and nucleic acid structures, virtual screening continues to grow as an effective method for the discovery of new inhibitors and drug molecules.Areas covered in this review: We describe virtual screening methods that are available in the AutoDock suite of programs and several of our successes in using AutoDock virtual screening in pharmaceutical lead discovery.What the reader will gain: A general overview of the challenges of virtual screening is presented, along with the tools available in the AutoDock suite of programs for addressing these challenges.Take home message: Virtual screening is an effective tool for the discovery of compounds for use as leads in drug discovery, and the free, open source program AutoDock is an effective tool for virtual screening.
David S Goodsell - One of the best experts on this subject based on the ideXlab platform.
-
The AutoDock suite at 30
Protein Science, 2020Co-Authors: David S Goodsell, Arthur J. Olson, Michel F. Sanner, Stefano ForliAbstract:The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third-party developers. This article is protected by copyright. All rights reserved.
-
covalent docking using AutoDock two point attractor and flexible side chain methods
Protein Science, 2016Co-Authors: Giulia Bianco, David S Goodsell, Stefano Forli, Arthur J. OlsonAbstract:We describe two methods of automated covalent docking using AutoDock4: the two-point attractor method and the flexible side chain method. Both methods were applied to a training set of 20 diverse protein–ligand covalent complexes, evaluating their reliability in predicting the crystallographic pose of the ligands. The flexible side chain method performed best, recovering the pose in 75% of cases, with failures for the largest inhibitors tested. Both methods are freely available at the AutoDock website (http://AutoDock.scripps.edu).
-
Covalent docking using AutoDock: Two‐point attractor and flexible side chain methods
Protein Science, 2015Co-Authors: Giulia Bianco, David S Goodsell, Stefano Forli, Arthur J. OlsonAbstract:We describe two methods of automated covalent docking using AutoDock4: the two-point attractor method and the flexible side chain method. Both methods were applied to a training set of 20 diverse protein–ligand covalent complexes, evaluating their reliability in predicting the crystallographic pose of the ligands. The flexible side chain method performed best, recovering the pose in 75% of cases, with failures for the largest inhibitors tested. Both methods are freely available at the AutoDock website (http://AutoDock.scripps.edu).
-
Virtual Screening with AutoDock: Theory and Practice.
Expert Opinion on Drug Discovery, 2010Co-Authors: Sandro Cosconati, David S Goodsell, Stefano Forli, Alexander L. Perryman, Rodney Harris, Arthur J. OlsonAbstract:Importance of the field: Virtual screening is a computer-based technique for identifying promising compounds to bind to a target molecule of known structure. Given the rapidly increasing number of protein and nucleic acid structures, virtual screening continues to grow as an effective method for the discovery of new inhibitors and drug molecules.Areas covered in this review: We describe virtual screening methods that are available in the AutoDock suite of programs and several of our successes in using AutoDock virtual screening in pharmaceutical lead discovery.What the reader will gain: A general overview of the challenges of virtual screening is presented, along with the tools available in the AutoDock suite of programs for addressing these challenges.Take home message: Virtual screening is an effective tool for the discovery of compounds for use as leads in drug discovery, and the free, open source program AutoDock is an effective tool for virtual screening.
-
a semiempirical free energy force field with charge based desolvation
Journal of Computational Chemistry, 2007Co-Authors: Ruth Huey, Garrett M. Morris, Arthur J. Olson, David S GoodsellAbstract:The authors describe the development and testing of a semiempirical free energy force field for use in AutoDock4 and similar grid-based docking methods. The force field is based on a comprehensive thermodynamic model that allows incorporation of intramolecular energies into the predicted free energy of binding. It also incorporates a charge-based method for evaluation of desolvation designed to use a typical set of atom types. The method has been calibrated on a set of 188 diverse protein–ligand complexes of known structure and binding energy, and tested on a set of 100 complexes of ligands with retroviral proteases. The force field shows improvement in redocking simulations over the previous AutoDock3 force field. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007
Robert Günther - One of the best experts on this subject based on the ideXlab platform.
