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MTR3D: identifying regions within protein tertiary structures under purifying selection

The identification of disease-causal variants is non-trivial. By mapping population variation from over 448,000 exome and genome sequences to over 81,000 experimental structures and homology models of the human proteome, we have calculated both regional intolerance to missense variation (Missense To...

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Autores principales: Silk, Michael, Pires, Douglas E V, Rodrigues, Carlos H M, D’Souza, Elston N, Olshansky, Moshe, Thorne, Natalie, Ascher, David B
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8265191/
https://www.ncbi.nlm.nih.gov/pubmed/34050760
http://dx.doi.org/10.1093/nar/gkab428
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author Silk, Michael
Pires, Douglas E V
Rodrigues, Carlos H M
D’Souza, Elston N
Olshansky, Moshe
Thorne, Natalie
Ascher, David B
author_facet Silk, Michael
Pires, Douglas E V
Rodrigues, Carlos H M
D’Souza, Elston N
Olshansky, Moshe
Thorne, Natalie
Ascher, David B
author_sort Silk, Michael
collection PubMed
description The identification of disease-causal variants is non-trivial. By mapping population variation from over 448,000 exome and genome sequences to over 81,000 experimental structures and homology models of the human proteome, we have calculated both regional intolerance to missense variation (Missense Tolerance Ratio, MTR), using a sliding window of 21–41 codons, and introduce a new 3D spatial intolerance to missense variation score (3D Missense Tolerance Ratio, MTR3D), using spheres of 5–8 Å. We show that the MTR3D is less biased by regions with limited data and more accurately identifies regions under purifying selection than estimates relying on the sequence alone. Intolerant regions were highly enriched for both ClinVar pathogenic and COSMIC somatic missense variants (Mann–Whitney U test P < 2.2 × 10(−16)). Further, we combine sequence- and spatial-based scores to generate a consensus score, MTRX, which distinguishes pathogenic from benign variants more accurately than either score separately (AUC = 0.85). The MTR3D server enables easy visualisation of population variation, MTR, MTR3D and MTRX scores across the entire gene and protein structure for >17,000 human genes and >42,000 alternative alternate transcripts, including both Ensembl and RefSeq transcripts. MTR3D is freely available by user-friendly web-interface and API at http://biosig.unimelb.edu.au/mtr3d/.
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spelling pubmed-82651912021-07-09 MTR3D: identifying regions within protein tertiary structures under purifying selection Silk, Michael Pires, Douglas E V Rodrigues, Carlos H M D’Souza, Elston N Olshansky, Moshe Thorne, Natalie Ascher, David B Nucleic Acids Res Web Server Issue The identification of disease-causal variants is non-trivial. By mapping population variation from over 448,000 exome and genome sequences to over 81,000 experimental structures and homology models of the human proteome, we have calculated both regional intolerance to missense variation (Missense Tolerance Ratio, MTR), using a sliding window of 21–41 codons, and introduce a new 3D spatial intolerance to missense variation score (3D Missense Tolerance Ratio, MTR3D), using spheres of 5–8 Å. We show that the MTR3D is less biased by regions with limited data and more accurately identifies regions under purifying selection than estimates relying on the sequence alone. Intolerant regions were highly enriched for both ClinVar pathogenic and COSMIC somatic missense variants (Mann–Whitney U test P < 2.2 × 10(−16)). Further, we combine sequence- and spatial-based scores to generate a consensus score, MTRX, which distinguishes pathogenic from benign variants more accurately than either score separately (AUC = 0.85). The MTR3D server enables easy visualisation of population variation, MTR, MTR3D and MTRX scores across the entire gene and protein structure for >17,000 human genes and >42,000 alternative alternate transcripts, including both Ensembl and RefSeq transcripts. MTR3D is freely available by user-friendly web-interface and API at http://biosig.unimelb.edu.au/mtr3d/. Oxford University Press 2021-05-29 /pmc/articles/PMC8265191/ /pubmed/34050760 http://dx.doi.org/10.1093/nar/gkab428 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Web Server Issue
Silk, Michael
Pires, Douglas E V
Rodrigues, Carlos H M
D’Souza, Elston N
Olshansky, Moshe
Thorne, Natalie
Ascher, David B
MTR3D: identifying regions within protein tertiary structures under purifying selection
title MTR3D: identifying regions within protein tertiary structures under purifying selection
title_full MTR3D: identifying regions within protein tertiary structures under purifying selection
title_fullStr MTR3D: identifying regions within protein tertiary structures under purifying selection
title_full_unstemmed MTR3D: identifying regions within protein tertiary structures under purifying selection
title_short MTR3D: identifying regions within protein tertiary structures under purifying selection
title_sort mtr3d: identifying regions within protein tertiary structures under purifying selection
topic Web Server Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8265191/
https://www.ncbi.nlm.nih.gov/pubmed/34050760
http://dx.doi.org/10.1093/nar/gkab428
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