0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      GTExome: Modeling commonly expressed missense mutations in the human genome

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          A web application, GTExome, is described that quickly identifies, classifies, and models missense mutations in commonly expressed human proteins. GTExome can be used to categorize genomic mutation data with tissue specific expression data from the Genotype-Tissue Expression (GTEx) project. Commonly expressed missense mutations in proteins from a wide range of tissue types can be selected and assessed for modeling suitability. Information about the consequences of each mutation is provided to the user including if disulfide bonds, hydrogen bonds, or salt bridges are broken, buried prolines introduced, buried charges are created or lost, charge is swapped, a buried glycine is replaced, or if the residue that would be removed is a proline in the cis configuration. Also, if the mutation site is in a binding pocket the number of pockets and their volumes are reported. The user can assess this information and then select from available experimental or computationally predicted structures of native proteins to create, visualize, and download a model of the mutated protein using Fast and Accurate Side-chain Protein Repacking (FASPR). For AlphaFold modeled proteins, confidence scores for native proteins are provided. Using this tool, we explored a set of 9,666 common missense mutations from a variety of tissues from GTEx and show that most mutations can be modeled using this tool to facilitate studies of protein-protein and protein-drug interactions. The open-source tool is freely available at https://pharmacogenomics.clas.ucdenver.edu/gtexome/.

          Related collections

          Most cited references21

          • Record: found
          • Abstract: found
          • Article: not found
          Is Open Access

          The Genotype-Tissue Expression (GTEx) project.

          Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            ColabFold: making protein folding accessible to all

            ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold’s 40−60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com . ColabFold is a free and accessible platform for protein folding that provides accelerated prediction of protein structures and complexes using AlphaFold2 or RoseTTAFold.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Highly accurate protein structure prediction for the human proteome

              Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure 1 . Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold 2 , at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective. AlphaFold is used to predict the structures of almost all of the proteins in the human proteome—the availability of high-confidence predicted structures could enable new avenues of investigation from a structural perspective.
                Bookmark

                Author and article information

                Contributors
                Role: Data curationRole: SoftwareRole: VisualizationRole: Writing – original draft
                Role: Data curationRole: Software
                Role: Data curation
                Role: Data curation
                Role: ConceptualizationRole: Data curationRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                30 May 2024
                2024
                : 19
                : 5
                : e0303604
                Affiliations
                [1 ] Computational Bioscience, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
                [2 ] Department of Chemistry, University of Colorado Denver, Denver, CO, United States of America
                Zhejiang University College of Life Sciences, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0003-2034-1999
                Article
                PONE-D-24-03381
                10.1371/journal.pone.0303604
                11139294
                38814966
                b68f08a1-1b35-440a-8c46-91a7cc876eed
                © 2024 Hoffman et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 25 January 2024
                : 26 April 2024
                Page count
                Figures: 4, Tables: 0, Pages: 10
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R15GM151726
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: GM096958
                Award Recipient :
                JH was supported by the MARC U-STAR program (NIGMS T34 T34 GM096958) Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R15GM151726 (SMR).
                Categories
                Research Article
                Biology and Life Sciences
                Molecular Biology
                Macromolecular Structure Analysis
                Protein Structure
                Protein Structure Prediction
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Structure
                Protein Structure Prediction
                Biology and Life Sciences
                Genetics
                Mutation
                Missense Mutation
                Biology and Life Sciences
                Molecular Biology
                Macromolecular Structure Analysis
                Protein Structure
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Structure
                Research and Analysis Methods
                Database and Informatics Methods
                Biological Databases
                Protein Structure Databases
                Biology and Life Sciences
                Molecular Biology
                Macromolecular Structure Analysis
                Protein Structure
                Protein Structure Databases
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Structure
                Protein Structure Databases
                Biology and Life Sciences
                Molecular Biology
                Macromolecular Structure Analysis
                Protein Structure
                Protein Structure Comparison
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Structure
                Protein Structure Comparison
                Research and Analysis Methods
                Database and Informatics Methods
                Biological Databases
                Mutation Databases
                Biology and Life Sciences
                Genetics
                Mutation
                Mutation Databases
                Biology and Life Sciences
                Molecular Biology
                Macromolecular Structure Analysis
                Protein Structure
                Protein Structure Determination
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Structure
                Protein Structure Determination
                Physical Sciences
                Chemistry
                Chemical Compounds
                Organic Compounds
                Amino Acids
                Cyclic Amino Acids
                Proline
                Physical Sciences
                Chemistry
                Organic Chemistry
                Organic Compounds
                Amino Acids
                Cyclic Amino Acids
                Proline
                Biology and Life Sciences
                Biochemistry
                Proteins
                Amino Acids
                Cyclic Amino Acids
                Proline
                Custom metadata
                All relevant data for this study are publicly available from the Github repository ( https://github.com/henrytan2/CU-Denver-Pharmacogenomics-Website) and from the GTEx Portal database ( https://www.gtexportal.org/home/downloads/adult-gtex/bulk_tissue_expression).

                Uncategorized
                Uncategorized

                Comments

                Comment on this article