-
research article pso AutoDock a fast flexible molecular docking program based on swarm intelligence
Chemical Biology & Drug Design, 2007Co-Authors: Vigneshwaran Namasivayam, Robert GüntherAbstract:On the quest of novel therapeutics, molecular docking methods have proven to be valuable tools for screening large libraries of compounds determining the interactions of potential drugs with the target proteins. A widely used docking approach is the simulation of the docking process guided by a binding energy function. On the basis of the molecular docking program AutoDock, we present pso@AutoDock as a tool for fast flexible molecular docking. Our novel Particle Swarm Optimization (PSO) algorithms varCPSO and varCPSO-ls are suited for rapid docking of highly flexible ligands. Thus, a ligand with 23 rotatable bonds was successfully docked within as few as 100 000 computing steps (rmsd = 0.87 A), which corresponds to only 10% of the computing time demanded by AutoDock. In comparison to other docking techniques as gold 3.0, dock 6.0, flexx 2.2.0, AutoDock 3.05, and sodock, pso@AutoDock provides the smallest rmsd values for 12 in 37 protein–ligand complexes. The average rmsd value of 1.4 A is significantly lower then those obtained with the other docking programs, which are all above 2.0 A. Thus, pso@AutoDock is suggested as a highly efficient docking program in terms of speed and quality for flexible peptide–protein docking and virtual screening studies.
-
pso@AutoDock: a fast flexible molecular docking program based on Swarm intelligence.
Chemical Biology & Drug Design, 2007Co-Authors: Namasivayam, Robert GüntherAbstract:On the quest of novel therapeutics, molecular docking methods have proven to be valuable tools for screening large libraries of compounds determining the interactions of potential drugs with the target proteins. A widely used docking approach is the simulation of the docking process guided by a binding energy function. On the basis of the molecular docking program AutoDock, we present pso@AutoDock as a tool for fast flexible molecular docking. Our novel Particle Swarm Optimization (PSO) algorithms varCPSO and varCPSO-ls are suited for rapid docking of highly flexible ligands. Thus, a ligand with 23 rotatable bonds was successfully docked within as few as 100 000 computing steps (rmsd = 0.87 A), which corresponds to only 10% of the computing time demanded by AutoDock. In comparison to other docking techniques as gold 3.0, dock 6.0, flexx 2.2.0, AutoDock 3.05, and sodock, pso@AutoDock provides the smallest rmsd values for 12 in 37 protein-ligand complexes. The average rmsd value of 1.4 A is significantly lower then those obtained with the other docking programs, which are all above 2.0 A. Thus, pso@AutoDock is suggested as a highly efficient docking program in terms of speed and quality for flexible peptide-protein docking and virtual screening studies.
-
pso AutoDock a fast flexible molecular docking program based on swarm intelligence
Chemical Biology & Drug Design, 2007Co-Authors: Robert GüntherAbstract:On the quest of novel therapeutics, molecular docking methods have proven to be valuable tools for screening large libraries of compounds determining the interactions of potential drugs with the target proteins. A widely used docking approach is the simulation of the docking process guided by a binding energy function. On the basis of the molecular docking program AutoDock, we present pso@AutoDock as a tool for fast flexible molecular docking. Our novel Particle Swarm Optimization (PSO) algorithms varCPSO and varCPSO-ls are suited for rapid docking of highly flexible ligands. Thus, a ligand with 23 rotatable bonds was successfully docked within as few as 100 000 computing steps (rmsd = 0.87 A), which corresponds to only 10% of the computing time demanded by AutoDock. In comparison to other docking techniques as gold 3.0, dock 6.0, flexx 2.2.0, AutoDock 3.05, and sodock, pso@AutoDock provides the smallest rmsd values for 12 in 37 protein-ligand complexes. The average rmsd value of 1.4 A is significantly lower then those obtained with the other docking programs, which are all above 2.0 A. Thus, pso@AutoDock is suggested as a highly efficient docking program in terms of speed and quality for flexible peptide-protein docking and virtual screening studies.
Stefano Forli - One of the best experts on this subject based on the ideXlab platform.
-
The AutoDock suite at 30
Protein Science, 2020Co-Authors: David S Goodsell, Arthur J. Olson, Michel F. Sanner, Stefano ForliAbstract:The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third-party developers. This article is protected by copyright. All rights reserved.
-
evaluating the energy efficiency of opencl accelerated AutoDock molecular docking
Parallel Distributed and Network-Based Processing, 2020Co-Authors: Leonardo Solisvasquez, Andreas Koch, Diogo Santosmartins, Stefano ForliAbstract:AutoDock is a molecular docking application that consists of a genetic algorithm coupled with the Solis-Wets local-search method. Despite its wide usage, its power consumption on heterogeneous systems has not been evaluated extensively. In this work, we evaluate the energy efficiency of an OpenCL-accelerated version of AutoDock that, along with the traditional SolisWets method, newly incorporates the ADADELTA gradient-based local search. Executions on a Nvidia V100 GPU yielded energy efficiency improvements of up to 297x (Solis-Wets) and 137x (ADADELTA) with respect to the original AutoDock baseline.
