• Radiation Oncology Journal
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• v.36 no.4
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• pp.265-275
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• 2018

Cancer is a complex multifaceted illness that affects different patients in discrete ways. For a number of cancers the use of chemotherapy has become standard practice. Chemotherapy is a use of cytostatic drugs to cure cancer. Cytostatic agents not only affect cancer cells but also affect the growth of normal cells; leading to side effects. Because of this, radiotherapy gained importance in treating cancer. Slaughtering of cancerous cells by radiotherapy depends on the radiosensitivity of the tumor cells. Efforts to improve the therapeutic ratio have resulted in the development of compounds that increase the radiosensitivity of tumor cells or protect the normal cells from the effects of radiation. Amifostine is the only chemical radioprotector approved by the US Food and Drug Administration (FDA), but due to its side effect and toxicity, use of this compound was also failed. Hence the use of herbal radioprotectors bearing pharmacological properties is concentrated due to their low toxicity and efficacy. Notably, in silico methods can expedite drug discovery process, to lessen the compounds with unfavorable pharmacological properties at an early stage of drug development. Hence a detailed perspective of these properties, in accordance with their prediction and measurement, are pivotal for a successful identification of radioprotectors by drug discovery process.

• Bulletin of the Korean Chemical Society
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• v.24 no.12
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• pp.1742-1750
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• 2003

In order to characterize the hydrophobic parameters of N-acetyl amino acid amides in 1-octanol/water, a theoretical calculation was carried out using a solvation free energy density model. The hydrophobicity parameters of the molecules are obtained with the consideration of the solvation free energy over the solvent volume surrounding the solute, using a grid model. Our method can account for the solvent accessible surface area of the molecules according to conformational variations. Through a comparison of the hydrophobicity of our calculation and that of other experimental/theoretical works, the solvation free energy density model is proven to be a useful tool for the evaluation of the hydrophobicity of amino acids and peptides. In order to evaluate the solvation free energy density model as a method of calculating the activity of drugs using the hydrophobicity of its building blocks, the contracture of Bradykinin potentiating pentapeptide was also predicted from the hydrophobicity of each residue. The solvation free energy density model can be used to employ descriptors for the prediction of peptide activities in drug discovery, as well as to calculate the hydrophobicity of amino acids.

• Microbiology and Biotechnology Letters
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• v.47 no.2
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• pp.278-288
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• 2019

In the present investigation, we attempted to design a protocol to develop a hybrid protein with better bioremediation capacity. Using in silico approaches, a Hybrid Open Reading Frame (Hybrid ORF) is developed targeting the genes of microorganisms known for degradation of organophosphates. Out of 21 genes identified through BLAST search, 8 structurally similar genes (opdA, opd, opaA, pte RO, pdeA, parC, mpd and phnE) involved in biodegradation were screened. Gene conservational analysis categorizes these organophosphates degrading 8 genes into 4 super families i.e., Metallo-dependent hydrolases, Lactamase B, MPP and TM_PBP2 superfamily. Hybrid protein structure was modeled using multi-template homology modeling (3S07_A; 99%, 1P9E_A; 98%, 2ZO9_B; 33%, 2DXL_A; 33%) by $Schr{\ddot{o}}dinger$ software suit version 10.4.018. Structural verification of protein models was done using Ramachandran plot, it was showing 96.0% residue in the favored region, which was verified using RAMPAGE. The phosphotriesterase protein was showing the highest structural similarity with hybrid protein having raw score 984. The 5 binding sites of hybrid protein were identified through binding site prediction. The docking study shows that hybrid protein potentially interacts with 10 different organophosphates. The study results indicate that the hybrid protein designed has the capability of degrading a wide range of organophosphate compounds.

