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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3

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    Artificial intelligence based methods for hot spot prediction
    (Current Biology Ltd, 2022) N/A; N/A; N/A; N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Department of Chemical and Biological Engineering; Övek, Damla; Abalı, Zeynep; Zeylan, Melisa Ece; Keskin, Özlem; Gürsoy, Attila; Tunçbağ, Nurcan; PhD Student; PhD Student; PhD Student; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; 26605; 8745; 245513
    Proteins interact through their interfaces to fulfill essential functions in the cell. They bind to their partners in a highly specific manner and form complexes that have a profound effect on understanding the biological pathways they are involved in. Any abnormal interactions may cause diseases. Therefore, the identification of small molecules which modulate protein interactions through their interfaces has high thera-peutic potential. However, discovering such molecules is challenging. Most protein-protein binding affinity is attributed to a small set of amino acids found in protein interfaces known as hot spots. Recent studies demonstrate that drug-like small molecules specifically may bind to hot spots. Therefore, hot spot prediction is crucial. As experimental data accumulates, artificial intelligence begins to be used for computational hot spot prediction. First, we review machine learning and deep learning for computational hot spot prediction and then explain the significance of hot spots toward drug design.
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    Conformational diversity and protein-protein interfaces in drug repurposing in ras signaling pathway
    (Nature Portfolio, 2024) Department of Computer Engineering;Department of Chemical and Biological Engineering; Sayın, Ahenk Zeynep; Abalı, Zeynep; Şenyüz, Simge; Cankara, Fatma; Gürsoy, Attila; Keskin, Özlem; Graduate School of Sciences and Engineering; College of Engineering
    We focus on drug repurposing in the Ras signaling pathway, considering structural similarities of protein-protein interfaces. The interfaces formed by physically interacting proteins are found from PDB if available and via PRISM (PRotein Interaction by Structural Matching) otherwise. The structural coverage of these interactions has been increased from 21 to 92% using PRISM. Multiple conformations of each protein are used to include protein dynamics and diversity. Next, we find FDA-approved drugs bound to structurally similar protein-protein interfaces. The results suggest that HIV protease inhibitors tipranavir, indinavir, and saquinavir may bind to EGFR and ERBB3/HER3 interface. Tipranavir and indinavir may also bind to EGFR and ERBB2/HER2 interface. Additionally, a drug used in Alzheimer's disease can bind to RAF1 and BRAF interface. Hence, we propose a methodology to find drugs to be potentially used for cancer using a dataset of structurally similar protein-protein interface clusters rather than pockets in a systematic way.
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    PPInterface: a comprehensive dataset of 3D protein-protein interface structures
    (Academic Press, 2024) Department of Computer Engineering;Department of Chemical and Biological Engineering; Abalı, Zeynep; Aydın, Zeynep; Khokkar, Moaaz Ur-Rehman; Gürsoy, Attila; Keskin, Özlem; Graduate School of Sciences and Engineering; College of Engineering
    The PPInterface dataset contains 815,082 interface structures, providing the most comprehensive structural information on protein–protein interfaces. This resource is extracted from over 215,000 three-dimensional protein structures stored in the Protein Data Bank (PDB). The dataset contains a wide range of protein complexes, providing a wealth of information for researchers investigating the structural properties of protein–protein interactions. The accompanying web server has a user-friendly interface that allows for efficient search and download functions. Researchers can access detailed information on protein interface structures, visualize them, and explore a variety of features, increasing the dataset's utility and accessibility.