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A General Framework to Learn Tertiary Structure for Protein Sequence Characterization
During the past five years, deep-learning algorithms have enabled ground-breaking progress towards the prediction of tertiary structure from a protein sequence. Very recently, we developed SAdLSA, a new computational algorithm for protein sequence comparison via deep-learning of protein structural a...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8301223/ https://www.ncbi.nlm.nih.gov/pubmed/34308415 http://dx.doi.org/10.3389/fbinf.2021.689960 |
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author | Gao, Mu Skolnick, Jeffrey |
author_facet | Gao, Mu Skolnick, Jeffrey |
author_sort | Gao, Mu |
collection | PubMed |
description | During the past five years, deep-learning algorithms have enabled ground-breaking progress towards the prediction of tertiary structure from a protein sequence. Very recently, we developed SAdLSA, a new computational algorithm for protein sequence comparison via deep-learning of protein structural alignments. SAdLSA shows significant improvement over established sequence alignment methods. In this contribution, we show that SAdLSA provides a general machine-learning framework for structurally characterizing protein sequences. By aligning a protein sequence against itself, SAdLSA generates a fold distogram for the input sequence, including challenging cases whose structural folds were not present in the training set. About 70% of the predicted distograms are statistically significant. Although at present the accuracy of the intra-sequence distogram predicted by SAdLSA self-alignment is not as good as deep-learning algorithms specifically trained for distogram prediction, it is remarkable that the prediction of single protein structures is encoded by an algorithm that learns ensembles of pairwise structural comparisons, without being explicitly trained to recognize individual structural folds. As such, SAdLSA can not only predict protein folds for individual sequences, but also detects subtle, yet significant, structural relationships between multiple protein sequences using the same deep-learning neural network. The former reduces to a special case in this general framework for protein sequence annotation. |
format | Online Article Text |
id | pubmed-8301223 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83012232022-04-01 A General Framework to Learn Tertiary Structure for Protein Sequence Characterization Gao, Mu Skolnick, Jeffrey Front Bioinform Bioinformatics During the past five years, deep-learning algorithms have enabled ground-breaking progress towards the prediction of tertiary structure from a protein sequence. Very recently, we developed SAdLSA, a new computational algorithm for protein sequence comparison via deep-learning of protein structural alignments. SAdLSA shows significant improvement over established sequence alignment methods. In this contribution, we show that SAdLSA provides a general machine-learning framework for structurally characterizing protein sequences. By aligning a protein sequence against itself, SAdLSA generates a fold distogram for the input sequence, including challenging cases whose structural folds were not present in the training set. About 70% of the predicted distograms are statistically significant. Although at present the accuracy of the intra-sequence distogram predicted by SAdLSA self-alignment is not as good as deep-learning algorithms specifically trained for distogram prediction, it is remarkable that the prediction of single protein structures is encoded by an algorithm that learns ensembles of pairwise structural comparisons, without being explicitly trained to recognize individual structural folds. As such, SAdLSA can not only predict protein folds for individual sequences, but also detects subtle, yet significant, structural relationships between multiple protein sequences using the same deep-learning neural network. The former reduces to a special case in this general framework for protein sequence annotation. Frontiers Media S.A. 2021-05-21 /pmc/articles/PMC8301223/ /pubmed/34308415 http://dx.doi.org/10.3389/fbinf.2021.689960 Text en Copyright © 2021 Gao and Skolnick. 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 | Bioinformatics Gao, Mu Skolnick, Jeffrey A General Framework to Learn Tertiary Structure for Protein Sequence Characterization |
title | A General Framework to Learn Tertiary Structure for Protein Sequence Characterization |
title_full | A General Framework to Learn Tertiary Structure for Protein Sequence Characterization |
title_fullStr | A General Framework to Learn Tertiary Structure for Protein Sequence Characterization |
title_full_unstemmed | A General Framework to Learn Tertiary Structure for Protein Sequence Characterization |
title_short | A General Framework to Learn Tertiary Structure for Protein Sequence Characterization |
title_sort | general framework to learn tertiary structure for protein sequence characterization |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8301223/ https://www.ncbi.nlm.nih.gov/pubmed/34308415 http://dx.doi.org/10.3389/fbinf.2021.689960 |
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