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 insight 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 1) abstracts the underlying protein sequence, structure or fold, 2) allows the use of shape retrieval methods to screen large database of protein structures to identify surficial homologs and possible interacting partners, 3) opens an extension of the protein structure-function paradigm towards a protein structure-surface(s)-function paradigm. AVAILABILITY All data are available online at http://datasetmachat.drugdesign.fr. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.