Summary
This year has seen a breakthrough in structural bioinformatics - deep learning-based methods, most notably Google DeepMind's AlphaFold2, have demonstrated near-experimental accuracy of protein structure predictions. However, even the best protein structure prediction methods do not automatically provide knowledge about protein dynamics and protein interactions, which is often essential to understand or predict the biological functions of proteins. Those functions are performed via intermolecular interactions, and such interactions almost always involve conformational changes of engaged partners. The problem of modeling dynamic protein structures and their complexes is still largely unsolved - this project aims to significantly contribute towards its future solution by exploring the link between computational geometry, statistical physics, and machine learning. The postdoctoral researcher will develop novel methods that: given a dynamic (moving) molecular structure, efficiently compute tessellation-derived contact areas; given a starting structure and its tessellation-derived contacts areas, predict (using a graph neural network) how the interatomic contact areas will change upon motion; given a protein complex model generated by docking, use the predicted statistical properties of the contact areas to estimate (using a graph neural network) the protein-protein binding energy score. If successfully developed, such methods will provide unique data about the dynamics of tessellation-derived interatomic contact areas. Most importantly, they will provide effective dynamics-aware scores for assessing and ranking structural models of protein complexes.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101059190 |
| Start date: | 01-09-2023 |
| End date: | 31-08-2025 |
| Total budget - Public funding: | - 195 914,00 Euro |
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Original description
This year has seen a breakthrough in structural bioinformatics - deep learning-based methods, most notably Google DeepMind's AlphaFold2, have demonstrated near-experimental accuracy of protein structure predictions. However, even the best protein structure prediction methods do not automatically provide knowledge about protein dynamics and protein interactions, which is often essential to understand or predict the biological functions of proteins. Those functions are performed via intermolecular interactions, and such interactions almost always involve conformational changes of engaged partners. The problem of modeling dynamic protein structures and their complexes is still largely unsolved - this project aims to significantly contribute towards its future solution by exploring the link between computational geometry, statistical physics, and machine learning. The postdoctoral researcher will develop novel methods that: given a dynamic (moving) molecular structure, efficiently compute tessellation-derived contact areas; given a starting structure and its tessellation-derived contacts areas, predict (using a graph neural network) how the interatomic contact areas will change upon motion; given a protein complex model generated by docking, use the predicted statistical properties of the contact areas to estimate (using a graph neural network) the protein-protein binding energy score. If successfully developed, such methods will provide unique data about the dynamics of tessellation-derived interatomic contact areas. Most importantly, they will provide effective dynamics-aware scores for assessing and ranking structural models of protein complexes.Status
SIGNEDCall topic
HORIZON-MSCA-2021-PF-01-01Update Date
09-02-2023
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