var bibbase_data = {"data":"\"Loading..\"\n\n
\n\n \n\n \n\n \n \n\n \n\n \n \n\n \n\n \n
\n generated by\n \n \"bibbase.org\"\n\n \n
\n \n\n
\n\n \n\n\n
\n\n Excellent! Next you can\n create a new website with this list, or\n embed it in an existing web page by copying & pasting\n any of the following snippets.\n\n
\n JavaScript\n (easiest)\n
\n \n <script src=\"https://bibbase.org/show?bib=https://kuis-ai-publications.s3.eu-central-1.amazonaws.com/cbm.txt?dl=0&jsonp=1&jsonp=1\"></script>\n \n
\n\n PHP\n
\n \n <?php\n $contents = file_get_contents(\"https://bibbase.org/show?bib=https://kuis-ai-publications.s3.eu-central-1.amazonaws.com/cbm.txt?dl=0&jsonp=1\");\n print_r($contents);\n ?>\n \n
\n\n iFrame\n (not recommended)\n
\n \n <iframe src=\"https://bibbase.org/show?bib=https://kuis-ai-publications.s3.eu-central-1.amazonaws.com/cbm.txt?dl=0&jsonp=1\"></iframe>\n \n
\n\n

\n For more details see the documention.\n

\n
\n
\n\n
\n\n This is a preview! To use this list on your own web site\n or create a new web site from it,\n create a free account. The file will be added\n and you will be able to edit it in the File Manager.\n We will show you instructions once you've created your account.\n
\n\n
\n\n

To the site owner:

\n\n

Action required! Mendeley is changing its\n API. In order to keep using Mendeley with BibBase past April\n 14th, you need to:\n

    \n
  1. renew the authorization for BibBase on Mendeley, and
    2. \n
  2. update the BibBase URL\n in your page the same way you did when you initially set up\n this page.\n
    3. \n
\n

\n\n

\n \n \n Fix it now\n

\n
\n\n
\n\n\n
\n \n \n
\n
\n  \n 2021\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n The structural basis of Akt PH domain interaction with calmodulin.\n \n \n \n \n\n\n \n Weako, J.; Jang, H.; Keskin, O.; Nussinov, R.; and Gursoy, A.\n\n\n \n\n\n\n Biophysical Journal. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"ThePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@article{WEAKO2021,\n    author = "Weako, Jackson and Jang, Hyunbum and Keskin, Ozlem and Nussinov, Ruth and Gursoy, Attila",\n    title = "The structural basis of Akt PH domain interaction with calmodulin",\n    journal = "Biophysical Journal",\n    year = "2021",\n    issn = "0006-3495",\n    doi = "https://doi.org/10.1016/j.bpj.2021.03.018",\n    url = "https://www.sciencedirect.com/science/article/pii/S0006349521002484",\n    abstract = "Akt plays a key role in the Ras/PI3K/Akt/mTOR signaling pathway. In breast cancer, Akt translocation to the plasma membrane is enabled by the interaction of its pleckstrin homology domain (PHD) with calmodulin (CaM). At the membrane, the conformational change promoted by PIP3 releases CaM and facilitates Thr308 and Ser473 phosphorylation and activation. Here, using modeling and molecular dynamics simulations, we aim to figure out how CaM interacts with Akt’s PHD at the atomic level. Our simulations show that CaM-PHD interaction is thermodynamically stable and involves a β-strand rather than an α-helix, in agreement with NMR data, and that electrostatic and hydrophobic interactions are critical. The PHD interacts with CaM lobes; however, multiple modes are possible. IP4, the polar head of PIP3, weakens the CaM-PHD interaction, implicating the release mechanism at the plasma membrane. Recently, we unraveled the mechanism of PI3Kα activation at the atomistic level and the structural basis for Ras role in the activation. Here, our atomistic structural data clarify the mechanism of how CaM interacts, delivers, and releases Akt—the next node in the Ras/PI3K pathway—at the plasma membrane.",\n    keywords = "CBM"\n}\n\n
\n
\n\n\n
