{"title":"The Present State and Impact of AI-Driven Computational Tools for Predicting Plant Protein Structures.","authors":"Stanislaus Antony Ceasar, Heba T Ebeed","doi":"10.2174/0109298665335283241003092139","DOIUrl":null,"url":null,"abstract":"<p><p>Several key functions of plants, such as photosynthesis, nutrient transport, disease resistance, and abiotic tolerance, are manifested by several classes of proteins. Prediction of 3- dimensional (3-D) structures of proteins and their working mechanisms can have a profound impact on plant proteomics research and could help improve key agricultural traits in crop plants. This review aims to present the current status of plant protein structure determination and discuss the way forward. Most experimentally proven protein structures are available only for the model plant Arabidopsis thaliana. Most of the key crop plants have only a few hundred or fewer experimentally proven 3-D structures. Fewer than 1% of the protein sequences in the majority of plants have had their 3D structures experimentally determined, and A. thaliana is the sole plant with the highest percentage of 1.4 % of protein sequences with experimentally determined structures. AI-based protein structure prediction tool AlphaFold has predicted models of several thousand proteins for many crop plants. In AlphaFold predicted protein models, soybean has the highest percentage (65%) of its UniProt protein sequences with predicted models, and a few other crop plants have also a considerable percentage of its UniProt sequences with AlphaFold predicted models. AlphaFold might help predict models and bridge the gap in plant structure determination studies. Protein structure information might lead to engineering key residues to improve the agronomical performance of crop plants.</p>","PeriodicalId":20736,"journal":{"name":"Protein and Peptide Letters","volume":" ","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Protein and Peptide Letters","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.2174/0109298665335283241003092139","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Several key functions of plants, such as photosynthesis, nutrient transport, disease resistance, and abiotic tolerance, are manifested by several classes of proteins. Prediction of 3- dimensional (3-D) structures of proteins and their working mechanisms can have a profound impact on plant proteomics research and could help improve key agricultural traits in crop plants. This review aims to present the current status of plant protein structure determination and discuss the way forward. Most experimentally proven protein structures are available only for the model plant Arabidopsis thaliana. Most of the key crop plants have only a few hundred or fewer experimentally proven 3-D structures. Fewer than 1% of the protein sequences in the majority of plants have had their 3D structures experimentally determined, and A. thaliana is the sole plant with the highest percentage of 1.4 % of protein sequences with experimentally determined structures. AI-based protein structure prediction tool AlphaFold has predicted models of several thousand proteins for many crop plants. In AlphaFold predicted protein models, soybean has the highest percentage (65%) of its UniProt protein sequences with predicted models, and a few other crop plants have also a considerable percentage of its UniProt sequences with AlphaFold predicted models. AlphaFold might help predict models and bridge the gap in plant structure determination studies. Protein structure information might lead to engineering key residues to improve the agronomical performance of crop plants.
期刊介绍：
Protein & Peptide Letters publishes letters, original research papers, mini-reviews and guest edited issues in all important aspects of protein and peptide research, including structural studies, advances in recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, and drug design. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallization and preliminary structure determination of biologically important proteins are considered only if they include significant new approaches or deal with proteins of immediate importance, and preliminary structure determinations of biologically important proteins. Purely theoretical/review papers should provide new insight into the principles of protein/peptide structure and function. Manuscripts describing computational work should include some experimental data to provide confirmation of the results of calculations.
Protein & Peptide Letters focuses on:
Structure Studies
Advances in Recombinant Expression
Drug Design
Chemical Synthesis
Function
Pharmacology
Enzymology
Conformational Analysis
Immunology
Biotechnology
Protein Engineering
Protein Folding
Sequencing
Molecular Recognition
Purification and Analysis
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