Researcher:
Bayrak, Çiğdem Sevim

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PhD Student

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Çiğdem Sevim

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Bayrak

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Bayrak, Çiğdem Sevim

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2012 - 20143

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    Statistical mechanics of proteins in the random coil state
    (SciTePress, 2012) Department of Chemical and Biological Engineering; N/A; Erman, Burak; Bayrak, Çiğdem Sevim; Faculty Member; PhD Student; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; 179997; N/A
    Denatured proteins are mostly partially folded and compact proteins. A statistical analysis on thermodynamic properties is presented to describe and characterize denatured proteins. Conformational free energy, energy, entropy and heat capacity expressions are derived using the Rotational Isomeric States model of polymer theory. The state space and the probabilities of each state are comprised from a coil database. Properties for the denatured state are obtained for a sample set of proteins taken from the Protein Data Bank. Thermodynamic expressions of denatured state are derived.
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    Conformational transitions in the ramachandran space of amino acids using the dynamic rotational isomeric state (DRIS) model
    (Royal Soc Chemistry, 2014) N/A; Department of Chemical and Biological Engineering; Bayrak, Çiğdem Sevim; Erman, Burak; PhD Student; Faculty Member; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 179997
    The dynamic rotational isomeric state model is applied to predict the internal dynamics of the 20 amino acids. Transition rates between rotational isomeric states are calculated from molecular dynamics simulations of Gly-Gly-X-Gly-Gly peptides where X represents one of the 20 amino acids. Predicted relaxation times are in good agreement with fluorescence quenching rate measurements.
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    Predicting most probable conformations of a given peptide sequence in the random coil state
    (Royal Society of Chemistry (RSC), 2012) N/A; Department of Chemical and Biological Engineering; Bayrak, Çiğdem Sevim; Erman, Burak; Faculty Member; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 179997
    In this work, we present a computational scheme for finding high probability conformations of peptides. The scheme calculates the probability of a given conformation of the given peptide sequence using the probability distribution of torsion states. Dependence of the states of a residue on the states of its first neighbors along the chain is considered. Prior probabilities of torsion states are obtained from a coil library. Posterior probabilities are calculated by the matrix multiplication Rotational Isomeric States Model of polymer theory. The conformation of a peptide with highest probability is determined by using a hidden Markov model Viterbi algorithm. First, the probability distribution of the torsion states of the residues is obtained. Using the highest probability torsion state, one can generate, step by step, states with lower probabilities. To validate the method, the highest probability state of residues in a given sequence is calculated and compared with probabilities obtained from the Coil Databank. Predictions based on the method are 32% better than predictions based on the most probable states of residues. The ensemble of ""n'' high probability conformations of a given protein is also determined using the Viterbi algorithm with multistep backtracking.