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Publication Metadata only A strategy based on protein-protein interface motifs may help in identifying drug off-targets(American Chemical Society (ACS), 2012) Nussinov, Ruth; Department of Chemical and Biological Engineering; Department of Computer Engineering; Keskin, Özlem; Gürsoy, Attila; Ergin, Billur Çelebi; Faculty Member; Faculty Member; Teaching Faculty; Department of Chemical and Biological Engineering; Department of Computer Engineering; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; College of Engineering; 26605; 8745; 261792Networks are increasingly used to study the impact of drugs at the systems level. From the algorithmic standpoint, a drug can "attack" nodes or edges of a protein-protein interaction network In this work, we propose a new network strategy, "The Interface Attack", based on protein-protein interfaces. Similar interface architectures can occur between unrelated proteins. Consequently, in principle, a drug that binds to one has a certain probability of binding to others. The interface attack strategy simultaneously removes from the network all interactions that consist of similar interface motifs. This strategy is inspired by network pharmacology and allows inferring potential off-targets. We introduce a network model that we call "Protein Interface and Interaction Network (P2IN)", which is the integration of protein-protein interface structures and protein interaction networks. This interface based, network organization clarifies which protein pairs have structurally similar interfaces and which proteins may compete to bind the same surface region. We built the P2IN with the p53 signaling network and performed network robustness analysis. We show that (1) "hitting" frequent interfaces (a set of edges distributed around the network) might be as destructive as eleminating high degree proteins (hub nodes), (2) frequent interfaces are not always topologically critical elements in the network, and (3) interface attack may reveal functional changes in the system better than the attack of single proteins. In the off target detection case study, we found that drugs blocking the interface between CDK6 and CDKN2D may also affect the interaction between CDK4 and CDKN2D.Publication Metadata only Analysis of single amino acid variations in singlet hot spots of protein-protein interfaces(Oxford Univ Press, 2018) N/A; N/A; Department of Computer Engineering; Department of Chemical and Biological Engineering; Özdemir, E. Sıla; Gürsoy, Attila; Keskin, Özlem; PhD Student; Faculty Member; 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); Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 8745; 26605Motivation: Single amino acid variations (SAVs) in protein-protein interaction (PPI) sites play critical roles in diseases. PPI sites (interfaces) have a small subset of residues called hot spots that contribute significantly to the binding energy, and they may form clusters called hot regions. Singlet hot spots are the single amino acid hot spots outside of the hot regions. The distribution of SAVs on the interface residues may be related to their disease association. Results: We performed statistical and structural analyses of SAVs with literature curated experimental thermodynamics data, and demonstrated that SAVs which destabilize PPIs are more likely to be found in singlet hot spots rather than hot regions and energetically less important interface residues. In contrast, non-hot spot residues are significantly enriched in neutral SAVs, which do not affect PPI stability. Surprisingly, we observed that singlet hot spots tend to be enriched in disease-causing SAVs, while benign SAVs significantly occur in non-hot spot residues. Our work demonstrates that SAVs in singlet hot spot residues have significant effect on protein stability and function.Publication Open Access Binding mechanism of neutralizing nanobodies targeting SARS-CoV-2 spike glycoprotein(American Chemical Society (ACS), 2021) Gölcük, M.; Hacısüleyman, A.; Yıldız, A.; Gür, M.; Department of Chemical and Biological Engineering; Erman, Burak; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 179997Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters human cells upon binding of its spike (S) glycoproteins to ACE2 receptors. Several nanobodies neutralize SARS-CoV-2 infection by binding to the receptor-binding domain (RBD) of the S protein, but how their binding antagonizes S-ACE2 interactions is not well understood. Here, we identified interactions between the RBD and nanobodies H11-H4, H11-D4, and Ty1 by performing all-atom molecular dynamics simulations. H11-H4 and H11-D4 can bind to RBD without overlapping with ACE2. H11-H4, and to a lesser extent H11-D4, binding dislocates ACE2 from its binding site due to electrostatic repulsion. In comparison, Ty1 overlaps with ACE2 on RBD and has a similar binding strength to ACE2. Mutations in the Alpha variant of SARS-CoV-2 had a minor effect