Gozde Yalcin

This study aimed to elucidate the chemical composition of Origanum minutiflorum essential oil (OmEO) and evaluate its antioxidant and antibacterial activities, while also investigating, for the first time, the antibacterial potential of its major volatile constituents using in silico approaches, including ADME profiling and molecular docking, together with a genotoxicity assessment. Thirteen constituents, accounting for 99.10% of the total oil composition, were identified, with p ‐cymene (36.7%), γ ‐clausenane (31.1%), and borneol (10.6%) as the major components. OmEO exhibited moderate antioxidant activity, with IC ₅₀ values of 1.0864 ± 0.0296 mg/mL in the DPPH• assay and 0.0055 ± 0.0002 mg/mL in the ABTS• ⁺ assay, as well as a TEAC value of 745.56 ± 1.47 µM. The total phenolic content was 558.57 ± 1.43 µg/mL GAE. OmEO exhibited dose‐dependent antibacterial activity against both Gram‐positive and Gram‐negative bacteria, with a MIC of 50 µg/mL for all tested strains except Enterobacter aerogenes , for which the MIC was 100 µg/mL. In the genotoxicity assessment, the oil inhibited stem elongation more strongly than root growth. In silico analyses identified borneol and γ ‐clausenane as promising candidates for further investigation of their antibacterial potential.

Black tea quality is governed by aroma chemistry: terpene alcohols (linalool, geraniol, nerolidol), methyl salicylate, and short-chain aldehydes whose abundance and release kinetics from the polyphenol-rich leaf matrix shape perceived grade. Grade information lies not only in the average headspace concentration but in the temporal shape of volatile organic compound (VOC) release under controlled heating. Conventional electronic noses obscure this signal: they rely on multi-sensor arrays, compress each response into summary statistics, and report accuracy only at the level of individual measurements. Whether a single low-cost metal–oxide–semiconductor (MOS) gas sensor can recover grade-defining aroma chemistry, and whether waveform-level modeling can exploit it, was therefore investigated. A portable electronic nose built around a Bosch BME688 sensor recorded 90 time series, each comprising four directly measured channels (temperature, humidity, pressure, gas sensor resistance) and a derived indoor-air-quality (IAQ) proxy computed from them by the on-chip BSEC library, from 16 commercial Turkish black teas across three quality grades. Two representations were compared on the same data: a feature-based pipeline reducing 25 statistical descriptors to seven principal components for six classifiers (best F1-macro = 0.624, MLP), and a raw-waveform Multi-Scale 1D-CNN with Squeeze–Excitation and temporal self-attention (MS-CNN-Attention). Under product-grouped cross-validation, the deep model reached F1-macro = 0.811 (+30%) and graded 14 of 16 products correctly by majority vote, against 11 of 16 for the MLP, with the largest gain in the medium grade (F1: 0.52 → 0.79), where summary-statistic compression destroys the release-kinetic signal. The contributions are threefold: one programmable MOS sensor operated as a thermal-desorption profiler rather than a sensor array; a direct comparison of feature-based classical learning against raw-waveform deep learning on the same small, non-normally distributed dataset; and a product-level decision-consistency metric suited to batch screening. Pairing a low-cost MOS sensor with waveform-level modeling offers a rapid, non-destructive route to aroma-chemistry-based tea quality screening.

Objective: In this study, it was aimed to determine an FDA-approved molecule that inhibits the IL-17 receptor, which is an important target for the prevention of inflammation in Ankylosing Spondylitis (AS), using the drug repositioning approach. Material and Method: Using the Drug-Gene Interaction database, 18 molecules specific to the active HLA-B gene were identified in AS. Then, the 3D structure of IL-17 was obtained from the RSCB database. I) Blind docking II) Computed Atlas of Surface Topography of Proteins web tool was used to determine the binding package. The interaction between the known inhibitor of IL-17, rhodomyrtone, and IL-17, was determined by molecular docking using grid boxes around the determined binding packages. Accordingly, configuration files were prepared with the selected grid box features, and docking was performed for 18 molecules with the AutoDock Vina program. Result and Discussion: The carbamazepine molecule shows the best binding affinity and binding profile with IL-17. It was also revealed that minocycline, sulfasalazine, and thalidomide are tightly packed in the active site. It has been demonstrated that these molecules may be lead molecules for the treatment of AS disease.

