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Lin YJ, Menon AS, Hu Z, Brenner SE. Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors. Hum Genomics 2024; 18:90. [PMID: 39198917 PMCID: PMC11360829 DOI: 10.1186/s40246-024-00663-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2024] [Accepted: 08/19/2024] [Indexed: 09/01/2024] Open
Abstract
BACKGROUND Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb). RESULTS The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods. CONCLUSIONS VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at https://genomeinterpretation.org/vipdb.
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Affiliation(s)
- Yu-Jen Lin
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA
- Center for Computational Biology, University of California, Berkeley, CA, 94720, USA
| | - Arul S Menon
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, CA, 94720, USA
| | - Zhiqiang Hu
- Department of Plant and Microbial Biology, University of California, 111 Koshland Hall #3102, Berkeley, CA, 94720-3102, USA
- Illumina, Foster City, CA, 94404, USA
| | - Steven E Brenner
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA.
- Center for Computational Biology, University of California, Berkeley, CA, 94720, USA.
- College of Computing, Data Science, and Society, University of California, Berkeley, CA, 94720, USA.
- Department of Plant and Microbial Biology, University of California, 111 Koshland Hall #3102, Berkeley, CA, 94720-3102, USA.
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Lin YJ, Menon AS, Hu Z, Brenner SE. Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.25.600283. [PMID: 38979289 PMCID: PMC11230257 DOI: 10.1101/2024.06.25.600283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb). Results The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods. Conclusions VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. Availability VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.
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Affiliation(s)
- Yu-Jen Lin
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- Center for Computational Biology, University of California, Berkeley, California 94720, USA
| | - Arul S. Menon
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA
| | - Zhiqiang Hu
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
- Currently at: Illumina, Foster City, California 94404, USA
| | - Steven E. Brenner
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- Center for Computational Biology, University of California, Berkeley, California 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
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Guzmán-Vega FJ, González-Álvarez AC, Peña-Guerra KA, Cardona-Londoño KJ, Arold ST. Leveraging AI Advances and Online Tools for Structure-Based Variant Analysis. Curr Protoc 2023; 3:e857. [PMID: 37540795 DOI: 10.1002/cpz1.857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2023]
Abstract
Understanding how a gene variant affects protein function is important in life science, as it helps explain traits or dysfunctions in organisms. In a clinical setting, this understanding makes it possible to improve and personalize patient care. Bioinformatic tools often only assign a pathogenicity score, rather than providing information about the molecular basis for phenotypes. Experimental testing can furnish this information, but this is slow and costly and requires expertise and equipment not available in a clinical setting. Conversely, mapping a gene variant onto the three-dimensional (3D) protein structure provides a fast molecular assessment free of charge. Before 2021, this type of analysis was severely limited by the availability of experimentally determined 3D protein structures. Advances in artificial intelligence algorithms now allow confident prediction of protein structural features from sequence alone. The aim of the protocols presented here is to enable non-experts to use databases and online tools to investigate the molecular effect of a genetic variant. The Basic Protocol relies only on the online resources AlphaFold, Protein Structure Database, and UniProt. Alternate Protocols document the usage of the Protein Data Bank, SWISS-MODEL, ColabFold, and PyMOL for structure-based variant analysis. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: 3D Mapping based on UniProt and AlphaFold Alternate Protocol 1: Using experimental models from the PDB Alternate Protocol 2: Using information from homology modeling with SWISS-MODEL Alternate Protocol 3: Predicting 3D structures with ColabFold Alternate Protocol 4: Structure visualization and analysis with PyMOL.
