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…

Integration of wine cultivation history for characterizing the terroirs of Côte d’Or (Burgundy, France)

Les aires d’appellations de la Côte d’Or résultent d’une sélection humaine empirique, historique et évolutive en adéquation avec les facteurs naturels. Afin de comprendre quels facteurs naturels et humains agissent sur le caractère et l’évolution des terroirs des Côtes de Nuits et de Beaune, une méthodologie de recherche a été développée. Elle s’articule autour de deux axes, la caractérisation physique des lieux-dits viticoles et l’historicité de la qualité de ces lieux-dits. Le travail avec un S.I.G permet d’étudier l’évolution spatiale et temporelle de la qualité.

Optimisation de la fertilisation du Cot sur le Causse de l’Appellation d’Origine Contrôlée Cahors

The Appellation d’Origine Contrôlée area of ​​Cahors (Lot) covers an area of ​​21,700 ha, spread over 45 municipalities, of which only 4,300 are planted with vines. The main grape variety of this AOC is the Cot noir which represents 70% of the grape varieties, thus giving their typicality to the wines of this region; but despite this importance, to our knowledge, its physiology has remained relatively unstudied.

Effect of post-harvest ozone treatments on the skin phenolic composition and extractability of red winegrapes cv Nebbiolo and Barbera

Wine industry is looking forward for innovative, safe and eco-friendly antimicrobial products allowing the reduction of chemical treatments in the grape defense and the winemaking process that can affect negatively the quality of the product. Ozone has been tested in food industry giving good results in preventing fungi and bacteria growth on a wide spectrum of vegetables and fruits, due to its oxidant activity and ability to attack numerous cellular constituents. Ozone leaves no chemical residues on the food surface, decomposing itself rapidly in oxygen. Gaseous ozone has been already tested for table grapes storage and on wine grapes during withering.

Pesticide – Free viticulture: towards agroecological wine-producing socio-ecosystems

Can we cultivate grapevine without pesticides? This is a huge challenge for this emblematic crop, which is one of the largest users of plant protection products. Pesticides are mainly used to protect the vine against leaf diseases (powdery mildew, mildew, black-rot), even in organic farming, which uses copper in particular. What are the research avenues that can help eliminate pesticides today?

Comparing different vineyard sampling densities and patterns for spatial interpolation of intrinsic water use efficiency

The need to rationalize agricultural inputs has recently increased interest in assessing vineyard variability in order to implement variable rate input applications, so-called ‘precision viticulture’. In many viticultural areas globally, precision viticulture is already widely used such as for selective harvesting and variable rate application (VRA) of inputs such as irrigation and/or fertilizer. Robust VRA relies on having a geostatistically accurate map (of one or more vineyard attributes) requiring high sampling densities, which can be cost- and time-prohibitive to obtain. Previous work on spatial interpolation using kriging have upscaled ground-based measurements, but such upscaling strategies are applicable only when vineyard conditions are spatially continuous and satisfies the assumption of second-order stationary processes. Alternatively, mixed models that combine kriging and auxiliary information, such as the regression kriging (RK) method, are more instructive for spatial predictions. In order to improve prediction accuracies, it is therefore necessary to incorporate additional information to achieve accurate spatial patterns with low error.