\n Akt plays a key role in the Ras/PI3K/Akt/mTOR signaling pathway. In breast cancer, Akt translocation to the plasma membrane is enabled by the interaction of its pleckstrin homology domain (PHD) with calmodulin (CaM). At the membrane, the conformational change promoted by PIP3 releases CaM and facilitates Thr308 and Ser473 phosphorylation and activation. Here, using modeling and molecular dynamics simulations, we aim to figure out how CaM interacts with Akt’s PHD at the atomic level. Our simulations show that CaM-PHD interaction is thermodynamically stable and involves a β-strand rather than an α-helix, in agreement with NMR data, and that electrostatic and hydrophobic interactions are critical. The PHD interacts with CaM lobes; however, multiple modes are possible. IP4, the polar head of PIP3, weakens the CaM-PHD interaction, implicating the release mechanism at the plasma membrane. Recently, we unraveled the mechanism of PI3Kα activation at the atomistic level and the structural basis for Ras role in the activation. Here, our atomistic structural data clarify the mechanism of how CaM interacts, delivers, and releases Akt—the next node in the Ras/PI3K pathway—at the plasma membrane.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Neuropsychiatric Symptoms of COVID-19 Explained by SARS-CoV-2 Proteins’ Mimicry of Human Protein Interactions.\n \n \n \n \n\n\n \n Yapici-Eser, H.; Koroglu, Y. E.; Oztop-Cakmak, O.; Keskin, O.; Gursoy, A.; and Gursoy-Ozdemir, Y.\n\n\n \n\n\n\n Frontiers in Human Neuroscience, 15: 126. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"NeuropsychiatricPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@article{10.3389/fnhum.2021.656313,\n    author = "Yapici-Eser, Hale and Koroglu, Yunus Emre and Oztop-Cakmak, Ozgur and Keskin, Ozlem and Gursoy, Attila and Gursoy-Ozdemir, Yasemin",\n    title = "Neuropsychiatric Symptoms of COVID-19 Explained by SARS-CoV-2 Proteins’ Mimicry of Human Protein Interactions",\n    journal = "Frontiers in Human Neuroscience",\n    volume = "15",\n    pages = "126",\n    year = "2021",\n    url = "https://www.frontiersin.org/article/10.3389/fnhum.2021.656313",\n    doi = "10.3389/fnhum.2021.656313",\n    issn = "1662-5161",\n    abstract = "The first clinical symptoms focused on the presentation of coronavirus disease 2019 (COVID-19) have been respiratory failure, however, accumulating evidence also points to its presentation with neuropsychiatric symptoms, the exact mechanisms of which are not well known. By using a computational methodology, we aimed to explain the molecular paths of COVID-19 associated neuropsychiatric symptoms, based on the mimicry of the human protein interactions with SARS-CoV-2 proteins.Methods: Available 11 of the 29 SARS-CoV-2 proteins’ structures have been extracted from Protein Data Bank. HMI-PRED (Host-Microbe Interaction PREDiction), a recently developed web server for structural PREDiction of protein-protein interactions (PPIs) between host and any microbial species, was used to find the “interface mimicry” through which the microbial proteins hijack host binding surfaces. Classification of the found interactions was conducted using the PANTHER Classification System.Results: Predicted Human-SARS-CoV-2 protein interactions have been extensively compared with the literature. Based on the analysis of the molecular functions, cellular localizations and pathways related to human proteins, SARS-CoV-2 proteins are found to possibly interact with human proteins linked to synaptic vesicle trafficking, endocytosis, axonal transport, neurotransmission, growth factors, mitochondrial and blood-brain barrier elements, in addition to its peripheral interactions with proteins linked to thrombosis, inflammation and metabolic control.Conclusion: SARS-CoV-2-human protein interactions may lead to the development of delirium, psychosis, seizures, encephalitis, stroke, sensory impairments, peripheral nerve diseases, and autoimmune disorders. Our findings are also supported by the previous in vivo and in vitro studies from other viruses. Further in vivo and in vitro studies using the proteins that are pointed here, could pave new targets both for avoiding and reversing neuropsychiatric presentations.",\n    keywords = "CBM"\n}\n\n
\n
\n\n\n
