Comparative Analysis of Protein-Bound Ligand Conformations with Respect to Catalyst's Conformational Space Subsampling Algorithms

Kirchmair, Johannes; Laggner, Christian; Wolber, Gerhard; Langer, Thomas

doi:10.1021/ci049753l

Cited by 132 publications

(198 citation statements)

References 25 publications

(30 reference statements)

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“…Random ligand conformations were generated from the initial ligand structure through high-temperature molecular dynamics. Due to the high flexibility of sialic acids, we docked for each ligand several conformations previously generated with the BEST algorithm (49) to cover the full range of conformers. The poses showing the lowest energy were retained and clustered according to their binding mode.…”

Section: Methodsmentioning

confidence: 99%

Successful Prediction of Substrate-binding Pocket in SLC17 Transporter Sialin

Pietrancosta

Anne

Prescher

et al. 2012

Journal of Biological Chemistry

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Background: Sialin is a clinically relevant lysosomal sialic acid exporter related to vesicular glutamate transporters and other SLC17 transporters. Results: Using synthetic sialic acid analogues, homology modeling, site-directed mutagenesis, and virtual screening, we built and validated a three-dimensional model of sialin. Conclusion:The three-dimensional model successfully predicts small molecule binding to sialin. Significance: The model will help identifying pharmacological tools targeted to SLC17 transporters.

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Section: Methodsmentioning

confidence: 99%

Successful Prediction of Substrate-binding Pocket in SLC17 Transporter Sialin

Pietrancosta

Anne

Prescher

et al. 2012

Journal of Biological Chemistry

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“…These databases only contain a limited number of low-energy conformations per molecule. 95,96 It may be possible that an active molecule cannot be identified as the conformation is missing. This is especially the case for the many different conformations of rotatable bonds of small molecular functionalities such as hydroxyl groups.…”

Section: Limitations Of Pharmacophore Methodsmentioning

confidence: 99%

Pharmacophore modeling: advances, limitations, and current utility in drug discovery

Qing¹,

Lee²,

Raeymaecker³

et al. 2014

JRLCR

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Pharmacophore modeling is a successful yet very diverse subfield of computer-aided drug design. The concept of the pharmacophore has been widely applied to the rational design of novel drugs. In this paper, we review the computational implementation of this concept and its common usage in the drug discovery process. Pharmacophores can be used to represent and identify molecules on a 2D or 3D level by schematically depicting the key elements of molecular recognition. The most common application of pharmacophores is virtual screening, and different strategies are possible depending on the prior knowledge. However, the pharmacophore concept is also useful for ADME-tox modeling, side effect, and off-target prediction as well as target identification. Furthermore, pharmacophores are often combined with molecular docking simulations to improve virtual screening. We conclude this review by summarizing the new areas where significant progress may be expected through the application of pharmacophore modeling; these include protein-protein interaction inhibitors and protein design. Keywords: ADME-tox, computer-aided drug design, pharmacophore fingerprint, protein design, virtual screening What is computer-aided drug design?Drug design is an expensive and laborious process of developing new medicine. This process has its origin in herbal remedies dating back millennia.1 Only since the last century have drugs had a (semi)synthetic origin.2 The first hit compounds often lack both potency and safety, and must therefore be optimized. While historically this was a trial-and-error process, 3,4 soon rational strategies were developed to improve potency. 5,6 As with any data handling procedures, computers have become a more prominent and ubiquitous tool in drug discovery since the 1980s. The crossover between computational and pharmaceutical research is typically designated computer-aided drug design (CADD). 8,9 CADD covers a broad range of applications spanning the drug discovery pipeline, although these are highly clustered in the early phases. The main purpose of CADD is to speed up and rationalize the drug design process while reducing costs. 10 The aim of the earliest phase in drug discovery is to identify the first hit compounds, which is sometimes attempted by high-throughput screening (HTS), the testing of many thousands of compounds with a suitable activity assay. The in silico counterpart of in vitro HTS is referred to as virtual screening and aims at filtering libraries of molecules using computational methods to prioritize those most likely to be active for a given

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“…Random ligand conformations were generated from the initial ligand structure through high-temperature molecular dynamics. Due to the high flexibility of the MraY ligand, we docked for each ligand several conformations previously generated with the BEST algorithm 43 to cover the full range of conformers. The poses showing the lowest energy were retained and clustered according to their binding mode.…”

Section: General Procedures For Phthalimide Isopentylidene and Tbs Grmentioning

confidence: 99%

5′-Methylene-triazole-substituted-aminoribosyl uridines as MraY inhibitors: synthesis, biological evaluation and molecular modeling

et al. 2015

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The straightforward synthesis of 5'-methylene-[1,4]-triazole-substituted aminoribosyl uridines is described. Two families of compounds were synthesized from a unique epoxide which was regioselectively opened by acetylide ions (for compounds II) or azide ions (for compounds III). Sequential diastereoselective glycosylation with a ribosyl fluoride derivative, Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) with various complementary azide and alkyne partners afforded the targeted compounds after final deprotection. The biological activity of the 16 resulting compounds together with that of 14 previously reported compounds I, lacking the 5' methylene group, was evaluated on the MraY transferase activity. Out of the 30 tested compounds, 18 compounds revealed MraY inhibition with IC 50 ranging from 15 to 150 µM. A molecular modeling study was performed to rationalize the observed structure-activity relationships (SAR), which allowed us to correlate the activity of the most potent compounds with an interaction involving Leu191 of MraY AA . The antibacterial activity was also evaluated and seven compounds exhibited a good activity against Gram-positive bacterial pathogens with MIC ranging from 8 to 32 µg mL −1

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Comparative Analysis of Protein-Bound Ligand Conformations with Respect to Catalyst's Conformational Space Subsampling Algorithms

Cited by 132 publications

References 25 publications

Successful Prediction of Substrate-binding Pocket in SLC17 Transporter Sialin

Successful Prediction of Substrate-binding Pocket in SLC17 Transporter Sialin

Pharmacophore modeling: advances, limitations, and current utility in drug discovery

5′-Methylene-triazole-substituted-aminoribosyl uridines as MraY inhibitors: synthesis, biological evaluation and molecular modeling

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