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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3

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Department: search.filters.department.Department of Chemical and Biological EngineeringSubject: search.filters.subject.Statistics

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    Prediction of protein-protein interactions by combining structure and sequence conservation in protein interfaces
    (Oxford Univ Press, 2005) N/A; Department of Computer Engineering; Department of Chemical and Biological Engineering; Aytuna, Ali Selim; Gürsoy, Attila; Keskin, Özlem; Master 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
    Motivation: Elucidation of the full network of protein- protein interactions is crucial for understanding of the principles of biological systems and processes. Thus, there is a need for in silico methods for predicting interactions. We present a novel algorithm for automated prediction of protein-protein interactions that employs a unique bottom-up approach combining structure and sequence conservation in protein interfaces. Results: Running the algorithm on a template dataset of 67 interfaces and a sequentially non-redundant dataset of 6170 protein structures, 62616 potential interactions are predicted. These interactions are compared with the ones in two publicly available interaction databases (Database of Interacting Proteins and Biomolecular Interaction Network Database) and also the Protein Data Bank. A significant number of predictions are verified in these databases. The unverified ones may correspond to (1) interactions that are not covered in these databases but known in literature, (2) unknown interactions that actually occur in nature and (3) interactions that do not occur naturally but may possibly be realized synthetically in laboratory conditions. Some unverified interactions, supported significantly with studies found in the literature, are discussed.
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    Folding dynamics of proteins from denatured to native state: principal component analysis
    (Mary Ann Liebert, Inc, 2004) N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Department of Chemical and Biological Engineering; Department of Chemical and Biological Engineering; Palazoğlu, Ahmet; Gürsoy, Attila; Arkun, Yaman; Erman, Burak; Other; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; College of Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; 8745; 108526; N/A
    Several trajectories starting from random configurations and ending in the native state for chymotrypsin inhibitor 2, CI2, are generated using a Go-type model where the backbone torsional angles execute random jumps on which a drift towards their native values is superposed. Bond lengths and bond angles are kept fixed, and the size of the backbone atoms and side groups are recognized. The large datasets obtained are analyzed using a particular type of principal component analysis known as Karhunen - Loeve expansion (KLE). Trajectories are decomposed separately into modes in residue space and time space. General features of different folding trajectories are compared in the modal space and relationships between the structure of CI2 and its folding dynamics are obtained. Dynamic scaling and order reduction of the folding trajectories are discussed. A continuous wavelet transform is used to decompose the nonstationary folding trajectories into windows exhibiting different features of folding dynamics. Analysis of correlations confirms the known two-state nature of folding of CI2. All of the conserved residues of the protein are shown to be stationary in the small modes of the residue space. The sequential nature of folding is shown by examining the slow modes of the trajectories. The present model of protein folding dynamics is compared with the simple Rouse model of polymer dynamics. Principal component analysis is shown to be a very effective tool for the characterization of the general folding features of proteins.