in RBD binding strengths of ACE2 and nanobodies, but reduced the ability of H11-H4 and H11-D4 to dislocate ACE2 from RBD. In comparison, the Beta variant weakened the RBD binding strengths of H11-H4 and H11-D4, which were less effective to dislocate ACE2 binding. Unexpectedly, mutations in Beta strengthened Ty1 binding to RBD, suggesting that this nanobody may be more effective to neutralize the Beta variant of SARS-CoV-2.Publication Metadata only Computers and chemical engineering virtual special issue in honor of professor george stephanopoulos foreword(Elsevier, 2022) Bakshi, Bhavik R.; Realff, Matthew; Morari, Manfred; Department of Chemical and Biological Engineering; Arkun, Yaman; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 108526Publication Metadata only Dynamic modeling of an industrial diesel hydroprocessing plant by the method of continuous lumping(Elsevier, 2015) Canan, Ümmuhan; İş, Gamze; Erdoğan, Murat; Department of Chemical and Biological Engineering; N/A; Aydın, Erdal; Çelebi, Ayşe Dilan; Şıldır, Hasan; Arkun, Yaman; Faculty Member; Master Student; PHD Student; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; 311745; N/A; 242076; 108526Diesel hydroprocessing is an important refinery process which consists of hydrodesulfurization to remove the undesired sulfur from the oil feedstock followed by hydrocracking and fractionation to obtain diesel with desired properties. Due to the new emission standards to improve the air quality, there is an increasing demand for the production of ultra low sulfur diesel fuel. This paper is addressing the development of a reliable dynamic process model which can be used for real-time optimization and control purposes to improve the process conditions of existing plants to meet the low-sulfur demand. The overall plant model consists of a hydrodesulfurization (HDS) model for the first two reactor beds followed by a hydrocracking (HC) model for the last cracking bed. The models are dynamic, non-isothermal, pseudo-homogeneous plug flow reactor models. Reaction kinetics are modeled using the method of continuous lumping which treats the reaction medium as a continuum of species whose reactivities depend on the true boiling point of the mixture. The key modeling parameters are estimated using industrial data. Steady-state and dynamic model predictions of the reactor bed temperatures, sulfur removal, and diesel production match closely the plant data. (C) 2015 Elsevier Ltd. All rights reserved.Publication Metadata only Equipment selection for coupling a microgrid with a power-to-gas system in the context of optimal design and operation(Elsevier Ltd, 2024) Akülker, Handan; Department of Chemical and Biological Engineering; Aydın, Erdal; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); College of EngineeringThis study proposes a one-layer deterministic Mixed-Integer Nonlinear Programming to design and schedule a PTG-integrated microgrid. The key contribution is that optimal equipment selection, design, and scheduling, considering the PTG system at the core of the problem, are determined just in a single formulation. Scenarios based on different carbon dioxide taxes and natural gas prices are investigated. Only one wind turbine farm is chosen when the carbon dioxide tax is increased from 50 $/ton to 100 $/ton. On the other hand, when the natural gas price is increased from 1.548 $/m3 to 1.72 $/m3, two wind turbine farms are selected. Solar panel arrays are not chosen in all the scenarios. Generated power by solar panels is not enough for installation despite their much lower carbon dioxide emissions and negligible operational costs. Consequently, the optimal equipment selections may change linked to the natural gas price and carbon dioxide tax.Publication Metadata only Folding dynamics of proteins from denatured to native state: principal component analysis(Mary Ann Liebert, Inc, 2004) N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Department of Chemical and Biological Engineering; Department of Chemical and Biological Engineering; Palazoğlu, Ahmet; Gürsoy, Attila; Arkun, Yaman; Erman, Burak; Other; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; College of Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; 8745; 108526; N/ASeveral trajectories starting from random configurations and ending in the native state for chymotrypsin inhibitor 2, CI2, are generated using a Go-type model where the backbone torsional angles execute random jumps on which a drift towards their native values is superposed. Bond lengths and bond angles are kept fixed, and the size of the backbone atoms and side groups are recognized. The large datasets obtained are analyzed using a particular type of principal component analysis known as Karhunen - Loeve expansion (KLE). Trajectories are decomposed separately into modes in residue space and time space. General features of different folding trajectories are compared in the modal space and relationships between the structure of CI2 and