• Genomics & Informatics
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• v.14 no.4
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• pp.241-254
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• 2016

Environmental microbes like Bordetella petrii has been established as a causative agent for various infectious diseases in human. Again, development of drug resistance in B. petrii challenged to combat against the infection. Identification of potential drug target and proposing a novel lead compound against the pathogen has a great aid and value. In this study, bioinformatics tools and technology have been applied to suggest a potential drug target by screening the proteome information of B. petrii DSM 12804 (accession No. PRJNA28135) from genome database of National Centre for Biotechnology information. In this regards, the inhibitory effect of nine natural compounds like ajoene (Allium sativum), allicin (A. sativum), cinnamaldehyde (Cinnamomum cassia), curcumin (Curcuma longa), gallotannin (active component of green tea and red wine), isoorientin (Anthopterus wardii), isovitexin (A. wardii), neral (Melissa officinalis), and vitexin (A. wardii) have been acknowledged with anti-bacterial properties and hence tested against identified drug target of B. petrii by implicating computational approach. The in silico studies revealed the hypothesis that lpxD could be a potential drug target and with recommendation of a strong inhibitory effect of selected natural compounds against infection caused due to B. petrii, would be further validated through in vitro experiments.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.6
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• pp.2161-2166
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• 2015

Estrogen receptors (ERs) are steroid receptors located in the cytoplasm and on the nuclear membrane. The sequence similarities of human $ER{\alpha}$, mouse $ER{\alpha}$, rat $ER{\alpha}$, dog $ER{\alpha}$, and cat $ER{\alpha}$ are above 90%, but structures of $ER{\alpha}$ may different among species. Estrogen can be agonist and antagonist depending on its target organs. This hormone play roles in several diseases including breast cancer. There are variety of the relative binding affinity (RBA) of ER and estrogen species in comparison to $17{\beta}-estradiol$ (E2), which is a natural ligand of both $ER{\alpha}$ and $ER{\beta}$. The RBA of the estrogen species are as following: diethyl stilbestrol (DES) > hexestrol > dienestrol > $17{\beta}-estradiol$ (E2) > 17- estradiol > moxestrol > estriol (E3) >4-OH estradiol > estrone-3-sulfate. Estrogen mimetic drugs, selective estrogen receptor modulators (SERMs), have been used as hormonal therapy for ER positive breast cancer and postmenopausal osteoporosis. In the postgenomic era, in silico models have become effective tools for modern drug discovery. These provide three dimensional structures of many transmembrane receptors and enzymes, which are important targets of de novo drug development. The estimated inhibition constants (Ki) from computational model have been used as a screening procedure before in vitro and in vivo studies.

• Biomolecules & Therapeutics
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• v.29 no.5
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• pp.519-526
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• 2021

In a search for effective PPAR-γ agonists, 110 clinical drugs were screened via molecular docking, and 9 drugs, including parecoxib, were selected for subsequent biological evaluation. Molecular docking of parecoxib to the ligand-binding domain of PPAR-γ showed high binding affinity and relevant binding conformation compared with the PPAR-γ ligand/antidiabetic drug rosiglitazone. Per the docking result, parecoxib showed the best PPAR-γ transactivation in Ac2F rat liver cells. Further docking simulation and a luciferase assay suggested parecoxib would be a selective (and partial) PPAR-γ agonist. PPAR-γ activation by parecoxib induced adipocyte differentiation in 3T3-L1 murine preadipocytes. Parecoxib promoted adipogenesis in a dose-dependent manner and enhanced the expression of adipogenesis transcription factors PPAR-γ, C/EBPα, and C/EBPβ. These data indicated that parecoxib might be utilized as a partial PPAR-γ agonist for drug repositioning study.

