Bringas, P. | Search bioRxiv

bioRxiv2026

Optimizing irrigation during heat events sustains grapevine physiology and fruit production

O_LIIncreasing frequency, intensity, and duration of heat waves (HWs) threaten agricultural production globally by constraining physiological function and fruit production. Supplemental irrigation mitigates heat stress in grapevine and other woody perennial crops, yet water scarcity necessitates optimized irrigation strategies during extreme heat. C_LIO_LIWe conducted a three-year field trial in a commercial Cabernet Sauvignon vineyard, applying differential irrigation only before and during naturally occurring HWs: baseline (50% ET), moderate (90-120% ET), and high (120-180% ET). We monitored water potentials, leaf gas exchange, canopy temperature, yield, and berry composition. C_LIO_LIBaseline irrigation consistently reduced net photosynthesis, stomatal conductance, and leaf cooling capacity during HWs. Moderate supplemental irrigation maintained gas exchange, transpiration, and leaf temperature, mitigating yield losses. Excessive irrigation beyond moderate levels provided no additional physiological benefit and decreased crop water use efficiency and berry quality. C_LIO_LIOur results demonstrate that targeted, event-based irrigation sustains grapevine physiological performance and fruit production under extreme heat, whereas both insufficient and excessive water negatively affect carbon assimilation, stomatal regulation, and crop productivity. These findings emphasize the importance of aligning water management with heat event timing to preserve vine function, optimize water use, and maintain yield and fruit quality in water-limited regions. C_LI

plant biology↗

bioRxiv2026

Origin-1: a generative AI platform for de novo antibody design against novel epitopes

0Generative artificial intelligence has advanced antibody discovery, yet de novo design of therapeutic antibodies against targets with "zero-prior" epitopes remains a fundamental challenge. We define "zero-prior" epitopes as target sites lacking structural data from any reported antibody-antigen or protein-protein complex involving the target. Here we present Origin-1, a generative AI platform that overcomes this by integrating epitope-conditioned all-atom structure generation, paired complementarity determining region sequence design, and a specialized co-folding-based scoring protocol to select antibody designs predicted to be high-confidence, specific binders with favorable developability. We evaluated Origin-1 on a panel of ten targets selected to have no available protein-protein complex structures and minimal homology ([≤]60% sequence identity) to proteins with known complexes, creating stringent design conditions. In fewer than one hundred design attempts per target, we identified developable, specific antibodies, validated across multiple biophysical and developability assays, for four targets: COL6A3, AZGP1, CHI3L2, and IL36RA, with functional inhibition demonstrated for IL36RA. Cryogenic electron microscopy confirmed the atomic accuracy of our designs, revealing complexes that closely matched the computational models with high structural fidelity (3.0-3.3 [A] resolution; 0.83-0.91 DockQ). Furthermore, we employed AI-guided affinity maturation to optimize a de novo antibody binder against IL36RA, producing functional antagonists with sub-nanomolar affinities and a top EC50 of 12.3 nM. These results demonstrate a framework for targeting epitopes without structural precedent, expanding the programmable therapeutic antibody landscape.

molecular biology↗