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Modeling to Understand Plant Protein Structure-Function Relationships—Implications for Seed Storage Proteins
Proteins are among the most important molecules on Earth. Their structure and aggregation behavior are key to their functionality in living organisms and in protein-rich products. Innovations, such as increased computer size and power, together with novel simulation tools have improved our understan...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071054/ https://www.ncbi.nlm.nih.gov/pubmed/32079172 http://dx.doi.org/10.3390/molecules25040873 |
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author | Rasheed, Faiza Markgren, Joel Hedenqvist, Mikael Johansson, Eva |
author_facet | Rasheed, Faiza Markgren, Joel Hedenqvist, Mikael Johansson, Eva |
author_sort | Rasheed, Faiza |
collection | PubMed |
description | Proteins are among the most important molecules on Earth. Their structure and aggregation behavior are key to their functionality in living organisms and in protein-rich products. Innovations, such as increased computer size and power, together with novel simulation tools have improved our understanding of protein structure-function relationships. This review focuses on various proteins present in plants and modeling tools that can be applied to better understand protein structures and their relationship to functionality, with particular emphasis on plant storage proteins. Modeling of plant proteins is increasing, but less than 9% of deposits in the Research Collaboratory for Structural Bioinformatics Protein Data Bank come from plant proteins. Although, similar tools are applied as in other proteins, modeling of plant proteins is lagging behind and innovative methods are rarely used. Molecular dynamics and molecular docking are commonly used to evaluate differences in forms or mutants, and the impact on functionality. Modeling tools have also been used to describe the photosynthetic machinery and its electron transfer reactions. Storage proteins, especially in large and intrinsically disordered prolamins and glutelins, have been significantly less well-described using modeling. These proteins aggregate during processing and form large polymers that correlate with functionality. The resulting structure-function relationships are important for processed storage proteins, so modeling and simulation studies, using up-to-date models, algorithms, and computer tools are essential for obtaining a better understanding of these relationships. |
format | Online Article Text |
id | pubmed-7071054 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70710542020-03-19 Modeling to Understand Plant Protein Structure-Function Relationships—Implications for Seed Storage Proteins Rasheed, Faiza Markgren, Joel Hedenqvist, Mikael Johansson, Eva Molecules Review Proteins are among the most important molecules on Earth. Their structure and aggregation behavior are key to their functionality in living organisms and in protein-rich products. Innovations, such as increased computer size and power, together with novel simulation tools have improved our understanding of protein structure-function relationships. This review focuses on various proteins present in plants and modeling tools that can be applied to better understand protein structures and their relationship to functionality, with particular emphasis on plant storage proteins. Modeling of plant proteins is increasing, but less than 9% of deposits in the Research Collaboratory for Structural Bioinformatics Protein Data Bank come from plant proteins. Although, similar tools are applied as in other proteins, modeling of plant proteins is lagging behind and innovative methods are rarely used. Molecular dynamics and molecular docking are commonly used to evaluate differences in forms or mutants, and the impact on functionality. Modeling tools have also been used to describe the photosynthetic machinery and its electron transfer reactions. Storage proteins, especially in large and intrinsically disordered prolamins and glutelins, have been significantly less well-described using modeling. These proteins aggregate during processing and form large polymers that correlate with functionality. The resulting structure-function relationships are important for processed storage proteins, so modeling and simulation studies, using up-to-date models, algorithms, and computer tools are essential for obtaining a better understanding of these relationships. MDPI 2020-02-17 /pmc/articles/PMC7071054/ /pubmed/32079172 http://dx.doi.org/10.3390/molecules25040873 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Rasheed, Faiza Markgren, Joel Hedenqvist, Mikael Johansson, Eva Modeling to Understand Plant Protein Structure-Function Relationships—Implications for Seed Storage Proteins |
title | Modeling to Understand Plant Protein Structure-Function Relationships—Implications for Seed Storage Proteins |
title_full | Modeling to Understand Plant Protein Structure-Function Relationships—Implications for Seed Storage Proteins |
title_fullStr | Modeling to Understand Plant Protein Structure-Function Relationships—Implications for Seed Storage Proteins |
title_full_unstemmed | Modeling to Understand Plant Protein Structure-Function Relationships—Implications for Seed Storage Proteins |
title_short | Modeling to Understand Plant Protein Structure-Function Relationships—Implications for Seed Storage Proteins |
title_sort | modeling to understand plant protein structure-function relationships—implications for seed storage proteins |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071054/ https://www.ncbi.nlm.nih.gov/pubmed/32079172 http://dx.doi.org/10.3390/molecules25040873 |
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