\n The first clinical symptoms focused on the presentation of coronavirus disease 2019 (COVID-19) have been respiratory failure, however, accumulating evidence also points to its presentation with neuropsychiatric symptoms, the exact mechanisms of which are not well known. By using a computational methodology, we aimed to explain the molecular paths of COVID-19 associated neuropsychiatric symptoms, based on the mimicry of the human protein interactions with SARS-CoV-2 proteins.Methods: Available 11 of the 29 SARS-CoV-2 proteins’ structures have been extracted from Protein Data Bank. HMI-PRED (Host-Microbe Interaction PREDiction), a recently developed web server for structural PREDiction of protein-protein interactions (PPIs) between host and any microbial species, was used to find the “interface mimicry” through which the microbial proteins hijack host binding surfaces. Classification of the found interactions was conducted using the PANTHER Classification System.Results: Predicted Human-SARS-CoV-2 protein interactions have been extensively compared with the literature. Based on the analysis of the molecular functions, cellular localizations and pathways related to human proteins, SARS-CoV-2 proteins are found to possibly interact with human proteins linked to synaptic vesicle trafficking, endocytosis, axonal transport, neurotransmission, growth factors, mitochondrial and blood-brain barrier elements, in addition to its peripheral interactions with proteins linked to thrombosis, inflammation and metabolic control.Conclusion: SARS-CoV-2-human protein interactions may lead to the development of delirium, psychosis, seizures, encephalitis, stroke, sensory impairments, peripheral nerve diseases, and autoimmune disorders. Our findings are also supported by the previous in vivo and in vitro studies from other viruses. Further in vivo and in vitro studies using the proteins that are pointed here, could pave new targets both for avoiding and reversing neuropsychiatric presentations.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2020\n \n \n (7)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n HMI-PRED: A Web Server for Structural Prediction of Host-Microbe Interactions Based on Interface Mimicry.\n \n \n \n\n\n \n Guven-Maiorov, E.; Hakouz, A.; Valjevac, S.; Keskin, O.; Tsai, C.; Gursoy, A.; and Nussinov, R.\n\n\n \n\n\n\n Journal of molecular biology. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@article{GuvenMaiorov2020HMIPREDAW,\n    author = "Guven-Maiorov, Emine and Hakouz, Asma and Valjevac, Sukejna and Keskin, O. and Tsai, Chung-Jung and Gursoy, A. and Nussinov, R.",\n    title = "HMI-PRED: A Web Server for Structural Prediction of Host-Microbe Interactions Based on Interface Mimicry.",\n    journal = "Journal of molecular biology",\n    year = "2020",\n    keywords = "CBM"\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Oncogenic K-Ras4B Dimerization Enhances Downstream Mitogen-activated Protein Kinase Signaling.\n \n \n \n\n\n \n Muratcioğlu, S.; Aydin, C.; Odabasi, E.; Ozdemir, E.; Firat-Karalar, E. N.; Jang, H.; Tsai, C.; Nussinov, R.; Kavakli, I.; Gursoy, A.; and Keskin, O.\n\n\n \n\n\n\n Journal of Molecular Biology, 432: 1199-1215. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@article{Muratciolu2020OncogenicKD,\n    author = "Muratcio{\\u{g}}lu, Serena and Aydin, Cihan and Odabasi, Ezgi and Ozdemir, E. and Firat-Karalar, E. N. and Jang, H. and Tsai, Chung-Jung and Nussinov, R. and Kavakli, I. and Gursoy, A. and Keskin, O.",\n    title = "Oncogenic K-Ras4B Dimerization Enhances Downstream Mitogen-activated Protein Kinase Signaling.",\n    journal = "Journal of Molecular Biology",\n    year = "2020",\n    volume = "432",\n    pages = "1199-1215",\n    keywords = "CBM"\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Embedding Alternative Conformations of Proteins in Protein-Protein Interaction Networks.\n \n \n \n\n\n \n Halakou, F.; Gürsoy, A.; and Keskin, O.\n\n\n \n\n\n\n Methods in molecular biology, 2074. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@article{Halakou2020EmbeddingAC,\n    author = {Halakou, Farideh and G{\\"u}rsoy, Attila and Keskin, O.},\n    title = "Embedding Alternative Conformations of Proteins in Protein-Protein Interaction Networks",\n    journal = "Methods in molecular biology",\n    year = "2020",\n    volume = "2074",\n    keywords = "CBM"\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Identification of potential inhibitors of human methionine aminopeptidase (type II) for cancer therapy: Structure-based virtual screening, ADMET prediction and molecular dynamics studies.\n \n \n \n \n\n\n \n Weako, J.; Uba, A. I.; Keskin, Ö.; Gürsoy, A.; and Yelekçi, K.\n\n\n \n\n\n\n Computational biology and chemistry, 86: 107244. June 2020.\n \n\n\n\n
