Researcher:
Abalı, Zeynep

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PhD Student

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Zeynep

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Abalı

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Abalı, Zeynep

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2021 - 20222

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Researcher Of Publications: search.filters.isAuthorOfPublication.677502f7-9931-4dae-a165-e2a27ae5e36c

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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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    PublicationOpen Access
    Web interface for 3D visualization and analysis of SARS-CoV-2-human mimicry and interactions
    (Oxford University Press (OUP), 2021) Tsai, Chung-Jung; Nussinov, Ruth; Department of Chemical and Biological Engineering; Department of Computer Engineering; Keskin, Özlem; Gürsoy, Attila; Övek, Damla; Taweel, Ameer; Abalı, Zeynep; Tezsezen, Ece; Köroğlu, Yunus Emre; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26605; 8745; N/A; N/A; N/A; N/A; N/A
    Summary: we present a web-based server for navigating and visualizing possible interactions between SARS-CoV-2 and human host proteins. The interactions are obtained from HMI_Pred which relies on the rationale that virus proteins mimic host proteins. The structural alignment of the viral protein with one side of the human protein-protein interface determines the mimicry. The mimicked human proteins and predicted interactions, and the binding sites are presented. The user can choose one of the 18 SARS-CoV-2 protein structures and visualize the potential 3D complexes it forms with human proteins. The mimicked interface is also provided. The user can superimpose two interacting human proteins in order to see whether they bind to the same site or different sites on the viral protein. The server also tabulates all available mimicked interactions together with their match scores and number of aligned residues. This is the first server listing and cataloging all interactions between SARS-CoV-2 and human protein structures, enabled by our innovative interface mimicry strategy.