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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/6

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Quartile: search.filters.quartile.Q3Subject: search.filters.subject.Biochemistry and molecular biology

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    PublicationOpen Access
    Gaussian network model revisited: effects of mutation and ligand binding on protein behavior
    (Institute of Physics (IOP) Publishing, 2022) Department of Chemical and Biological Engineering; Erman, Burak; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 179997
    The coarse-grained Gaussian network model (GNM), considers only the alpha carbons of the folded protein. Therefore it is not directly applicable to the study of mutation or ligand binding problems where atomic detail is required. This shortcoming is improved by including all atom pairs within the coordination shell of each other into the Kirchoff adjacency matrix. Counting all contacts rather than only alpha carbon contacts diminishes the magnitude of fluctuations in the system. But more importantly, it changes the graph-like connectivity structure, i.e., the Kirchoff adjacency matrix of the protein. This change depends on amino acid type which introduces amino acid specific and position specific information into the classical coarse-grained GNM which was originally modeled in analogy with the phantom network model of rubber elasticity. With this modification, it is now possible to explain the consequences of mutation and ligand binding on residue fluctuations, their pair-correlations and mutual information shared by each pair. We refer to the new model as 'all-atom GNM'. Using examples from published data we show that the all-atom GNM gives B-factors that are in better agreement with experiment, can explain effects of mutation on long range communication in PDZ domains and can predict effects of GDP and GTP binding on the dimerization of KRAS.
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    PublicationOpen Access
    Mode coupling points to functionally important residues in myosin II
    (Wiley, 2014) Department of Computer Engineering; Department of Chemical and Biological Engineering; Department of Physics; Varol, Onur; Yüret, Deniz; Erman, Burak; Kabakçıoğlu, Alkan; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; Department of Physics; Graduate School of Sciences and Engineering; College of Engineering; College of Sciences; N/A; 179996; 179997; 49854
    Relevance of mode coupling to energy/information transfer during protein function, particularly in the context of allosteric interactions is widely accepted. However, existing evidence in favor of this hypothesis comes essentially from model systems. We here report a novel formal analysis of the near-native dynamics of myosin II, which allows us to explore the impact of the interaction between possibly non-Gaussian vibrational modes on fluctutational dynamics. We show that an information-theoretic measure based on mode coupling alone yields a ranking of residues with a statistically significant bias favoring the functionally critical locations identified by experiments on myosin II.
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    PublicationOpen Access
    Interaction prediction and classification of PDZ domains
    (BioMed Central, 2010) Kalyoncu, Sibel; Keskin, Özlem; Gürsoy, Attila; Master Student; Faculty Member; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; N/A; 26605; 8745
    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.