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The transformative power of transformers in protein structure prediction

Transformer neural networks have revolutionized structural biology with the ability to predict protein structures at unprecedented high accuracy. Here, we report the predictive modeling performance of the state-of-the-art protein structure prediction methods built on transformers for 69 protein targ...

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Detalles Bibliográficos
Autores principales: Moussad, Bernard, Roche, Rahmatullah, Bhattacharya, Debswapna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10410766/
https://www.ncbi.nlm.nih.gov/pubmed/37523536
http://dx.doi.org/10.1073/pnas.2303499120
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author Moussad, Bernard
Roche, Rahmatullah
Bhattacharya, Debswapna
author_facet Moussad, Bernard
Roche, Rahmatullah
Bhattacharya, Debswapna
author_sort Moussad, Bernard
collection PubMed
description Transformer neural networks have revolutionized structural biology with the ability to predict protein structures at unprecedented high accuracy. Here, we report the predictive modeling performance of the state-of-the-art protein structure prediction methods built on transformers for 69 protein targets from the recently concluded 15th Critical Assessment of Structure Prediction (CASP15) challenge. Our study shows the power of transformers in protein structure modeling and highlights future areas of improvement.
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spelling pubmed-104107662023-08-10 The transformative power of transformers in protein structure prediction Moussad, Bernard Roche, Rahmatullah Bhattacharya, Debswapna Proc Natl Acad Sci U S A Biological Sciences Transformer neural networks have revolutionized structural biology with the ability to predict protein structures at unprecedented high accuracy. Here, we report the predictive modeling performance of the state-of-the-art protein structure prediction methods built on transformers for 69 protein targets from the recently concluded 15th Critical Assessment of Structure Prediction (CASP15) challenge. Our study shows the power of transformers in protein structure modeling and highlights future areas of improvement. National Academy of Sciences 2023-07-31 2023-08-08 /pmc/articles/PMC10410766/ /pubmed/37523536 http://dx.doi.org/10.1073/pnas.2303499120 Text en Copyright © 2023 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Biological Sciences
Moussad, Bernard
Roche, Rahmatullah
Bhattacharya, Debswapna
The transformative power of transformers in protein structure prediction
title The transformative power of transformers in protein structure prediction
title_full The transformative power of transformers in protein structure prediction
title_fullStr The transformative power of transformers in protein structure prediction
title_full_unstemmed The transformative power of transformers in protein structure prediction
title_short The transformative power of transformers in protein structure prediction
title_sort transformative power of transformers in protein structure prediction
topic Biological Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10410766/
https://www.ncbi.nlm.nih.gov/pubmed/37523536
http://dx.doi.org/10.1073/pnas.2303499120
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