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Affiliation(s)
- Francisco J Guzmán-Vega
- Bioscience Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, KAUST, Thuwal, Saudi Arabia
| | - Ana C González-Álvarez
- Bioengineering Program, Biological and Environmental Science and Engineering Division, KAUST, Thuwal, Saudi Arabia
- Computational Bioscience Research Center, KAUST, Thuwal, Saudi Arabia
| | - Karla A Peña-Guerra
- Bioengineering Program, Biological and Environmental Science and Engineering Division, KAUST, Thuwal, Saudi Arabia
- Computational Bioscience Research Center, KAUST, Thuwal, Saudi Arabia
| | - Kelly J Cardona-Londoño
- Bioengineering Program, Biological and Environmental Science and Engineering Division, KAUST, Thuwal, Saudi Arabia
- Computational Bioscience Research Center, KAUST, Thuwal, Saudi Arabia
| | - Stefan T Arold
- Bioscience Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
- Bioengineering Program, Biological and Environmental Science and Engineering Division, KAUST, Thuwal, Saudi Arabia
- Computational Bioscience Research Center, KAUST, Thuwal, Saudi Arabia
- Centre de Biologie Structurale (CBS), INSERM, CNRS, Université de Montpellier, Montpellier, France
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Gress A, Srikakulam SK, Keller S, Ramensky V, Kalinina OV. d-StructMAn: Containerized structural annotation on the scale from genetic variants to whole proteomes. Gigascience 2022; 11:giac086. [PMID: 36130085 PMCID: PMC9487898 DOI: 10.1093/gigascience/giac086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/06/2022] [Accepted: 08/18/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Structural annotation of genetic variants in the context of intermolecular interactions and protein stability can shed light onto mechanisms of disease-related phenotypes. Three-dimensional structures of related proteins in complexes with other proteins, nucleic acids, or ligands enrich such functional interpretation, since intermolecular interactions are well conserved in evolution. RESULTS We present d-StructMAn, a novel computational method that enables structural annotation of local genetic variants, such as single-nucleotide variants and in-frame indels, and implements it in a highly efficient and user-friendly tool provided as a Docker container. Using d-StructMAn, we annotated several very large sets of human genetic variants, including all variants from ClinVar and all amino acid positions in the human proteome. We were able to provide annotation for more than 46% of positions in the human proteome representing over 60% proteins. CONCLUSIONS d-StructMAn is the first of its kind and a highly efficient tool for structural annotation of protein-coding genetic variation in the context of observed and potential intermolecular interactions. d-StructMAn is readily applicable to proteome-scale datasets and can be an instrumental building machine-learning tool for predicting genotype-to-phenotype relationships.
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Affiliation(s)
- Alexander Gress
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)/Helmholtz Centre for Infection Research (HZI), Saarbrücken 8: 66123, Germany
- Graduate School of Computer Science, Saarland University, Saarbrücken 5: 101990, Germany
| | - Sanjay K Srikakulam
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)/Helmholtz Centre for Infection Research (HZI), Saarbrücken 8: 66123, Germany
- Graduate School of Computer Science, Saarland University, Saarbrücken 5: 101990, Germany
- Interdisciplinary Graduate School of Natural Product Research, Saarland University, Saarbrücken 6: 119991, Germany
| | - Sebastian Keller
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)/Helmholtz Centre for Infection Research (HZI), Saarbrücken 8: 66123, Germany
- Graduate School of Computer Science, Saarland University, Saarbrücken 5: 101990, Germany
- Research Group Computational Biology, Max Planck Institute for Informatics, Saarbrücken 7: 66421, Germany
| | - Vasily Ramensky
- National Medical Research Center for Therapy and Preventive Medicine of the Ministry of Healthcare of Russian Federation, Moscow, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
| | - Olga V Kalinina
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)/Helmholtz Centre for Infection Research (HZI), Saarbrücken 8: 66123, Germany
- Medical Faculty, Saarland University, Homburg, Germany
- Center for Bioinformatics, Saarland Informatics Campus, Saarbrücken, Germany
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Martín M, Brunello FG, Modenutti CP, Nicola JP, Marti MA. MotSASi: Functional short linear motifs (SLiMs) prediction based on genomic single nucleotide variants and structural data. Biochimie 2022; 197:59-73. [DOI: 10.1016/j.biochi.2022.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 01/17/2022] [Accepted: 02/02/2022] [Indexed: 11/28/2022]
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Abstract
Background: Iodide transport defect is an uncommon cause of dyshormonogenic congenital hypothyroidism due to homozygous or compound heterozygous pathogenic variants in the SLC5A5 gene, which encodes the sodium/iodide symporter (NIS), causing deficient iodide accumulation in thyroid follicular cells, thus impairing thyroid hormonogenesis. Methods:SLC5A5 gene variants were compiled from public databases and research articles exploring the molecular bases of congenital hypothyroidism. Using a dataset of 198 missense NIS variants classified as either benign or pathogenic, we developed and validated a machine learning-based NIS-specific variant classifier to predict the impact of missense NIS variants. Results: We generated a manually curated dataset containing 7793 unique SLC5A5 variants. As most databases compiled exome sequencing data, variant mapping revealed an increased density of variants in SLC5A5 coding exons. Based on allele frequency (AF) analysis, we established an AF threshold of 1:10,000 above which a variant should be considered benign. Most pathogenic NIS variants were located in the protein-coding region, as most patients were genetically diagnosed by using a candidate gene strategy limited to this region. Significantly, we evidenced that 94.5% of missense NIS variants were classified as of uncertain significance. Therefore, we developed an NIS-specific variant classifier to improve the prediction of pathogenicity of missense variants. Our classifier predicted the clinical outcome of missense variants with high accuracy (90%), outperforming state-of-the-art pathogenicity predictors, such as REVEL, PolyPhen-2, and SIFT. Based on the excellent performance of our classifier, we predicted the mutational landscape of NIS. The analysis of the mutational landscape revealed that most missense variants located in transmembrane segments are frequently pathogenic. Moreover, we predicted that ∼28% of all single-nucleotide variants that could cause missense NIS variants are pathogenic, thus putatively leading to congenital hypothyroidism if present in homozygous or compound heterozygous state. Conclusions: We reported the first NIS-specific variant classifier aiming at improving the interpretation of missense NIS variants in clinical practice. Deciphering the mutational landscape for every protein involved in thyroid hormonogenesis is a relevant task for a deep understanding of the molecular mechanisms causing dyshormonogenic congenital hypothyroidism.