• Genomics & Informatics
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• v.3 no.1
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• pp.24-29
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• 2005

TRPV2 is a non-specific cation channel expressed in sensory neurons, and activated by noxious heat. Particularly, TRPV2 has six transmembrane domains and three ankyrin repeats. TRPV2 has been cloned from various species such as human, rat, and mouse. Oocytes of Xenopus laevis - an African clawed frog ­have been widely used for decades in characterization of various receptors and ion channels. The functional property of rat TRPV2 was also identified by this oocyte expression system. However, no TRPV2 orthologue of Xenopus laevis has been reported so far. Hence, we have focused to clone a TRPV2 orthologue of Xenopus laevis with the aid of bioinformatic tools. Because the genome sequence of Xenopus laevis is not available until now, a genome sequence of Xenopus tropicalis - a close relative species of Xenopus laevis - was used. After a number of bioinformatic searches in silico, a predicted full-length sequence of TRPV2 orthologue of Xenopus tropicalis was found. Based on this predicted sequence, various approaches such as RT-PCR and 5' -RACE technique were applied to clone a full length of Xenopus laevis TRV2. Consequently, a full-length Xenopus laevis TRPV2 was cloned from heart cDNA.

• Natural Product Sciences
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• v.25 no.3
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• pp.215-221
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• 2019

Inflammation is the crucial biological process of immune system which acts as body's defense and protective response against the injuries or infection. However, the systemic inflammation devotes the adverse effects such as multiple inflammation associated diseases. One of the best ways to treat this entity is by blocking the tumor necrosis factor alpha ($TNF-{\alpha}$) and TNF-related apoptosis-inducing ligand (TRAIL) to avoid the proinflammation cytokines production. Thus, this study aims to evaluate the potency of Sambucus bioactive compounds as anti-inflammation through in silico approach. In order to assess that, molecular docking was performed to evaluate the interaction properties between the $TNF-{\alpha}$ or TRAIL with the ligands. The 2D structure of ligands were retrieved online via PubChem and the 3D protein modeling was done by using SWISS Model. The prediction results of the study showed that caffeic acid (-6.4 kcal/mol) and homovanillic acid (-6.6 kcal/mol) have the greatest binding affinity against the $TNF-{\alpha}$ and TRAIL respectively. This evidence suggests that caffeic acid and homovanillic acid may potent as anti-inflammatory agent against the inflammation associated diseases. Finally, this study needs further examination and evaluation to validate the potency of Sambucus bioactive compounds.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

• Korean Journal of Plant Resources
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• v.27 no.1
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• pp.22-28
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• 2014

In this study, we conducted the anti-HIV-1 enzymes assay in vitro and its active components were predicted by QSAR in silico for the elucidation of action mechanism on anti-HIV of natural resources. The extracts of Gardenia jasminoides var. radicans Makino were tested for their inhibitory effects on the reverse transcriptase (RT), protease and ${\alpha}$-glucosidase. In the enzyme inhibition assay, the methanol extracts of Gardenia jasminoides var. radicans Makino stem showed a strong activity of 32.5% on the enzyme activity to cleave an oligopeptide, resembling one of the cleavage sites in the viral polyprotein which can only be processed by HIV-1 protease. Moreover the methanol extracts of stem exhibited alpha-glucosidase inhibitory activities of 26.1%. The methanol extracts ($100{\mu}g/ml$) of stem showed a weak activity of 13.4% on anti-HIV-1 RT using Enzyme Linked Oligonucleotide Sorbent Assay (ELOSA) method. However, all extracts of leaf and stem didn't exhibit the HIV-1-induced cytopathic effects with IC (inhibitory concentration) of $100{\mu}g/ml$ in HIV-1-infected human T-cell line. From these results, it is suggested that Gardenia jasminoides var. radicans Makino extracts may possibly be involved in the inhibition of reverse transcriptase, protease and alpha-glucosidase but can't vitally concerned with the viral replication in vitro.

