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Identifying well-folded de novo proteins in the new era of accurate structure prediction
Computational de novo protein design tailors proteins for target structures and oligomerisation states with high stability, which allows overcoming many limitations of natural proteins when redesigned for new functions. Despite significant advances in the field over the past decade, it remains chall...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581288/ https://www.ncbi.nlm.nih.gov/pubmed/36275629 http://dx.doi.org/10.3389/fmolb.2022.991380 |
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author | Peñas-Utrilla, Daniel Marcos, Enrique |
author_facet | Peñas-Utrilla, Daniel Marcos, Enrique |
author_sort | Peñas-Utrilla, Daniel |
collection | PubMed |
description | Computational de novo protein design tailors proteins for target structures and oligomerisation states with high stability, which allows overcoming many limitations of natural proteins when redesigned for new functions. Despite significant advances in the field over the past decade, it remains challenging to predict sequences that will fold as stable monomers in solution or binders to a particular protein target; thereby requiring substantial experimental resources to identify proteins with the desired properties. To overcome this, here we leveraged the large amount of design data accumulated in the last decade, and the breakthrough in protein structure prediction from last year to investigate on improved ways of selecting promising designs before experimental testing. We collected de novo proteins from previous studies, 518 designed as monomers of different folds and 2112 as binders against the Botulinum neurotoxin, and analysed their structures with AlphaFold2, RoseTTAFold and fragment quality descriptors in combination with other properties related to surface interactions. These features showed high complementarity in rationalizing the experimental results, which allowed us to generate quite accurate machine learning models for predicting well-folded monomers and binders with a small set of descriptors. Cross-validating designs with varied orthogonal computational techniques should guide us for identifying design imperfections, rescuing designs and making more robust design selections before experimental testing. |
format | Online Article Text |
id | pubmed-9581288 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95812882022-10-20 Identifying well-folded de novo proteins in the new era of accurate structure prediction Peñas-Utrilla, Daniel Marcos, Enrique Front Mol Biosci Molecular Biosciences Computational de novo protein design tailors proteins for target structures and oligomerisation states with high stability, which allows overcoming many limitations of natural proteins when redesigned for new functions. Despite significant advances in the field over the past decade, it remains challenging to predict sequences that will fold as stable monomers in solution or binders to a particular protein target; thereby requiring substantial experimental resources to identify proteins with the desired properties. To overcome this, here we leveraged the large amount of design data accumulated in the last decade, and the breakthrough in protein structure prediction from last year to investigate on improved ways of selecting promising designs before experimental testing. We collected de novo proteins from previous studies, 518 designed as monomers of different folds and 2112 as binders against the Botulinum neurotoxin, and analysed their structures with AlphaFold2, RoseTTAFold and fragment quality descriptors in combination with other properties related to surface interactions. These features showed high complementarity in rationalizing the experimental results, which allowed us to generate quite accurate machine learning models for predicting well-folded monomers and binders with a small set of descriptors. Cross-validating designs with varied orthogonal computational techniques should guide us for identifying design imperfections, rescuing designs and making more robust design selections before experimental testing. Frontiers Media S.A. 2022-10-05 /pmc/articles/PMC9581288/ /pubmed/36275629 http://dx.doi.org/10.3389/fmolb.2022.991380 Text en Copyright © 2022 Peñas-Utrilla and Marcos. https://creativecommons.org/licenses/by/4.0/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. |
spellingShingle | Molecular Biosciences Peñas-Utrilla, Daniel Marcos, Enrique Identifying well-folded de novo proteins in the new era of accurate structure prediction |
title | Identifying well-folded de novo proteins in the new era of accurate structure prediction |
title_full | Identifying well-folded de novo proteins in the new era of accurate structure prediction |
title_fullStr | Identifying well-folded de novo proteins in the new era of accurate structure prediction |
title_full_unstemmed | Identifying well-folded de novo proteins in the new era of accurate structure prediction |
title_short | Identifying well-folded de novo proteins in the new era of accurate structure prediction |
title_sort | identifying well-folded de novo proteins in the new era of accurate structure prediction |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581288/ https://www.ncbi.nlm.nih.gov/pubmed/36275629 http://dx.doi.org/10.3389/fmolb.2022.991380 |
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