AMIDE v2: High-Throughput Screening Based on AutoDock-GPU and Improved Workflow Leading to Better Performance and Reliability (original) (raw)
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Computational protein-ligand docking and virtual drug screening with the AutoDock suite
Nature protocols, 2016
Computational docking can be used to predict bound conformations and free energies of binding for small-molecule ligands to macromolecular targets. Docking is widely used for the study of biomolecular interactions and mechanisms, and it is applied to structure-based drug design. The methods are fast enough to allow virtual screening of ligand libraries containing tens of thousands of compounds. This protocol covers the docking and virtual screening methods provided by the AutoDock suite of programs, including a basic docking of a drug molecule with an anticancer target, a virtual screen of this target with a small ligand library, docking with selective receptor flexibility, active site prediction and docking with explicit hydration. The entire protocol will require ∼5 h.
GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer
Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, 2020
Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadershipclass computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic. 1
Fast, accurate, and reliable molecular docking with QuickVina 2
Bioinformatics, 2015
Motivation: The need for efficient molecular docking tools for high-throughput screening is growing alongside the rapid growth of drug-fragment databases. AutoDock Vina (‘Vina’) is a widely used docking tool with parallelization for speed. QuickVina (‘QVina 1’) then further enhanced the speed via a heuristics, requiring high exhaustiveness. With low exhaustiveness, its accuracy was compromised. We present in this article the latest version of QuickVina (‘QVina 2’) that inherits both the speed of QVina 1 and the reliability of the original Vina. Results: We tested the efficacy of QVina 2 on the core set of PDBbind 2014. With the default exhaustiveness level of Vina (i.e. 8), a maximum of 20.49-fold and an average of 2.30-fold acceleration with a correlation coefficient of 0.967 for the first mode and 0.911 for the sum of all modes were attained over the original Vina. A tendency for higher acceleration with increased number of rotatable bonds as the design variables was observed. On ...
MetaDOCK: A Combinatorial Molecular Docking Approach
ACS Omega, 2023
Molecular docking plays a major role in academic and industrial drug screening and discovery processes. Despite the availability of numerous docking software packages, there is a lot of scope for improvement for the docking algorithms in terms of becoming more reliable to replicate the experimental binding results. Here, we propose a combinatorial or consensus docking approach where complementary powers of the existing methods are captured. We created a meta-docking protocol by combining the results of AutoDock4.2, LeDock, and rDOCK programs as these are freely available, easy to use, and suitable for large-scale analysis and produced better performance on benchmarking studies. Rigorous benchmarking analyses were undertaken to evaluate the scoring, posing, and screening capability of our approach. Further, the performance measures were compared against one standard state-of-the-art commercial docking software, GOLD, and one freely available software, PLANTS. Performances of MetaDOCK for scoring, posing, and screening the protein−ligand complexes were found to be quite superior compared to the reference programs. Exhaustive molecular dynamics simulation and molecular mechanics Poisson−Boltzmann and surface area-based free energy estimation also suggest better energetic stability of the docking solutions produced by our meta-approach. We believe that the MetaDOCK approach is a useful packaging of the freely available software and provides a better alternative to the scientific community who are unable to afford costly commercial packages.
2019
Molecular Docking (MD) is a key tool in computer-aided drug design that aims to predict the binding pose between a small molecule and a macromolecular target. At its core, MD calculates the strength of possible binding poses, and searches for the energetically-stronger ones among those generated during simulation. Automatic Docking (AutoDock) is a widely-used MD code that employs a physics-based scoring function to quantify the binding strength. AutoDock also uses a Lamarckian Genetic Algorithm (LGA), and in turn, the Solis-Wets method, as a local-search algorithm, in order to find strong interactions of such molecular systems. Due to the highly-parallel nature of the LGA tasks involved, AutoDock can benefit from runtime acceleration based on parallelization. This thesis presents an OpenCL-based parallelization of AutoDock, and a corresponding evaluation in terms of execution performance, quality-of-results, and compute-energy efficiency, achieved on different platforms based on: mu...
BMC Bioinformatics, 2008
Background: The number of protein targets with a known or predicted tri-dimensional structure and of drug-like chemical compounds is growing rapidly and so is the need for new therapeutic compounds or chemical probes. Performing flexible structure-based virtual screening computations on thousands of targets with millions of molecules is intractable to most laboratories nor indeed desirable. Since shape complementarity is of primary importance for most proteinligand interactions, we have developed a tool/protocol based on rigid-body docking to select compounds that fit well into binding sites.
Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective
Current Drug Targets, 2019
Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don’t always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, ...
rDock: A Fast, Versatile and Open Source Program for Docking Ligands to Proteins and Nucleic Acids
PLoS Computational Biology, 2014
Identification of chemical compounds with specific biological activities is an important step in both chemical biology and drug discovery. When the structure of the intended target is available, one approach is to use molecular docking programs to assess the chemical complementarity of small molecules with the target; such calculations provide a qualitative measure of affinity that can be used in virtual screening (VS) to rank order a list of compounds according to their potential to be active. rDock is a molecular docking program developed at Vernalis for high-throughput VS (HTVS) applications. Evolved from RiboDock, the program can be used against proteins and nucleic acids, is designed to be computationally very efficient and allows the user to incorporate additional constraints and information as a bias to guide docking. This article provides an overview of the program structure and features and compares rDock to two reference programs, AutoDock Vina (open source) and Schrö dinger's Glide (commercial). In terms of computational speed for VS, rDock is faster than Vina and comparable to Glide. For binding mode prediction, rDock and Vina are superior to Glide. The VS performance of rDock is significantly better than Vina, but inferior to Glide for most systems unless pharmacophore constraints are used; in that case rDock and Glide are of equal performance. The program is released under the Lesser General Public License and is freely available for download, together with the manuals, example files and the complete test sets, at http://rdock. sourceforge.net/ Citation: Ruiz-Carmona S, Alvarez-Garcia D, Foloppe N, Garmendia-Doval AB, Juhos S, et al. (2014) rDock: A Fast, Versatile and Open Source Program for Docking Ligands to Proteins and Nucleic Acids. PLoS Comput Biol 10(4): e1003571.
Surrogate docking: structure-based virtual screening at high throughput speed
Journal of Computer-Aided Molecular Design, 2005
Structure-based screening using fully flexible docking is still too slow for large molecular libraries. High quality docking of a million molecule library can take days even on a cluster with hundreds of CPUs. This performance issue prohibits the use of fully flexible docking in the design of large combinatorial libraries. We have developed a fast structure-based screening method, which utilizes docking of a limited number of compounds to build a 2D QSAR model used to rapidly score the rest of the database. We compare here a model based on radial basis functions and a Bayesian categorization model. The number of compounds that need to be actually docked depends on the number of docking hits found. In our case studies reasonable quality models are built after docking of the number of molecules containing 50dockinghits.TherestofthelibraryisscreenedbytheQSARmodel.OptionallyafractionoftheQSAR−prioritizedlibrarycanbedockedinordertofindthetruedockinghits.Thequalityofthemodelonlydependsonthetrainingsetsize−notonthesizeofthelibrarytobescreened.Therefore,forlargerlibrariesthemethodyieldshighergaininspeednochangeinperformance.Prioritizingalargelibrarywiththesemodelsprovidesasignificantenrichmentwithdockinghits:itattainsthevaluesof50 docking hits. The rest of the library is screened by the QSAR model. Optionally a fraction of the QSAR-prioritized library can be docked in order to find the true docking hits. The quality of the model only depends on the training set size-not on the size of the library to be screened. Therefore, for larger libraries the method yields higher gain in speed no change in performance. Prioritizing a large library with these models provides a significant enrichment with docking hits: it attains the values of 50dockinghits.TherestofthelibraryisscreenedbytheQSARmodel.OptionallyafractionoftheQSAR−prioritizedlibrarycanbedockedinordertofindthetruedockinghits.Thequalityofthemodelonlydependsonthetrainingsetsize−notonthesizeofthelibrarytobescreened.Therefore,forlargerlibrariesthemethodyieldshighergaininspeednochangeinperformance.Prioritizingalargelibrarywiththesemodelsprovidesasignificantenrichmentwithdockinghits:itattainsthevaluesof13 and $35 at the beginning of the score-sorted libraries in our two case studies: screening of the NCI collection and a combinatorial libraries on CDK2 kinase structure. With such enrichments, only a fraction of the database must actually be docked to find many of the true hits. The throughput of the method allows its use in screening of large compound collections and in the design of large combinatorial libraries. The strategy proposed has an important effect on efficiency but does not affect retrieval of actives, the latter being determined by the quality of the docking method itself.
Comparative Study of the Efficiency of Three Protein-Ligand Docking Programs
Journal of Proteomics & Bioinformatics, 2008
Structure-based lead optimization approaches are increasingly playing a role in the drug-discovery process. Virtual screening by molecular docking has become a largely used approach to lead discovery in the pharmaceutical industry when a high-resolution structure of the biological target of interest is available. The performance of three docking programs (Arguslab, Autodock and FlexX), for virtual database screening, is studied. Autodock and FlexX are well established commercial packages while Arguslab is distributed freely for Windows platforms by Planaria Software. Comparisons of these docking programs and scoring functions using a large and diverse data set of pharmaceutically interesting targets and active compounds are carried out. We focus on the problem of docking and scoring flexible compounds which are sterically capable of docking into a rigid conformation of the receptor. The three dimensional structures of a carefully chosen set of 126 pharmaceutically relevant protein-ligand complexes were used for the comparative study. The Autodock methodology is shown to consistently yield enrichments superior to the two alternative methods, while FlexX outperforms largely Arguslab.