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Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15
Since the 14th Critical Assessment of Techniques for Protein Structure Prediction (CASP14), AlphaFold2 has become the standard method for protein tertiary structure prediction. One remaining challenge is to further improve its prediction. We developed a new version of the MULTICOM system to sample d...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484931/ https://www.ncbi.nlm.nih.gov/pubmed/37679431 http://dx.doi.org/10.1038/s42004-023-00991-6 |
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author | Liu, Jian Guo, Zhiye Wu, Tianqi Roy, Raj S. Chen, Chen Cheng, Jianlin |
author_facet | Liu, Jian Guo, Zhiye Wu, Tianqi Roy, Raj S. Chen, Chen Cheng, Jianlin |
author_sort | Liu, Jian |
collection | PubMed |
description | Since the 14th Critical Assessment of Techniques for Protein Structure Prediction (CASP14), AlphaFold2 has become the standard method for protein tertiary structure prediction. One remaining challenge is to further improve its prediction. We developed a new version of the MULTICOM system to sample diverse multiple sequence alignments (MSAs) and structural templates to improve the input for AlphaFold2 to generate structural models. The models are then ranked by both the pairwise model similarity and AlphaFold2 self-reported model quality score. The top ranked models are refined by a novel structure alignment-based refinement method powered by Foldseek. Moreover, for a monomer target that is a subunit of a protein assembly (complex), MULTICOM integrates tertiary and quaternary structure predictions to account for tertiary structural changes induced by protein-protein interaction. The system participated in the tertiary structure prediction in 2022 CASP15 experiment. Our server predictor MULTICOM_refine ranked 3rd among 47 CASP15 server predictors and our human predictor MULTICOM ranked 7th among all 132 human and server predictors. The average GDT-TS score and TM-score of the first structural models that MULTICOM_refine predicted for 94 CASP15 domains are ~0.80 and ~0.92, 9.6% and 8.2% higher than ~0.73 and 0.85 of the standard AlphaFold2 predictor respectively. |
format | Online Article Text |
id | pubmed-10484931 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104849312023-09-09 Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15 Liu, Jian Guo, Zhiye Wu, Tianqi Roy, Raj S. Chen, Chen Cheng, Jianlin Commun Chem Article Since the 14th Critical Assessment of Techniques for Protein Structure Prediction (CASP14), AlphaFold2 has become the standard method for protein tertiary structure prediction. One remaining challenge is to further improve its prediction. We developed a new version of the MULTICOM system to sample diverse multiple sequence alignments (MSAs) and structural templates to improve the input for AlphaFold2 to generate structural models. The models are then ranked by both the pairwise model similarity and AlphaFold2 self-reported model quality score. The top ranked models are refined by a novel structure alignment-based refinement method powered by Foldseek. Moreover, for a monomer target that is a subunit of a protein assembly (complex), MULTICOM integrates tertiary and quaternary structure predictions to account for tertiary structural changes induced by protein-protein interaction. The system participated in the tertiary structure prediction in 2022 CASP15 experiment. Our server predictor MULTICOM_refine ranked 3rd among 47 CASP15 server predictors and our human predictor MULTICOM ranked 7th among all 132 human and server predictors. The average GDT-TS score and TM-score of the first structural models that MULTICOM_refine predicted for 94 CASP15 domains are ~0.80 and ~0.92, 9.6% and 8.2% higher than ~0.73 and 0.85 of the standard AlphaFold2 predictor respectively. Nature Publishing Group UK 2023-09-07 /pmc/articles/PMC10484931/ /pubmed/37679431 http://dx.doi.org/10.1038/s42004-023-00991-6 Text en © The Author(s) 2023 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 Liu, Jian Guo, Zhiye Wu, Tianqi Roy, Raj S. Chen, Chen Cheng, Jianlin Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15 |
title | Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15 |
title_full | Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15 |
title_fullStr | Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15 |
title_full_unstemmed | Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15 |
title_short | Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15 |
title_sort | improving alphafold2-based protein tertiary structure prediction with multicom in casp15 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484931/ https://www.ncbi.nlm.nih.gov/pubmed/37679431 http://dx.doi.org/10.1038/s42004-023-00991-6 |
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