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Deep Green Unannotated Protein Structures

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The Deep Green list is based on the identification and curation of conserved unannotated proteins in three green lineage (Viridiplantae) model organisms; Arabidopsis thaliana, Chlamydomonas reinhardtii, and Setaria viridis. Preliminary characterization of Deep Green proteins and genes was done using various informatics tools and published data sets and is presented in Knoshaug, Sun, et al., 2023, submitted. The structures of these unannotated proteins were also predicted using AlphaFold (Jumper et al., 2021). The data deposited here are the AlphaFold structural predictions having the highest pLDDT score and thus identified as the best folded structure (ranked_0). These data enable others to do in-depth structural characterizations to aid in functional characterization leading to deeper understanding of plant biology. References: Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., ?ídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P. and Hassabis, D. (2021) Highly accurate protein structure prediction with AlphaFold. Nature, 596:583-589. Knoshaug, E. P., Sun, P., Nag, A., Nguyen, H., Mattoon, E. M., Zhang, N., Liu, J., Chen, C., Cheng, J., Zhang, R., St. John, P., and Umen, J. (submitted) Identification and preliminary characterization of conserved uncharacterized proteins from Chlamydomonas reinhardtii, Arabidopsis thaliana, and Setaria viridis.

Citation Formats

TY - DATA AB - The Deep Green list is based on the identification and curation of conserved unannotated proteins in three green lineage (Viridiplantae) model organisms; Arabidopsis thaliana, Chlamydomonas reinhardtii, and Setaria viridis. Preliminary characterization of Deep Green proteins and genes was done using various informatics tools and published data sets and is presented in Knoshaug, Sun, et al., 2023, submitted. The structures of these unannotated proteins were also predicted using AlphaFold (Jumper et al., 2021). The data deposited here are the AlphaFold structural predictions having the highest pLDDT score and thus identified as the best folded structure (ranked_0). These data enable others to do in-depth structural characterizations to aid in functional characterization leading to deeper understanding of plant biology. References: Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P. and Hassabis, D. (2021) Highly accurate protein structure prediction with AlphaFold. Nature, 596:583-589. Knoshaug, E. P., Sun, P., Nag, A., Nguyen, H., Mattoon, E. M., Zhang, N., Liu, J., Chen, C., Cheng, J., Zhang, R., St. John, P., and Umen, J. (submitted) Identification and preliminary characterization of conserved uncharacterized proteins from Chlamydomonas reinhardtii, Arabidopsis thaliana, and Setaria viridis. AU - Knoshaug, Eric A2 - Sun, Peipei A3 - Nag, Ambarish A4 - Nguyen, Huong A5 - Mattoon, Erin A6 - Zhang, Ningning A7 - Liu, Jian A8 - Chen, Chen A9 - Cheng, Jianlin A10 - Zhang, Ru A11 - St. John, Peter A12 - Umen, James DB - Open Energy Data Initiative (OEDI) DP - Open EI | National Laboratory of the Rockies DO - KW - Donald Danforth Plant Science Center KW - unannotated proteins KW - protein structure KW - Arabidopsis thaliana KW - Setaria viridis KW - Chlamydomonas reinhardtii KW - energy crop KW - model species KW - green lineage KW - AlphaFold LA - English DA - 2023/04/20 PY - 2023 PB - National Renewable Energy Laboratory T1 - Deep Green Unannotated Protein Structures UR - https://data.openei.org/submissions/8267 ER -
Export Citation to RIS
Knoshaug, Eric, et al. Deep Green Unannotated Protein Structures. National Renewable Energy Laboratory, 20 April, 2023, NREL. https://data.nlr.gov/submissions/216.
Knoshaug, E., Sun, P., Nag, A., Nguyen, H., Mattoon, E., Zhang, N., Liu, J., Chen, C., Cheng, J., Zhang, R., St. John, P., & Umen, J. (2023). Deep Green Unannotated Protein Structures. [Data set]. NREL. National Renewable Energy Laboratory. https://data.nlr.gov/submissions/216
Knoshaug, Eric, Peipei Sun, Ambarish Nag, Huong Nguyen, Erin Mattoon, Ningning Zhang, Jian Liu, Chen Chen, Jianlin Cheng, Ru Zhang, Peter St. John, and James Umen. Deep Green Unannotated Protein Structures. National Renewable Energy Laboratory, April, 20, 2023. Distributed by NREL. https://data.nlr.gov/submissions/216
@misc{OEDI_Dataset_8267, title = {Deep Green Unannotated Protein Structures}, author = {Knoshaug, Eric and Sun, Peipei and Nag, Ambarish and Nguyen, Huong and Mattoon, Erin and Zhang, Ningning and Liu, Jian and Chen, Chen and Cheng, Jianlin and Zhang, Ru and St. John, Peter and Umen, James}, abstractNote = {The Deep Green list is based on the identification and curation of conserved unannotated proteins in three green lineage (Viridiplantae) model organisms; Arabidopsis thaliana, Chlamydomonas reinhardtii, and Setaria viridis. Preliminary characterization of Deep Green proteins and genes was done using various informatics tools and published data sets and is presented in Knoshaug, Sun, et al., 2023, submitted. The structures of these unannotated proteins were also predicted using AlphaFold (Jumper et al., 2021). The data deposited here are the AlphaFold structural predictions having the highest pLDDT score and thus identified as the best folded structure (ranked_0). These data enable others to do in-depth structural characterizations to aid in functional characterization leading to deeper understanding of plant biology.\ References:\ Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., ?ídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P. and Hassabis, D. (2021) Highly accurate protein structure prediction with AlphaFold. Nature, 596:583-589.\ Knoshaug, E. P., Sun, P., Nag, A., Nguyen, H., Mattoon, E. M., Zhang, N., Liu, J., Chen, C., Cheng, J., Zhang, R., St. John, P., and Umen, J. (submitted) Identification and preliminary characterization of conserved uncharacterized proteins from Chlamydomonas reinhardtii, Arabidopsis thaliana, and Setaria viridis.}, url = {https://data.nlr.gov/submissions/216}, year = {2023}, howpublished = {NREL, National Renewable Energy Laboratory, https://data.nlr.gov/submissions/216}, note = {Accessed: 2026-07-08} }

Details

Data from Apr 20, 2023

Last updated Mar 12, 2026

Submitted Apr 20, 2023

Organization

National Renewable Energy Laboratory

Contact

Eric Knoshaug

Authors

Eric Knoshaug

National Renewable Energy Laboratory

Peipei Sun

Donald Danforth Plant Science Center

Ambarish Nag

National Renewable Energy Laboratory

Huong Nguyen

Donald Danforth Plant Science Center

Erin Mattoon

Donald Danforth Plant Science Center

Ningning Zhang

Donald Danforth Plant Science Center

Jian Liu

University of Missouri - Columbia

Chen Chen

University of Missouri - Columbia

Jianlin Cheng

University of Missouri - Columbia

Ru Zhang

Donald Danforth Plant Science Center

Peter St. John

National Renewable Energy Laboratory

James Umen

Donald Danforth Plant Sciences Center

Research Areas

DOE Project Details

Project Name Deep Green: Structural and Functional Genomic Characterization of Conserved Unannotated Green Lineage Proteins

Project Number ERW9098

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