Location: Corn Insects and Crop Genetics Research
2023 Annual Report
Accomplishments
1. Tools developed to streamline protein structure determination and improve comparisons across various species. Determining protein structures was previously time-consuming and costly, leading to a bottleneck in the field of structural biology. Unfortunately, the absence of a protein structure poses limitations in comparing proteins, which makes it challenging to understand their roles in gene function and impedes efforts to improve traits. ARS scientists in Ames, Iowa, at the Maize Genetics and Genomics Database (MaizeGDB) developed a suite of tools to overcome these obstacles, and published reports of them in a leading genetic journal. These tools employ machine learning for rapid 3-D protein structure prediction, streamlining protein comparisons within maize and across diverse species, from crops to humans and yeast. This paves the way for deeper exploration of maize's genetic diversity, accelerating research, and enhancing our understanding of gene functions, ultimately driving advancements for crucial traits. These advancements offer valuable information and support for maize researchers and breeders, surpassing the limitations faced just a few years ago and contributing to enhanced research outcomes.
Review Publications
Woodhouse, M.H., Portwood II, J.L., Sen, S., Hayford, R.K., Gardiner, J.M., Cannon, E.K., Harper, L.C., Andorf, C.M. 2023. Maize protein structure resources at the maize genetics and genomics database. Genetics. 224(1).Article iyad016. https://doi.org/10.1093/genetics/iyad016.
Mural, R.V., Sun, G., Grzybowski, M., Tross, M.C., Jin, H., Smith, C., Newton, L., Andorf, C.M., Woodhouse, M.H., Thompson, A.M., Sigmon, B., Schnable, J.C. 2022. Association mapping across a multitude of traits collected in diverse environments in maize. Gigascience. 11.Article giac080. https://doi.org/10.1093/gigascience/giac080.
Cho, K., Sen, T.Z., Andorf, C.M. 2022. Predicting tissue-specific mRNA and protein abundance in maize: A machine learning approach. Frontiers in Artificial Intelligence. 5. Article 830170. https://doi.org/10.3389/frai.2022.830170.
Cagirici, B.H., Andorf, C.M., Sen, T.Z. 2022. Co-expression pan-network reveals genes involved in complex traits within maize pan-genome. BMC Plant Biology. 22. Article 595. https://doi.org/10.1186/s12870-022-03985-z.
