Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Amoozadeh, Sahel, Johnston, Jodie, Meisrimler, Claudia-Nicole
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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
_version_ 1784612547043786752
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
work_keys_str_mv AT amoozadehsahel exploitingstructuralmodellingtoolstoexplorehosttranslocatedeffectorproteins
AT johnstonjodie exploitingstructuralmodellingtoolstoexplorehosttranslocatedeffectorproteins
AT meisrimlerclaudianicole exploitingstructuralmodellingtoolstoexplorehosttranslocatedeffectorproteins