Researcher: Ünal, Evrim Besray
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Ünal, Evrim Besray
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Publication Metadata only Computational search of the interaction between melanopsin and cryptochrome-2 proteins(Blackwell Publishing, 2006) N/A; Department of Chemical and Biological Engineering; Department of Chemical and Biological Engineering; Ünal, Evrim Besray; Erman, Burak; Kavaklı, İbrahim Halil; PhD Student; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 179997; 40319N/APublication Metadata only Conformational energies and entropies of peptides, and the peptide-protein binding problem(IOP Publishing Ltd, 2009) N/A; Department of Computer Engineering; Department of Chemical and Biological Engineering; Ünal, Evrim Besray; Gürsoy, Attila; Erman, Burak; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; The Center for Computational Biology and Bioinformatics (CCBB); Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 8745; 179997A novel statistical thermodynamic approach is applied to free-peptide segments in order to classify them according to their conformational energies, entropies and heat capacities. Our approach employs the rotational isomeric state (RIS) model in which the states are described by the Ramachandran map of backbone torsion angles. The statistical weight matrices for the pairwise-dependent states are derived from the torsion angle probabilities of the consecutive dipeptides in a coil library. The partition function is determined for a given sequence via RIS multiplication of the pre-determined matrices. The conformational partition function, Helmholtz free energy, energy, entropy and heat capacity are obtained. The model is applied to randomly produced peptides and also to known peptide inhibitors to analyze their thermodynamic properties. Peptides with low energy, low entropy and low-heat capacity are determined to be essential for a peptide to be a good candidate inhibitor. Free energy changes in peptide binding are also discussed.Publication Metadata only Inhibitor peptide design for NF- KB: Markov model and genetic algorithm(IEEE, 2010) N/A; Department of Computer Engineering; Department of Chemical and Biological Engineering; Ünal, Evrim Besray; Gürsoy, Attila; Erman, Burak; PhD 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; 179997Two peptide design approaches are proposed to block activities of disease related proteins. First approach employs a probabilistic method; the problem is set as Markov chain. The possible binding site of target protein and a path on this binding site are determined. 20 natural amino acids and 400 dipeptides are docked to the selected path using the AutoDock software. The statistical weight matrices for the binding energies are derived from AutoDock results; matrices are used to determine top 100 peptide sequences with affinity to target protein. Second approach utilizes a heuristic method for peptide sequence determination; genetic algorithm (GA) with tournament selection. The amino acids are the genes; the peptide sequences are the chromosomes of GA. Initial random population of 100 chromosomes leads to determination of 100 possible binding peptides, after 8-10 generations of GA. Thermodynamic properties of the peptides are analyzed by a method that we proposed previously. NF-κB protein is selected as case-study.Publication Open Access Identification of two amino acids in the c-terminal domain of mouse CRY2 essential for PER2 interaction(BioMed Central, 2010) Department of Chemical and Biological Engineering; Özber, Natali; Barış, İbrahim; Tatlıcı, Gülnaz; Gür, İbrahim; Kılınç, Seda; Ünal, Evrim Besray; Kavaklı, İbrahim Halil; Master Student; Teaching Faculty; PhD Student; Department of Chemical and Biological Engineering; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; N/A; 111629; N/A; N/A; N/A; N/A; 40319Background: Cryptochromes (CRYs) are a class of flavoprotein blue-light signaling receptors found in plants and animals, and they control plant development and the entrainment of circadian rhythms. They also act as integral parts of the central circadian oscillator in humans and other animals. In mammals, the CLOCK-BMAL1 heterodimer activates transcription of the Per and Cry genes as well as clock-regulated genes. The PER2 proteins interact with CRY and CKI epsilon, and the resulting ternary complexes translocate into the nucleus, where they negatively regulate the transcription of Per and Cry core clock genes and other clock-regulated output genes. Recent studies have indicated that the extended C-termini of the mammalian CRYs, as compared to photolyase proteins, interact with PER proteins. Results: We identified a region on mCRY2 (between residues 493 and 512) responsible for direct physical interaction with mPER2 by mammalian two-hybrid and co immunoprecipitation assays. Moreover, using oligonucleotide-based degenerate PCR, we discovered that mutation of Arg-501 and Lys-503 of mCRY2 within this C-terminal region totally abolishes interaction with PER2. Conclusions: Our results identify mCRY2 amino acid residues that interact with the mPER2 binding region and suggest the potential for rational drug design to inhibit CRYs for specific therapeutic approaches.Publication Open Access VitAL: viterbi algorithm for de novo peptide design(Public Library of Science, 2010) Department of Computer Engineering; Department of Chemical and Biological Engineering; Ünal, Evrim Besray; Gürsoy, Attila; Erman, Burak; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; College of Engineering; N/A; N/A; 179997Background: Drug design against proteins to cure various diseases has been studied for several years. Numerous design techniques were discovered for small organic molecules for specific protein targets. The specificity, toxicity and selectivity of small molecules are hard problems to solve. The use of peptide drugs enables a partial solution to the toxicity problem. There has been a wide interest in peptide design, but the design techniques of a specific and selective peptide inhibitor against a protein target have not yet been established. Methodology/Principal Findings: A novel de novo peptide design approach is developed to block activities of disease related protein targets. No prior training, based on known peptides, is necessary. The method sequentially generates the peptide by docking its residues pair by pair along a chosen path on a protein. The binding site on the protein is determined via the coarse grained Gaussian Network Model. A binding path is determined. The best fitting peptide is constructed by generating all possible peptide pairs at each point along the path and determining the binding energies between these pairs and the specific location on the protein using AutoDock. The Markov based partition function for all possible choices of the peptides along the path is generated by a matrix multiplication scheme. The best fitting peptide for the given surface is obtained by a Hidden Markov model using Viterbi decoding. The suitability of the conformations of the peptides that result upon binding on the surface are included in the algorithm by considering the intrinsic Ramachandran potentials. Conclusions/Significance: The model is tested on known protein-peptide inhibitor complexes. The present algorithm predicts peptides that have better binding energies than those of the existing ones. Finally, a heptapeptide is designed for a protein that has excellent binding affinity according to AutoDock results.