\n\n\n\n \n \n \"IdentificationPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@article{PMID:32252002,\n    author = {Weako, Jackson and Uba, Abdullahi Ibrahim and Keskin, {\\"O}zlem and G{\\"u}rsoy, Attila and Yelek{\\c{c}}i, Kemal},\n    title = "Identification of potential inhibitors of human methionine aminopeptidase (type II) for cancer therapy: Structure-based virtual screening, ADMET prediction and molecular dynamics studies",\n    doi = "10.1016/j.compbiolchem.2020.107244",\n    volume = "86",\n    month = "June",\n    year = "2020",\n    journal = "Computational biology and chemistry",\n    issn = "1476-9271",\n    pages = "107244",\n    abstract = "Methionine Aminopeptidases MetAPs are divalent-cofactor dependent enzymes that are responsible for the cleavage of the initiator Methionine from the nascent polypeptides. MetAPs are classified into two isoforms: namely, MetAP1 and MetAP2. Several studies have revealed that MetAP2 is upregulated in various cancers, and its inhibition has shown to suppress abnormal or excessive blood vessel formation and tumor growth in model organisms. Clinical studies show that the natural product fumagillin, and its analogs are potential inhibitors of MetAP2. However, due to their poor pharmacokinetic properties and neurotoxicities in clinical studies, their further developments have received a great setback. Here, we apply structure-based virtual screening and molecular dynamics methods to identify a new class of potential inhibitors for MetAP2. We screened Otava's Chemical Library, which consists of about 3 200 000 tangible-chemical compounds, and meticulously selected the top 10 of these compounds based on their inhibitory potentials against MetAP2. The top hit compounds subjected to ADMET predictor using 3 independent ADMET prediction programs, were found to be drug-like. To examine the stability of ligand binding mode, and efficacy, the unbound form of MetAP2, its complexes with fumagillin, spiroepoxytriazole, and the best promising compounds compound-3369841 and compound-3368818 were submitted to 100 ns molecular dynamics simulation. Like fumagillin, spiroepoxytriazole, and both compound-3369841 and compound-3368818 showed stable binding mode over time during the simulations. Taken together, these uninherited-fumagillin compounds may serve as new class of inhibitors or provide scaffolds for further optimization towards the design of more potent MetAP2 inhibitors -development of such inhibitors would be essential strategy against various cancer types.",\n    url = "https://doi.org/10.1016/j.compbiolchem.2020.107244",\n    keywords = "CBM"\n}\n\n
\n
\n\n\n
\n Methionine Aminopeptidases MetAPs are divalent-cofactor dependent enzymes that are responsible for the cleavage of the initiator Methionine from the nascent polypeptides. MetAPs are classified into two isoforms: namely, MetAP1 and MetAP2. Several studies have revealed that MetAP2 is upregulated in various cancers, and its inhibition has shown to suppress abnormal or excessive blood vessel formation and tumor growth in model organisms. Clinical studies show that the natural product fumagillin, and its analogs are potential inhibitors of MetAP2. However, due to their poor pharmacokinetic properties and neurotoxicities in clinical studies, their further developments have received a great setback. Here, we apply structure-based virtual screening and molecular dynamics methods to identify a new class of potential inhibitors for MetAP2. We screened Otava's Chemical Library, which consists of about 3 200 000 tangible-chemical compounds, and meticulously selected the top 10 of these compounds based on their inhibitory potentials against MetAP2. The top hit compounds subjected to ADMET predictor using 3 independent ADMET prediction programs, were found to be drug-like. To examine the stability