Receptor–ligand molecular docking
TLDR
The main topics and recent computational and methodological advances in protein–ligand docking are summarised, including protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction.Abstract:
Docking methodology aims to predict the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets and is currently used as a standard computational tool in drug design for lead compound optimisation and in virtual screening studies to find novel biologically active molecules. The basic tools of a docking methodology include a search algorithm and an energy scoring function for generating and evaluating ligand poses. In this review, we present the search algorithms and scoring functions most commonly used in current molecular docking methods that focus on protein–ligand applications. We summarise the main topics and recent computational and methodological advances in protein–ligand docking. Protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction are important and interconnected challenges to be overcome by further methodological developments in the docking field.read more
Citations
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Journal ArticleDOI
Key Topics in Molecular Docking for Drug Design.
Pedro Henrique Monteiro Torres,Ana Carolina Rennó Sodero,Paula Jofily,Floriano Paes Silva-Jr +3 more
TL;DR: This review presents an overview of the method and attempts to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking.
Journal ArticleDOI
Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective
TL;DR: 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, comparison of performance for existing tools and algorithms, state of art in docking.
Journal ArticleDOI
Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges.
TL;DR: Some recent successful applications and methodological advances are covered, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics.
Journal ArticleDOI
Advancement and prospects of bioinformatics analysis for studying bioactive peptides from food-derived protein: Sequence, structure, and functions
TL;DR: An overview of research progress in the bioinformatics methods used for identifying, characterizing, elaborating bioactive mechanisms of, and producing food-derived bioactive peptides is provided to present an effective workflow.
Journal ArticleDOI
A dynamic niching genetic algorithm strategy for docking highly flexible ligands
Camila S. de Magalhães,Camila S. de Magalhães,Diogo Marinho Almeida,Helio J. C. Barbosa,Laurent E. Dardenne +4 more
TL;DR: A new multi-solution genetic algorithm method, named Dynamic Modified Restricted Tournament Selection (DMRTS), was developed for the effective docking of highly flexible ligands, which can adequately sample the conformational search space, producing a diverse set of high quality solutions.
References
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Journal ArticleDOI
AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility
Garrett M. Morris,Ruth Huey,William Lindstrom,Michel F. Sanner,Richard K. Belew,David S. Goodsell,Arthur J. Olson +6 more
TL;DR: AutoDock4 incorporates limited flexibility in the receptor and its utility in analysis of covalently bound ligands is reported, using both a grid‐based docking method and a modification of the flexible sidechain technique.
Journal ArticleDOI
Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function
Garrett M. Morris,David S. Goodsell,Robert Scott Halliday,Ruth Huey,William E. Hart,Richard K. Belew,Arthur J. Olson +6 more
TL;DR: It is shown that both the traditional and Lamarckian genetic algorithms can handle ligands with more degrees of freedom than the simulated annealing method used in earlier versions of AUTODOCK, and that the Lamarckia genetic algorithm is the most efficient, reliable, and successful of the three.
Journal ArticleDOI
Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.
Richard A. Friesner,Jay L. Banks,Robert B. Murphy,Thomas A. Halgren,Jasna Klicic,Daniel T. Mainz,Matthew P. Repasky,Eric H. Knoll,Mee Shelley,Jason K. Perry,David E. Shaw,Perry Francis,Peter S Shenkin +12 more
TL;DR: Glide approximates a complete systematic search of the conformational, orientational, and positional space of the docked ligand to find the best docked pose using a model energy function that combines empirical and force-field-based terms.
Journal ArticleDOI
Development and validation of a genetic algorithm for flexible docking.
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.
Journal ArticleDOI
Extra Precision Glide: Docking and Scoring Incorporating a Model of Hydrophobic Enclosure for Protein-Ligand Complexes
Richard A. Friesner,Robert B. Murphy,Matthew P. Repasky,Leah L. Frye,Jeremy R. Greenwood,Thomas A. Halgren,Paul C. Sanschagrin,Daniel T. Mainz +7 more
TL;DR: Enrichment results demonstrate the importance of the novel XP molecular recognition and water scoring in separating active and inactive ligands and avoiding false positives.
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