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Publication Metadata only Artificial intelligence based methods for hot spot prediction(Current Biology Ltd, 2022) N/A; N/A; N/A; N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Department of Chemical and Biological Engineering; Övek, Damla; Abalı, Zeynep; Zeylan, Melisa Ece; Keskin, Özlem; Gürsoy, Attila; Tunçbağ, Nurcan; PhD Student; PhD Student; PhD Student; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; 26605; 8745; 245513Proteins interact through their interfaces to fulfill essential functions in the cell. They bind to their partners in a highly specific manner and form complexes that have a profound effect on understanding the biological pathways they are involved in. Any abnormal interactions may cause diseases. Therefore, the identification of small molecules which modulate protein interactions through their interfaces has high thera-peutic potential. However, discovering such molecules is challenging. Most protein-protein binding affinity is attributed to a small set of amino acids found in protein interfaces known as hot spots. Recent studies demonstrate that drug-like small molecules specifically may bind to hot spots. Therefore, hot spot prediction is crucial. As experimental data accumulates, artificial intelligence begins to be used for computational hot spot prediction. First, we review machine learning and deep learning for computational hot spot prediction and then explain the significance of hot spots toward drug design.Publication Metadata only Constructing structural networks of signaling pathways on the proteome scale(Current Biology Ltd, 2012) Nussinov, Ruth; Department of Chemical and Biological Engineering; N/A; Department of Computer Engineering; Keskin, Özlem; Kuzu, Güray; Gürsoy, Attila; Faculty Member; PhD Student; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; 26605; N/A; 8745Proteins function through their interactions, and the availability of protein interaction networks could help in understanding cellular processes. However, the known structural data are limited and the classical network node-and-edge representation, where proteins are nodes and interactions are edges, shows only which proteins interact; not how they interact. Structural networks provide this information. Protein-protein interface structures can also indicate which binding partners can interact simultaneously and which are competitive, and can help forecasting potentially harmful drug side effects. Here, we use a powerful protein-protein interactions prediction tool which is able to carry out accurate predictions on the proteome scale to construct the structural network of the extracellular signal-regulated kinases (ERK) in the mitogen-activated protein kinase (MAPK) signaling pathway. This knowledge-based method, PRISM, is motif-based, and is combined with flexible refinement and energy scoring. PRISM predicts protein interactions based on structural and evolutionary similarity to known protein interfaces.Publication Metadata only Developments in integrative modeling with dynamical interfaces(Current Biology Ltd, 2019) Nussinov, Ruth; N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Özdemir, E. Sıla; Gürsoy, Attila; Keskin, Özlem; PhD Student; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); N/A; College of Engineering; College of Engineering; N/A; 8745; 40548Proteins are dynamic, and this holds especially for their surfaces. They display ensembles of conformations, which allows them to interact with diverse partners, often via the same patch of surface, and execute their distinct functions. Binding a specific partner can stimulate - or suppress - a distinct signaling pathway. This diversity poses a challenge: how to reliably model a specific protein-protein interaction (PPI)? This problem is compounded in protein assemblies, which are typically large, involving multiple protein-protein interfaces. Integrative modeling (IM), which combines diverse data, has emerged as the most promising strategy; however, modeling dynamical interfaces, often at the detailed level, which are at the heart of reliable predictions of assemblies, still poses a challenge. Here we review hurdles and advances in integrative modeling of dynamical interfaces; while some could have been predicted or expected, others transformed modeling in unanticipated ways. We further comment on what we believe could be possible future advances.Publication Metadata only Enriching the human apoptosis pathway by predicting the structures of protein-protein complexes(Elsevier, 2012) Nussinov, Ruth; Department of Chemical and Biological Engineering; Department of Computer Engineering; N/A; Keskin, Özlem; Gürsoy, Attila; Özbabacan, Saliha Ece Acuner; Faculty Member; Faculty Member; PhD Student; Department of