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Predicting and validating a model of suppressor of IKKepsilon through biophysical characterization.

ABSTRACT: Suppressor of IKKepsilon (SIKE) is a 207 residue protein that is implicated in the TLR3-TANK-binding kinase-1-mediated response to viral infection. SIKE's function in this pathway is unknown, but SIKE forms interactions with two distinct cytoskeletal proteins, ?-actinin and tubulin, and SIKE knockout reduces cell migration. As structure informs function and in the absence of solved structural homologs, our studies were directed toward creating a structural model of SIKE through biochemical and biophysical characterization to probe and interrogate SIKE function. Circular dichroism revealed a primarily (73%) helical structure of minimal stability (?=32°C) but reversibly denatured. Limited proteolysis (LP) and chemical modification identified the N-terminal 2/3 of the protein as dynamic and accessible, whereas size exclusion chromatography (SEC) confirmed three homo-oligomeric species. SEC coupled to chemical crosslinking characterized the primary species as dimeric, a secondary hexameric species, and a higher order aggregate/polymer. Fluorescence polarization using intrinsic tryptophan fluorescence contextualized the anisotropy value for the SIKE dimer (molecular weight 51.8 kDa) among proteins of known structure, bovine serum albumin (BSA; 66?kDa), and glutamate dehydrogenase (GDH; 332?kDa). Radii of gyration for BSA and GDH provided exclusionary values for SIKE tertiary and dimeric quaternary models that otherwise conformed to secondary structure, LP, and modification data. Dimeric quaternary models were further culled using acrylamide quenching data of SIKE's single tryptophan that showed a single, protected environment. The low cooperativity of folding and regions of dynamic and potentially disordered structure advance the hypothesis that SIKE forms a conformational ensemble of native states that accommodate SIKE's interactions with multiple, distinct protein-binding partners.

SUBMITTER: Machek ML

PROVIDER: S-EPMC6635840 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Predicting and validating a model of suppressor of IKKepsilon through biophysical characterization.

Machek Megan L ML Sonnenschein Halie A HA Graham Sasha-Kaye I SI Shikwana Flowreen F Kim Seung-Hwan L SL Garcia DuBar Selena S Minzer Ian D ID Wey Ryan R Bell Jessica K JK

Protein science : a publication of the Protein Society 20190523 8

Suppressor of IKKepsilon (SIKE) is a 207 residue protein that is implicated in the TLR3-TANK-binding kinase-1-mediated response to viral infection. SIKE's function in this pathway is unknown, but SIKE forms interactions with two distinct cytoskeletal proteins, α-actinin and tubulin, and SIKE knockout reduces cell migration. As structure informs function and in the absence of solved structural homologs, our studies were directed toward creating a structural model of SIKE through biochemical and b ...[more]

PMID: 31074891

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Project description:PurposeTools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach.Methods and materialsClinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org.ResultsVariables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (P<.001). Learning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26).ConclusionsDistributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data-sharing barriers. We believe that distributed learning is the future of sharing data in health care.

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Predicting and validating a model of suppressor of IKKepsilon through biophysical characterization.

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Predicting and validating a model of suppressor of IKKepsilon through biophysical characterization.

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