Researcher: Makinacı, Gözde Kar
Loading...
Name Variants
Makinacı, Gözde Kar
Email Address
Birth Date
6 results
Search Results
Now showing 1 - 6 of 6
Publication Metadata only 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/AHot 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.Publication Metadata only Human proteome-scale structural modeling of E2-E3 interactions exploiting interface motifs(Amer Chemical Soc, 2012) Nussinov, Ruth; N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Makinacı, Gözde Kar; Keskin, Özlem; Gürsoy, Attila; PhD Student; Faculty Member; Faculty Member; 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; 8745Ubiquitination is crucial for many cellular processes such as protein degradation, DNA repair, transcription regulation, and cell signaling. Ubiquitin attachment takes place via a sequential enzymatic cascade involving ubiquitin activation (by El enzymes), ubiquitin conjugation (by E2 enzymes), and ubiquitin substrate tagging (by E3 enzymes). E3 ligases mediate ubiquitin transfer from E2s to substrates and as such confer substrate specificity. Although E3s can interact and function with numerous E2s, it is still unclear how they choose which E2 to use. Identifying all E2 partners of an E3 is essential for inferring the principles guiding E2 selection by an E3. Here we model the interactions of E3 and E2 proteins in a large, proteome-scale strategy based on interface structural motifs, which allows elucidation of (1) which E3s interact with which E2s in the human ubiquitination pathway and (2) how they interact with each other. Interface analysis of E2-E3 complexes reveals that loop L1 of E2s is critical for binding; the residue in the sixth position in loop L1 is widely utilized as an interface hot spot and appears indispensible for E2 interactions. Other loop L1 residues also confer specificity on the E2-E3 interactions: HECT E3s are in contact with the residue in the second position in loop L1 of E2s, but this is not the case for the RING finger type E3s. Our modeled E2-E3 complexes illuminate how slight sequence variations in E2 residues may contribute to specificity in E3 binding. These findings may be important for discovering drug candidates targeting E3s, which have been implicated in many diseases.Publication Metadata only Towards inferring time dimensionality in protein-protein interaction networks by integrating structures: the p53 example(Royal Society of Chemistry (RSC), 2009) Nussinov, Ruth; Department of Chemical and Biological Engineering; Department of Computer Engineering; (TBD); N/A; Keskin, Özlem; Gürsoy, Attila; Tunçbağ, Nurcan; Makinacı, Gözde Kar; Faculty Member; Faculty Member; Faculty Member; PhD Student; Department of Chemical and Biological Engineering; Department of Computer Engineering; (TBD); The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26605; 8745; 245513; N/AInspection of protein-protein interaction maps illustrates that a hub protein can interact with a very large number of proteins, reaching tens and even hundreds. Since a single protein cannot interact with such a large number of partners at the same time, this presents a challenge: can we figure out which interactions can occur simultaneously and which are mutually excluded? Addressing this question adds a fourth dimension into interaction maps: that of time. Including the time dimension in structural networks is an immense asset; time dimensionality transforms network node-and-edge maps into cellular processes, assisting in the comprehension of cellular pathways and their regulation. While the time dimensionality can be further enhanced by linking protein complexes to time series of mRNA expression data, current robust, network experimental data are lacking. Here we outline how, using structural data, efficient structural comparison algorithms and appropriate datasets and filters can assist in getting an insight into time dimensionality in interaction networks; in predicting which interactions can and cannot co-exist; and in obtaining concrete predictions consistent with experiment. As an example, we present p53-linked processes.Publication Metadata only Allostery and population shift in drug discovery(Elsevier, 2010) Nussinov, Ruth; 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; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26605; 8745; N/AProteins can exist in a large number of conformations around their native states that can be characterized by an energy landscape. The landscape illustrates individual valleys, which are the conformational substates. From the functional standpoint, there are two key points: first, all functionally relevant substates pre-exist; and second, the landscape is dynamic and the relative populations of the substates will change following allosteric events. Allosteric events perturb the structure, and the energetic strain propagates and shifts the population. This can lead to changes in the shapes and properties of target binding sites. Here we present an overview of dynamic conformational ensembles focusing on allosteric events in signaling. We propose that combining equilibrium fluctuation concepts with genomic screens could help drug discovery.Publication Metadata only A survey of available tools and web servers for analysis of protein-protein interactions and interfaces(Oxford University Press (OUP), 2009) Nussinov, Ruth; Department of Chemical and Biological Engineering; Department of Computer Engineering; N/A; Department of Chemical and Biological Engineering; Keskin, Özlem; Gürsoy, Attila; Makinacı, Gözde Kar; Tunçbağ, Nurcan; Faculty Member; Faculty Member; PhD Student; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; 26605; 8745; N/A; 245513The unanimous agreement that cellular processes are (largely) governed by interactions between proteins has led to enormous community efforts culminating in overwhelming information relating to these proteins; to the regulation of their interactions, to the way in which they interact and to the function which is determined by these interactions. These data have been organized in databases and servers. However, to make these really useful, it is essential not only to be aware of these, but in particular to have a working knowledge of which tools to use for a given problem; what are the tool advantages and drawbacks; and no less important how to combine these for a particular goal since usually it is not one tool, but some combination of tool-modules that is needed. This is the goal of this review.Publication Metadata only 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; 8745The 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.