• The Plant Pathology Journal
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• v.33 no.6
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• pp.529-542
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• 2017

Control of plant diseases is largely dependent on use of agrochemicals. However, there are widening gaps between our knowledge on plant diseases gained from genetic/mechanistic studies and rapid translation of the knowledge into target-oriented development of effective agrochemicals. Here we propose that the time is ripe for computer-aided drug discovery/design (CADD) in molecular plant pathology. CADD has played a pivotal role in development of medically important molecules over the last three decades. Now, explosive increase in information on genome sequences and three dimensional structures of biological molecules, in combination with advances in computational and informational technologies, opens up exciting possibilities for application of CADD in discovery and development of agrochemicals. In this review, we outline two categories of the drug discovery strategies: structure- and ligand-based CADD, and relevant computational approaches that are being employed in modern drug discovery. In order to help readers to dive into CADD, we explain concepts of homology modelling, molecular docking, virtual screening, and de novo ligand design in structure-based CADD, and pharmacophore modelling, ligand-based virtual screening, quantitative structure activity relationship modelling and de novo ligand design for ligand-based CADD. We also provide the important resources available to carry out CADD. Finally, we present a case study showing how CADD approach can be implemented in reality for identification of potent chemical compounds against the important plant pathogens, Pseudomonas syringae and Colletotrichum gloeosporioides.

• Journal of Biomedical Engineering Research
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• v.43 no.2
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• pp.71-80
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• 2022

The interaction between dual-ligand decorated particle-based delivery system and target cell under shear flow is predicted using probability model developed. We assumed the two kinds of ligand are decorated on the surface of the particle with 10% length difference. Fixed with other biophysical parameters, a study on the particle-cell interaction for the different non-specific interaction parameter is performed. To induce the firm adhesion, short ligand-receptor should be engaged. Also, it is shown that the rational design of ligand-receptor interaction, including receptor number, specific interaction parameter, kinds of ligand-receptor, etc., should be considered.

• Journal of Integrative Natural Science
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• v.3 no.3
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• pp.143-147
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• 2010

Since it is of great importance to know how a ligand binds to a receptor, there have been a lot of efforts to improve the quality of prediction of docking poses. Earlier efforts were focused on improving search algorithm and scoring function in a docking program resulting in a partial improvement with a lot of variations. Although these are basically very important and essential, more tangible improvements came from the reduction of search space. In a normal docking study, the approximate active site is assumed to be known. After defining active site, scoring functions and search algorithms are used to locate the expected binding pose within this search space. A good search algorithm will sample wisely toward the correct binding pose. By careful study of receptor structure, it was possible to prioritize sub-space in the active site using "receptor-based pharmacophores" or "hot spots". In a sense, these techniques reduce the search space from the beginning. Further improvements were made when the bound ligand structure is available, i.e., the searching could be directed by molecular similarity using ligand information. This could be very helpful to increase the accuracy of binding pose. In addition, if the biological activity data is available, docking program could be improved to the level of being useful in affinity prediction for a series of congeneric ligands. Since the number of co-crystal structures is increasing in protein databank, "Ligand-Guided Docking" to reduce the search space would be more important to improve the accuracy of docking pose prediction and the efficiency of virtual screening. Further improvements in this area would be useful to produce more reliable docking programs.

• Bulletin of the Korean Chemical Society
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• v.32 no.7
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• pp.2433-2442
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• 2011

The three dimensional quantitative structure activity relationship (3D QSAR) models were developed using Comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and docking studies. The fit of Quinazolinone antifolates inside the active site of modeled bovine dihydrofolate reductase (DHFR) was assessed. Both ligand based (LB) and receptor based (RB) QSAR models were generated, these models showed good internal and external statistical reliability that is evident from the $q^2_{loo}$, $r^2_{ncv}$ and $r^2_{pred}$. The identified key features enabled us to design new Quinazolinone analogues as DHFR inhibitors. This study is a building bridge between docking studies of homology modeled bovine DHFR protein as well as ligand and target based 3D QSAR techniques of CoMFA and CoMSIA approaches.