• Bulletin of the Korean Chemical Society
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• v.34 no.4
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• pp.1051-1054
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• 2013

Closo-carborane has been considered as an efficient boron-carrier for boron neutron capture therapy (BNCT) and an attractive surrogate of lipophilic phenyl or cyclohexyl ring in drug design. Despite a great number of carborane-containing ligands have been synthesized and evaluated, molecular modeling studies of carborane ligands with macromolecules have been rarely reported. We herein describe a facile docking and scoring-function strategy of 16 carborane ligands with an estrogen receptor by using the commercial Gaussian, Chem3D Pro and Discovery Studio (DS) computational programs. Docked poses of the carborane ligands in silico exhibited similar binding modes to that of the crystal ligand in the active site of estrogen receptor. Score analysis of the best docked pose for each ligand indicated that the Ligscore1 and the Dockscore have a moderate correlation with in vitro biological activity. This is the first report on the scoring-correlation studies of carborane ligands with macromolecules. The integrated Gaussian-DS approach has a potential application for virtual screening, De novo design, and optimization of carborane ligands in medicinal chemistry.

• Genomics & Informatics
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• v.19 no.1
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• pp.7.1-7.13
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• 2021

Levodopa (L-DOPA) therapy is normally practised to treat motor pattern associated with Parkinson disease (PD). Additionally, several inhibitory drugs such as Entacapone and Opicapone are also cosupplemented to protect peripheral inactivation of exogenous L-DOPA (~80%) that occurs due to metabolic activity of the enzyme catechol-O-methyltransferase (COMT). Although, both Entacapone and Opicapone have U.S. Food and Drug Administration approval but regular use of these drugs is associated with high risk of side effects. Thus, authors have focused on in silico discovery of phytochemicals and evaluation of their effectiveness against human soluble COMT using virtual screening, molecular docking, drug-like property prediction, generation of pharmacophoric property, and molecular dynamics simulation. Overall, study proposed, nine phytochemicals (withaphysalin D, withaphysalin N, withaferin A, withacnistin, withaphysalin C, withaphysalin O, withanolide B, withasomnine, and withaphysalin F) of plant Withania somnifera have strong binding efficiency against human COMT in comparison to both of the drugs i.e., Opicapone and Entacapone, thus may be used as putative bioenhancer in L-DOPA therapy. The present study needs further experimental validation to be used as an adjuvant in PD treatment.

• Biomolecules & Therapeutics
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• v.32 no.3
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• pp.390-398
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• 2024

FoxO1, a member of the Forkhead transcription factor family subgroup O (FoxO), is expressed in a range of cell types and is crucial for various pathophysiological processes, such as apoptosis and inflammation. While FoxO1's roles in multiple diseases have been recognized, the target has remained largely unexplored due to the absence of cost-effective and efficient inhibitors. Therefore, there is a need for natural FoxO1 inhibitors with minimal adverse effects. In this study, docking, MMGBSA, and ADMET analyses were performed to identify natural compounds that exhibit strong binding affinity to FoxO1. The top candidates were then subjected to molecular dynamics (MD) simulations. A natural product library was screened for interaction with FoxO1 (PDB ID-3CO6) using the Glide module of the Schrödinger suite. In silico ADMET profiling was conducted using SwissADME and pkCSM web servers. Binding free energies of the selected compounds were assessed with the Prime-MMGBSA module, while the dynamics of the top hits were analyzed using the Desmond module of the Schrödinger suite. Several natural products demonstrated high docking scores with FoxO1, indicating their potential as FoxO1 inhibitors. Specifically, the docking scores of neochlorogenic acid and fraxin were both below -6.0. These compounds also exhibit favorable drug-like properties, and a 25 ns MD study revealed a stable interaction between fraxin and FoxO1. Our findings highlight the potential of various natural products, particularly fraxin, as effective FoxO1 inhibitors with strong binding affinity, dynamic stability, and suitable ADMET profiles.