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Affiliation(s)
- Mariano Martín
- Departamento de Bioquímica Clínica, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Córdoba, Argentina
- Centro de Investigaciones en Bioquímica Clínica e Inmunología Consejo Nacional de Investigaciones Científicas y Técnicas (CIBICI-CONICET), Córdoba, Argentina
| | - Juan Pablo Nicola
- Departamento de Bioquímica Clínica, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Córdoba, Argentina
- Centro de Investigaciones en Bioquímica Clínica e Inmunología Consejo Nacional de Investigaciones Científicas y Técnicas (CIBICI-CONICET), Córdoba, Argentina
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Milanesio B, Pepe C, Defelipe LA, Eandi Eberle S, Avalos Gomez V, Chaves A, Albero A, Aguirre F, Fernandez D, Aizpurua L, Paula Dieuzeide M, Turjanski A, Bianchi P, Fermo E, Feliu-Torres A. Six novel variants in the PKLR gene associated with pyruvate kinase deficiency in Argentinian patients. Clin Biochem 2021; 91:26-30. [PMID: 33631127 DOI: 10.1016/j.clinbiochem.2021.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/29/2021] [Accepted: 02/08/2021] [Indexed: 10/22/2022]
Abstract
BACKGROUND Pyruvate kinase deficiency (PKD) is a rare recessive congenital hemolytic anemia caused by mutations in the PKLR gene. The disease shows a marked variability in clinical expression. We studied the molecular features of nine unrelated Argentinian patients with congenital hemolytic anemia associated with erythrocyte pyruvate kinase deficiency. DESIGN AND METHODS Routine hematologic investigations were performed to rule out other causes of chronic hemolytic anemia. Sanger sequencing and in-sílico analysis were carried out to identify and characterize the genetics variants. RESULTS Six different novel missense variants were detected among the 18 studied alleles: c.661 G > C (Asp221His), c.956 G > T (Gly319Val), c.1595 G > C (Arg532Pro), c.347 G > A (Arg116Gln), c.1232 G > T (Gly411Val), c.1021G > A (Gly341Ser). Structural implications of amino-acid substitutions were correlated with the clinical phenotypes seen in the probands. CONCLUSIONS This is the first comprehensive report on molecular characterization of pyruvate kinase deficiency in Argentina and the second from South America that would contribute to our knowledge on the distribution and frequency of PKLR variants in our population but also offer new insights into the interpretation of the effect of PKLR variants and phenotype.
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Affiliation(s)
- Berenice Milanesio
- Servicio de Hematología-Oncología, Hospital de Pediatría "Prof. Dr. Juan P. Garrahan", Combate de los Pozos 1881, (C1245AAM) Buenos Aires, Argentina
| | - Carolina Pepe
- Servicio de Hematología-Oncología, Hospital de Pediatría "Prof. Dr. Juan P. Garrahan", Combate de los Pozos 1881, (C1245AAM) Buenos Aires, Argentina
| | - Lucas A Defelipe
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina; IQUIBICEN/UBA-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Silvia Eandi Eberle
- Servicio de Hematología-Oncología, Hospital de Pediatría "Prof. Dr. Juan P. Garrahan", Combate de los Pozos 1881, (C1245AAM) Buenos Aires, Argentina
| | - Vanesa Avalos Gomez
- Servicio de Hematología-Oncología, Hospital de Pediatría "Prof. Dr. Juan P. Garrahan", Combate de los Pozos 1881, (C1245AAM) Buenos Aires, Argentina
| | - Alejandro Chaves
- Servicio de Hematología-Oncología, Hospital de Pediatría "Prof. Dr. Juan P. Garrahan", Combate de los Pozos 1881, (C1245AAM) Buenos Aires, Argentina
| | - Agustina Albero
- Servicio de Hematología-Oncología, Hospital de Pediatría "Prof. Dr. Juan P. Garrahan", Combate de los Pozos 1881, (C1245AAM) Buenos Aires, Argentina
| | - Fernando Aguirre
- Servicio de Hematología-Oncología, Hospital de Pediatría "Prof. Dr. Juan P. Garrahan", Combate de los Pozos 1881, (C1245AAM) Buenos Aires, Argentina
| | - Diego Fernandez
- Servicio de Hematología-Oncología, Hospital de Pediatría "Prof. Dr. Juan P. Garrahan", Combate de los Pozos 1881, (C1245AAM) Buenos Aires, Argentina
| | - Luciana Aizpurua
- Servicio de Hematología-Oncología, Hospital de Pediatría "Prof. Dr. Juan P. Garrahan", Combate de los Pozos 1881, (C1245AAM) Buenos Aires, Argentina
| | - María Paula Dieuzeide
- Servicio de Hematología-Oncología, Hospital de Pediatría "Prof. Dr. Juan P. Garrahan", Combate de los Pozos 1881, (C1245AAM) Buenos Aires, Argentina
| | - Adrián Turjanski
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina; IQUIBICEN/UBA-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Paola Bianchi
- U.O.C. Ematologia, U.O.S. FisiopatologiadelleAnemie, Fondazione IRCCS Ca Granada, OspedaleMaggiore Policlínico, Milan, Italy
| | - Elisa Fermo
- U.O.C. Ematologia, U.O.S. FisiopatologiadelleAnemie, Fondazione IRCCS Ca Granada, OspedaleMaggiore Policlínico, Milan, Italy
| | - Aurora Feliu-Torres
- Servicio de Hematología-Oncología, Hospital de Pediatría "Prof. Dr. Juan P. Garrahan", Combate de los Pozos 1881, (C1245AAM) Buenos Aires, Argentina.
