Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation.
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.
About: This article is published in Journal of Molecular Biology.The article was published on 1995-01-01. It has received 1522 citations till now. The article focuses on the topics: Scoring functions for docking & Lead Finder.
Citations
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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.
5,882 citations
Cites background or methods from "Molecular recognition of receptor s..."
...With the exception that binary-coding was now preferred to Gray-coding, the methods described by Jones et al. (1995a) were used to generate the active site and ligand conformations....
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...In the algorithm described by Jones et al. (1995a) we treated the nitrogen atoms in all lysine residues as solvated and used a separate set of hydrogen-bonding energies for these donors....
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...Figure 9 of Jones et al. (1995a))....
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...Although acceptors were now used in preference to lone-pairs as ®tting points for generating hydrogen-bond motifs, the same two-pass least-squares ®tting technique described by Jones et al. (1995a) was used to dock the ligand within the active site....
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...Unlike the encoding used by Jones et al. (1995a), Gray-coding was not employed, as it was found to disrupt crossover....
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TL;DR: This work presents an automatic method for docking organic ligands into protein binding sites that combines an appropriate model of the physico-chemical properties of the docked molecules with efficient methods for sampling the conformational space of the ligand.
2,607 citations
Cites methods from "Molecular recognition of receptor s..."
...Recently, two flexible docking methods based on a genetic algorithm have been published (Oshiro et al., 1995; Jones et al., 1995)....
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TL;DR: In terms of producing binding energy estimates, the Goldscore function appears to perform better than the Chemscore function and the two consensus protocols, particularly for faster search settings.
Abstract: The Chemscore function was implemented as a scoring function for the protein-ligand docking program GOLD, and its performance compared to the original Goldscore function and two consensus docking protocols, "Goldscore-CS" and "Chemscore-GS," in terms of docking accuracy, prediction of binding affinities, and speed. In the "Goldscore-CS" protocol, dockings produced with the Goldscore function are scored and ranked with the Chemscore function; in the "Chemscore-GS" protocol, dockings produced with the Chemscore function are scored and ranked with the Goldscore function. Comparisons were made for a "clean" set of 224 protein-ligand complexes, and for two subsets of this set, one for which the ligands are "drug-like," the other for which they are "fragment-like." For "drug-like" and "fragment-like" ligands, the docking accuracies obtained with Chemscore and Goldscore functions are similar. For larger ligands, Goldscore gives superior results. Docking with the Chemscore function is up to three times faster than docking with the Goldscore function. Both combined docking protocols give significant improvements in docking accuracy over the use of the Goldscore or Chemscore function alone. "Goldscore-CS" gives success rates of up to 81% (top-ranked GOLD solution within 2.0 A of the experimental binding mode) for the "clean list," but at the cost of long search times. For most virtual screening applications, "Chemscore-GS" seems optimal; search settings that give docking speeds of around 0.25-1.3 min/compound have success rates of about 78% for "drug-like" compounds and 85% for "fragment-like" compounds. In terms of producing binding energy estimates, the Goldscore function appears to perform better than the Chemscore function and the two consensus protocols, particularly for faster search settings. Even at docking speeds of around 1-2 min/compound, the Goldscore function predicts binding energies with a standard deviation of approximately 10.5 kJ/mol.
2,505 citations
Cites methods from "Molecular recognition of receptor s..."
...On the 84-processor cluster used in this work, we can screen approximately 1.2 million compounds/week using GOLD....
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...The force constants kBA and the ideal bond angles o,BA are taken from GOLD.(5) Ccov is a constant used to balance the covalent bond term against the rest of the Chemscore function (see Table II)....
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...Chemscore is the scoring function used in PRO_LEADS, and, because higher success rates are quoted for this program than for GOLD (see above), it seemed an obvious improvement to make to GOLD....
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...Most parts of the GOLD program have been described by Jones et al.4,5 Like all other docking programs, GOLD consists of three main parts: 1....
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...Most parts of the GOLD program have been described by Jones et al.(4,5) Like all other docking programs, GOLD consists of three main parts:...
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TL;DR: Three-dimensional pharmacophore models are constructed, which are sufficiently selective to identify the described binding mode and are thus a useful tool for in-silico screening of large compound databases.
Abstract: From the historically grown archive of protein−ligand complexes in the Protein Data Bank small organic ligands are extracted and interpreted in terms of their chemical characteristics and features. Subsequently, pharmacophores representing ligand−receptor interaction are derived from each of these small molecules and its surrounding amino acids. Based on a defined set of only six types of chemical features and volume constraints, three-dimensional pharmacophore models are constructed, which are sufficiently selective to identify the described binding mode and are thus a useful tool for in-silico screening of large compound databases. The algorithms for ligand extraction and interpretation as well as the pharmacophore creation technique from the automatically interpreted data are presented and applied to a rhinovirus capsid complex as application example.
1,480 citations
TL;DR: A fast force field generation tool, called SwissParam, able to generate, for arbitrary small organic molecule, topologies, and parameters based on the Merck molecular force field, but in a functional form that is compatible with the CHARMM force field is presented.
Abstract: The drug discovery process has been deeply transformed recently by the use of computational ligand-based or structure-based methods, helping the lead compounds identification and optimization, and finally the delivery of new drug candidates more quickly and at lower cost. Structure-based computational methods for drug discovery mainly involve ligand-protein docking and rapid binding free energy estimation, both of which require force field parameterization for many drug candidates. Here, we present a fast force field generation tool, called SwissParam, able to generate, for arbitrary small organic molecule, topologies, and parameters based on the Merck molecular force field, but in a functional form that is compatible with the CHARMM force field. Output files can be used with CHARMM or GROMACS. The topologies and parameters generated by SwissParam are used by the docking software EADock2 and EADock DSS to describe the small molecules to be docked, whereas the protein is described by the CHARMM force field, and allow them to reach success rates ranging from 56 to 78%. We have also developed a rapid binding free energy estimation approach, using SwissParam for ligands and CHARMM22/27 for proteins, which requires only a short minimization to reproduce the experimental binding free energy of 214 ligand-protein complexes involving 62 different proteins, with a standard error of 2.0 kcal mol(-1), and a correlation coefficient of 0.74. Together, these results demonstrate the relevance of using SwissParam topologies and parameters to describe small organic molecules in computer-aided drug design applications, together with a CHARMM22/27 description of the target protein. SwissParam is available free of charge for academic users at www.swissparam.ch.
1,370 citations
References
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Book•
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher:
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields
Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required
52,797 citations
01 Jan 1989
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Abstract: From the Publisher:
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required.
33,034 citations
Book•
01 Jan 1975
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Abstract: Name of founding work in the area. Adaptation is key to survival and evolution. Evolution implicitly optimizes organisims. AI wants to mimic biological optimization { Survival of the ttest { Exploration and exploitation { Niche nding { Robust across changing environments (Mammals v. Dinos) { Self-regulation,-repair and-reproduction 2 Artiicial Inteligence Some deenitions { "Making computers do what they do in the movies" { "Making computers do what humans (currently) do best" { "Giving computers common sense; letting them make simple deci-sions" (do as I want, not what I say) { "Anything too new to be pidgeonholed" Adaptation and modiication is root of intelligence Some (Non-GA) branches of AI: { Expert Systems (Rule based deduction)
32,573 citations
Book•
01 Jan 1954
TL;DR: Molecular theory of gases and liquids as mentioned in this paper, molecular theory of gas and liquids, Molecular theory of liquid and gas, molecular theories of gases, and liquid theory of liquids, مرکز
Abstract: Molecular theory of gases and liquids , Molecular theory of gases and liquids , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی
11,807 citations