MARMOTTE · Tessellation-based analysis of dynamic protein structures and their complexes - MoleculAR MOTions meet TEssellations (MARMOTTE).

Tessellation-based analysis of dynamic protein structures and their complexes - MoleculAR MOTions meet TEssellations (MARMOTTE).

Structural biology is being revolutionized by deep learning-based methods that produce high-quality protein structure models. However, current state-of-the-art prediction methods have a key limitation: they generate only static conformational models and do not predict structural heterogeneity. Different regions of a protein can exhibit different propensities to change conformation, for various reasons. Some may be partially disordered or participate in structurally ambiguous (fuzzy) interactions essential for biological function. Another limitation is that accurate prediction of protein-protein complexes typically requires large multiple sequence alignments across homologous chains, which are often unavailable for complexes with unclear evolutionary relationships, such as hetero-oligomeric, transient, or ad hoc assemblies. Thus, reliably modeling protein complexes remains challenging. Overall, while recent breakthroughs mark a transformative advance, they do not provide insight into protein dynamics or interactions, which are crucial for understanding biological function. Protein functions arise through intermolecular interactions that almost always involve conformational changes of the interacting partners. The MARMOTTE project aimed to address these challenges by improving computational analysis of protein structures and their complexes through the exploration and prediction of structural heterogeneity of interatomic contacts. The project focused on a Voronoi tessellation-based approach to interaction analysis, which has been shown to be more descriptive than traditional pairwise distance-based methods. Specifically, the project had three scientific objectives: to develop methods that (1) efficiently compute tessellation-derived contact areas in dynamic structures represented as sets of conformational snapshots, (2) predict how these contact areas change upon motion, and (3) use the predicted statistical properties of contact areas to estimate protein-protein binding energy scores suitable for ranking and selecting complex models.

Data: CORDIS, © European Union