Dataset Information

Modeling transcriptional activation changes to Gal4 variants via structure-based computational mutagenesis.

ABSTRACT: As a DNA binding transcriptional activator, Gal4 promotes the expression of genes responsible for galactose metabolism. The Gal4 protein from Saccharomyces 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 an in silico mutagenesis 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 as r = 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.

SUBMITTER: Masso M

PROVIDER: S-EPMC5983003 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Modeling transcriptional activation changes to Gal4 variants via structure-based computational mutagenesis.

Masso Majid M Rao Nitin N Pyarasani Purnima P

PeerJ 20180529

As a DNA binding transcriptional activator, Gal4 promotes the expression of genes responsible for galactose metabolism. The Gal4 protein from <i>Saccharomyces cerevisiae</i> (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 an <i>in silico</i> mutagenesis technique that ...[more]

PMID: 29868268

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Project description:BackgroundWith the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality.ResultsMotivated by inferring transcriptional compensation (TC) interactions in yeast, a stepwise structural equation modeling algorithm (SSEM) is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations) from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC) SSEM works best. SSEM with Bayesian information criterion (BIC) results in the highest true positive rates, the largest percentage of correctly predicted interactions from all existing interactions, and the highest true negative (non-existing interactions) rates. Next, we apply SSEM using real microarray data to infer TC interactions among (1) small groups of genes that are synthetic sick or lethal (SSL) to SGS1, and (2) a group of SSL pairs of 51 yeast genes involved in DNA synthesis and repair that are of interest. For (1), SSEM with BIC is shown to outperform three Bayesian network algorithms and a multivariate autoregressive model, checked against the results of qRT-PCR experiments. The predictions for (2) are shown to coincide with several known pathways of Sgs1 and its partners that are involved in DNA replication, recombination and repair. In addition, experimentally testable interactions of Rad27 are predicted.ConclusionSSEM is a useful tool for inferring genetic networks, and the results reinforce the possibility of predicting pathways of protein complexes via genetic interactions.

Project description:The proteasome inhibitor MG132 had been shown to prevent galactose induction of the S. cerevisiae GAL1 gene, demonstrating that ubiquitin proteasome-dependent degradation of transcription factors plays an important role in the regulation of gene expression. The deletion of the gene encoding the F-box protein Mdm30 had been reported to stabilize the transcriptional activator Gal4 under inducing conditions and to lead to defects in galactose utilization, suggesting that recycling of Gal4 is required for its function. Subsequently, however, it was argued that Gal4 remains stably bound to the enhancer under inducing conditions, suggesting that proteolytic turnover of Gal4 might not be required for its function. We have performed an alanine-scanning mutagenesis of ubiquitin and isolated a galactose utilization-defective ubiquitin mutant. We have used it for an unbiased suppressor screen and identified the inhibitor Gal80 as a suppressor of the transcriptional defects of the ubiquitin mutant, indicating that the protein degradation of the inhibitor Gal80, and not of the activator Gal4, is required for galactose induction of the GAL genes. We also show that in the absence of Gal80, Mdm30 is not required for Gal4 function, strongly supporting this hypothesis. Furthermore, we have found that Mediator controls the galactose-induced protein degradation of Gal80, which places Mediator genetically upstream of the activator Gal4. Mediator had originally been isolated by its ability to respond to transcriptional activators, and here we have discovered a leading role for Mediator in the process of transcription. The protein kinase Snf1 senses the inducing conditions and transduces the signal to Mediator, which initiates the degradation of the inhibitor Gal80 with the help of the E3 ubiquitin ligase SCF(Mdm30). The ability of Mediator to control the protein degradation of transcriptional inhibitors indicates that Mediator is actually able to direct its own recruitment to gene promoters.

Project description:Motivation:Genetic variation that disrupts gene function by altering gene splicing between individuals can substantially influence traits and disease. In those cases, accurately predicting the effects of genetic variation on splicing can be highly valuable for investigating the mechanisms underlying those traits and diseases. While methods have been developed to generate high quality computational predictions of gene structures in reference genomes, the same methods perform poorly when used to predict the potentially deleterious effects of genetic changes that alter gene splicing between individuals. Underlying that discrepancy in predictive ability are the common assumptions by reference gene finding algorithms that genes are conserved, well-formed and produce functional proteins. Results:We describe a probabilistic approach for predicting recent changes to gene structure that may or may not conserve function. The model is applicable to both coding and non-coding genes, and can be trained on existing gene annotations without requiring curated examples of aberrant splicing. We apply this model to the problem of predicting altered splicing patterns in the genomes of individual humans, and we demonstrate that performing gene-structure prediction without relying on conserved coding features is feasible. The model predicts an unexpected abundance of variants that create de novo splice sites, an observation supported by both simulations and empirical data from RNA-seq experiments. While these de novo splice variants are commonly misinterpreted by other tools as coding or non-coding variants of little or no effect, we find that in some cases they can have large effects on splicing activity and protein products and we propose that they may commonly act as cryptic factors in disease. Availability and implementation:The software is available from geneprediction.org/SGRF. Supplementary information:Supplementary information is available at Bioinformatics online.

Project description:Directed evolution methods mimic in vitro Darwinian evolution, inducing random mutations and selective pressure in genes to obtain proteins with enhanced characteristics. These techniques are developed using trial-and-error testing at an experimental level with a high degree of uncertainty. Therefore, in silico modeling of directed evolution is required to support experimental assays. Several in silico approaches have reproduced directed evolution, using statistical, thermodynamic, and kinetic models in an attempt to recreate experimental conditions. Likewise, optimization techniques using heuristic models have been used to understand and find the best scenarios of directed evolution. Our study uses an in silico model named HeurIstics DirecteD EvolutioN, which is based on a genetic algorithm designed to generate chimeric libraries from 2 parental genes, cry11Aa and cry11Ba, of Bacillus thuringiensis. These genes encode crystal-shaped ?-endotoxins with 3 conserved domains. Cry11 toxins are of biotechnological interest because they have shown to be effective as biopesticides for disease-spreading vectors. With our heuristic model, we considered experimental parameters such as DNA fragmentation length, number of generations or simulation cycles, and mutation rate, to get characteristics of Cry11 chimeric libraries such as percentage of population identity, truncation of variants obtained from the presence of internal stop codons, percentage of thermodynamic diversity, and stability of variants. Our study allowed us to focus on experimental conditions that may be useful for the design of in vitro and in silico experiments of directed evolution with Cry toxins of 3 conserved domains. Furthermore, we obtained in silico libraries of Cry11 variants, in which structural characteristics of wild Cry families were observed in a review of a sample of in silico sequences. We consider that future studies could use our in silico libraries and heuristic computational models, as the one suggested here, to support in vitro experiments of directed evolution.

Dataset Information

Modeling transcriptional activation changes to Gal4 variants via structure-based computational mutagenesis.

Publications

Modeling transcriptional activation changes to Gal4 variants via structure-based computational mutagenesis.

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