Pharmacophore‐based molecular docking to account for ligand flexibility

Joseph‐McCarthy, Diane; Thomas, Bert E.; Belmarsh, Michael; Moustakas, Demetri T.; Álvarez, Juan Carlos

doi:10.1002/prot.10266

Cited by 82 publications

(68 citation statements)

References 37 publications

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“…The "multiple-run" paradigm consists of performing a series of independent runs for each conformation, using traditional rigid-protein docking programs [57], [61], [62]. The "single-run" paradigm consists of performing a single docking run by combining all the conformations into a single representation, such as a gridbased average of the ensemble [58], [63] or a dynamic pharmacophore model [64], [65]. In all cases, receptor conformations can be provided by experimental techniques, such as X-ray crystallography [66]- [68] or NMR spectroscopy [69], [70].…”

Section: Ensemble Dockingmentioning

confidence: 99%

Understanding the challenges of protein flexibility in drug design

Antunes

Devaurs

Kavraki

2015

Expert Opinion on Drug Discovery

109

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Section: Ensemble Dockingmentioning

confidence: 99%

Understanding the challenges of protein flexibility in drug design

Antunes

Devaurs

Kavraki

2015

Expert Opinion on Drug Discovery

109

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“…The genetic algorithm (GA) behind the GOLD program [53,74,75,113] makes it easy to include different types of constraints in the fitness function, thus enabling the generation of biased poses. In the PhDock approach [114], as implemented in DOCK 4.0 [115], one can perform pharmacophore-based docking by overlaying precomputed conformers of molecules based on to their largest 3D pharmacophore. The pharmacophore is then matched to predefined site points representing putative receptor interactions.…”

Section: Improvement 2 -Docking With Constraintsmentioning

confidence: 99%

Structure-Based Drug Design: Docking and Scoring

Kroemer

2007

CPPS

282

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This review gives an introduction into ligand -receptor docking and illustrates the basic underlying concepts. An overview of different approaches and algorithms is provided. Although the application of docking and scoring has led to some remarkable successes, there are still some major challenges ahead, which are outlined here as well. Approaches to address some of these challenges and the latest developments in the area are presented. Some aspects of the assessment of docking program performance are discussed. A number of successful applications of structure-based virtual screening are described.

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“…6,9 Our approach uses multiple operators (e.g., discrete and continuous genetic operators) that cooperate using family competition (similar to a local search procedure) to balance exploration and exploitation. Like some VS methods, 18,22,23 GEMDOCK evolves the pharmacological preferences from a number of known active ligands to take advantage of the similarity of a putative ligand to those that are known to bind to a protein's active site, thereby guiding the docking of the putative ligand. However, unlike existing pharmacophore-based docking methods, we developed and incorporated a new scoring function that evolves a pharmacological consensus (e.g., hot spots) and ligand preferences using the target protein and known active ligands.…”

Section: Introductionmentioning

confidence: 99%

A pharmacophore‐based evolutionary approach for screening selective estrogen receptor modulators

Yang

Shen

2005

Proteins

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We developed a pharmacophorebased evolutionary approach for virtual screening. This tool, termed the Generic Evolutionary Method for molecular DOCKing (GEMDOCK), combines an evolutionary approach with a new pharmacophorebased scoring function. The former integrates discrete and continuous global search strategies with local search strategies to expedite convergence. The latter, integrating an empirical-based energy function and pharmacological preferences (binding-site pharmacological interactions and ligand preferences), simultaneously serves as the scoring function for both molecular docking and postdocking analyses to improve screening accuracy. We apply pharmacological interaction preferences to select the ligands that form pharmacological interactions with target proteins, and use the ligand preferences to eliminate the ligands that violate the electrostatic or hydrophilic constraints. We assessed the accuracy of our approach using human estrogen receptor (ER) and a ligand database from the comparative studies of Bissantz et al. (J Med Chem 2000;43:4759 -4767). Using GEMDOCK, the average goodness-of-hit (GH) score was 0.83 and the average false-positive rate was 0.13% for ER antagonists, and the average GH score was 0.48 and the average false-positive rate was 0.75% for ER agonists. The performance of GEMDOCK was superior to competing methods such as GOLD and DOCK. We found that our pharmacophore-based scoring function indeed was able to reduce the number of false positives; moreover, the resulting pharmacological interactions at the binding site, as well as ligand preferences, were important to the screening accuracy of our experiments. These results suggest that GEMDOCK constitutes a robust tool for virtual database screening. Proteins 2005;59:205-220.

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Pharmacophore‐based molecular docking to account for ligand flexibility

Cited by 82 publications

References 37 publications

Understanding the challenges of protein flexibility in drug design

Understanding the challenges of protein flexibility in drug design

Structure-Based Drug Design: Docking and Scoring

A pharmacophore‐based evolutionary approach for screening selective estrogen receptor modulators

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