of ligand binding mode, and efficacy, the unbound form of MetAP2, its complexes with fumagillin, spiroepoxytriazole, and the best promising compounds compound-3369841 and compound-3368818 were submitted to 100 ns molecular dynamics simulation. Like fumagillin, spiroepoxytriazole, and both compound-3369841 and compound-3368818 showed stable binding mode over time during the simulations. Taken together, these uninherited-fumagillin compounds may serve as new class of inhibitors or provide scaffolds for further optimization towards the design of more potent MetAP2 inhibitors -development of such inhibitors would be essential strategy against various cancer types.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Beyond the heterodimer model for mineralocorticoid and glucocorticoid receptor interactions in nuclei and at DNA.\n \n \n \n\n\n \n Pooley, J.; Rivers, C.; Kilcooley, M.; Paul, S.; Cavga, A.; Kershaw, Y.; Muratcioglu, S.; Gursoy, A.; Keskin, O.; and Lightman, S.\n\n\n \n\n\n\n PLoS ONE, 15. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@article{b01431ce9b1e48a7b4452b7ad91e552a,\n    author = "Pooley, John and Rivers, {Caroline A} and Kilcooley, Michael and Paul, {Susana N} and Cavga, {Ayse Derya} and Kershaw, {Yvonne M} and Muratcioglu, Serena and Gursoy, Attila and Keskin, Ozlem and Lightman, {Stafford L}",\n    title = "Beyond the heterodimer model for mineralocorticoid and glucocorticoid receptor interactions in nuclei and at DNA.",\n    abstract = "Glucocorticoid (GR) and mineralocorticoid receptors (MR) are believed to classically bind DNA as homodimers or MR-GR heterodimers to influence gene regulation in response to pulsatile basal or stress-evoked glucocorticoid secretion. Pulsed corticosterone presentation reveals MR and GR co-occupy DNA only at the peaks of glucocorticoid oscillations, allowing interaction. GR DNA occupancy was pulsatile, while MR DNA occupancy was prolonged through the inter-pulse interval. In mouse mammary 3617 cells MR-GR interacted in the nucleus and at a chromatin-associated DNA binding site. Interactions occurred irrespective of ligand type and receptors formed complexes of higher order than heterodimers. We also detected MR-GR interactions ex-vivo in rat hippocampus. An expanded range of MR-GR interactions predicts structural allostery allowing a variety of transcriptional outcomes and is applicable to the multiple tissue types that co-express both receptors in the same cells whether activated by the same or different hormones.",\n    year = "2020",\n    doi = "10.1371/journal.pone.0227520",\n    volume = "15",\n    journal = "PLoS ONE",\n    publisher = "Public Library of Science",\n    keywords = "CBM"\n}\n\n
\n
\n\n\n
\n Glucocorticoid (GR) and mineralocorticoid receptors (MR) are believed to classically bind DNA as homodimers or MR-GR heterodimers to influence gene regulation in response to pulsatile basal or stress-evoked glucocorticoid secretion. Pulsed corticosterone presentation reveals MR and GR co-occupy DNA only at the peaks of glucocorticoid oscillations, allowing interaction. GR DNA occupancy was pulsatile, while MR DNA occupancy was prolonged through the inter-pulse interval. In mouse mammary 3617 cells MR-GR interacted in the nucleus and at a chromatin-associated DNA binding site. Interactions occurred irrespective of ligand type and receptors formed complexes of higher order than heterodimers. We also detected MR-GR interactions ex-vivo in rat hippocampus. An expanded range of MR-GR interactions predicts structural allostery allowing a variety of transcriptional outcomes and is applicable to the multiple tissue types that co-express both receptors in the same cells whether activated by the same or different hormones.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n The structural basis of the oncogenic mutant K-Ras4B homodimers.\n \n \n \n \n\n\n \n Kosoglu, K.; Omur, M. E.; Jang, H.; Nussinov, R.; Keskin, O.; and Gursoy, A.\n\n\n \n\n\n\n bioRxiv. