Comparative evaluation of shape retrieval methods on macromolecular surfaces: an application of computer vision methods in structural bioinformatics

Motivation: The investigation of the structure of biological systems at the molecular level gives insights about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geometry can lead to more adequate predictions of their interactions. In the present work, we assess the performance of reference shape retrieval methods from the computer vision community on protein shapes. Results: Shape retrieval methods are efficient in identifying orthologous proteins and tracking large conformational changes. This work illustrates the interest for the protein surface shape as a higher-level representation of the protein structure that (i) abstracts the underlying protein sequence, structure or fold, (ii) allows the use of shape retrieval methods to screen large databases of protein structures to identify surficial homologs and possible interacting partners and (iii) opens an extension of the protein structure-function paradigm toward a protein structure-surface(s)function paradigm.

Automated summary

Investigating the structure of biological systems at the molecular level can provide insights into their functions and dynamics. The shape and surface of biomolecules are crucial for molecular recognition events, and characterizing their geometry can enhance predictions of interactions. Evaluating shape retrieval methods from the computer vision community on protein shapes shows the importance of protein surface shape as a higher-level representation of protein structure, allowing for the identification of surficial homologs and potential interacting partners.