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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2023
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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. |
format | Online Article Text |
id | pubmed-10410766 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
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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