The BRPF1 protein is encoded by the BRPF1 gene. In addition, the BRPF1 gene is known to be upregulated in leukaemia. Recent studies have shown that it is also overexpressed in hepatocellular carcinoma (HCC) as well. Therefore, BRPF1 is a significant target for anti-cancer drug development studies, especially on HCC. 40 terpenoids and flavonoids were chosen because of their anticancer properties given in the literature. In this study, the biological activity of molecules was also investigated with in silico structure-activity relationship analysis. In addition, interactions between a series of terpenoids and flavonoids and the BRPF1 protein were investigated by molecular docking and molecular dynamics simulations. The energy change caused by the interactions of BRPF1 with different compounds was also evaluated by MM/PBSA calculations. It has been revealed that compound 5 (−9.2 kcal/mol), a kind of secoclerodane type diterpenoid, has a higher affinity both compared to other flavonoids and terpenoids, and 9F9 (−7.9 kcal/mol), a selective BRPF1 inhibitor. The study presented in this article demonstrates that compound 5, as a natural product, could form a chemical scaffold for the development of selective BRPF1 bromodomain inhibitors.

Background: Medicinal plant oils are used in the treatment of various human diseases due to their phytochemical components. Recently, enzyme inhibition studies have been increasing in cosmetics, the food industry, and especially pharmaceuticals.Objective: The main goal of this study is to focus on a specific interaction between the essential oil components of Mentha longifolia ssp. longifolia and carbonic anhydrase (CA) enzyme in vitro and in silico.Methods: The chemical composition of the essential oil was identified by gas chromatography coupled with mass spectrometry (GC-MS). The CA inhibitory activity of M. longifolia essential oil was investigated by using esterase activity for the first time in this study. Molecular docking was performed separately for two different CA isoforms (CA-II and CA-IX).Results: Among fourteen components identified, piperitone (27.14%), 2-acetylcyclopentanone (21.05%), p-menthan-3-one (13.90%), menthan (6.60%), and piperitone oxide (6.52%) were defined as the major compounds. The essential oil showed remarkable inhibitory activity against CA with an IC50 value of 0.010 mg/mL. According to the molecular docking analysis, caryophyllene oxide (-6.5 kcal/mol for CAIX isoform, -6.8 kcal/mol for CA-II isoform) and trans-caryophyllene (-6.3 kcal/mol for CA-IX isoform, - 6.7 kcal/mol for CA-II isoform) molecules showed the best inhibitory activity in two different CA isoforms. In this study, it was determined that all molecules are bioavailable by ADMET analyses.Conclusion: The results of this study are valuable for the development of natural and new CA enzyme inhibitors without side effects in the treatment of diseases, such as glaucoma, obesity, and epilepsy.

