Back

Decoding Rice Seed Storage Proteins: From Gene Identification to Structural Prediction

Yadav, A.; Jaiswal, P.; Mathew, I. E.; Panwar, A.; Agarwal, P.

2025-10-15 plant biology
10.1101/2025.10.15.682491 bioRxiv
Show abstract

Rice seed storage proteins (SSPs) serve as the primary source of dietary protein, energy, and nutrition; however, our understanding of these proteins remains limited, hindering efforts aimed at enhancing grain nutritional quality. To address this, we conducted a comprehensive genome-wide analysis of rice SSPs, encompassing phylogenetic relationships, domain and motif characterization, promoter cis-element mapping, expression profiling, and 3D structural modeling. Through homology- and domain-based searches, we identified 65 SSP-encoding genes, including 19 previously uncharacterized members. Phylogenetic analysis and domain comparisons revealed evolutionary proximity between albumins and prolamins; and between globulins and glutelins. Albumins, glutelins and prolamins clustered tandemly implying that duplication has driven SSP expansion. Transcriptomic evidence indicated that albumins, globulins, and glutelins were transcriptionally active from S2 seed development stage, whereas prolamins initiated expression from S3 stage. Promoter analysis revealed several seed-specific cis-regulatory elements, with CAATBOX1, EBOXBNNAPA, and DOFCOREZM being the most prevalent. Structural modeling showed that albumins and prolamins predominantly adopted -helical conformations, whereas globulins and glutelins were enriched in {beta}-strands and coils. This integrated analysis offers valuable insights into the classification, evolution, regulation, and structure of rice SSPs, providing a foundational resource for advancing grain nutritional improvement strategies.

Published in Annals of Botany · training set

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.