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Publication Metadata only A kernel-based multilayer perceptron framework to identify pathways related to cancer stages(Springer Science and Business Media Deutschland GmbH, 2023) Mokhtaridoost, Milad; N/A; Department of Industrial Engineering; Soleimanpoor, Marzieh; Gönen, Mehmet; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 237468Standard machine learning algorithms have limited knowledge extraction capability in discriminating cancer stages based on genomic characterizations, due to the strongly correlated nature of high-dimensional genomic data. Moreover, activation of pathways plays a crucial role in the growth and progression of cancer from early-stage to late-stage. That is why we implemented a kernel-based neural network framework that integrates pathways and gene expression data using multiple kernels and discriminates early- and late-stages of cancers. Our goal is to identify the relevant molecular mechanisms of the biological processes which might be driving cancer progression. As the input of developed multilayer perceptron (MLP), we constructed kernel matrices on multiple views of expression profiles of primary tumors extracted from pathways. We used Hallmark and Pathway Interaction Database (PID) datasets to restrict the search area to interpretable solutions. We applied our algorithm to 12 cancer cohorts from the Cancer Genome Atlas (TCGA), including more than 5100 primary tumors. The results showed that our algorithm could extract meaningful and disease-specific mechanisms of cancers. We tested the predictive performance of our MLP algorithm and compared it against three existing classification algorithms, namely, random forests, support vector machines, and multiple kernel learning. Our MLP method obtained better or comparable predictive performance against these algorithms.Publication Metadata only Computational analysis of the binding free energy of H3K9ME3 peptide to the tandem tudor domains of JMJD2A(IEEE, 2010) N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Department of Chemical and Biological Engineering; N/A; Keskin, Özlem; Gürsoy, Attila; Erman, Burak; Özboyacı, Musa; Faculty Member; Faculty Member; Faculty Member; PhD Student; Department of Computer Engineering; Department of Chemical and Biological Engineering; College of Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26605; 8745; 179997; N/AJMJD2A is a histone lysine demethylase enzyme which plays a prominent role in the development of prostate and esophageal squamous cancers. Consisting of a JmjC, a JmjN, two PHD and two tandem tudor domains, JMJD2A recognizes and binds to four different methylated histone peptides: H3K4me3, H4K20me3, H4K20me2 and H3K9me3, via its tudor domains. Of the four histone peptides, only recognition of the H3K4me3 and H4K20me3 by JMJD2A-tudor has been identified. In this study, we investigated the recognition of trimethylated H3K9 by the tandem tudor domains of JMJD2A. Using the molecular dynamics simulations, we performed normal mode and molecular mechanics - Poisson Boltzmann / generalized born - surface area (MM-PB/GB-SA) analysis to find the entropic and enthalpic contributions to binding free energy respectively. We showed that binding of the ligand is mainly driven by favorable van der Waals interactions made after complexation. Our findings suggest that, upon complex formation, H3K9me3 peptide adopts a similar binding mode and the same orientation with H3K4me3 peptide.Publication Metadata only Coupling between energy and residue position fluctuations in native proteins(IEEE, 2010) Department of Chemical and Biological Engineering; N/A; Erman, Burak; Gür, Mert; Faculty Member; PhD Student; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; 179997; 216930The coupling between energy fluctuations and positional fluctuations in molecular dynamics trajectories of Crambin at 310 K is studied. Coupling with energy fluctuation is evaluated for both atomic positions and residue positions. Couplings show values which fluctuate around the previously proposed theoretical value under harmonic approximation. The magnitude of these correlations is in agreement, on the average, with the harmonic approximation. Additionally coupling between energy fluctuations and atom-atom distance fluctuations are evaluated. This coupling indicates how much each interaction among different atoms/residues is correlated with the protein's total energy fluctuations. Some atom's/residue's interactions have shown outstanding correlation. Moreover coupling of residue fluctuations between different modes is studied. © 2009 IEEE.Publication Metadata only Determination of the correspondence between mobility (rigidity) and conservation of the interface residues(IEEE, 2010) N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; N/A; Keskin, Özlem; Gürsoy, Attila; Makinacı, Gözde Kar; Faculty Member; Faculty Member; PhD Student; Department of Chemical and Biological Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26605; 8745; N/AHot spots at protein interfaces may play specific functional roles and contribute to the stability of the protein complex. These residues are not homogeneously distributed along the protein interfaces; rather they are clustered within locally tightly packed regions forming a network of interactions among themselves. Here, we investigate the organization of computational hot spots at protein interfaces. A list of proteins whose free and bound forms exist is examined. Inter-residue distances of the interface residues are compared for both forms. Results reveal that there exist rigid block regions at protein interfaces. More interestingly, these regions correspond to computational hot regions. Hot spots can be determined with an average positive predictive value (PPV) of 0.73 and average sensitivity value of 0.70 for seven protein complexes.