Researcher:
Kuzu, Güray

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PhD Student

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Güray

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Kuzu

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Kuzu, Güray

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2012 - 20188

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Researcher Of Publications: search.filters.isAuthorOfPublication.6d98ae0e-4a96-49f3-93fd-9ee4fbda1581

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Now showing 1 - 8 of 8
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    Expanding the conformational selection paradigm in protein-ligand docking
    (Humana Press Inc, 2012) Nussinov, Ruth; N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Kuzu, Güray; Keskin, Özlem; Gürsoy, Attila; PhD Student; Faculty Member; Faculty Member; N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 26605; 8745
    Conformational selection emerges as a theme in macromolecular interactions. Data validate it as a prevailing mechanism in protein-protein, protein-DNA, protein-RNA, and protein-small molecule drug recognition. This raises the question of whether this fundamental biomolecular binding mechanism can be used to improve drug docking and discovery. Actually, in practice this has already been taking place for some years in increasing numbers. Essentially, it argues for using not a single conformer, but an ensemble. The paradigm of conformational selection holds that because the ensemble is heterogeneous, within it there will be states whose conformation matches that of the ligand. Even if the population of this state is low, since it is favorable for binding the ligand, it will bind to it with a subsequent population shift toward this conformer. Here we suggest expanding it by first modeling all protein interactions in the cell by using Prism, an efficient motif-based protein-protein interaction modeling strategy, followed by ensemble generation. Such a strategy could be particularly useful for signaling proteins, which are major targets in drug discovery and bind multiple partners through a shared binding site, each with some-minor or major-conformational change.
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    Exploiting conformational ensembles in modeling protein-protein interactions on the proteome scale
    (American Chemical Society (ACS), 2013) Nussinov, Ruth; Department of Chemical and Biological Engineering; Department of Computer Engineering; N/A; Keskin, Özlem; Gürsoy, Attila; Kuzu, Güray; Faculty Member; Faculty Member; PhD Student; Department of Chemical and Biological Engineering; Department of Computer Engineering; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26605; 8745; N/A
    Cellular functions are performed through protein-protein interactions; therefore, identification of these interactions is crucial for understanding biological processes. Recent studies suggest that knowledge-based approaches are more useful than "blind" docking for modeling at large scales. However, a caveat of knowledge-based approaches is that they treat molecules as rigid structures. The Protein Data Bank (PDB) offers a wealth of conformations. Here, we exploited an ensemble of the conformations in predictions by a knowledge-based method, PRISM. We tested "difficult" cases in a docking-benchmark data set, where the unbound and bound protein forms are structurally different. Considering alternative conformations for each protein, the percentage of successfully predicted interactions increased from similar to 26 to 66%, and 57% of the interactions were successfully predicted in an "unbiased" scenario, in which data related to the bound forms were not utilized. If the appropriate conformation, or relevant template interface, is unavailable in the PDB, PRISM could not predict the interaction successfully. The pace of the growth of the PDB promises a rapid increase of ensemble conformations emphasizing the merit of such knowledge-based ensemble strategies for higher success rates in protein-protein interaction predictions on an interactome scale. We constructed the structural network of ERK interacting proteins as a case study.
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    Constructing structural networks of signaling pathways on the proteome scale
    (Current Biology Ltd, 2012) Nussinov, Ruth; Department of Chemical and Biological Engineering; N/A; Department of Computer Engineering; Keskin, Özlem; Kuzu, Güray; Gürsoy, Attila; Faculty Member; PhD Student; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; 26605; N/A; 8745
    Proteins function through their interactions, and the availability of protein interaction networks could help in understanding cellular processes. However, the known structural data are limited and the classical network node-and-edge representation, where proteins are nodes and interactions are edges, shows only which proteins interact; not how they interact. Structural networks provide this information. Protein-protein interface structures can also indicate which binding partners can interact simultaneously and which are competitive, and can help forecasting potentially harmful drug side effects. Here, we use a powerful protein-protein interactions prediction tool which is able to carry out accurate predictions on the proteome scale to construct the structural network of the extracellular signal-regulated kinases (ERK) in the mitogen-activated protein kinase (MAPK) signaling pathway. This knowledge-based method, PRISM, is motif-based, and is combined with flexible refinement and energy scoring. PRISM predicts protein interactions based on structural and evolutionary similarity to known protein interfaces.
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    Protein-protein interfaces integrated into interaction networks: implications on drug design
    (Bentham Science Publ Ltd, 2012) N/A; N/A; N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Makinacı, Gözde Kar; Kuzu, Güray; Keskin, Özlem; Gürsoy, Attila; PhD Student; PhD Student; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; 26605; 8745
    The growing perception that diseases are often consequences of multiple molecular abnormalities rather than being the result of a single defect highlights the importance of network-centric view in therapeutic approaches. Protein interaction networks may contribute to understanding of disease, assist in drug design and discovery. Here, we review some recent advances in disease-associated protein interaction networks taking a structural approach. We first describe structural aspects of protein-protein interactions and properties of protein interfaces as related to drug design; we address protein interactions in a network perspective; in particular, we illustrate how integrating protein interfaces onto interaction networks can guide the identification of selective drug targets or drugs targeting multiple proteins in a network.
