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Inter-Residue Distance Prediction From Duet Deep Learning Models
Residue distance prediction from the sequence is critical for many biological applications such as protein structure reconstruction, protein–protein interaction prediction, and protein design. However, prediction of fine-grained distances between residues with long sequence separations still remains...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9148999/ https://www.ncbi.nlm.nih.gov/pubmed/35651930 http://dx.doi.org/10.3389/fgene.2022.887491 |
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author | Zhang, Huiling Huang, Ying Bei, Zhendong Ju, Zhen Meng, Jintao Hao, Min Zhang, Jingjing Zhang, Haiping Xi, Wenhui |
author_facet | Zhang, Huiling Huang, Ying Bei, Zhendong Ju, Zhen Meng, Jintao Hao, Min Zhang, Jingjing Zhang, Haiping Xi, Wenhui |
author_sort | Zhang, Huiling |
collection | PubMed |
description | Residue distance prediction from the sequence is critical for many biological applications such as protein structure reconstruction, protein–protein interaction prediction, and protein design. However, prediction of fine-grained distances between residues with long sequence separations still remains challenging. In this study, we propose DuetDis, a method based on duet feature sets and deep residual network with squeeze-and-excitation (SE), for protein inter-residue distance prediction. DuetDis embraces the ability to learn and fuse features directly or indirectly extracted from the whole-genome/metagenomic databases and, therefore, minimize the information loss through ensembling models trained on different feature sets. We evaluate DuetDis and 11 widely used peer methods on a large-scale test set (610 proteins chains). The experimental results suggest that 1) prediction results from different feature sets show obvious differences; 2) ensembling different feature sets can improve the prediction performance; 3) high-quality multiple sequence alignment (MSA) used for both training and testing can greatly improve the prediction performance; and 4) DuetDis is more accurate than peer methods for the overall prediction, more reliable in terms of model prediction score, and more robust against shallow multiple sequence alignment (MSA). |
format | Online Article Text |
id | pubmed-9148999 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91489992022-05-31 Inter-Residue Distance Prediction From Duet Deep Learning Models Zhang, Huiling Huang, Ying Bei, Zhendong Ju, Zhen Meng, Jintao Hao, Min Zhang, Jingjing Zhang, Haiping Xi, Wenhui Front Genet Genetics Residue distance prediction from the sequence is critical for many biological applications such as protein structure reconstruction, protein–protein interaction prediction, and protein design. However, prediction of fine-grained distances between residues with long sequence separations still remains challenging. In this study, we propose DuetDis, a method based on duet feature sets and deep residual network with squeeze-and-excitation (SE), for protein inter-residue distance prediction. DuetDis embraces the ability to learn and fuse features directly or indirectly extracted from the whole-genome/metagenomic databases and, therefore, minimize the information loss through ensembling models trained on different feature sets. We evaluate DuetDis and 11 widely used peer methods on a large-scale test set (610 proteins chains). The experimental results suggest that 1) prediction results from different feature sets show obvious differences; 2) ensembling different feature sets can improve the prediction performance; 3) high-quality multiple sequence alignment (MSA) used for both training and testing can greatly improve the prediction performance; and 4) DuetDis is more accurate than peer methods for the overall prediction, more reliable in terms of model prediction score, and more robust against shallow multiple sequence alignment (MSA). Frontiers Media S.A. 2022-05-16 /pmc/articles/PMC9148999/ /pubmed/35651930 http://dx.doi.org/10.3389/fgene.2022.887491 Text en Copyright © 2022 Zhang, Huang, Bei, Ju, Meng, Hao, Zhang, Zhang and Xi. 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 | Genetics Zhang, Huiling Huang, Ying Bei, Zhendong Ju, Zhen Meng, Jintao Hao, Min Zhang, Jingjing Zhang, Haiping Xi, Wenhui Inter-Residue Distance Prediction From Duet Deep Learning Models |
title | Inter-Residue Distance Prediction From Duet Deep Learning Models |
title_full | Inter-Residue Distance Prediction From Duet Deep Learning Models |
title_fullStr | Inter-Residue Distance Prediction From Duet Deep Learning Models |
title_full_unstemmed | Inter-Residue Distance Prediction From Duet Deep Learning Models |
title_short | Inter-Residue Distance Prediction From Duet Deep Learning Models |
title_sort | inter-residue distance prediction from duet deep learning models |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9148999/ https://www.ncbi.nlm.nih.gov/pubmed/35651930 http://dx.doi.org/10.3389/fgene.2022.887491 |
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