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"ThePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@article{Kosoglu2020.09.07.285783,\n    author = "Kosoglu, Kayra and Omur, Meltem Eda and Jang, Hyunbum and Nussinov, Ruth and Keskin, Ozlem and Gursoy, Attila",\n    title = "The structural basis of the oncogenic mutant K-Ras4B homodimers",\n    year = "2020",\n    doi = "10.1101/2020.09.07.285783",\n    publisher = "Cold Spring Harbor Laboratory",\n    abstract = "Ras proteins activate their effectors through physical interactions in response to the various extracellular stimuli at the plasma membrane. Oncogenic Ras forms dimer and nanoclusters at the plasma membrane, boosting the downstream MAPK signal. It was reported that K-Ras4B can dimerize through two major interfaces: (i) the effector lobe interface, mapped to Switch I and effector binding regions; (ii) the allosteric lobe interface involving α3 and α4 helices. Recent experiments showed that constitutively active, oncogenic mutant K-Ras4BG12D dimers are enriched in the plasma membrane. Here, we perform molecular dynamics simulations of K-Ras4BG12D homodimers aiming to quantify the two major interfaces in atomic level. To examine the effect of mutations on dimerization, two double mutations, K101D/R102E on the allosteric lobe and R41E/K42D on the effector lobe interfaces were added to the K-Ras4BG12D dimer simulations. We observed that the effector lobe K-Ras4BG12D dimer is stable, while the allosteric lobe dimer alters its helical interface during the simulations, presenting multiple conformations. The K101D/R102E mutations slightly weakens the allosteric lobe interface. However, the R41E/K42D mutations disrupt the effector lobe interface. Using the homo-oligomers prediction server, we obtained trimeric, tetrameric, and pentameric complexes with the allosteric lobe K-Ras4BG12D dimers. However, the allosteric lobe dimer with the K101D/R102E mutations is not capable of generating multiple higher order structures. Our detailed interface analysis may help to develop inhibitor design targeting functional Ras dimerization and high order oligomerization at the membrane signaling platform.",\n    url = "https://www.biorxiv.org/content/early/2020/09/07/2020.09.07.285783",\n    journal = "bioRxiv",\n    keywords = "CBM"\n}\n\n
\n
\n\n\n
\n Ras proteins activate their effectors through physical interactions in response to the various extracellular stimuli at the plasma membrane. Oncogenic Ras forms dimer and nanoclusters at the plasma membrane, boosting the downstream MAPK signal. It was reported that K-Ras4B can dimerize through two major interfaces: (i) the effector lobe interface, mapped to Switch I and effector binding regions; (ii) the allosteric lobe interface involving α3 and α4 helices. Recent experiments showed that constitutively active, oncogenic mutant K-Ras4BG12D dimers are enriched in the plasma membrane. Here, we perform molecular dynamics simulations of K-Ras4BG12D homodimers aiming to quantify the two major interfaces in atomic level. To examine the effect of mutations on dimerization, two double mutations, K101D/R102E on the allosteric lobe and R41E/K42D on the effector lobe interfaces were added to the K-Ras4BG12D dimer simulations. We observed that the effector lobe K-Ras4BG12D dimer is stable, while the allosteric lobe dimer alters its helical interface during the simulations, presenting multiple conformations. The K101D/R102E mutations slightly weakens the allosteric lobe interface. However, the R41E/K42D mutations disrupt the effector lobe interface. Using the homo-oligomers prediction server, we obtained trimeric, tetrameric, and pentameric complexes with the allosteric lobe K-Ras4BG12D dimers. However, the allosteric lobe dimer with the K101D/R102E mutations is not capable of generating multiple higher order structures. Our detailed interface analysis may help to develop inhibitor design targeting functional Ras dimerization and high order oligomerization at the membrane signaling platform.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Embedding Alternative Conformations of Proteins in Protein-Protein Interaction Networks.\n \n \n \n\n\n \n Halakou, F.; ; and - Ozlem Keskin, A. G.