• Journal of the Korean Magnetic Resonance Society
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• v.6 no.2
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• pp.94-102
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• 2002

An interesting recent application of intermolecular NOE experiment is the saturation transfer difference NMR(STD-NMR) method that is useful in screening compound libraries to identify bio-active ligands. This technique also identifies the group epitopes of the bound ligand in a reversibly forming protein-ligand complex. We present here a complete relaxation and conformational exchange matrix (CORCEMA) theory (Moseley et al., J. Magn. Reson. B, 108, 243-261 (1995)) applicable for the STD-NMR experiment. Using some ideal model systems we have analyzed the factors that influence the STD intensity changes in the ligand proton NMR spectrum when the resonances from some protons on the receptor protein are saturated. These factors will be discussed and some examples of its application in some model systems will be presented. This CORCEMA theory for STD-NMR and the associated algorithm are useful in a quantitative interpretation of the STD-NMR effects, and are likely to be useful in structure-based drug design efforts. They are also useful in a quantitative characterization of protein-protein (or protein-nucleic acid) contact surfaces from an intermolecular cross-saturation NMR experiment.

• Bulletin of the Korean Chemical Society
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• v.28 no.2
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• pp.211-219
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• 2007

The subtle but significant differences and thereby the lack of consensus in active site structures among the crystal structures of cyclin-dependent kinase 2 (CDK2) has hampered structure-based drug design. In this study, we devised a simple but effective ‘mutation, pharmacophore-guided docking, followed by mutation' strategy to generate an “average” CDK2 structure, which was used for ligand docking study to successfully reproduce 30 out of 32 X-ray ligand positions within 2.0 A of heavy atom RMSD. This novel docking method was applied for structure-based 3D QSAR with CoMSIA study of a series of structurally related ligands, which showed a good discrimination between CDK2 binders and nonbinders.

• Bulletin of the Korean Chemical Society
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• v.26 no.11
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• pp.1695-1700
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• 2005

Catechol-O-methyltransferase (COMT, EC 2.1.1.6) is an S-adenosylmethionine (SAM, AdoMet) dependent methyltransferase, and is related to the functions of the neurotransmitters in various mental processes, such as Parkinson’s disease. COMT inhibitors represent a new class of antiparkinson drugs, when they are coadministered with levodopa. Based on x-ray structure of rat COMT (rCOMT), the three dimensional structure of human COMT (hCOMT) was constructed by comparative homology modeling using MODELLER. The catalytic site of these two proteins showed subtle differences, but these differences are important to determine the characterization of COMT inhibitor. Ligand docking study is carried out for complex of hCOMT and COMT inhibitors using AutoDock. Among fifteen inhibitors chosen from world patent, nine models were energetically favorable. The average value of heavy atomic RMSD was 1.5 $\AA$. Analysis of ligand-protein binding model implies that Arg201 on hCOMT plays important roles in the interactions with COMT inhibitors. This study may give insight to develop new ways of antiparkinson drug.

• Rapid Communication in Photoscience
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• v.4 no.4
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• pp.88-90
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• 2015

The design and development of fluorescent chemosensors have recently been intensively explored for sensitive and specific detection of environmentally and biologically relevant metal ions in aqueous solution and living cells. Herein, we report the photophysical results of rhodamine B based fluorogenic and chromogenic receptor for selective copper detection in the complete organic or mixed aqueous-organic media at neutral pH under ambient condition. The ligand exhibited the remarkable increment in the fluorescence emission and UV-visible absorption signal intensities at 587 and 547 nm, respectively, on induction of copper ion while the ligand solution remain completely silent on addition of varieties of other metal ions.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.134-138
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• 2005

The design of ion channel targeted library is a valuable methodology that can aid in the selection and prioritization of potential ion channel-likeness for ion-channel-targeted bio-screening from large commercial available chemical pool. The differences of property profiling between the 93 ion-channel active compounds from MDDR and CMC database and the ACDSC compounds were classified by suitable descriptors calculated with preADME software. Through the PCA, clustering, and similarity analysis, the compounds capable of ion channel activity were defined in ACDSC compounds pool. The designed library showed a tendency to follow the property profile of ion-channel active compounds and can be implemented with great time and economical efficiencies of ligand-based drug design or virtual high throughput screening from an enormous small molecule space.

• Journal of Integrative Natural Science
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• v.8 no.3
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• pp.214-220
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• 2015

Cancer has emerged as one of the leading cause of deaths worldwide. A three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis was performed on a series of quinazoline-based anticancer agents. Purpose of the study is to understand the structural basis for their inhibitory activity. Comparative molecular field analysis (CoMFA) technique was employed to develop 3D-QSAR model. Ligand-based alignment scheme was used to generate a reliable CoMFA model. The model produced statistically significant results with a cross-validated correlation coefficient ($q^2$) of 0.589 and a non-cross-validated correlation coefficient ($r^2$) of 0.928. Model was further validated by bootstrapping and progressive scrambling analysis. This study could assist in the design of novel and more potent anticancer agents.