Researcher:
Gürsoy, Attila

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Attila

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Gürsoy

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Now showing 1 - 10 of 128
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    Determination of the correspondence between mobility (rigidity) and conservation of the interface residues
    (IEEE, 2010) N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; N/A; Keskin, Özlem; Gürsoy, Attila; Makinacı, Gözde Kar; Faculty Member; Faculty Member; PhD Student; Department of Chemical and Biological Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26605; 8745; N/A
    Hot spots at protein interfaces may play specific functional roles and contribute to the stability of the protein complex. These residues are not homogeneously distributed along the protein interfaces; rather they are clustered within locally tightly packed regions forming a network of interactions among themselves. Here, we investigate the organization of computational hot spots at protein interfaces. A list of proteins whose free and bound forms exist is examined. Inter-residue distances of the interface residues are compared for both forms. Results reveal that there exist rigid block regions at protein interfaces. More interestingly, these regions correspond to computational hot regions. Hot spots can be determined with an average positive predictive value (PPV) of 0.73 and average sensitivity value of 0.70 for seven protein complexes.
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    Enriching the human apoptosis pathway by predicting the structures of protein-protein complexes
    (Elsevier, 2012) Nussinov, Ruth; Department of Chemical and Biological Engineering; Department of Computer Engineering; N/A; Keskin, Özlem; Gürsoy, Attila; Özbabacan, Saliha Ece Acuner; 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; 264351
    Apoptosis is a matter of life and death for cells and both inhibited and enhanced apoptosis may be involved in the pathogenesis of human diseases. The structures of protein-protein complexes in the apoptosis signaling pathway are important as the structural pathway helps in understanding the mechanism of the regulation and information transfer, and in identifying targets for drug design. Here, we aim to predict the structures toward a more informative pathway than currently available. Based on the 3D structures of complexes in the target pathway and a protein-protein interaction modeling tool which allows accurate and proteome-scale applications, we modeled the structures of 29 interactions, 21 of which were previously unknown. Next, 27 interactions which were not listed in the KEGG apoptosis pathway were predicted and subsequently validated by the experimental data in the literature. Additional interactions are also predicted. The multi-partner hub proteins are analyzed and interactions that can and cannot co-exist are identified. Overall, our results enrich the understanding of the pathway with interactions and provide structural details for the human apoptosis pathway. They also illustrate that computational modeling of protein-protein interactions on a large scale can help validate experimental data and provide accurate, structural atom-level detail of signaling pathways in the human cell.
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    Hot spots in protein-protein interfaces: towards drug discovery
    (Elsevier, 2014) N/A; N/A; Department of Computer Engineering; Department of Chemical and Biological Engineering; Çukuroğlu, Engin; Engin, Hatice Billur; Gürsoy, Attila; Keskin, Özlem; PhD Student; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; N/A; College of Engineering; College of Engineering; N/A; N/A; 8745; 26605
    Identification of drug-like small molecules that alter protein-protein interactions might be a key step in drug discovery. However, it is very challenging to find such molecules that target interface regions in protein complexes. Recent findings indicate that such molecules usually target specifically energetically favored residues (hot spots) in protein protein interfaces. These residues contribute to the stability of protein-protein complexes. Computational prediction of hot spots on bound and unbound structures might be useful to find druggable sites on target interfaces. We review the recent advances in computational hot spot prediction methods in the first part of the review and then provide examples on how hot spots might be crucial in drug design. (C) 2014 Published by Elsevier Ltd.
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    Modeling structural protein interaction networks for betweenness analysis
    (Springer-Verlag Berlin, 2014) N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Demircioğlu, Deniz; Keskin, Özlem; Gürsoy, Attila; Researcher; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; N/A; College of Engineering; College of Engineering; N/A; 26605; 8745
    Protein-protein interactions are usually represented as interaction networks (graphs), where the proteins are represented as nodes and the connections between the interacting proteins are shown as edges. Proteins or interactions with high betweenness are considered as key connector members of the network. The interactions of a protein are dictated by its structure. In this study, we propose a new protein interaction network model taking structures of proteins into consideration. With this model, it is possible to reveal simultaneous and mutually exclusive interactions of a protein. Effect of mutually exclusive interactions on information flow in a network is studied with weighted edge betweenness analysis and it is observed that a total of 68% of bottlenecks found in p53 pathway network differed from bottlenecks found via regular edge betweenness analysis. The new network model favored the proteins which have regulatory roles and take part in cell cycle and molecular functions like protein binding, transcription factor binding, and kinase activity.
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    Genome-wide RNA and DNA high throughput sequencing reveals proinflammatory and death gene signatures in head and neck squamous cell carcinoma lines with different HPV status
    (American Association for Cancer Research (AACR), 2014) Yang, Xinping; Cheng, Hui; Si, Han; Saleh, Anthony; Coupar, Jamie; Ferris, Robert L.; Yarbrough, Wendell G.; Prince, Mark E.; Carey, Thomas E.; Van Waes, Carter; Chen, Zhong; N/A; N/A; Department of Computer Engineering; N/A; Maiorov, Emine Güven; Keskin, Özlem; Gürsoy, Attila; PhD Student; Faculty Member; Faculty Member; N/A; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; 26605; 8745; N/A
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    Structural cooperativity in histone H3 tail modifications
    (Wiley, 2011) N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Department of Chemical and Biological Engineering; Şanlı, Deniz; Keskin, Özlem; Gürsoy, Attila; Erman, Burak; Researcher; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; 26605; 8745; 179997
