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      Assessing Human Genetic Variations in Glucose Transporter SLC2A10 and Their Role in Altering Structural and Functional Properties

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          Abstract

          Purpose: Demand is increasing for clinical genomic sequencing to provide diagnoses for patients presenting phenotypes indicative of genetic diseases, but for whom routine genetic testing failed to yield a diagnosis. DNA-based testing using high-throughput technologies often identifies variants with insufficient evidence to determine whether they are disease-causal or benign, leading to categorization as variants of uncertain significance (VUS).

          Methods: We used molecular modeling and simulation to generate specific hypotheses for the molecular effects of variants in the human glucose transporter, GLUT10 ( SLC2A10). Similar to many disease-relevant membrane proteins, no experimentally derived 3D structure exists. An atomic model was generated and used to evaluate multiple variants, including pathogenic, benign, and VUS.

          Results: These analyses yielded detailed mechanistic data, not currently predictable from sequence, including altered protein stability, charge distribution of ligand binding surfaces, and shifts toward or away from transport-competent conformations. Consideration of the two major conformations of GLUT10 was important as variants have conformation-specific effects. We generated detailed molecular hypotheses for the functional impact of variants in GLUT10 and propose means to determine their pathogenicity.

          Conclusion: The type of workflow we present here is valuable for increasing the throughput and resolution with which VUS effects can be assessed and interpreted.

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          Most cited references21

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          dbNSFP v3.0: A One-Stop Database of Functional Predictions and Annotations for Human Nonsynonymous and Splice-Site SNVs.

          The purpose of the dbNSFP is to provide a one-stop resource for functional predictions and annotations for human nonsynonymous single-nucleotide variants (nsSNVs) and splice-site variants (ssSNVs), and to facilitate the steps of filtering and prioritizing SNVs from a large list of SNVs discovered in an exome-sequencing study. A list of all potential nsSNVs and ssSNVs based on the human reference sequence were created and functional predictions and annotations were curated and compiled for each SNV. Here, we report a recent major update of the database to version 3.0. The SNV list has been rebuilt based on GENCODE 22 and currently the database includes 82,832,027 nsSNVs and ssSNVs. An attached database dbscSNV, which compiled all potential human SNVs within splicing consensus regions and their deleteriousness predictions, add another 15,030,459 potentially functional SNVs. Eleven prediction scores (MetaSVM, MetaLR, CADD, VEST3, PROVEAN, 4× fitCons, fathmm-MKL, and DANN) and allele frequencies from the UK10K cohorts and the Exome Aggregation Consortium (ExAC), among others, have been added. The original seven prediction scores in v2.0 (SIFT, 2× Polyphen2, LRT, MutationTaster, MutationAssessor, and FATHMM) as well as many SNV and gene functional annotations have been updated. dbNSFP v3.0 is freely available at http://sites.google.com/site/jpopgen/dbNSFP.
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            Protein disorder prediction: implications for structural proteomics.

            A great challenge in the proteomics and structural genomics era is to predict protein structure and function, including identification of those proteins that are partially or wholly unstructured. Disordered regions in proteins often contain short linear peptide motifs (e.g., SH3 ligands and targeting signals) that are important for protein function. We present here DisEMBL, a computational tool for prediction of disordered/unstructured regions within a protein sequence. As no clear definition of disorder exists, we have developed parameters based on several alternative definitions and introduced a new one based on the concept of "hot loops," i.e., coils with high temperature factors. Avoiding potentially disordered segments in protein expression constructs can increase expression, foldability, and stability of the expressed protein. DisEMBL is thus useful for target selection and the design of constructs as needed for many biochemical studies, particularly structural biology and structural genomics projects. The tool is freely available via a web interface (http://dis.embl.de) and can be downloaded for use in large-scale studies.
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              VADAR: a web server for quantitative evaluation of protein structure quality.

              VADAR (Volume Area Dihedral Angle Reporter) is a comprehensive web server for quantitative protein structure evaluation. It accepts Protein Data Bank (PDB) formatted files or PDB accession numbers as input and calculates, identifies, graphs, reports and/or evaluates a large number (>30) of key structural parameters both for individual residues and for the entire protein. These include excluded volume, accessible surface area, backbone and side chain dihedral angles, secondary structure, hydrogen bonding partners, hydrogen bond energies, steric quality, solvation free energy as well as local and overall fold quality. These derived parameters can be used to rapidly identify both general and residue-specific problems within newly determined protein structures. The VADAR web server is freely accessible at http://redpoll.pharmacy.ualberta.ca/vadar.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                25 July 2018
                2018
                : 9
                : 276
                Affiliations
                [1] 1Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic , Rochester, MN, United States
                [2] 2Bioinformatics Research and Development Laboratory, Genomics Sciences and Precision Medicine Center, Medical College of Wisconsin , Milwaukee, WI, United States
                [3] 3Laboratory of Epigenetics and Chromatin Dynamics, Department of Biochemistry and Molecular Biology, Epigenomics Translational Program, Center for Individualized Medicine, Mayo Clinic , Rochester, MN, United States
                [4] 4Center for Individualized Medicine, Mayo Clinic , Rochester, MN, United States
                Author notes

                Edited by: Enrico Baruffini, Università degli Studi di Parma, Italy

                Reviewed by: Michael Lawrence, Walter and Eliza Hall Institute of Medical Research, Australia; Andras Szilagyi, Hungarian Academy of Sciences (MTA), Hungary

                *Correspondence: Raul Urrutia, rurrutia@ 123456mcw.edu Eric W. Klee, klee.eric@ 123456mayo.edu

                This article was submitted to Genetic Disorders, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2018.00276
                6068234
                ae1462ba-94ee-436c-97f6-8362d32bd13f
                Copyright © 2018 Zimmermann, Urrutia, Cousin, Oliver and Klee.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 25 April 2018
                : 05 July 2018
                Page count
                Figures: 5, Tables: 0, Equations: 0, References: 41, Pages: 11, Words: 0
                Funding
                Funded by: Mayo Clinic 10.13039/100000871
                Funded by: National Institute of Diabetes and Digestive and Kidney Diseases 10.13039/100000062
                Award ID: RO1 52913, P30 084567, P50CA102701
                Categories
                Genetics
                Original Research

                Genetics
                genetics,molecular modeling,natural variation,variant of uncertain significance,ats
                Genetics
                genetics, molecular modeling, natural variation, variant of uncertain significance, ats

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