Browsing by Author "Yavuz, Ahmet Sinan"

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Adaptive phenotypic modulations lead to therapy resistance in chronic myeloid leukemia cells
(PUBLIC LIBRARY SCIENCE, 2020-01-01) Baykal-Kose, Seda; Acikgoz, Eda; Yavuz, Ahmet Sinan; Geyik, Oyku Gonul; Ate, Halil; Sezerman, Osman Ugur; Ozsan, Guner Hayri; Yuce, Zeynep
Tyrosine kinase inhibitor (TKI) resistance is a major problem in chronic myeloid leukemia (CML). We generated a TKI-resistant K562 sub-population, K562-IR, under selective imatinib-mesylate pressure. K562-IR cells are CD34(-)/CD38(-), BCR-Abl-independent, proliferate slowly, highly adherent and form intact tumor spheroids. Loss of CD45 and other hematopoietic markers reveal these cells have diverged from their hematopoietic origin. CD34 negativity, high expression of E-cadherin and CD44
Multiplex-PCR-Based Screening and Computational Modeling of Virulence Factors and T-Cell Mediated Immunity in Helicobacter pylori Infections for Accurate Clinical Diagnosis
(PUBLIC LIBRARY SCIENCE, 2015-01-01) Oktem-Okullu, Sinem; Tiftikci, Arzu; Saruc, Murat; Cicek, Bahattin; Vardareli, Eser; Tozun, Nurdan; Kocagoz, Tanil; Sezerman, Ugur; Yavuz, Ahmet Sinan; Sayi-Yazgan, Ayca
The outcome of H. pylori infection is closely related with bacteria's virulence factors and host immune response. The association between T cells and H. pylori infection has been identified, but the effects of the nine major H. pylori specific virulence factors
Prediction of neddylation sites from protein sequences and sequence-derived properties
(BMC, 2015-01-01) Yavuz, Ahmet Sinan; Sozer, Namik Berk; Sezerman, Osman Ugur
Background: Neddylation is a reversible post-translational modification that plays a vital role in maintaining cellular machinery. It is shown to affect localization, binding partners and structure of target proteins. Disruption of protein neddylation was observed in various diseases such as Alzheimer's and cancer. Therefore, understanding the neddylation mechanism and determining neddylation targets possibly bears a huge importance in further understanding the cellular processes. This study is the first attempt to predict neddylated sites from protein sequences by using several sequence and sequence-based structural features. Results: We have developed a neddylation site prediction method using a support vector machine based on various sequence properties, position-specific scoring matrices, and disorder. Using 21 amino acid long lysinecentred windows, our model was able to predict neddylation sites successfully, with an average 5-fold stratified cross validation performance of 0.91, 0.91, 0.75, 0.44, 0.95 for accuracy, specificity, sensitivity, Matthew's correlation coefficient and area under curve, respectively. Independent test set results validated the robustness of reported new method. Additionally, we observed that neddylation sites are commonly flexible and there is a significant positively charged amino acid presence in neddylation sites. Conclusions: In this study, a neddylation site prediction method was developed for the first time in literature. Common characteristics of neddylation sites and their discriminative properties were explored for further in silico studies on neddylation. Lastly, up-to-date neddylation dataset was provided for researchers working on post-translational modifications in the accompanying supplementary material of this article.