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Department: search.filters.department.Department of Industrial EngineeringSubject: search.filters.subject.Biophysics

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Now showing 1 - 5 of 5
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    Analysis and network representation of hotspots in protein interfaces using minimum cut trees
    (Wiley, 2010) Department of Chemical and Biological Engineering; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Department of Computer Engineering; Tunçbağ, Nurcan; Salman, Fatma Sibel; Keskin, Özlem; Gürsoy, Attila; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; College of Engineering; College of Engineering; 245513; 178838; 26605; 8745
    We propose a novel approach to analyze and visualize residue contact networks of protein interfaces by graph-based algorithms using a minimum cut tree (mincut tree). Edges in the network are weighted according to an energy function derived from knowledge-based potentials. The mincut tree, which is constructed from the weighted residue network, simplifies and summarizes the complex structure of the contact network by an efficient and informative representation. This representation offers a comprehensible view of critical residues and facilitates the inspection of their organization. We observed, on a nonredundant data set of 38 protein complexes with experimental hotspots that the highest degree node in the mincut tree usually corresponds to an experimental hotspot. Further, hotspots are found in a few paths in the mincut tree. In addition, we examine the organization of hotspots (hot regions) using an iterative clustering algorithm on two different case studies. We find that distinct hot regions are located on specific sites of the mincut tree and some critical residues hold these clusters together. Clustering of the interface residues provides information about the relation of hot regions with each other. Our new approach is useful at the molecular level for both identification of critical paths in the protein interfaces and extraction of hot regions by clustering of the interface residues.
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    Computational basis of knowledge-based conformational probabilities derived from local- and long-range interactions in proteins
    (Wiley-Liss, 2007) N/A; Department of Industrial Engineering; Department of Computer Engineering; N/A; Department of Chemical and Biological Engineering; Örmeci, Lerzan; Gürsoy, Attila; Tunca, Güzin; Erman, Burak; Faculty Member; Faculty Member; Master Student; Faculty Member; Department of Industrial Engineering; Department of Computer Engineering; Department of Chemical and Biological Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; 32863; 8745; N/A; 179997
    The probabilities of the various basins in Ramachandran maps are examined critically. The theoretical basis of probability calculations both from molecular computations and from protein libraries are discussed. The well-defined basins of the Ramachandran maps are treated as rotational isomeric states. Statistical independence and dependence of the states of different residues along the peptide chain are discussed. The Flory isolated pair hypothesis, near neighbor correlations, context effects, and long-range correlations are examined critically. A method of evaluating long-range correlations in helical and extended sequences is introduced in analogy with earlier polymer theory. Three different protein libraries are constructed where data is considered from residues in the M coiled regions, (ii) all regions, and (iii) only the helical and extended regions of proteins. Singlet and pairwise dependent probabilities calculated from these libraries are used to predict whether a given sequence is helical or extended. Predictions using pairwise dependence were not better than those using singlet probabilities. Modeling of long-range correlations improved the predictions significantly. Removal of the Chameleon sequences from the data set also improved the predictions, but to a lesser extent.
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    Protein dynamics analysis identifies candidate cancer driver genes and mutations in TCGA data
    (Wiley, 2021) Haliloglu, Turkan; N/A; Department of Industrial Engineering; Sayılgan, Jan Fehmi; Gönen, Mehmet; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 237468
    Recently, it has been showed that cancer missense mutations selectively target the neighborhood of hinge residues, which are key sites in protein dynamics. Here, we show that this approach can be extended to find previously unknown candidate mutations and genes. To this aim, we developed a computational pipeline to detect significantly enriched three-dimensional (3D) clustering of missense mutations around hinge residues. The hinge residues were detected by applying a Gaussian network model. By systematically analyzing the PanCancer compendium of somatic mutations in nearly 10 000 tumors from the Cancer Genome Atlas, we identified candidate genes and mutations in addition to well known ones. For instance, we found significantly enriched 3D clustering of missense mutations in known cancer genes including CDK4, CDKN2A, TCL1A, and MAPK1. Beside these known genes, we also identified significantly enriched 3D clustering of missense mutations around hinge residues in PLA2G4A, which may lead to excessive phosphorylation of the extracellular signal-regulated kinases. Furthermore, we demonstrated that hinge-based features improves pathogenicity prediction for missense mutations. Our results show that the consideration of clustering around hinge residues can help us explain the functional role of the mutations in known cancer genes and identify candidate genes.
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    Protein dynamics analysis reveals that missense mutations in cancer-related genes appear frequently on hinge-neighboring residues
    (Wiley, 2019) Haliloğlu, Türkan; N/A; Department of Industrial Engineering; Sayılgan, Jan Fehmi; Gönen, Mehmet; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 237468
    Missense mutations have various effects on protein structures, also leading to distorted protein dynamics that plausibly affects the function. We hypothesized that missense mutations in cancer-related genes selectively target hinge-neighboring residues that orchestrate collective structural dynamics. To test our hypothesis, we selected 69 cancer-related genes from the Cancer Gene Census database and their representative protein structures from the Protein Data Bank. We first identified the hinge residues in two global modes of motion by applying the Gaussian Network Model. We then showed that missense mutations are significantly enriched on hinge-neighboring residues in oncogenes and tumor suppressor genes. We observed that several oncogenes (eg, MAP2K1, PTPN11, and KRAS) and tumor suppressor genes (eg, EZH2, CDKN2C, and RHOA) strongly exhibit this phenomenon. This study highlights and rationalizes the functional importance of missense mutations on hinge-neighboring residues in cancer.
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    Relationships between amino acid sequence and backbone torsion angle preferences
    (Wiley, 2004) N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Department of Computer Engineering; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Keskin, Özlem; Yüret, Deniz; Gürsoy, Attila; Türkay, Metin; Erman, Burak; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Industrial Engineering; Department of Chemical and Biological Engineering; College of Engineering; College of Engineering; College of Engineering; College of Engineering; College of Engineering; 26605; 179996; 8745; 24956; 179997
    Statistical averages and correlations for backbone torsion angles of chymotrypsin inhibitor 2 are calculated by using the Rotational Isomeric States model of chain statistics. Statistical weights of torsional states of phipsi pairs, needed for the statistics of the full chain, are obtained in two different ways: 1) by using knowledge-based pair-wise dependent phipsi energy maps from Protein Data Bank (PDB) and 2) by collecting torsion angle data from a large number of random coil configurations of an all-atom protein model with volume exclusion. Results obtained by using PDB data show strong correlations between adjacent torsion angle pairs belonging to both the same and different residues. These correlations favor the choice of the native-state torsion angles, and they are strongly context dependent, determined by the specific amino acid sequence of the protein. Excluded volume or steric clashes, only, do not introduce context-dependent ( correlations into the chain that would affect the choice of native-state torsional angles.