Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Article
    Citation - Scopus: 6
    Functional Characterization of a Novel Cyp119 Variant To Explore Its Biocatalytic Potential
    (John Wiley and Sons Inc, 2022) Sürmeli, Nur Başak; Surmeli, N.B.; 01. Izmir Institute of Technology; 03.01. Department of Bioengineering; 03. Faculty of Engineering
    Biocatalysts are increasingly applied in the pharmaceutical and chemical industry. Cytochrome P450 enzymes (P450s) are valuable biocatalysts due to their ability to hydroxylate unactivated carbon atoms using molecular oxygen. P450s catalyze reactions using nicotinamide adenine dinucleotide phosphate (NAD(P)H) cofactor and electron transfer proteins. Alternatively, P450s can utilize hydrogen peroxide (H2O2) as an oxidant, but this pathway is inefficient. P450s that show higher efficiency with peroxides are sought after in industrial applications. P450s from thermophilic organisms have more potential applications as they are stable toward high temperature, high and low pH, and organic solvents. CYP119 is an acidothermophilic P450 from Sulfolobus acidocaldarius. In our previous study, a novel T213R/T214I (double mutant [DM]) variant of CYP119 was obtained by screening a mutant library for higher peroxidation activity utilizing H2O2. Here, we characterized the substrate scope; stability toward peroxides; and temperature and organic solvent tolerance of DM CYP119 to identify its potential as an industrial biocatalyst. DM CYP119 displayed higher stability than wild-type (WT) CYP119 toward organic peroxides. It shows higher peroxidation activity for non-natural substrates and higher affinity for progesterone and other bioactive potential substrates compared to WT CYP119. DM CYP119 emerges as a new biocatalyst with a wide range of potential applications in the pharmaceutical and chemical industry. © 2021 International Union of Biochemistry and Molecular Biology, Inc.
  • Article
    Citation - Scopus: 4
    Quasi-Supervised Strategies for Compound-Protein Interaction Prediction
    (John Wiley and Sons Inc, 2022) Çakı, O.; Karaçalı, Bilge; Karaçalı, B.; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    In-silico compound-protein interaction prediction addresses prioritization of drug candidates for experimental biochemical validation because the wet-lab experiments are time-consuming, laborious and costly. Most machine learning methods proposed to that end approach this problem with supervised learning strategies in which known interactions are labeled as positive and the rest are labeled as negative. However, treating all unknown interactions as negative instances may lead to inaccuracies in real practice since some of the unknown interactions are bound to be positive interactions waiting to be identified as such. In this study, we propose to address this problem using the Quasi-Supervised Learning (QSL) algorithm. In this framework, potential interactions are predicted by estimating the overlap between a true positive dataset of compound-protein pairs with known interactions and an unknown dataset of all the remaining compound-protein pairs. The potential interactions are then identified as those in the unknown dataset that overlap with the interacting pairs in the true positive dataset in terms of the associated similarity structure. We also address the class-imbalance problem by modifying the conventional cost function of the QSL algorithm. Experimental results on GPCR and Nuclear Receptor datasets show that the proposed method can identify actual interactions from all possible combinations. © 2021 Wiley-VCH GmbH.