Modeling transcriptional activation changes to Gal4 variants via structure-based computational mutagenesis
| العنوان: | Modeling transcriptional activation changes to Gal4 variants via structure-based computational mutagenesis |
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| المؤلفون: | Purnima Pyarasani, Nitin Rao, Majid Masso |
| المصدر: | PeerJ PeerJ, Vol 6, p e4844 (2018) |
| بيانات النشر: | PeerJ, 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | 0301 basic medicine, Variant function prediction, Bioinformatics, Feature vector, Data Mining and Machine Learning, Saccharomyces cerevisiae, lcsh:Medicine, Mutagenesis (molecular biology technique), Computational biology, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Knowledge-based potential, Machine learning, A-DNA, Gene, biology, General Neuroscience, lcsh:R, fungi, Computational Biology, General Medicine, biology.organism_classification, Regression, Structure–function relationships, 030104 developmental biology, Order (biology), Computational mutagenesis, Gal4, General Agricultural and Biological Sciences, Function (biology) |
| الوصف: | As a DNA binding transcriptional activator, Gal4 promotes the expression of genes responsible for galactose metabolism. The Gal4 protein fromSaccharomyces cerevisiae(baker’s yeast) has become a model for studying eukaryotic transcriptional activation in general because its regulatory properties mirror those of several eukaryotic organisms, including mammals. Given the availability of a crystallographic structure for Gal4, here we implement anin silicomutagenesis technique that makes use of a four-body knowledge-based energy function, in order to empirically quantify the structural impacts associated with single residue substitutions on the Gal4 protein. These results were used to examine the structure-function relationship in Gal4 based on a recently published experimental mutagenesis study, whereby functional changes to a uniformly distributed set of 1,068 single residue Gal4 variants were obtained by measuring their transcriptional activation levels relative to wild-type. A significant correlation was observed between computed (scalar) structural effect data and measured activity values for this collection of single residue Gal4 variants. Additionally, attribute vectors quantifying position-specific environmental impacts were generated for each of the Gal4 variants via computational mutagenesis, and we implemented supervised classification and regression statistical machine learning algorithms to train predictive models of variant Gal4 activity based on these structural changes. All models performed well under cross-validation testing, with balanced accuracy reaching 91% among the classification models, and with the actual and predicted activity values displaying a correlation as high asr = 0.80 for the regression models. Reliable predictions of transcriptional activation levels for Gal4 variants that have yet to be studied can be instantly generated by submitting their respective structure-based feature vectors to the trained models for testing. Such a computational pre-screening of Gal4 variants may potentially reduce costs associated with running large-scale mutagenesis experiments. |
| تدمد: | 2167-8359 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa3bba80d47de300763fde6410b2e137Test https://doi.org/10.7717/peerj.4844Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....aa3bba80d47de300763fde6410b2e137 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21678359 |
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