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 Metadata only Interaction prediction of PDZ domains using a machine learning approach(IEEE, 2010) N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; N/A; Keskin, Özlem; Gürsoy, Attila; Kalyoncu, Sibel; Faculty Member; Faculty Member; Master Student; Department of Chemical and Biological Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26605; 8745; N/AProtein interaction domains play crucial roles in many complex cellular pathways. PDZ domains are one of the most common protein interaction domains. Prediction of binding specificity of PDZ domains by a computational manner could eliminate unnecessary, time-consuming experiments. In this study, interactions of PDZ domains are predicted by using a machine learning approach in which only primary sequences of PDZ domains and peptides are used. In order to encode feature vectors for each interaction, trigram frequencies of primary sequences of PDZ domains and corresponding peptides are calculated. After construction of numerical interaction dataset, we compared different classifiers and ended up with Random Forest (RF) algorithm which gave the top performance. We obtained very high prediction accuracy (91.4%) for binary interaction prediction which outperforms all previous similar methods.Publication Metadata only Reaction path analysis for demethylation process of histone tail lysine residues(IEEE, 2010) N/A; Department of Chemical and Biological Engineering; Department of Chemical and Biological Engineering; N/A; Keskin, Özlem; Erman, Burak; Karasulu, Bora; Faculty Member; Faculty Member; Master Student; Department of Chemical and Biological Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26605; 179997; N/AHistone proteins control many crucial cell regulatory processes post-translational modifications. Among these modifications, methylation is recently shown to be reversible with the discovery of Lysine-specific Demethylase (LSD1) enzyme. As many studies have showed the relation of some cancer-type and other diseases with the abnormalities in the balance of methylation/demethylation, drug molecule design based on the information gained from reaction path analysis becomes very useful. In this paper, a chemically-consistent reaction mechanism is proposed for the demethylation of histone tail lysine residues and the reaction path analysis of this mechanism is carried out. Potential and free energy profiles of the system, which does not include the residues of the enzyme, are calculated with semi-empirical and quantum mechanical (QM) methods. These results create a fundamental basis for further analysis of the demethylation process with enzyme and/or inhibitor molecules available in the literature.Publication Open Access Recent advances in operations research in computational biology, bioinformatics and medicine(EDP Sciences, 2014) Felici, Giovanni; Szachniuk, Marta; Lukasiak, Piotr; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956The EURO Working Group on Operations Research in Computational Biology, Bioinformatics and Medicine held its fourth conference in Poznan-Biedrusko, Poland, June 26-28, 2014. The editorial board of RAIRO-OR invited submissions of papers to a special issue on Recent Advances in Operations Research in Computational Biology, Bioinformatics and Medicine. This special issue includes nine papers that were selected among forty presentations and included in this special issue after two rounds of reviewing.Publication Metadata only Structural properties of hub proteins(IEEE, 2010) Ozkirimli, Elif; N/A; Department of Chemical and Biological Engineering; Keskin, Özlem; Çukuroğlu, Engin; Faculty Member; PhD Student; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26605; N/AProtein-protein interaction networks are scale free networks with a few hub proteins that have many interaction partners in the network. In this work, we examined the flexibility of the hubs by using a structural perspective and compared date hubs, which bind their partners at different times, and party hubs, which bind their partners simultaneously, with non hub proteins. The flexibility of the proteins is evaluated using temperature factors. Party hubs are found to be more flexible than date hubs, which in turn are more flexible than non-hub proteins. These may explain how a hub interacts with its partners specifically.