Determining the Underlying Distributions of Change in Free Energy Change for Pathogenic and Benign Protein Mutations

A mutation in a patient's genome can affect a protein in that patient’s body, resulting in either no change in the health of the patient or a disease experienced by the patient. Assigning terminology, the mutations can therefore be referred to as benign or pathogenic, respectively. When these benign or pathogenic mutations occur, there is an associated change in change in free energy (ΔΔG) when the protein folds, which essentially means the act of the protein folding can become more or less stabilizing. The questions we were interested in are the following: are pathogenic protein mutations stabilizing or destabilizing when compared to benign protein mutations and is there a difference between ΔΔG distributions for benign and pathogenic mutations. In order to analyze the distribution of the ΔΔG’s, we looked at both data from a previous study and data obtained from an extensive literature search for pathogenic mutations found in patients who exhibit a disease. We found that there appears to be a statistical difference between the distribution of benign ΔΔG’s and pathogenic ΔΔG’s when organizing proteins by general function and that pathogenic mutations appear to be more destabilizing than benign mutations. Furthermore, pathogenic distributions appear better described by two gaussians, or a bimodal distribution, whereas benign distributions are adequately described by a single gaussian. Pathogenic distributions also appear to have greater range and variance. While the causes are not yet entirely understood, these results can play a role in understanding what, if any, role ΔΔG has on the pathogenicity of a mutation and could be one day used alongside other methods to generate a model that can help predict the pathogenicity of an arbitrary mutation.http://deepblue.lib.umich.edu/bitstream/2027.42/176952/1/Honors_Capstone_Protein_Mutation_Distributions_Report_-_Jorden_Thompson.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/176952/2/Honors_Capstone_Protein_Mutation_Distributions_poster_-_Jorden_Thompson.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/176952/3/Honors_Capstone_Protein_Mutation_Distributions_methyltransferase_mutations_-_Jorden_Thompson.xlsxhttp://deepblue.lib.umich.edu/bitstream/2027.42/176952/4/Honors_Capstone_Protein_Mutation_Distributions_transporter_mutations_-_Jorden_Thompson.xls

QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science

Bolyen E, Rideout JR, Dillon MR, et al. QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science. PeerJ. 2018

Author Correction: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

In the version of this article initially published, some reference citations were incorrect. The three references to Jupyter Notebooks should have cited Kluyver et al. instead of Gonzalez et al. The reference to Qiita should have cited Gonzalez et al. instead of Schloss et al. The reference to mothur should have cited Schloss et al. instead of McMurdie & Holmes. The reference to phyloseq should have cited McMurdie & Holmes instead of Huber et al. The reference to Bioconductor should have cited Huber et al. instead of Franzosa et al. And the reference to the biobakery suite should have cited Franzosa et al. instead of Kluyver et al. The errors have been corrected in the HTML and PDF versions of the article.</p

Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

An amendment to this paper has been published and can be accessed via a link at the top of the paper