Grasspea (Lathyrus sativus L.) is a versatile, climate-resilient legume valued for its nutritional richness and low input requirements. However, the presence of the neurotoxin β-N-oxalyl-L-α, β-diaminopropionic acid (β-ODAP) has limited its popularity. The present study aimed to identify promising low-ODAP grasspea genotypes for rice-fallow ecosystems of the Gangetic Plains of India through comprehensive morpho-biochemical characterisation. Fifteen genotypes of diverse origin were evaluated in a randomized complete block design (RCBD) with 3 replications and observations were recorded from five plants per plot. Biochemical analyses were performed using standard protocols, including anthrone (carbohydrates), Lowry’s (protein) and modified Rao’s colourimetric assay for ß-ODAP quantification. Significant variability was observed among genotypes. Total soluble sugars ranged from 3.01–12.43 %, proteins from 14.08–33.76 % and starch from 9.46–43.8 %, reflecting up to four-fold variation in carbohydrates and over two-fold variation in protein content. ß-ODAP content ranged from 0.07–0.51 %, with values ≤ 0.15 % considered low, identifying genotypes such as BANG-227 (0.07 %) as a low-ODAP line. Yield traits also varied widely, with seed yield ranging from 6.30–17.09 g per plant and pods per plant from 53 to 139. Genotypes BANG-227, IFLA-2341 and BCK-19-43 exhibited a desirable combination of low ODAP, higher nutritional attributes and superior yield performance. Correlation and multivariate studies revealed that primary branches, pods per plant and seed weight were key contributors to yield, while ODAP and mineral composition contributed to genetic divergence. The study conclusively identified BANG-227, IFLA-2341 and BCK-19-43 with superior nutritional and agronomic performance for breeding and deployment in rice-fallow agro-ecosystems.
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Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
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