CE23 - Intelligence artificielle et science des données

GRaphs and Algorithms for 3D proteIn structurE and dyNamics classificaTion – GRADIENT

Submission summary

Shape classification is one of the most important tasks in computer vision as demonstrated by the large body of work dealing with 3D shape analysis. Recent advances in 3D data acquisition and the availability of large 3D repositories have been instrumental in the design of new and more efficient algorithms for shape classification. 3D shapes may be represented by graphs and consequently, graph techniques may be strongly useful for their classification. In the GRADIENT project, we address the problem of 3D protein deformable shapes classification. Proteins are macromolecules characterized by deformable and complex shapes which are linked to their function making their classification an important task namely for drug discovery and biological processes characterization. Protein shapes can be generated from their high-resolution 3D structures available in the Protein Data Bank. Their conformational space can be sampled using molecular dynamics simulations. In the GRADIENT project, proteins are assimilated to 3D dynamic deformable objects and their surfaces are represented by graphs (meshes). Since molecular dynamics can be used to efficiently sample the trajectories of molecular 3D objects, they constitute a perfect case of study for dynamic graph matching.
The goal of the GRADIENT project is to develop a platform containing robust techniques and algorithms based on graphs, algorithms and machine learning able to classify and analyze 3D deformable (dynamic) shapes of proteins. To do that, we will address the following research hypothesis and challenges: 1) Data representation, descriptors and features for 3D protein deformable structures. 2) Graph distances to compare 3D protein deformable shapes. 3) Robust models algorithms combining graph and machine learning techniques to classify 3D protein deformable shapes. 4) Trajectories tracking analysis of protein dynamics.
The platform will be freely available for academics and under commercial license for industrials.

Project coordination

HAMAMACHE KHEDDOUCI (TRESOR PUBLIC)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

UMANIS
CGI France
LIRIS TRESOR PUBLIC
GBCM Conservatoire National des Arts et Métiers Paris

Help of the ANR 841,918 euros
Beginning and duration of the scientific project: January 2023 - 48 Months

Useful links

Explorez notre base de projets financés

 

 

ANR makes available its datasets on funded projects, click here to find more.

Sign up for the latest news:
Subscribe to our newsletter