its folding dynamics are obtained. Dynamic scaling and order reduction of the folding trajectories are discussed. A continuous wavelet transform is used to decompose the nonstationary folding trajectories into windows exhibiting different features of folding dynamics. Analysis of correlations confirms the known two-state nature of folding of CI2. All of the conserved residues of the protein are shown to be stationary in the small modes of the residue space. The sequential nature of folding is shown by examining the slow modes of the trajectories. The present model of protein folding dynamics is compared with the simple Rouse model of polymer dynamics. Principal component analysis is shown to be a very effective tool for the characterization of the general folding features of proteins.Publication Open Access HMI-PRED 2.0: a biologist-oriented web application for prediction of host-microbe protein-protein interaction by interface mimicry(Oxford University Press (OUP), 2022) Lim, H., Tsai, C.J.; Nussinov, R.; Department of Computer Engineering; Department of Chemical and Biological Engineering; Keskin, Özlem; Gürsoy, Attila; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; College of Engineering; 26605; 8745HMI-PRED 2.0 is a publicly available web service for the prediction of host-microbe protein-protein interaction by interface mimicry that is intended to be used without extensive computational experience. A microbial protein structure is screened against a database covering the entire available structural space of complexes of known human proteins.Publication Open Access Hypothetical yet effective: computational identification of high-performing MOFs for CO2 capture(Elsevier, 2022) Department of Chemical and Biological Engineering; Demir, Hakan; Keskin, Seda; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; N/A; 40548With the advances in computational resources and algorithms, computer simulations are being increasingly used to tackle the most challenging problems of the world. Among them, CO2 capture is a topic that needs imminent attention as the presence of high levels of CO2 in the air can lead to drastic shifts in global climate. Here, a recently developed hypothetical metal-organic framework (MOF) database comprised of anion-pillared (AP) MOFs is computationally screened for the separation of CO2/CO, CO2/H-2, and CO2/N-2 gas mixtures at room temperature. The best performing MOFs are identified using three performance metrics, adsorption selectivity, working capacity, and regenerability, in conjunction. In these top materials, the preferential adsorption sites are illustrated, which will be useful in guiding the experimental design of new MOFs with extraordinarily high CO2 selectivities. The favorable separation performances of AP MOFs suggest that efficient gas separations can be conducted using MOFs without open metal sites.Publication Metadata only Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy(Oxford Univ Press, 2009) N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Department of Chemical and Biological Engineering; Tunçbağ, Nurcan; Gürsoy, Attila; Keskin, Özlem; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; College of Engineering; College of Engineering; College of Engineering; 245513; 8745; 26605Motivation: Hot spots are residues comprising only a small fraction of interfaces yet accounting for the majority of the binding energy. These residues are critical in understanding the principles of protein interactions. Experimental studies like alanine scanning mutagenesis require significant effort; therefore, there is a need for computational methods to predict hot spots in protein interfaces. Results: We present a new intuitive efficient method to determine computational hot spots based on conservation (C), solvent accessibility [accessible surface area (ASA)] and statistical pairwise residue potentials (PP) of the interface residues. Combination of these features is examined in a comprehensive way to study their effect in hot spot detection. The predicted hot spots are observed to match with the experimental hot spots with an accuracy of 70% and a precision of 64% in Alanine Scanning Energetics Database (ASEdb), and accuracy of 70% and a precision of 73% in Binding Interface Database (BID). Several machine learning methods are also applied to predict hot spots. Performance of our empirical approach exceeds learning-based methods and other existing hot spot prediction methods. Residue occlusion from solvent in the complexes and pairwise potentials are found to be the main discriminative features in hot spot prediction. Conclusion: Our empirical method is a simple approach in hot spot prediction yet with its high accuracy and computational effectiveness. We believe that this method provides insights for the researchers working on characterization of protein binding sites and design of specific therapeutic agents for protein interactions.
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