WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 22Citation - Scopus: 28Prediction of Linear Cationic Antimicrobial Peptides Active Against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models(MDPI, 2022-04-03) Soylemez, Ummu Gulsum; Yousef, Malik; Kesmen, Zulal; Buyukkiraz, Mine Erdem; Bakir-Gungor, BurcuAntimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in vitro tests. In this study, we focused on the linear cationic peptides with non-hemolytic activity, which are downloaded from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). Referring to the MIC (Minimum inhibition concentration) values, we have assigned a positive label to a peptide if it shows antimicrobial activity; otherwise, the peptide is labeled as negative. Here, we focused on the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately, and we created two datasets accordingly. Ten different physico-chemical properties of the peptides are calculated and used as features in our study. Following data exploration and data preprocessing steps, a variety of classification algorithms are used with 100-fold Monte Carlo Cross-Validation to build models and to predict the antimicrobial activity of the peptides. Among the generated models, Random Forest has resulted in the best performance metrics for both Gram-negative dataset (Accuracy: 0.98, Recall: 0.99, Specificity: 0.97, Precision: 0.97, AUC: 0.99, F1: 0.98) and Gram-positive dataset (Accuracy: 0.95, Recall: 0.95, Specificity: 0.95, Precision: 0.90, AUC: 0.97, F1: 0.92) after outlier elimination is applied. This prediction approach might be useful to evaluate the antibacterial potential of a candidate peptide sequence before moving to the experimental studies.Article Citation - WoS: 15Citation - Scopus: 16Natural Molecule-Incorporated Magnetic Organic-Inorganic Nanoflower: Investigation of Its Dual Fenton Reaction-Dependent Enzyme-Like Catalytic Activities With Cyclic Use(Wiley-VCH Verlag GmbH, 2023-04-03) Dadi, Seyma; Cardoso, Marlon Henrique; Mandal, Amit Kumar; Franco, Octavio Luiz; Ildiz, Nilay; Ocsoy, IsmailThe functional organic-inorganic hybrid nanoflowers (hNFs) have recently attracted considerable attention due to enhanced catalytic activity and stability. The main purpose of this study is to synthesize new Fenton reagents and investigate their catalytic activity, dye degradation performance and antimicrobial activity. This magnetic gallic acid nanoflowers (FeGANF) were self-assembled via incorporating magnetic nanoparticles (Fe3O4 NPs) into gallic acid (GA) as organic part and copper(II) phosphate (Cu-3(PO4)(2)) as inorganic parts. The FeGANF were characterized by SEM, EDX, FT-IR and XRD. The peroxidase-like activity and dye degradation performance of FeGANF and GANF based on Fenton reaction in the presence of H2O2 was studied toward guaiacol as substrate, using methylene blue (MB) and congo red (CR) as a cationic and anionic dyes, respectively. FeGANF shows much high catalytic activity and decoloration efficiency (97 % for MB and 99 % for CR) because of dual active center in Fenton reaction on the surface of FeGANF. FeGANF exhibited more antimicrobial activity against Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 25923, and Candida albicans ATCC 10231 than that of the GA and GANF. The results of these studies suggest that magnetic hNFs has proved to be promising Fenton reagents for biological and environmental applications including treatment of wastewater.