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Research Project: MaizeGDB: Enabling Access to Basic, Translational, and Applied Research Information

Location: Corn Insects and Crop Genetics Research

Title: MaizeGDB: Maize protein structure resources

item Andorf, Carson
item Portwood, John
item SEN, SHATABDI - Iowa State University
item HAYFORD, RITA - Orise Fellow
item Cannon, Ethalinda
item GARDINER, JACK - University Of Missouri
item Woodhouse, Margaret

Submitted to: Maize Annual Meetings
Publication Type: Abstract Only
Publication Acceptance Date: 2/10/2023
Publication Date: 3/16/2023
Citation: Andorf, C.M., Portwood II, J.L., Sen, S., Hayford, R., Cannon, E.K., Gardiner, J., Woodhouse, M.H. 2023. MaizeGDB: Maize protein structure resources. Maize Annual Meetings. 65.

Interpretive Summary: N/A

Technical Abstract: MaizeGDB offers new tools to accelerate protein structural comparisons between maize and other plants as well as human and yeast outgroups. These tools leverage recent technological breakthroughs in protein structure prediction, such as the release of AlphaFold and ESMFold, which have reduced the structural biology bottleneck by several orders of magnitude. MaizeGDB also offers bulk downloads of comparative protein structure data, along with predicted functional annotation information, to assist maize researchers in assessing functional homology, gene model annotation quality, and other information unavailable to maize scientists even a few years ago. A new method called Functional Annotations using Sequence and Structure Orthology (FASSO) combines sequence- and structure-based approaches to obtain a more accurate and complete set of orthologs across diverse species, and was used to annotate orthologs between five plant species (maize, sorghum, rice, soybean, Arabidopsis) and three distance outgroups (human, budding yeast, and fission yeast) resulting in over 270,000 functional annotations across the eight proteomes, including annotations for over 5,600 uncharacterized proteins. FASSO also provides confidence labels on ortholog predictions and flags potential misannotations in existing proteomes and demonstrates the utility of the approach by exploring the annotation of the maize proteome.