\n\n\n \n\n\n\n In Protein-Protein Interaction Networks, Methods and Protocols. Springer, 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@incollection{DBLP:series/mimb/HalakouGK20,\n    author = {Halakou, Farideh and and- Ozlem Keskin, Attila G{\\"{u}}rsoy},\n    title = "Embedding Alternative Conformations of Proteins in Protein-Protein Interaction Networks",\n    booktitle = "Protein-Protein Interaction Networks, Methods and Protocols",\n    publisher = "Springer",\n    year = "2020",\n    keywords = "CBM"\n}\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2017\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n Androgen receptor binding sites are highly mutated in prostate cancer.\n \n \n \n\n\n \n Morova, T.; Gönen, M.; Gursoy, A.; Keskin, Ö.; and Lack, N. A.\n\n\n \n\n\n\n bioRxiv. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@article{Morova2017AndrogenRB,\n    author = {Morova, Tun{\\c{c}} and G{\\"o}nen, M. and Gursoy, A. and Keskin, {\\"O}zlem and Lack, Nathan A.},\n    title = "Androgen receptor binding sites are highly mutated in prostate cancer",\n    journal = "bioRxiv",\n    year = "2017",\n    keywords = "CBM"\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Androgen receptor binding sites are highly mutated in prostate cancer.\n \n \n \n \n\n\n \n Morova, T.; Gönen, M.; Gursoy, A.; Keskin, Ö.; and Lack, N. A.\n\n\n \n\n\n\n bioRxiv. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"AndrogenPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@article{Morova225433,\n    author = {Morova, Tun{\\c c} and G{\\"o}nen, Mehmet and Gursoy, Attila and Keskin, {\\"O}zlem and Lack, Nathan A.},\n    title = "Androgen receptor binding sites are highly mutated in prostate cancer",\n    year = "2017",\n    doi = "10.1101/225433",\n    publisher = "Cold Spring Harbor Laboratory",\n    abstract = "Androgen receptor (AR) signalling is essential to nearly all prostate cancer cells. Any alterations to AR-mediated transcription can have a profound effect on prostate carcinogenesis and tumour growth. While the AR protein has been extensively studied, little is know about mutations to the non-coding regions where AR binds to DNA. Using clinical whole genome sequencing, we demonstrate that AR binding sites have a dramatically increased rate of mutations that is greater than any other transcription factor and specific to only prostate cancer. Demonstrating this may be common to lineage-specific transcription factors, estrogen receptor binding sites had an elevated rate of mutations in breast cancer. Based on the mutations observed at the binding site of AR and other related transcription factors, we proposed that AR occupancy impairs access of base excision repair enzymes to endogenous DNA damage. Overall, this work demonstrates that non-coding AR binding sites are frequently mutated in prostate cancer and may potentially act as driver mutations.",\n    url = "https://www.biorxiv.org/content/early/2017/11/27/225433",\n    journal = "bioRxiv",\n    keywords = "CBM"\n}\n\n
\n
\n\n\n
\n Androgen receptor (AR) signalling is essential to nearly all prostate cancer cells. Any alterations to AR-mediated transcription can have a profound effect on prostate carcinogenesis and tumour growth. While the AR protein has been extensively studied, little is know about mutations to the non-coding regions where AR binds to DNA. Using clinical whole genome sequencing, we demonstrate that AR binding sites have a dramatically increased rate of mutations that is greater than any other transcription factor and specific to only prostate cancer. Demonstrating this may be common to lineage-specific transcription factors, estrogen receptor binding sites had an elevated rate of mutations in breast cancer. Based on the mutations observed at the binding site of AR and other related transcription factors, we proposed that AR occupancy impairs access of base excision repair enzymes to endogenous DNA damage. Overall, this work demonstrates that non-coding AR binding sites are frequently mutated in prostate cancer and may potentially act as driver mutations.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n\n\n \n\n \n \n \n \n\n
\n"}; document.write(bibbase_data.data);