Computational Tools for In Silico Fragment-Based Drug Design

Mortier, Jérémie; Rakers, Christin; Frédérick, Raphaël; Wolber, Gerhard

doi:10.2174/156802612804547371

Cited by 29 publications

(26 citation statements)

References 93 publications

(94 reference statements)

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“…In the continuation of this study, a computer‐aided method was developed to compile key ligand–enzyme interactions for α‐amylase inhibition and to use this information to identify novel drug‐like compounds able to bind α‐amylase specifically. Analyzing ligand conformations to identify required 3D chemical feature arrangements for optimal binding is state of the art in structure‐based drug design and virtual screening strategies . Recently, human pancreatic α‐amylase (HPA) has been crystallized with the ligand myricetin (IC 50 =30.2 μ m ), the first non‐carbohydrate ligand co‐crystallized with HPA (PDB ID: 4GQR).…”

Section: Figurementioning

confidence: 99%

α‐Amylase Modulation: Discovery of Inhibitors Using a Multi‐Pharmacophore Approach for Virtual Screening

et al. 2016

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Better control of postprandial hyperglycemia can be achieved by delaying the absorption of glucose resulting from carbohydrate digestion. Because α-amylase initiates the hydrolysis of polysaccharides, the design of α-amylase inhibitors can lead to the development of new treatments for metabolic disorders such as type II diabetes and obesity. In this study, a rational computer-aided approach was developed to identify novel α-amylase inhibitors. Three-dimensional pharmacophores were developed based on the binding mode analysis of six different families of compounds that bind to this enzyme. In a stepwise virtual screening workflow, seven molecules were selected from a library of 1.4 million. Five out of seven biologically tested compounds showed α-amylase inhibition, and the two most potent compounds inhibited α-amylase with IC values of 17 and 27 μm. The scaffold benzylideneacetohydrazide was shared by four of the discovered inhibitors, emerging as a novel drug-like non-carbohydrate fragment and constituting a promising lead scaffold for α-amylase inhibition.

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Section: Figurementioning

confidence: 99%

α‐Amylase Modulation: Discovery of Inhibitors Using a Multi‐Pharmacophore Approach for Virtual Screening

et al. 2016

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“…Further improvement might be possible in some cases by using MD-simulations to generate a canonical ligand ensemble weighted according to the Boltzmann distribution to identify the most populated and thereby most probable ligand conformations. For large, flexible ligands, a fragment-based docking approach might be more suitable and has already been applied in many drug design studies [56]–[58].…”

Section: Resultsmentioning

confidence: 99%

Assessment and Challenges of Ligand Docking into Comparative Models of G-Protein Coupled Receptors

et al. 2013

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The rapidly increasing number of high-resolution X-ray structures of G-protein coupled receptors (GPCRs) creates a unique opportunity to employ comparative modeling and docking to provide valuable insight into the function and ligand binding determinants of novel receptors, to assist in virtual screening and to design and optimize drug candidates. However, low sequence identity between receptors, conformational flexibility, and chemical diversity of ligands present an enormous challenge to molecular modeling approaches. It is our hypothesis that rapid Monte-Carlo sampling of protein backbone and side-chain conformational space with Rosetta can be leveraged to meet this challenge. This study performs unbiased comparative modeling and docking methodologies using 14 distinct high-resolution GPCRs and proposes knowledge-based filtering methods for improvement of sampling performance and identification of correct ligand-receptor interactions. On average, top ranked receptor models built on template structures over 50% sequence identity are within 2.9 Å of the experimental structure, with an average root mean square deviation (RMSD) of 2.2 Å for the transmembrane region and 5 Å for the second extracellular loop. Furthermore, these models are consistently correlated with low Rosetta energy score. To predict their binding modes, ligand conformers of the 14 ligands co-crystalized with the GPCRs were docked against the top ranked comparative models. In contrast to the comparative models themselves, however, it remains difficult to unambiguously identify correct binding modes by score alone. On average, sampling performance was improved by 103 fold over random using knowledge-based and energy-based filters. In assessing the applicability of experimental constraints, we found that sampling performance is increased by one order of magnitude for every 10 residues known to contact the ligand. Additionally, in the case of DOR, knowledge of a single specific ligand-protein contact improved sampling efficiency 7 fold. These findings offer specific guidelines which may lead to increased success in determining receptor-ligand complexes.

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“…[67][68][69][70][71] Alternatively, molecules are built by assembling molecular fragment rather than single atoms. [72][73][74][75][76][77][78][79][80][81][82][83] While the use of multi-atom building blocks implies a low resolution in the exploration of the chemical space, 84 it also allows to control the type of functionalities generated and to avoid most of the unrealistic candidates. The combination of selected molecular fragment and connection rules efficiently confines the chemical space according to the specific needs and defines a subspace referred as to the fragment space.…”

Section: Building Blocks: Atoms Vs Fragmentsmentioning

confidence: 99%

Automated Design of Realistic Organometallic Molecules from Fragments

Foscato

Occhipinti

Venkatraman

et al. 2014

J. Chem. Inf. Model.

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A method for the automated generation of realistic, synthetically accessible transition metal and organometallic complexes is described. Computational tools were designed to generate molecular fragments, preferably harvested from libraries of existing, stable compounds, to be used as building blocks for the construction of new molecules. These fragments are enriched with information about the number and type of possible connections to other fragments and are stored in library files. When connecting fragments in the subsequent building process, compatibility matrices, which define the connection rules between fragments, are used to delineate organometallic fragment spaces from which molecules can be generated in an automated fashion. The approach is flexible and allows ample structural variation at the same time as the combination of known fragments is easily restrained to avoid generation of exotic and unrealistic substructures and molecules. The method was tested in the generation of ruthenium complexes, with a given coordination environment, which can serve as candidates in catalyst development. The results demonstrate that molecules generated with the described method do not contain exotic arrangements of atoms and are by far more realistic than those obtained by the application of valence rules alone.

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Computational Tools for In Silico Fragment-Based Drug Design

Cited by 29 publications

References 93 publications

α‐Amylase Modulation: Discovery of Inhibitors Using a Multi‐Pharmacophore Approach for Virtual Screening

α‐Amylase Modulation: Discovery of Inhibitors Using a Multi‐Pharmacophore Approach for Virtual Screening

Assessment and Challenges of Ligand Docking into Comparative Models of G-Protein Coupled Receptors

Automated Design of Realistic Organometallic Molecules from Fragments

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