PubMed İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397

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Now showing 1 - 4 of 4
  • Article
    Citation - WoS: 7
    Citation - Scopus: 7
    In Silico Analyses and Global Transcriptional Profiling Reveal Novel Putative Targets for Pea3 Transcription Factor Related to Its Function in Neurons
    (Public Library Science, 2017-02-03) Kandemir, Basak; Dag, Ugur; Gungor, Burcu Bakir; Durasi, Ilknur Melis; Erdogan, Burcu; Sahin, Eray; Kurnaz, Isil Aksan
    Pea3 transcription factor belongs to the PEA3 subfamily within the ETS domain transcription factor superfamily, and has been largely studied in relation to its role in breast cancer metastasis. Nonetheless, Pea3 plays a role not only in breast tumor, but also in other tissues with branching morphogenesis, including kidneys, blood vasculature, bronchi and the developing nervous system. Identification of Pea3 target promoters in these systems are important for a thorough understanding of how Pea3 functions. Present study particularly focuses on the identification of novel neuronal targets of Pea3 in a combinatorial approach, through curation, computational analysis and microarray studies in a neuronal model system, SH-SY5Y neuroblastoma cells. We not only show that quite a number of genes in cancer, immune system and cell cycle pathways, among many others, are either up- or down-regulated by Pea3, but also identify novel targets including ephrins and ephrin receptors, semaphorins, cell adhesion molecules, as well as metalloproteases such as kallikreins, to be among potential target promoters in neuronal systems. Our overall results indicate that rather than early stages of neurite extension and axonal guidance, Pea3 is more involved in target identification and synaptic maturation.
  • Article
    Citation - WoS: 4
    Citation - Scopus: 4
    Elucidating the Complex Membrane Binding of a Protein With Multiple Anchoring Domains Using extHMMM
    (Public Library Science, 2024-07-08) Madsen, Jesper J.; Ohkubo, Y. Zenmei
    Membrane binding is a crucial mechanism for many proteins, but understanding the specific interactions between proteins and membranes remains a challenging endeavor. Coagulation factor Va (FVa) is a large protein whose membrane interactions are complicated due to the presence of multiple anchoring domains that individually can bind to lipid membranes. Using molecular dynamics simulations, we investigate the membrane binding of FVa and identify the key mechanisms that govern its interaction with membranes. Our results reveal that FVa can either adopt an upright or a tilted molecular orientation upon membrane binding. We further find that the domain organization of FVa deviates (sometimes significantly) from its crystallographic reference structure, and that the molecular orientation of the protein matches with domain reorganization to align the C2 domain toward its favored membrane-normal orientation. We identify specific amino acid residues that exhibit contact preference with phosphatidylserine lipids over phosphatidylcholine lipids, and we observe that mostly electrostatic effects contribute to this preference. The observed lipid-binding process and characteristics, specific to FVa or common among other membrane proteins, in concert with domain reorganization and molecular tilt, elucidate the complex membrane binding dynamics of FVa and provide important insights into the molecular mechanisms of protein-membrane interactions. An updated version of the HMMM model, termed extHMMM, is successfully employed for efficiently observing membrane bindings of systems containing the whole FVa molecule. Understanding the intricacies of protein-membrane interaction is essential for fleshing out the functional roles of membrane proteins. Substantial evidence indicates that the binding of some proteins, such as coagulation factor Va, proceeds in a multi-step manner requiring some form of rearrangements within the structure and dynamics of the protein, as well as in the protein's orientation relative to the membrane surface. In the case of factor Va, the intricate binding process can be attributed to the presence of multiple membrane-anchoring domains. In order to feasibly study the dynamics of these mechanisms, we employ an enhanced membrane-mimetic model, HMMM, capable of capturing such processes and rearrangements and making new discoveries possible using the molecular dynamics technique. The successful execution of these studies demanded several modifications to the original implementation of the HMMM. Our exploration not only improves our understanding of FVa's membrane binding dynamics but also contributes to the broader molecular mechanisms governing protein-membrane interactions.
  • Article
    Citation - WoS: 6
    Citation - Scopus: 7
    BLSTM Based Night-Time Wildfire Detection From Video
    (Public Library Science, 2022-06-03) Agirman, Ahmet K.; Tasdemir, Kasim
    Distinguishing fire from non-fire objects in night videos is problematic if only spatial features are to be used. Those features are highly disrupted under low-lit environments because of several factors, such as the dynamic range limitations of the cameras. This makes the analysis of temporal behavior of night-time fire indispensable for classification. To this end, a BLSTM based night-time wildfire event detection from a video algorithm is proposed. It is shown in the experiments that the proposed algorithm attains 95.15% of accuracy when tested against a wide variety of actual recordings of night-time wildfire incidents and 23.7 ms per frame detection time. Moreover, to pave the way for more targeted solutions to this challenging problem, experiment-based thorough investigations of possible sources of incorrect predictions and discussion of the unique nature of night-time wildfire videos are presented in the paper.
  • Article
    3Mont: A Multi-Omics Integrative Tool for Breast Cancer Subtype Stratification
    (Public Library Science, 2025-06-27) Unlu Yazici, Miray; Marron, J. S.; Bakir-Gungor, Burcu; Zou, Fei; Yousef, Malik; Yazici, Miray Unlu
    Breast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategies that address diverse types of BRCA is crucial. Notably, among different BRCA molecular sub-types, Hormone Receptor negative (HR-) BRCA cases, especially Basal-like BRCA sub-types, lack estrogen and progesterone hormone receptors and they exhibit a higher tumor growth rate compared to HR+ cases. Improving survival time and predicting prognosis for distinct molecular profiles is substantial. In this study, we propose a novel approach called 3-Multi-Omics Network and Integration Tool (3Mont), which integrates various -omics data by applying a grouping function, detecting pro-groups, and assigning scores to each pro-group using Feature importance scoring (FIS) component. Following that, machine learning (ML) models are constructed based on the prominent pro-groups, which enable the extraction of promising biomarkers for distinguishing BRCA sub-types. Our tool allows users to analyze the collective behavior of features in each pro-group (biological groups) utilizing ML algorithms. In addition, by constructing the pro-groups and equalizing the feature numbers in each pro-group using the FIS component, this process achieves a significant 20% speedup over the 3Mint tool. Contrary to conventional methods, 3Mont generates networks that illustrate the interplay of the prominent biomarkers of different -omics data. Accordingly, exploring the concerted actions of features in pro-groups facilitates understanding the dynamics of the biomarkers within the generated networks and developing effective strategies for better cancer sub-type stratification. The 3Mont tool, along with all supporting materials, can be found at https://github.com/malikyousef/3Mont.git.