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Docking (molecular)

About: Docking (molecular) is a(n) research topic. Over the lifetime, 15375 publication(s) have been published within this topic receiving 307552 citation(s). The topic is also known as: interaction simulation & molecular docking.


Papers
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Journal ArticleDOI
TL;DR: GOLD (Genetic Optimisation for Ligand Docking) is an automated ligand docking program that uses a genetic algorithm to explore the full range of ligand conformational flexibility with partial flexibility of the protein, and satisfies the fundamental requirement that the ligand must displace loosely bound water on binding.
Abstract: Prediction of small molecule binding modes to macromolecules of known three-dimensional structure is a problem of paramount importance in rational drug design (the “docking” problem). We report the development and validation of the program GOLD (Genetic Optimisation for Ligand Docking). GOLD is an automated ligand docking program that uses a genetic algorithm to explore the full range of ligand conformational flexibility with partial flexibility of the protein, and satisfies the fundamental requirement that the ligand must displace loosely bound water on binding. Numerous enhancements and modifications have been applied to the original technique resulting in a substantial increase in the reliability and the applicability of the algorithm. The advanced algorithm has been tested on a dataset of 100 complexes extracted from the Brookhaven Protein DataBank. When used to dock the ligand back into the binding site, GOLD achieved a 71% success rate in identifying the experimental binding mode.

5,300 citations

Journal ArticleDOI
TL;DR: An approach called HADDOCK (High Ambiguity Driven protein-protein Docking) that makes use of biochemical and/or biophysical interaction data such as chemical shift perturbation data resulting from NMR titration experiments or mutagenesis data to drive the docking process.
Abstract: The structure determination of protein-protein complexes is a rather tedious and lengthy process, by both NMR and X-ray crystallography. Several methods based on docking to study protein complexes have also been well developed over the past few years. Most of these approaches are not driven by experimental data but are based on a combination of energetics and shape complementarity. Here, we present an approach called HADDOCK (High Ambiguity Driven protein-protein Docking) that makes use of biochemical and/or biophysical interaction data such as chemical shift perturbation data resulting from NMR titration experiments or mutagenesis data. This information is introduced as Ambiguous Interaction Restraints (AIRs) to drive the docking process. An AIR is defined as an ambiguous distance between all residues shown to be involved in the interaction. The accuracy of our approach is demonstrated with three molecular complexes. For two of these complexes, for which both the complex and the free protein structures have been solved, NMR titration data were available. Mutagenesis data were used in the last example. In all cases, the best structures generated by HADDOCK, that is, the structures with the lowest intermolecular energies, were the closest to the published structure of the respective complexes (within 2.0 A backbone RMSD).

2,313 citations

Journal ArticleDOI
TL;DR: The docking scoring function of MolDock is an extension of the piecewise linear potential including new hydrogen bonding and electrostatic terms, which identifies the most promising docking solution from the solutions obtained by the docking algorithm.
Abstract: In this article we introduce a molecular docking algorithm called MolDock. MolDock is based on a new heuristic search algorithm that combines differential evolution with a cavity prediction algorithm. The docking scoring function of MolDock is an extension of the piecewise linear potential (PLP) including new hydrogen bonding and electrostatic terms. To further improve docking accuracy, a re-ranking scoring function is introduced, which identifies the most promising docking solution from the solutions obtained by the docking algorithm. The docking accuracy of MolDock has been evaluated by docking flexible ligands to 77 protein targets. MolDock was able to identify the correct binding mode of 87% of the complexes. In comparison, the accuracy of Glide and Surflex is 82% and 75%, respectively. FlexX obtained 58% and GOLD 78% on subsets containing 76 and 55 cases, respectively.

1,585 citations

Journal ArticleDOI
TL;DR: This work reports results based on software using a genetic algorithm that uses an evolutionary strategy in exploring the full conformational flexibility of the ligand with partial flexibility ofThe protein, and which satisfies the fundamental requirement that theligand must displace loosely bound water on binding.
Abstract: Understanding the principles whereby macromolecular biological receptors can recognise small molecule substrates or inhibitors is the subject of a major effort. This is of paramount importance in rational drug design where the receptor structure is known (the "docking" problem). Current theoretical approaches utilise models of the steric and electrostatic interaction of bound ligands and recently conformational flexibility has been incorporated. We report results based on software using a genetic algorithm that uses an evolutionary strategy in exploring the full conformational flexibility of the ligand with partial flexibility of the protein, and which satisfies the fundamental requirement that the ligand must displace loosely bound water on binding. Results are reported on five test systems showing excellent agreement with experimental data. The design of the algorithm offers insight into the molecular recognition mechanism.

1,464 citations

Journal ArticleDOI
TL;DR: A novel protein-ligand docking method that accurately accounts for both ligand and receptor flexibility by iteratively combining rigid receptor docking (Glide) with protein structure prediction (Prime) techniques is presented.
Abstract: We present a novel protein-ligand docking method that accurately accounts for both ligand and receptor flexibility by iteratively combining rigid receptor docking (Glide) with protein structure prediction (Prime) techniques. While traditional rigid-receptor docking methods are useful when the receptor structure does not change substantially upon ligand binding, success is limited when the protein must be "induced" into the correct binding conformation for a given ligand. We provide an in-depth description of our novel methodology and present results for 21 pharmaceutically relevant examples. Traditional rigid-receptor docking for these 21 cases yields an average RMSD of 5.5 A. The average ligand RMSD for docking to a flexible receptor for the 21 pairs is 1.4 A; the RMSD is < or =1.8 A for 18 of the cases. For the three cases with RMSDs greater than 1.8 A, the core of the ligand is properly docked and all key protein/ligand interactions are captured.

1,384 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202239
20211,527
20201,427
20191,183
20181,136
20171,177