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Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning
A major challenge to the characterization of intrinsically disordered regions (IDRs), which are widespread in the proteome, but relatively poorly understood, is the identification of molecular features that mediate functions of these regions, such as short motifs, amino acid repeats and physicochemi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9275697/ https://www.ncbi.nlm.nih.gov/pubmed/35767567 http://dx.doi.org/10.1371/journal.pcbi.1010238 |
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author | Lu, Alex X. Lu, Amy X. Pritišanac, Iva Zarin, Taraneh Forman-Kay, Julie D. Moses, Alan M. |
author_facet | Lu, Alex X. Lu, Amy X. Pritišanac, Iva Zarin, Taraneh Forman-Kay, Julie D. Moses, Alan M. |
author_sort | Lu, Alex X. |
collection | PubMed |
description | A major challenge to the characterization of intrinsically disordered regions (IDRs), which are widespread in the proteome, but relatively poorly understood, is the identification of molecular features that mediate functions of these regions, such as short motifs, amino acid repeats and physicochemical properties. Here, we introduce a proteome-scale feature discovery approach for IDRs. Our approach, which we call “reverse homology”, exploits the principle that important functional features are conserved over evolution. We use this as a contrastive learning signal for deep learning: given a set of homologous IDRs, the neural network has to correctly choose a held-out homolog from another set of IDRs sampled randomly from the proteome. We pair reverse homology with a simple architecture and standard interpretation techniques, and show that the network learns conserved features of IDRs that can be interpreted as motifs, repeats, or bulk features like charge or amino acid propensities. We also show that our model can be used to produce visualizations of what residues and regions are most important to IDR function, generating hypotheses for uncharacterized IDRs. Our results suggest that feature discovery using unsupervised neural networks is a promising avenue to gain systematic insight into poorly understood protein sequences. |
format | Online Article Text |
id | pubmed-9275697 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-92756972022-07-13 Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning Lu, Alex X. Lu, Amy X. Pritišanac, Iva Zarin, Taraneh Forman-Kay, Julie D. Moses, Alan M. PLoS Comput Biol Research Article A major challenge to the characterization of intrinsically disordered regions (IDRs), which are widespread in the proteome, but relatively poorly understood, is the identification of molecular features that mediate functions of these regions, such as short motifs, amino acid repeats and physicochemical properties. Here, we introduce a proteome-scale feature discovery approach for IDRs. Our approach, which we call “reverse homology”, exploits the principle that important functional features are conserved over evolution. We use this as a contrastive learning signal for deep learning: given a set of homologous IDRs, the neural network has to correctly choose a held-out homolog from another set of IDRs sampled randomly from the proteome. We pair reverse homology with a simple architecture and standard interpretation techniques, and show that the network learns conserved features of IDRs that can be interpreted as motifs, repeats, or bulk features like charge or amino acid propensities. We also show that our model can be used to produce visualizations of what residues and regions are most important to IDR function, generating hypotheses for uncharacterized IDRs. Our results suggest that feature discovery using unsupervised neural networks is a promising avenue to gain systematic insight into poorly understood protein sequences. Public Library of Science 2022-06-29 /pmc/articles/PMC9275697/ /pubmed/35767567 http://dx.doi.org/10.1371/journal.pcbi.1010238 Text en © 2022 Lu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Lu, Alex X. Lu, Amy X. Pritišanac, Iva Zarin, Taraneh Forman-Kay, Julie D. Moses, Alan M. Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning |
title | Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning |
title_full | Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning |
title_fullStr | Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning |
title_full_unstemmed | Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning |
title_short | Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning |
title_sort | discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9275697/ https://www.ncbi.nlm.nih.gov/pubmed/35767567 http://dx.doi.org/10.1371/journal.pcbi.1010238 |
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