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Exploiting Structural Modelling Tools to Explore Host-Translocated Effector Proteins
Oomycete and fungal interactions with plants can be neutral, symbiotic or pathogenic with different impact on plant health and fitness. Both fungi and oomycetes can generate so-called effector proteins in order to successfully colonize the host plant. These proteins modify stress pathways, developme...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8657640/ https://www.ncbi.nlm.nih.gov/pubmed/34884778 http://dx.doi.org/10.3390/ijms222312962 |
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author | Amoozadeh, Sahel Johnston, Jodie Meisrimler, Claudia-Nicole |
author_facet | Amoozadeh, Sahel Johnston, Jodie Meisrimler, Claudia-Nicole |
author_sort | Amoozadeh, Sahel |
collection | PubMed |
description | Oomycete and fungal interactions with plants can be neutral, symbiotic or pathogenic with different impact on plant health and fitness. Both fungi and oomycetes can generate so-called effector proteins in order to successfully colonize the host plant. These proteins modify stress pathways, developmental processes and the innate immune system to the microbes’ benefit, with a very different outcome for the plant. Investigating the biological and functional roles of effectors during plant–microbe interactions are accessible through bioinformatics and experimental approaches. The next generation protein modeling software RoseTTafold and AlphaFold2 have made significant progress in defining the 3D-structure of proteins by utilizing novel machine-learning algorithms using amino acid sequences as their only input. As these two methods rely on super computers, Google Colabfold alternatives have received significant attention, making the approaches more accessible to users. Here, we focus on current structural biology, sequence motif and domain knowledge of effector proteins from filamentous microbes and discuss the broader use of novel modelling strategies, namely AlphaFold2 and RoseTTafold, in the field of effector biology. Finally, we compare the original programs and their Colab versions to assess current strengths, ease of access, limitations and future applications. |
format | Online Article Text |
id | pubmed-8657640 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86576402021-12-10 Exploiting Structural Modelling Tools to Explore Host-Translocated Effector Proteins Amoozadeh, Sahel Johnston, Jodie Meisrimler, Claudia-Nicole Int J Mol Sci Review Oomycete and fungal interactions with plants can be neutral, symbiotic or pathogenic with different impact on plant health and fitness. Both fungi and oomycetes can generate so-called effector proteins in order to successfully colonize the host plant. These proteins modify stress pathways, developmental processes and the innate immune system to the microbes’ benefit, with a very different outcome for the plant. Investigating the biological and functional roles of effectors during plant–microbe interactions are accessible through bioinformatics and experimental approaches. The next generation protein modeling software RoseTTafold and AlphaFold2 have made significant progress in defining the 3D-structure of proteins by utilizing novel machine-learning algorithms using amino acid sequences as their only input. As these two methods rely on super computers, Google Colabfold alternatives have received significant attention, making the approaches more accessible to users. Here, we focus on current structural biology, sequence motif and domain knowledge of effector proteins from filamentous microbes and discuss the broader use of novel modelling strategies, namely AlphaFold2 and RoseTTafold, in the field of effector biology. Finally, we compare the original programs and their Colab versions to assess current strengths, ease of access, limitations and future applications. MDPI 2021-11-30 /pmc/articles/PMC8657640/ /pubmed/34884778 http://dx.doi.org/10.3390/ijms222312962 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Amoozadeh, Sahel Johnston, Jodie Meisrimler, Claudia-Nicole Exploiting Structural Modelling Tools to Explore Host-Translocated Effector Proteins |
title | Exploiting Structural Modelling Tools to Explore Host-Translocated Effector Proteins |
title_full | Exploiting Structural Modelling Tools to Explore Host-Translocated Effector Proteins |
title_fullStr | Exploiting Structural Modelling Tools to Explore Host-Translocated Effector Proteins |
title_full_unstemmed | Exploiting Structural Modelling Tools to Explore Host-Translocated Effector Proteins |
title_short | Exploiting Structural Modelling Tools to Explore Host-Translocated Effector Proteins |
title_sort | exploiting structural modelling tools to explore host-translocated effector proteins |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8657640/ https://www.ncbi.nlm.nih.gov/pubmed/34884778 http://dx.doi.org/10.3390/ijms222312962 |
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