• Genomics & Informatics
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• v.19 no.4
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• pp.44.1-44.9
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• 2021

Autism is a complex neurodevelopmental disorder, the prevalence of which has increased drastically in India in recent years. Neuroligin is a type I transmembrane protein that plays a crucial role in synaptogenesis. Alterations in synaptic genes are most commonly implicated in autism and other cognitive disorders. The present study investigated the neuroligin 3 gene in the Indian autistic population by sequencing and in silico pathogenicity prediction of molecular changes. In total, 108 clinically described individuals with autism were included from the North Karnataka region of India, along with 150 age-, sex-, and ethnicity-matched healthy controls. Genomic DNA was extracted from peripheral blood, and exonic regions were sequenced. The functional and structural effects of variants of the neuroligin 3 protein were predicted. One coding sequence variant (a missense variant) and four non-coding variants (two 5'-untranslated region [UTR] variants and two 3'-UTR variants) were recorded. The novel missense variant was found in 25% of the autistic population. The C/C genotype of c.551T>C was significantly more common in autistic children than in controls (p = 0.001), and a significantly increased risk of autism (24.7-fold) was associated with this genotype (p = 0.001). The missense variant showed pathogenic effects and high evolutionary conservation over the functions of the neuroligin 3 protein. In the present study, we reported a novel missense variant, V184A, which causes abnormal neuroligin 3 and was found with high frequency in the Indian autistic population. Therefore, neuroligin is a candidate gene for future molecular investigations and functional analysis in the Indian autistic population.

• Genomics & Informatics
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• v.20 no.2
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• pp.19.1-19.15
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• 2022

Alfalfa (Medicago sativa) is an important food and feed crop which rich in mineral sources. The WUSCHEL-related homeobox (WOX) gene family plays important roles in plant development and identification of putative gene families, their structure, and potential functions is a primary step for not only understanding the genetic mechanisms behind various biological process but also for genetic improvement. A variety of computational tools, including MAFFT, HMMER, hidden Markov models, Pfam, SMART, MEGA, ProtTest, BLASTn, and BRAD, among others, were used. We identified 34 MsWOX genes based on a systematic analysis of the alfalfa plant genome spread in eight chromosomes. This is an expansion of the gene family which we attribute to observed chromosomal duplications. Sequence alignment analysis revealed 61 conserved proteins containing a homeodomain. Phylogenetic study sung reveal five evolutionary clades with 15 motif distributions. Gene structure analysis reveals various exon, intron, and untranslated structures which are consistent in genes from similar clades. Functional analysis prediction of promoter regions reveals various transcription binding sites containing key growth, development, and stress-responsive transcription factor families such as MYB, ERF, AP2, and NAC which are spread across the genes. Most of the genes are predicted to be in the nucleus. Also, there are duplication events in some genes which explain the expansion of the family. The present research provides a clue on the potential roles of MsWOX family genes that will be useful for further understanding their functional roles in alfalfa plants.

• The Korean Journal of Physiology and Pharmacology
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• v.21 no.1
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• pp.107-115
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• 2017

Over the last decade, physiologically based pharmacokinetics (PBPK) application has been extended significantly not only to predicting preclinical/human PK but also to evaluating the drug-drug interaction (DDI) liability at the drug discovery or development stage. Herein, we describe a case study to illustrate the use of PBPK approach in predicting human PK as well as DDI using in silico, in vivo and in vitro derived parameters. This case was composed of five steps such as: simulation, verification, understanding of parameter sensitivity, optimization of the parameter and final evaluation. Caffeine and ciprofloxacin were used as tool compounds to demonstrate the "fit for purpose" application of PBPK modeling and simulation for this study. Compared to caffeine, the PBPK modeling for ciprofloxacin was challenging due to several factors including solubility, permeability, clearance and tissue distribution etc. Therefore, intensive parameter sensitivity analysis (PSA) was conducted to optimize the PBPK model for ciprofloxacin. Overall, the increase in $C_{max}$ of caffeine by ciprofloxacin was not significant. However, the increase in AUC was observed and was proportional to the administered dose of ciprofloxacin. The predicted DDI and PK results were comparable to observed clinical data published in the literatures. This approach would be helpful in identifying potential key factors that could lead to significant impact on PBPK modeling and simulation for challenging compounds.