terclim by ICS banner
IVES 9 IVES Conference Series 9 Artificial intelligence (AI)-based protein modeling for the interpretation of grapevine genetic variants

Artificial intelligence (AI)-based protein modeling for the interpretation of grapevine genetic variants

Abstract

Genetic variants known to produce single residue missense mutations have been associated with phenotypic traits of commercial interest in grapevine. This is the case of the K284N substitution in VviDXS1 associated with muscat aroma, or the R197L in VviAGL11 causing stenospermocarpic seedless grapes. The impact of such mutations on protein structure, stability, dynamics, interactions, or functional mechanism can be studied by computational methods, including our pyDock scoring, previously developed. For this, knowledge on the 3D structure of the protein and its complexes with other proteins and biomolecules is required, but such knowledge is not available for virtually none of the proteins and complexes in grapevine. Fortunately, the possibility of modeling proteins and complex structures with Artificial Intelligence (AI)-based methods like AlphaFold2 and AlphaFold2-Multimer will facilitate the application of this approach to proteins and complexes without available structure. Moreover, we are developing new methods based on AI to combine AlphaFold models, molecular dynamics (MD), pyDock energy scoring, and CCharPPI descriptors to predict the impact of protein mutations at the molecular level. As a case study, we have modelled the impact of the R197L seedlessness-associated substitution in VviAGL11. This protein is a homo-dimeric transcription factor that interacts with VviMADS4 dimeric protein to form a functional hetero-tetramer. Structural modeling of this complex provides insights into the functional mechanism of this protein and the role of the mentioned mutation. This protein modeling approach could be extended for grapevine mutation analysis at the genomic level.

DOI:

Publication date: June 14, 2024

Issue: Open GPB 2024

Type: Poster

Authors

Luis Ángel Rodríguez-Lumbreras1, Víctor Monteagudo1, Pablo Carbonell-Bejerano1, Fabian Glaser2, Juan Fernández-Recio1*

1 Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC-UR-Gobierno de La Rioja, Spain
2 Technion Institute of Technology, Israel

Contact the author*

Keywords

AI-based modeling, Seedless grapes, Protein-protein interactions, Mutation impact analysis, Protein structure

Tags

IVES Conference Series | Open GPB | Open GPB 2024

Citation

Related articles…

Shift of Nitrogen Resources by biotic interaction in grapevine

Grape phylloxera (Daktulosphaira vitifoliae Fitch), a monophagous pest of the grapevine, induces nodosities on the roots through its sap-sucking activity.

The plantation frame as a measure of adaptation to climate change

The mechanization of vineyard work originally led to a reduction in planting densities due to the lack of machinery adapted to the vineyard. The current availability of specific machinery makes it possible to establish higher planting densities. In this work, three planting densities (1.40×0.80 m, 1.80×1 m and 2.20×1.20 m, corresponding to 8928, 5555 and 3787 plants/ha respectively) were studied with four varieties autochthonous of Galicia (northwestern Spain): Albariño and Treixadura (white), Sousón and Mencía (red). The vines were trained in a vertical shoot positioning system using a single Royat cordon, and pruned to spurs with two buds each. Agronomic data (yield, pruning wood weight, Ravaz index) and oenological data in must were collected. The higher planting density (1.40×0.80 m) had no significant effect on grape yield per vine in white varieties, although production per hectare was much higher due to the greater number of plants. In red varieties, this planting density resulted in a significantly lower production per vine, compensated by the greater number of plants. In addition, it significantly reduced the Brix degree in the must of the Albariño, Treixadura and Sousón varieties, and increased the total acidity in the latter two and Mencía. It also caused an increase in extractable and total anthocyanins and IPT in red grapes. The effects of high planting density on grapes are of great interest for the adaptation of varieties in the context of climate change. In the future, it could be advisable to modify the limits imposed by the appellations of origin on the planting density of these varieties in order to obtain more balanced wines.

RED WINE AGING WITHOUT SO₂: WHAT IMPACT ON MICROBIAL COMMUNITY?

Nowadays, the use of food preservatives is controversial, SO2 being no exception. Microbial communities have been particularly studied during the prefermentary and fermentation stages in a context of without added SO2. However, microbial risks associated with SO2 reduction or absence, particularly during the wine aging process, have so far been little studied. The microbiological control of wine aging is a key issue for winemakers wishing to produce wines without added SO2. The aim of the present study is to evaluate the impact of different wine aging strategies according to the addition or not of SO2 on the microbiological population levels and diversity.

La caracterización de los moscateles

Ya en 1964 GIOVANNI DALMASSO et alii describiendo el Moscato bianco (12) ponían de manifiesto la dificultad realmente ardua en descubrir “si no todas, por lo menos las más importantes variedades que llevan el nombre de Moscateles

VOLATILE AND GLYCOSYLATED MARKERS OF SMOKE IMPACT: EVOLUTION IN BOTTLED WINE

Smoke impact in wines is caused by a wide range of volatile phenols found in wildfire smoke. These compounds are absorbed and accumulate in berries, where they may also become glycosylated. Both volatile and glycosylated forms eventually end up in wine where they can cause off-flavors. The impact on wine aroma is mainly attributed to volatile phenols, while in-mouth hydrolysis of glycosylated forms may be responsible for long-lasting “ashy” aftertastes (1).