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ADDRESS: A Database of Disease-associated Human Variants Incorporating Protein Structure and Folding Stabilities. J Mol Biol 2021; 433:166840. [PMID: 33539887 DOI: 10.1016/j.jmb.2021.166840] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 01/17/2021] [Accepted: 01/20/2021] [Indexed: 11/22/2022]
Abstract
Numerous human diseases are caused by mutations in genomic sequences. Since amino acid changes affect protein function through mechanisms often predictable from protein structure, the integration of structural and sequence data enables us to estimate with greater accuracy whether and how a given mutation will lead to disease. Publicly available annotated databases enable hypothesis assessment and benchmarking of prediction tools. However, the results are often presented as summary statistics or black box predictors, without providing full descriptive information. We developed a new semi-manually curated human variant database presenting information on the protein contact-map, sequence-to-structure mapping, amino acid identity change, and stability prediction for the popular UniProt database. We found that the profiles of pathogenic and benign missense polymorphisms can be effectively deduced using decision trees and comparative analyses based on the presented dataset. The database is made publicly available through https://zhanglab.ccmb.med.umich.edu/ADDRESS.
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Stephenson JD, Laskowski RA, Nightingale A, Hurles ME, Thornton JM. VarMap: a web tool for mapping genomic coordinates to protein sequence and structure and retrieving protein structural annotations. Bioinformatics 2020; 35:4854-4856. [PMID: 31192369 PMCID: PMC6853667 DOI: 10.1093/bioinformatics/btz482] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 05/17/2019] [Accepted: 06/07/2019] [Indexed: 12/16/2022] Open
Abstract
Motivation Understanding the protein structural context and patterning on proteins of genomic variants can help to separate benign from pathogenic variants and reveal molecular consequences. However, mapping genomic coordinates to protein structures is non-trivial, complicated by alternative splicing and transcript evidence. Results Here we present VarMap, a web tool for mapping a list of chromosome coordinates to canonical UniProt sequences and associated protein 3D structures, including validation checks, and annotating them with structural information. Availability and implementation https://www.ebi.ac.uk/thornton-srv/databases/VarMap. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- James D Stephenson
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK.,Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK
| | - Roman A Laskowski
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - Andrew Nightingale
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - Matthew E Hurles
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK
| | - Janet M Thornton
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
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Stein A, Fowler DM, Hartmann-Petersen R, Lindorff-Larsen K. Biophysical and Mechanistic Models for Disease-Causing Protein Variants. Trends Biochem Sci 2019; 44:575-588. [PMID: 30712981 PMCID: PMC6579676 DOI: 10.1016/j.tibs.2019.01.003] [Citation(s) in RCA: 96] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 01/04/2019] [Accepted: 01/08/2019] [Indexed: 12/13/2022]
Abstract
The rapid decrease in DNA sequencing cost is revolutionizing medicine and science. In medicine, genome sequencing has revealed millions of missense variants that change protein sequences, yet we only understand the molecular and phenotypic consequences of a small fraction. Within protein science, high-throughput deep mutational scanning experiments enable us to probe thousands of variants in a single, multiplexed experiment. We review efforts that bring together these topics via experimental and computational approaches to determine the consequences of missense variants in proteins. We focus on the role of changes in protein stability as a driver for disease, and how experiments, biophysical models, and computation are providing a framework for understanding and predicting how changes in protein sequence affect cellular protein stability.
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Affiliation(s)
- Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| | - Douglas M Fowler
- Departments of Genome Sciences and Bioengineering, University of Washington, Seattle, WA, USA
| | - Rasmus Hartmann-Petersen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
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