PLANTS: Application of Ant Colony Optimization to Structure-Based Drug Design

Korb, Oliver; Stützle, Thomas; Exner, Thomas E.

doi:10.1007/11839088_22

Cited by 238 publications

(261 citation statements)

References 16 publications

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“…During the refinement and energy optimization of the model, YASARA automatically merged glutathione from the template protein (2imi) into the protein model, resulting in an optimal position for the co-factor. Using this structure, docking studies were performed with PLANTS (30,31). In each case, 30 different docking poses were calculated and subsequently analyzed.…”

Section: Methodsmentioning

confidence: 99%

Isomerization of the Phytohormone Precursor 12-Oxophytodienoic Acid (OPDA) in the Insect Gut

Dąbrowska

Shabab

Brandt

et al. 2011

Journal of Biological Chemistry

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12-Oxophytodienoic acid (OPDA) is isomerized in the gut of herbivorous insects to tetrahydrodicranenone B (iso-OPDA).The transformation is achieved by a glutathione S-transferase present in the gut epithelium. Experiments with 9-[ 2 H]-iso-OPDA demonstrated the complete retention of the deuterium atom in the product 11-[ 2 H]-OPDA consistent with an intramolecular 1,3-hydrogen shift. Homology modeling based on the x-ray structure of a glutathione S-transferase from Anopheles gambiae revealed that the co-factor glutathione does not covalently bind to the substrate but appears to be involved in the initial deprotonation and enolization of the OPDA. The transformation resembles that of a mammalian GST-catalyzed isomerization of ⌬ 5 -3-ketosteroids to ⌬ 4 -3-ketosteroids or the conversion of prostaglandin A 1 to the biologically inactive prostaglandin B 1 .

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Section: Methodsmentioning

confidence: 99%

Isomerization of the Phytohormone Precursor 12-Oxophytodienoic Acid (OPDA) in the Insect Gut

Dąbrowska

Shabab

Brandt

et al. 2011

Journal of Biological Chemistry

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“…A large variety of different approaches for a solution of this problem has been applied [6,8,162,163,164,165] ranging from fragment-based (e.g., DOCK [166,169], FlexX [6,8,11,170], SLIDE [171], SURFLEX [172], and GLIDE [173,174,175]) over stochastic optimization methods for finding the global minimum (e.g., GOLD [176,177,178], AutoDock [179,180], PLANTS [181,182,183], ICM [184], QXP [185], and PRO_LEADS [186,187]) to multiconformer docking (e.g., FRED [188]). …”

Section: Influence Of Ph and Protonation States In Docking Studiesmentioning

confidence: 99%

“…Together with the scoring functions this leads in some cases to over favoring of highly charged ligands because of the possibility of forming charged interactions to the protein thus creating false positive docking results. When comparing two docking programs, PLANTS and GOLD, it is noticeable that PLANTS gives an overall better performance, but the influence of the protonation states is about the same for both programs despite the different approaches in terms of scoring function [176,181]. But the inclusion of additional protonation states may prove useful when less information is available about the targets or when the performance of the standard protonation is poor for the specific target.…”

Section: Influence Of Ph and Protonation States In Docking Studiesmentioning

confidence: 99%

Some Critical Aspects of Molecular Interactions Between Drugs and Receptors

Yunta¹

2014

AJMO

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Protonation states are sometimes crucial for free energy calculations to predict binding ability of molecules to receptors, a main subject for drug design. This mini-review seeks to identify the importance of knowing the influence of pH in such studies to better achieve correct predictions for drug candidates. Protonation states, for both proteins and drugs, need to be considered in docking studies although they have been usually neglected.

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“…They also report the importance of an efficient visualization method. Korb et al (2011) enhance the PLANTS (Korb et al, 2006) approach for protein-ligand docking using GPUs. They report speedup factors of up to 50x in their GPU implementation compared to an optimized CPU based implementation for the evaluation of interaction potentials in the context of rigid protein.…”

Section: Autodockmentioning

confidence: 99%