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A database of calculated solution parameters for the AlphaFold predicted protein structures
Recent spectacular advances by AI programs in 3D structure predictions from protein sequences have revolutionized the field in terms of accuracy and speed. The resulting “folding frenzy” has already produced predicted protein structure databases for the entire human and other organisms’ proteomes. H...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9072687/ https://www.ncbi.nlm.nih.gov/pubmed/35513443 http://dx.doi.org/10.1038/s41598-022-10607-z |
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author | Brookes, Emre Rocco, Mattia |
author_facet | Brookes, Emre Rocco, Mattia |
author_sort | Brookes, Emre |
collection | PubMed |
description | Recent spectacular advances by AI programs in 3D structure predictions from protein sequences have revolutionized the field in terms of accuracy and speed. The resulting “folding frenzy” has already produced predicted protein structure databases for the entire human and other organisms’ proteomes. However, rapidly ascertaining a predicted structure’s reliability based on measured properties in solution should be considered. Shape-sensitive hydrodynamic parameters such as the diffusion and sedimentation coefficients ([Formula: see text] , [Formula: see text] ) and the intrinsic viscosity ([η]) can provide a rapid assessment of the overall structure likeliness, and SAXS would yield the structure-related pair-wise distance distribution function p(r) vs. r. Using the extensively validated UltraScan SOlution MOdeler (US-SOMO) suite, a database was implemented calculating from AlphaFold structures the corresponding [Formula: see text] , [Formula: see text] , [η], p(r) vs. r, and other parameters. Circular dichroism spectra were computed using the SESCA program. Some of AlphaFold’s drawbacks were mitigated, such as generating whenever possible a protein’s mature form. Others, like the AlphaFold direct applicability to single-chain structures only, the absence of prosthetic groups, or flexibility issues, are discussed. Overall, this implementation of the US-SOMO-AF database should already aid in rapidly evaluating the consistency in solution of a relevant portion of AlphaFold predicted protein structures. |
format | Online Article Text |
id | pubmed-9072687 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90726872022-05-07 A database of calculated solution parameters for the AlphaFold predicted protein structures Brookes, Emre Rocco, Mattia Sci Rep Article Recent spectacular advances by AI programs in 3D structure predictions from protein sequences have revolutionized the field in terms of accuracy and speed. The resulting “folding frenzy” has already produced predicted protein structure databases for the entire human and other organisms’ proteomes. However, rapidly ascertaining a predicted structure’s reliability based on measured properties in solution should be considered. Shape-sensitive hydrodynamic parameters such as the diffusion and sedimentation coefficients ([Formula: see text] , [Formula: see text] ) and the intrinsic viscosity ([η]) can provide a rapid assessment of the overall structure likeliness, and SAXS would yield the structure-related pair-wise distance distribution function p(r) vs. r. Using the extensively validated UltraScan SOlution MOdeler (US-SOMO) suite, a database was implemented calculating from AlphaFold structures the corresponding [Formula: see text] , [Formula: see text] , [η], p(r) vs. r, and other parameters. Circular dichroism spectra were computed using the SESCA program. Some of AlphaFold’s drawbacks were mitigated, such as generating whenever possible a protein’s mature form. Others, like the AlphaFold direct applicability to single-chain structures only, the absence of prosthetic groups, or flexibility issues, are discussed. Overall, this implementation of the US-SOMO-AF database should already aid in rapidly evaluating the consistency in solution of a relevant portion of AlphaFold predicted protein structures. Nature Publishing Group UK 2022-05-05 /pmc/articles/PMC9072687/ /pubmed/35513443 http://dx.doi.org/10.1038/s41598-022-10607-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Brookes, Emre Rocco, Mattia A database of calculated solution parameters for the AlphaFold predicted protein structures |
title | A database of calculated solution parameters for the AlphaFold predicted protein structures |
title_full | A database of calculated solution parameters for the AlphaFold predicted protein structures |
title_fullStr | A database of calculated solution parameters for the AlphaFold predicted protein structures |
title_full_unstemmed | A database of calculated solution parameters for the AlphaFold predicted protein structures |
title_short | A database of calculated solution parameters for the AlphaFold predicted protein structures |
title_sort | database of calculated solution parameters for the alphafold predicted protein structures |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9072687/ https://www.ncbi.nlm.nih.gov/pubmed/35513443 http://dx.doi.org/10.1038/s41598-022-10607-z |
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