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Publication Open Access Artificial intelligence approaches to human-microbiome protein-protein interactions(Elsevier, 2022) Lim, Hansaim; Tsai, Chung-Jung; Nussinov, Ruth; Department of Computer Engineering; Department of Chemical and Biological Engineering; Gürsoy, Attila; Keskin, Özlem; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); College of Engineering; Graduate School of Sciences and Engineering; 8745; 26605; N/AHost-microbiome interactions play significant roles in human health and disease. Artificial intelligence approaches have been developed to better understand and predict the molecular interplay between the host and its microbiome. Here, we review recent advancements in computational methods to predict microbial effects on human cells with a special focus on protein–protein interactions. We categorize recent methods from traditional ones to more recent deep learning methods, followed by several challenges and potential solutions in structure-based approaches. This review serves as a brief guide to the current status and future directions in the field.
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 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.Publication Open Access Web interface for 3D visualization and analysis of SARS-CoV-2-human mimicry and interactions(Oxford University Press (OUP), 2021) Tsai, Chung-Jung; Nussinov, Ruth; Department of Chemical and Biological Engineering; Department of Computer Engineering; Keskin, Özlem; Gürsoy, Attila; Övek, Damla; Taweel, Ameer; Abalı, Zeynep; Tezsezen, Ece; Köroğlu, Yunus Emre; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26605; 8745; N/A; N/A; N/A; N/A; N/ASummary: we present a web-based server for navigating and visualizing possible interactions between SARS-CoV-2 and human host proteins. The interactions are obtained from HMI_Pred which relies on the rationale that virus proteins mimic host proteins. The structural alignment of the viral protein with one side of the human protein-protein interface determines the mimicry. The mimicked human proteins and predicted interactions, and the binding sites are presented. The user can choose one of the 18 SARS-CoV-2 protein structures and visualize the potential 3D complexes it forms with human proteins. The mimicked interface is also provided. The user can superimpose two interacting human proteins in order to see whether they bind to the same site or different sites on the viral protein. The server also tabulates all available mimicked interactions together with their match scores and number of aligned residues. This is the first server listing and cataloging all interactions between SARS-CoV-2 and human protein structures, enabled by our innovative interface mimicry strategy.