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    PublicationOpen Access
    PRISM-EM: template interface-based modelling of multi-protein complexes guided by cryo-electron microscopy density maps (corrigendum)
    (International Union of Crystallography, 2018) Nussinov, Ruth; Department of Chemical and Biological Engineering; Kuzu, Güray; Keskin, Özlem; Gürsoy, Attila; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; N/A; 26605; 8745
    A revised Table 6 and Supporting Information are provided for the article by Kuzu et al. [(2016 ), Acta Cryst. D72, 1137-1148].
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    PublicationOpen Access
    Modeling protein assemblies in the proteome
    (American Society for Biochemistry and Molecular Biology (ASBMB), 2014) Nussinov, Ruth; Department of Chemical and Biological Engineering; Department of Computer Engineering; Kuzu, Güray; Keskin, Özlem; Gürsoy, Attila; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; College of Engineering; N/A; 26605; 8745
    Most (if not all) proteins function when associated in multimolecular assemblies. Attaining the structures of protein assemblies at the atomic scale is an important aim of structural biology. Experimentally, structures are increasingly available, and computations can help bridge the resolution gap between high- and low-resolution scales. Existing computational methods have made substantial progress toward this aim; however, current approaches are still limited. Some involve manual adjustment of experimental data; some are automated docking methods, which are computationally expensive and not applicable to large-scale proteome studies; and still others exploit the symmetry of the complexes and thus are not applicable to nonsymmetrical complexes. Our study aims to take steps toward overcoming these limitations. We have developed a strategy for the construction of protein assemblies computationally based on binary interactions predicted by a motif-based protein interaction prediction tool, PRISM (Protein Interactions by Structural Matching). Previously, we have shown its power in predicting pairwise interactions. Here we take a step toward multimolecular assemblies, reflecting the more prevalent cellular scenarios. With this method we are able to construct homo-/hetero-complexes and symmetric/asymmetric complexes without a limitation on the number of components. The method considers conformational changes and is applicable to large-scale studies. We also exploit electron microscopy density maps to select a solution from among the predictions. Here we present the method, illustrate its results, and highlight its current limitations.
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    PublicationOpen Access
    The structural network of Interleukin-10 and its implications in inflammation and cancer
    (BioMed Central, 2014) Chen, Zhong; Van Waes, Carter; Nussinov, Ruth; Department of Computer Engineering; Özbabacan, Saliha Ece Acuner; Engin, Hatice Billur; Maiorov, Emine Güven; Kuzu, Güray; Muratçıoğlu, Serena; Başpınar, Alper; Gürsoy, Attila; PhD Student; Faculty Member; Department of Computer Engineering; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; N/A; N/A; N/A; N/A; N/A; N/A; 8745
    Background: Inflammation has significant roles in all phases of tumor development, including initiation, progression and metastasis. Interleukin-10 (IL-10) is a well-known immuno-modulatory cytokine with an anti-inflammatory activity. Lack of IL-10 allows induction of pro-inflammatory cytokines and hinders anti-tumor immunity, thereby favoring tumor growth. The IL-10 network is among the most important paths linking cancer and inflammation. The simple node-and-edge network representation is useful, but limited, hampering the understanding of the mechanistic details of signaling pathways. Structural networks complete the missing parts, and provide details. The IL-10 structural network may shed light on the mechanisms through which disease-related mutations work and the pathogenesis of malignancies. Results: Using PRISM (a PRotein Interactions by Structural Matching tool), we constructed the structural network of IL-10, which includes its first and second degree protein neighbor interactions. We predicted the structures of complexes involved in these interactions, thereby enriching the available structural data. In order to reveal the significance of the interactions, we exploited mutations identified in cancer patients, mapping them onto key proteins of this network. We analyzed the effect of these mutations on the interactions, and demonstrated a relation between these and inflammation and cancer. Our results suggest that mutations that disrupt the interactions of IL-10 with its receptors (IL-10RA and IL-10RB) and alpha 2-macroglobulin (A2M) may enhance inflammation and modulate anti-tumor immunity. Likewise, mutations that weaken the A2M-APP (amyloid precursor protein) association may increase the proliferative effect of APP through preventing beta-amyloid degradation by the A2M receptor, and mutations that abolish the A2M-Kallikrein-13 (KLK13) interaction may lead to cell proliferation and metastasis through the destructive effect of KLK13 on the extracellular matrix. Conclusions: Prediction of protein-protein interactions through structural matching can enrich the available cellular pathways. In addition, the structural data of protein complexes suggest how oncogenic mutations influence the interactions and explain their potential impact on IL-10 signaling in cancer and inflammation.
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    PublicationOpen Access
    PRISM-EM: template interface-based modelling of multi-protein complexes guided by cryo-electron microscopy density maps
    (International Union of Crystallography, 2016) Nussinov, Ruth; Department of Chemical and Biological Engineering; Kuzu, Güray; Keskin, Özlem; Gürsoy, Attila; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; N/A; 26605; 8745
    The structures of protein assemblies are important for elucidating cellular processes at the molecular level. Three-dimensional electron microscopy (3DEM) is a powerful method to identify the structures of assemblies, especially those that are challenging to study by crystallography. Here, a new approach, PRISM-EM, is reported to computationally generate plausible structural models using a procedure that combines crystallographic structures and density maps obtained from 3DEM. The predictions are validated against seven available structurally different crystallographic complexes. The models display mean deviations in the backbone of <5 angstrom. PRISM-EM was further tested on different benchmark sets; the accuracy was evaluated with respect to the structure of the complex, and the correlation with EM density maps and interface predictions were evaluated and compared with those obtained using other methods. PRISM-EM was then used to predict the structure of the ternary complex of the HIV-1 envelope glycoprotein trimer, the ligand CD4 and the neutralizing protein m36.