Research Project: Kanserle ilintili proteinlerin ve sinyal yolaklarının incelenmesi için yapısal protein-protein etkileşim ağlarının bulunması
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Contributors
Funders
ID
TB.00019
Authors
Keskin, Özlem
Faculty Member
Publications
Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM
(Nature Portfolio, 2011) Gürsoy, Attila; Keskin, Özlem; Tunçbağ, Nurcan; Nussinov, Ruth; Department of Computer Engineering; Department of Chemical and Biological Engineering; CCBB (The Center for Computational Biology and Bioinformatics); Yes; College of Engineering; Research Center
Prediction of protein-protein interactions at the structural level on the proteome scale is important because it allows prediction of protein function, helps drug discovery and takes steps toward genome-wide structural systems biology. We provide a protocol (termed PRISM, protein interactions by structural matching) for large-scale prediction of protein-protein interactions and assembly of protein complex structures. The method consists of two components: rigid-body structural comparisons of target proteins to known template protein-protein interfaces and flexible refinement using a docking energy function. The PRISM rationale follows our observation that globally different protein structures can interact via similar architectural motifs. PRISM predicts binding residues by using structural similarity and evolutionary conservation of putative binding residue 'hot spots'. Ultimately, PRISM could help to construct cellular pathways and functional, proteome-scale annotation. PRISM is implemented in Python and runs in a UNIX environment. The program accepts Protein Data Bank-formatted protein structures and is available at http://prism.ccbb.ku.edu.tr/prism_protocol/.
Interaction prediction and classification of PDZ domains
(BioMed Central, 2010) Gürsoy, Attila; Kalyoncu, Sibel; Keskin, Özlem; CCBB (The Center for Computational Biology and Bioinformatics); Yes; College of Engineering; Research Center
Background: PDZ domain is a well-conserved, structural protein domain found in hundreds of signaling proteins that are otherwise unrelated. PDZ domains can bind to the C-terminal peptides of different proteins and act as glue, clustering different protein complexes together, targeting specific proteins and routing these proteins in signaling pathways. These domains are classified into classes I, II and III, depending on their binding partners and the nature of bonds formed. Binding specificities of PDZ domains are very crucial in order to understand the complexity of signaling pathways. It is still an open question how these domains recognize and bind their partners. Results: The focus of the current study is two folds: 1) predicting to which peptides a PDZ domain will bind and 2) classification of PDZ domains, as Class I, II or I-II, given the primary sequences of the PDZ domains. Trigram and bigram amino acid frequencies are used as features in machine learning methods. Using 85 PDZ domains and 181 peptides, our model reaches high prediction accuracy (91.4%) for binary interaction prediction which outperforms previously investigated similar methods. Also, we can predict classes of PDZ domains with an accuracy of 90.7%. We propose three critical amino acid sequence motifs that could have important roles on specificity pattern of PDZ domains. Conclusions: Our model on PDZ interaction dataset shows that our approach produces encouraging results. The method can be further used as a virtual screening technique to reduce the search space for putative candidate target proteins and drug-like molecules of PDZ domains.
Enriching the human apoptosis pathway by predicting the structures of protein-protein complexes
(Elsevier, 2012) Gürsoy, Attila; Keskin, Özlem; Özbabacan, Saliha Ece Acuner; Nussinov, Ruth; Department of Computer Engineering; Department of Chemical and Biological Engineering; CCBB (The Center for Computational Biology and Bioinformatics); Yes; College of Engineering; Research Center
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.
HotRegion: a database of predicted hot spot clusters
(Oxford University Press (OUP), 2012) Çukuroğlu, Engin; Gürsoy, Attila; Keskin, Özlem; Department of Computer Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; Yes; College of Engineering; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
Hot spots are energetically important residues at protein interfaces and they are not randomly distributed across the interface but rather clustered. These clustered hot spots form hot regions. Hot regions are important for the stability of protein complexes, as well as providing specificity to binding sites. We propose a database called HotRegion, which provides the hot region information of the interfaces by using predicted hot spot residues, and structural properties of these interface residues such as pair potentials of interface residues, accessible surface area (ASA) and relative ASA values of interface residues of both monomer and complex forms of proteins. Also, the 3D visualization of the interface and interactions among hot spot residues are provided.
Analysis of hot region organization in hub proteins
(Springer, 2010) Çukuroğlu, Engin; Gürsoy, Attila; Keskin, Özlem; Department of Computer Engineering; Department of Chemical and Biological Engineering; CCBB (The Center for Computational Biology and Bioinformatics); Yes; College of Engineering; Research Center
Protein interaction maps constructed from binary interactions reveal that some proteins are highly connected to others (acting as hub proteins), whereas some others have a few interactions (at the edges of the map). This paper addresses hub proteins from a structural point: interfaces. It investigates how hot spots are organized in hub proteins (hot regions). We annotate interfaces as the ones between two date-hubs (DD), two party hubs (PP), and two non-hubs (NN). We investigate the physico-chemical properties of these three types of interfaces focusing on the accessible surface area distribution, hot region organization, and amino acid composition differences. Results reveal that there are significant differences between DD and PP interfaces. More of the hot spots are organized into the hot regions in DD interfaces compared to PP ones. A high fraction of the interfaces are covered by hot regions in DD interfaces. There are more distinct hot regions in DDs. Since the same (or overlapping) DD interfaces should be used repeatedly, different hot regions can be used to bind to different partners. Further, these hot region characteristics can be used to predict whether a given hub interface is involved in a DD or a PP interface type with 80% accuracy.
