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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...

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Autores principales: Peñas-Utrilla, Daniel, Marcos, Enrique
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
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.
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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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