Docking and scoring in virtual screening for drug discovery: methods and applications

Kitchen, Douglas B.; Decornez, Hélène; Furr, John R.; Bajorath, Jürgen

doi:10.1038/nrd1549

Cited by 3,198 publications

(2,405 citation statements)

References 131 publications

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“…Ligand docking is a well established computational technique that has been successfully employed in medicinal chemistry to assist drug discovery and lead optimization efforts [1]. The aim of ligand docking is to find the binding pose of a small organic molecule in a receptor pocket, and, if multiple ligands are compared, an estimate of the ligand binding affinity, referred to as the docking score.…”

Section: Introductionmentioning

confidence: 99%

A new method for ligand docking to flexible receptors by dual alanine scanning and refinement (SCARE)

Bottegoni

Kufareva

Totrov

et al. 2008

J Comput Aided Mol Des

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Protein binding sites undergo ligand specific conformational changes upon ligand binding. However, most docking protocols rely on a fixed conformation of the receptor, or on the prior knowledge of multiple conformations representing the variation of the pocket, or on a known bounding box for the ligand. Here we described a general induced fit docking protocol that requires only one initial pocket conformation and identifies most of the correct ligand positions as the lowest score. We expanded a previously used diverse "cross-docking" benchmark to thirty ligand-protein pairs extracted from different crystal structures. The algorithm systematically scans pairs of neighbouring side chains, replaces them by alanines, and docks the ligand to each 'gapped' version of the pocket. All docked positions are scored, refined with original side chains and flexible backbone and re-scored. In the optimal version of the protocol pairs of residues were replaced by alanines and only one best scoring conformation was selected from each 'gapped' pocket for refinement. The optimal SCARE (SCan Alanines and REfine) protocol identifies a near native conformation (under 2Å RMSD) as the lowest rank for 80% of pairs if the docking bounding box is defined by the predicted pocket envelope, and for as many as 90% of the pairs if the bounding box is derived from the known answer with ~5 Å margin as used in most previous publications. The presented fully automated algorithm takes about two hours per pose of a single processor time, requires only one pocket structure and no prior knowledge about the binding site location. Furthermore, the results for conformationally conserved pockets do not deteriorate due to substantial increase of the pocket variability.

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Section: Introductionmentioning

confidence: 99%

A new method for ligand docking to flexible receptors by dual alanine scanning and refinement (SCARE)

Bottegoni

Kufareva

Totrov

et al. 2008

J Comput Aided Mol Des

View full text Add to dashboard Cite

show abstract

“…The popular structure-based approaches require the availability of a 3D structure for the biological target of interest, this information permitting the use of methods based on de novo design or ligand-protein docking [5][6][7], these methods becoming widely used with the continuing growth in the availability of 3D protein data [8,9]. If the necessary 3D information is not available then it may be possible to identify the structural requirements for activity by analysis of those structures that have already been shown to be active.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of machine-learning methods for ligand-based virtual screening

Chen

Harrison

Papadatos

et al. 2007

J Comput Aided Mol Des

115

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Abstract. Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of a naive Bayesian classifier when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed.

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“…Virtual screening is a method applied to identify new chemical entities and structural scaffolds for a protein target [52,53]. The virtual screening process is carried out in virtual screening work flow of the Schrödinger suite.…”

Section: Virtual Screening and Pharmacokinetic Properties Calculationmentioning

confidence: 99%

Targeting the ubiquitin-conjugating enzyme E2D4 for cancer drug discovery–a structure-based approach

et al. 2016

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Cancer progression is a global burden. The incidence and mortality now reach 30 million deaths per year. Several pathways of cancer are under investigation for the discovery of effective therapeutics. The present study highlights the structural details of the ubiquitin protein 'Ubiquitin-conjugating enzyme E2D4' (UBE2D4) for the novel lead structure identification in cancer drug discovery process. The evaluation of 3D structure of UBE2D4 was carried out using homology modelling techniques. The optimized structure was validated by standard computational protocols. The active site region of the UBE2D4 was identified using computational tools like CASTp, Q-site Finder and SiteMap. The hydrophobic pocket which is responsible for binding with its natural receptor ubiquitin ligase CHIP (C-terminal of Hsp 70 interacting protein) was identified through proteinprotein docking study. Corroborating the results obtained from active site prediction tools and protein-protein docking study, the domain of UBE2D4 which is responsible for cancer cell progression is sorted out for further docking study. Virtual screening with large structural database like CB_Div Set and Asinex BioDesign small molecular structural database was carried out. The obtained new ligand molecules that have shown affinity towards UBE2D4 were considered for ADME prediction studies. The identified new ligand molecules with acceptable parameters of docking, ADME are considered as potent UBE2D4 enzyme inhibitors for cancer therapy.

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Docking and scoring in virtual screening for drug discovery: methods and applications

Cited by 3,198 publications

References 131 publications

A new method for ligand docking to flexible receptors by dual alanine scanning and refinement (SCARE)

A new method for ligand docking to flexible receptors by dual alanine scanning and refinement (SCARE)

Evaluation of machine-learning methods for ligand-based virtual screening

Targeting the ubiquitin-conjugating enzyme E2D4 for cancer drug discovery–a structure-based approach

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