    Post-translational modifications of histone H3 tails have crucial roles in regulation of cellular processes. There is cross-regulation between the modifications of K4, K9, and K14 residues. The modifications on these residues drastically promote or inhibit each other. In this work, we studied the structural changes of the histone H3 tail originating from the three most important modifications; tri-methylation of K4 and K9, and acetylation of K14. We performed extensive molecular dynamics simulations of four types of H3 tails: (i) the unmodified H3 tail having no chemical modification on the residues, (ii) the tri-methylated lysine 4 and lysine 9 H3 tail (K4me3K9me3), (iii) the tri-methylated lysine 4 and acetylated lysine 14 H3 tail (K4me3K14ace), and (iv) tri-methylated lysine 9 and acetylated lysine 14 H3 tail (K9me3K14ace). Here, we report the effects of K4, K9, and K14 modifications on the backbone torsion angles and relate these changes to the recognition and binding of histone modifying enzymes. According to the Ramachandran plot analysis; (i) the dihedral angles of K4 residue are significantly affected by the addition of three methyl groups on this residue regardless of the second modification, (ii) the dihedral angle values of K9 residue are similarly altered majorly by the tri-methylation of K4 residue, (iii) different combinations of modifications (tri-methylation of K4 and K9, and acetylation of K14) have different influences on phi and psi values of K14 residue. Finally, we discuss the consequences of these results on the binding modes and specificity of the histone modifying enzymes such as DIM-5, GCN5, and JMJD2A.
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    Prediction of protein interactions by structural matching: Prediction of PPI networks and the effects of mutations on PPIs that combines sequence and structural information
    (Humana Press Inc, 2017) Tunçbağ, Nurcan; Nussinov, Ruth; Department of Chemical and Biological Engineering; Department of Computer Engineering; Keskin, Özlem; Gürsoy, Attila; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; 26605; 8745
    Structural details of protein interactions are invaluable to the understanding of cellular processes. However, the identification of interactions at atomic resolution is a continuing challenge in the systems biology era. Although the number of structurally resolved complexes in the Protein Databank increases exponentially, the complexes only cover a small portion of the known structural interactome. In this chapter, we review the PRISM system that is a protein-protein interaction (PPI) prediction tool-its rationale, principles, and applications. We further discuss its extensions to discover the effect of single residue mutations, to model large protein assemblies, to improve its performance by exploiting conformational protein ensembles, and to reconstruct large PPI networks or pathway maps.
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    Transient proteinprotein interactions
    (Oxford Univ Press, 2011) N/A; N/A; N/A; Department of Computer Engineering; Department of Chemical and Biological Engineering; Özbabacan, Saliha Ece Acuner; Engin, Hatice Billur; Gürsoy, Attila; Keskin, Özlem; PhD Student; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; The Center for Computational Biology and Bioinformatics (CCBB); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; 264351; N/A; 8745; 26605
    Transient complexes are crucial for diverse biological processes such as biochemical pathways and signaling cascades in the cell. Here, we give an overview of the transient interactions; the importance of transient interactions as drug targets; and the structural characterization of transient proteinprotein complexes based on the geometrical and physicochemical features of the transient complexes' interfaces. To better understand and eventually design transient proteinprotein interactions (TPPIs), a molecular perspective of the proteinprotein interfaces is necessary. Obtaining high-quality structures of proteinprotein interactions could be one way of achieving this goal. After introducing the association kinetics of TPPIs, we elaborate on the experimental techniques detecting TPPIs in combination with the computational methods which classify transient and/or non-obligate complexes. In this review, currently available databases and servers that can be used to identify and predict TPPIs are also compiled.
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    TRAF3 signaling: Competitive binding and evolvability of adaptive viral molecular mimicry
    (Elsevier, 2016) Guven-Maiorov, Emine; VanWaes, Carter; Chen, Zhong; Tsai, Chung-Jung; Nussinov, Ruth; Department of Chemical and Biological Engineering; Department of Computer Engineering; Keskin, Özlem; Gürsoy, Attila; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; College of Engineering; 26605; 8745
    Background: The tumor necrosis factor receptor (TNFR) associated factor 3 (TRAF3) is a key node in innate and adaptive immune signaling pathways. TRAF3 negatively regulates the activation of the canonical and non canonical NF-kappa B pathways and is one of the key proteins in antiviral immunity. Scope of Review: Here we provide a structural overview of TRAF3 signaling in terms of its competitive binding and consequences to the cellular network. For completion, we also include molecular mimicry of TRAF3 physiological partners by some viral proteins. Major Conclusions: By out-competing host partners, viral proteins aim to subvert TRAF3 antiviral action. Mechanistically, dynamic, competitive binding by the organism's own proteins and same-site adaptive pathogen mimicry follow the same conformational selection principles. General Significance: Our premise is that irrespective of the eliciting event - physiological or acquired pathogenic trait - pathway activation (or suppression) may embrace similar conformational principles. However, even though here we largely focus on competitive binding at a shared site, similar to physiological signaling other pathogen subversion mechanisms can also be at play. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
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    Embedding alternative conformations of proteins in protein–protein interaction networks
    (Humana Press inc, 2020) N/A; N/A; Department of Computer Engineering; Department of Chemical and Biological Engineering; Halakou, Farideh; Gürsoy, Attila; Keskin, Özlem; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 8745; 26605
    While many proteins act alone, the majority of them interact with others and form molecular complexes to undertake biological functions at both cellular and systems levels. Two proteins should have complementary shapes to physically connect to each other. As proteins are dynamic and changing their conformations, it is vital to track in which conformation a specific interaction can happen. Here, we present a step-by-step guide to embedding the protein alternative conformations in each protein–protein interaction in a systems level. All external tools/websites used in each step are explained, and some notes and suggestions are provided to clear any ambiguous point.