-
PDP - Evaluating the Energy Efficiency of OpenCL-accelerated AutoDock Molecular Docking
2020 28th Euromicro International Conference on Parallel Distributed and Network-Based Processing (PDP), 2020Co-Authors: Leonardo Solis-vasquez, Diogo Santos-martins, Andreas Koch, Stefano ForliAbstract:AutoDock is a molecular docking application that consists of a genetic algorithm coupled with the Solis-Wets local-search method. Despite its wide usage, its power consumption on heterogeneous systems has not been evaluated extensively. In this work, we evaluate the energy efficiency of an OpenCL-accelerated version of AutoDock that, along with the traditional SolisWets method, newly incorporates the ADADELTA gradient-based local search. Executions on a Nvidia V100 GPU yielded energy efficiency improvements of up to 297x (Solis-Wets) and 137x (ADADELTA) with respect to the original AutoDock baseline.
-
D3R Grand Challenge 4: prospective pose prediction of BACE1 ligands with AutoDock-GPU.
Journal of Computer-Aided Molecular Design, 2019Co-Authors: Diogo Santos-martins, Francesca Alessandra Ambrosio, Giulia Bianco, Jerome Eberhardt, Leonardo Solis-vasquez, Andreas Koch, Stefano ForliAbstract:In this paper we describe our approaches to predict the binding mode of twenty BACE1 ligands as part of Grand Challenge 4 (GC4), organized by the Drug Design Data Resource. Calculations for all submissions (except for one, which used AutoDock4.2) were performed using AutoDock-GPU, the new GPU-accelerated version of AutoDock4 implemented in OpenCL, which features a gradient-based local search. The pose prediction challenge was organized in two stages. In Stage 1a, the protein conformations associated with each of the ligands were undisclosed, so we docked each ligand to a set of eleven receptor conformations, chosen to maximize the diversity of binding pocket topography. Protein conformations were made available in Stage 1b, making it a re-docking task. For all calculations, macrocyclic conformations were sampled on the fly during docking, taking the target structure into account. To leverage information from existing structures containing BACE1 bound to ligands available in the PDB, we tested biased docking and pose filter protocols to facilitate poses resembling those experimentally determined. Both pose filters and biased docking resulted in more accurate docked poses, enabling us to predict for both Stages 1a and 1b ligand poses within 2 A RMSD from the crystallographic pose. Nevertheless, many of the ligands could be correctly docked without using existing structural information, demonstrating the usefulness of physics-based scoring functions, such as the one used in AutoDock4, for structure based drug design.
-
AutoDock bias improving binding mode prediction and virtual screening using known protein ligand interactions
Bioinformatics, 2019Co-Authors: Juan Pablo Arcon, Carlos P. Modenutti, Demian Avendaño, Elias D. Lopez, Lucas A. Defelipe, Francesca Alessandra Ambrosio, Adrian G. Turjanski, Stefano Forli, Marcelo A. MartíAbstract: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.
Rami M Reddy - One of the best experts on this subject based on the ideXlab platform.
-
performance evaluation of docking programs glide gold AutoDock surflexdock using free energy perturbation reference data a case study of fructose 1 6 bisphosphatase amp analogs
Mini-reviews in Medicinal Chemistry, 2020Co-Authors: K. K. Reddy, Ravindranath S. Rathore, P. Srujana, R.r. Burri, M. Sumakanth, Pallu Reddanna, Ravikumar C Reddy, Rami M ReddyAbstract:BACKGROUND The accurate ranking of analogs of lead molecules with respect to their estimated binding free energies to drug targets remains highly challenging in molecular docking due to small relative differences in their free energy values. METHODS Free energy perturbation (FEP) method, which provides the most accurate relative binding free energy values were earlier used to calculate free energies of many ligands for several important drug targets including Fructose-1,6-BisphosPhatase (FBPase). The availability of abundant structural and experimental binding affinity data for FBPase inhibitors provided an ideal system to evaluate four widely used docking programs, AutoDock, Glide, GOLD and SurflexDock, distinct from earlier comparative evaluation studies. RESULTS The analyses suggested that, considering various parameters such as docking pose, scoring and ranking accuracy, sensitivity analysis and newly introduced relative ranking score, Glide provided reasonably consistent results in all respects for the system studied in the present work. Whereas GOLD and AutoDock also demonstrated better performance, AutoDock results were found to be significantly superior in terms of scoring accuracy compared to the rest. CONCLUSION Present analysis serves as a useful guide for researchers working in the field of lead optimization and for developers in upgradation of the docking programs.