Chemical and Biological Engineering; Department of Computer Engineering; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26605; 8745; 264351Apoptosis is a matter of life and death for cells and both inhibited and enhanced apoptosis may be involved in the pathogenesis of human diseases. The structures of protein-protein complexes in the apoptosis signaling pathway are important as the structural pathway helps in understanding the mechanism of the regulation and information transfer, and in identifying targets for drug design. Here, we aim to predict the structures toward a more informative pathway than currently available. Based on the 3D structures of complexes in the target pathway and a protein-protein interaction modeling tool which allows accurate and proteome-scale applications, we modeled the structures of 29 interactions, 21 of which were previously unknown. Next, 27 interactions which were not listed in the KEGG apoptosis pathway were predicted and subsequently validated by the experimental data in the literature. Additional interactions are also predicted. The multi-partner hub proteins are analyzed and interactions that can and cannot co-exist are identified. Overall, our results enrich the understanding of the pathway with interactions and provide structural details for the human apoptosis pathway. They also illustrate that computational modeling of protein-protein interactions on a large scale can help validate experimental data and provide accurate, structural atom-level detail of signaling pathways in the human cell.Publication Metadata only GTP-dependent K-Ras dimerization(Cell Press, 2015) Chavan, Tanmay S.; Freed, Benjamin C.; Jang, Hyunbum; Khavrutskii, Lyuba; Freed, R. Natasha; Dyba, Marzena A.; Stefanisko, Karen; Tarasov, Sergey G.; Gursoy, Attila; Keskin, Ozlem; Tarasova, Nadya I.; Gaponenko, Vadim; Nussinov, Ruth; N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Muratçıoğlu, Serena; Keskin, Özlem; Gürsoy, Attila; PhD Student; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 26605; 8745Ras proteins recruit and activate effectors, including Raf, that transmit receptor-initiated signals. Monomeric Ras can bind Raf; however, activation of Raf requires its dimerization. It has been suspected that dimeric Ras may promote dimerization and activation of Raf. Here, we show that the GTP-bound catalytic domain of K-Ras4B, a highly oncogenic splice variant of the K-Ras isoform, forms stable homodimers. We observe two major dimer interfaces. The first, highly populated beta-sheet dimer interface is at the Switch I and effector binding regions, overlapping the binding surfaces of Raf, PI3K, RalGDS, and additional effectors. This interface has to be inhibitory to such effectors. The second, helical interface also overlaps the binding sites of some effectors. This interface may promote activation of Raf. Our data reveal how Ras self-association can regulate effector binding and activity, and suggest that disruption of the helical dimer interface by drugs may abate Raf signaling in cancer.Publication Metadata only The key role of calmodulin in kras-driven adenocarcinomas(Amer Assoc Cancer Research, 2015) Nussinov, Ruth; Tsai, Chung-Jung; Jang, Hyunbum; N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Muratçıoğlu, Serena; Keskin, Özlem; Gürsoy, Attila; PhD Student; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 26605; 8745KRAS4B is a highly oncogenic splice variant of the KRAS isoform. It is the only isoform associated with initiation of adenocarcinomas. Insight into why and how KRAS4B can mediate ductal adenocarcinomas, particularly of the pancreas, is vastly important for its therapeutics. Here we point out the overlooked critical role of calmodulin (CaM). Calmodulin selectively binds to GTP-bound K-Ras4B; but not to other Ras isoforms. Cell proliferation and growth require the MAPK (Raf/MEK/ERK) and PI3K/Akt pathways. We propose that Ca2+/calmodulin promote PI3K alpha/Akt signaling, and suggest how. The elevated calcium levels clinically observed in adenocarcinomas may explain calmodulin's involvement in recruiting and stimulating PI3K alpha through interaction with its n/cSH2 domains as well as K-Ras4B; importantly, it also explains why K-Ras4B specifically is a key player in ductal carcinomas, such as pancreatic (PDAC), colorectal (CRC), and lung cancers. We hypothesize that calmodulin recruits and helps activate PI3K alpha at the membrane, and that this is the likely reason for Ca2+/calmodulin dependence in adenocarcinomas. Calmodulin can contribute to initiation/progression of ductal cancers via both PI3K alpha/Akt and Raf/MEK/ERK pathways. Blocking the K-Ras4B/MAPK pathway and calmodulin/PI3Ka binding in a K-Ras4B/calmodulin/PI3K alpha trimer could be a promising adenocarcinoma-specific therapeutic strategy.