• Journal of Microbiology and Biotechnology
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• v.23 no.6
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• pp.779-784
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• 2013

Leptospirosis is a worldwide zoonosis of global concern caused by Leptospira interrogans. The availability of ligand libraries has facilitated the search for novel drug targets using chemogenomics approaches, compared with the traditional method of drug discovery, which is time consuming and yields few leads with little intracellular information for guiding target selection. Recent subtractive genomics studies have revealed the putative drug targets in peptidoglycan biosynthesis pathways in Leptospira interrogans. Aligand library for the murD ligase enzyme in the peptidoglycan pathway has also been identified. Our approach in this research involves screening of the pre-existing ligand library of murD with related protein family members in the putative drug target assembly in the peptidoglycan biosynthesis pathway. A chemogenomics approach has been implemented here, which involves screening of known ligands of a protein family having analogous domain architecture for identification of leads for existing druggable protein family members. By means of this approach, one murC and one murF inhibitor were identified, providing a platform for developing an anti-leptospirosis drug targeting the peptidoglycan biosynthesis pathway. Given that the peptidoglycan biosynthesis pathway is exclusive to bacteria, the in silico identified mur ligase inhibitors are expected to be broad-spectrum Gram-negative inhibitors if synthesized and tested in in vitro and in vivo assays.

• BMB Reports
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• v.56 no.11
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• pp.594-599
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• 2023

A number of therapeutic drugs have been developed from functional chemicals found in plants. Knowledge of plants used for medicinal purposes has historically been transmitted by word of mouth or through literature. The aim of the present study is to provide a systemic platform for the development of lead compounds against breast cancer based on a traditional medical text. To verify our systematic approach, integrating processes consisted of text mining of traditional medical texts, 3-D virtual docking screening, and in vitro and in vivo experimental validations were demonstrated. Our text analysis system identified rutin as a specific phytochemical traditionally used for cancer treatment. 3-D virtual screening predicted that rutin could block EGFR signaling. Thus, we validated significant anti-cancer effects of rutin against breast cancer cells through blockade of EGFR signaling pathway in vitro. We also demonstrated in vivo anti-cancer effects of rutin using the breast cancer recurrence in vivo models. In summary, our innovative approach might be proper for discovering new phytochemical lead compounds designing for blockade of malignant neoplasm including breast cancer.

• Applied Microscopy
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• v.51
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• pp.13.1-13.7
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• 2021

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has arisen as a global pandemic affecting the respiratory system showing acute respiratory distress syndrome (ARDS). However, there is no targeted therapeutic agent yet and due to the growing cases of infections and the rising death tolls, discovery of the possible drug is the need of the hour. In general, the study for discovering therapeutic agent for SARS-CoV-2 is largely focused on large-scale screening with fragment-based drug discovery (FBDD). With the recent advancement in cryo-electron microscopy (Cryo-EM), it has become one of the widely used tools in structural biology. It is effective in investigating the structure of numerous proteins in high-resolution and also had an intense influence on drug discovery, determining the binding reaction and regulation of known drugs as well as leading the design and development of new drug candidates. Here, we review the application of cryo-EM in a structure-based drug design (SBDD) and in silico screening of the recently acquired FBDD in SARS-CoV-2. Such insights will help deliver better understanding in the procurement of the effective remedial solution for this pandemic.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.273-275
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• 2022

The use of drugs by pregnant women poses a potential risk to the fetus. Therefore, it is essential to classify drugs that pregnant women should prohibit. However, the fetal toxicity of most drugs has not been identified. This takes a lot of time and cost. In silico approaches, such as virtual screening, can identify compounds that may present a high risk to the fetus for a wide range of compounds at the low cost and time. We collected class information of each drug from the hazard classification lists for prescribing drugs in pregnancy by the government of Korea and Australia. Using the structural and chemical features of each drug, various machine learning models were constructed to predict fetal toxicity of drugs. For all models, the quantitative performance evaluation was performed. Based on the attention algorithm, important molecular substructures of compounds were identified in the process of predicting the fetal toxicity of the drug by the proposed model. From the results, we confirmed that drugs with a high risk of fetal toxicity can be predicted for a wide range of compounds by machine learning. This study can be used as a pre-screening tool for fetal toxicity predictions, as it provides key molecular substructures associated with the fetal toxicity of compounds.