Hesaplamalı ilaç tasarımı; ilaç geliştirme prosesini hızlandırmakla birlikte maliyeti de düşürerek tıp mühendisliğinin önde gelen alanlarından birine dönüşmüştür. MS hastalığı ölümle sonuçlanmasının yanı sıra, hastalarda yaşam kalitesini düşürmesi nedeniyle de ilaç geliştirme çalışmaları için büyük bir hedef olmayı sürdürmektedir. Sfingosin-1-fosfat reseptörü 1 (S1P1) bir G protein kapılı reseptördür ve MS hastalığının semptomlarının ekspresyonuna ve ilerlemesine yol açan önemli bir mekanizmada etkindir. Bu durum S1P1’i ilaç geliştirme çalışmaları için önemli bir hedef haline getirmektedir. Literatürde S1P1 modülasyonu hakkında ön klinik ve klinik çalışmalar tespit edilmiş olmasına rağmen seçiciliği yüksek modülatörlere rastlanmamıştır. Çalışmamız kapsamında BindingDB veri tabanı vasıtasıyla ulaşılan S1P1 modülatörlerine PharmaGist Web sunucusu vasıtasıyla farmakofor modelleme çalışmasına uygulanmıştır. Bu yöntem ligandların esnek bir biçimde üst üste çakıştırılması prosesi temeline dayanmaktadır. Ardından Autodock Vina programıyla moleküler kenetleme işlemi gerçekleştirilmiş, sonuçlar literatürdeki S1P1 antagonisti ile kıyaslanmıştır. PharmaGist'den alınan en iyi farmakofor modellerine göre ZINCPharmer veri tabanı üzerinden 80 molekül elde edilmiş bu moleküllere in siliko ADME/Toksikoloji işlemi uygulanmıştır. ADME/toksikoloji incelemesi ile elde edilen 4 molekül ZINC00390492, ZINC67740009, ZINC19847253 ve ZINC19847241’dir. Bütün moleküllerin bağlanma profili literatüre ve ML5 antagonistine benzer olarak belirlenmiştir ancak özellikle ZINC00390492 molekülünün bağlanma afinitesi (-8.6 kcal/mol) ML5 antagonistinin bağlanma afinitesinden (-8.4 kcal/mol) düşük bulunmuştur. Sonuç olarak bu çalışma ile ZINC00390492 molekülünün MS Hastalığının tedavisi için önder bir bileşik olabileceği hesaplamalı çalışmalar ile ortaya konulmuştur.

Background: Alzheimer's disease affects a large part of the world’s population by prolonging the human life span and becoming an economic burden in the health system. Therefore, its treatment becomes more and more important every day. With the insufficiency of existing drug molecules, new drug targets are being searched. The most important of these is the Glycogen Synthase Kinase 3β enzyme, which is thought to be of key importance in Tau hyperphosphorylation and Amyloid β accumulation mechanisms. Objective: In this research, computational studies were conducted to develop a new GSK3β enzyme inhibitor. Method: Leading compounds suitable for pharmacophore models obtained by the 3D QSAR method were scanned in databases. In silico ADME/Tox analyses were performed on the obtained molecules. Results: Although the three molecules (ENA99104, CNR13756, TIM405938) had strong Dock Scores (42.869, 53.344, and 41.119, respectively) in molecular docking calculations, only the CNR13756 molecule was found successful according to molecular dynamics simulations. Conclusion: All computational studies have revealed that the CNR13756 molecule can exhibit a therapeutic scaffold property, thus obtaining a selective GSK3β inhibitor with minimal side effects.

The increase in the drug-resistant strains of Mycobacterium tuberculosis has led researchers to new drug targets. The development of new compounds that have effective inhibitory properties with the selective vital structure of Mycobacterium tuberculosis is required in new scientific approaches. The most important of these approaches is the development of inhibitor molecules for Mycobacterium cell wall targets. In this study, first of all, the antitubercular activity of 23 benzimidazole derivatives was experimentally determined. And then molecular docking studies were carried out with 4 different targets: Arabinosyltransferase C (EmbC), Filamentous Temperature Sensitive Mutant Z (FtsZ), Protein Tyrosine Phosphatase B (PtpB), and Decaprenylphosphoryl-β-D-ribose-2′-oxidase (DprE1). It has been determined that benzimidazole derivatives show activity through the DprE1 enzyme. It is known that DprE1, which has an important role in the synthesis of the cell envelope from Arabinogalactan, is also effective in the formation of drug resistance. Due to this feature, the DprE1 enzyme has become an important target for drug development studies. Also, it was chosen as a target for this study. This study aims to identify molecules that inhibit DprE1 for the development of more potent and selective antitubercular drugs. For this purpose, molecular docking studies by AutoDock Vina, and CDOCKER and molecular dynamics (MD) simulations and in silico ADME/Tox analysis were implemented for 23 molecules. The molecules exhibited binding affinity values of less than −8.0 kcal/mol. After determining the compound’s anti-TB activities by a screening test, the best-docked results were detected using compounds 20, 21, and 30. It was found that 21, was the best molecule with its binding affinity value, which was supported by MD simulations and in silico ADME modeling results. Communicated by Ramaswamy H. Sarma.