Mammalian glycosaminoglycans (GAGs) are linear complex polysaccharides comprising heparan sulfate, heparin, dermatan sulfate, chondroitin sulfate, keratan sulfate and hyaluronic acid. They bind to numerous proteins and these interactions mediate their biological activities. GAG–protein interaction data reported in the literature are curated mostly in MatrixDB database (http://matrixdb. univ-lyon1.fr/). However, a standard nomenclature and a machine-readable format of GAGs together with bioinformatics tools for mining these interaction data are lacking.
We report here the building of an automated pipeline to (i) standardize the format of GAG sequences interacting with proteins manually curated from the literature, (ii) translate them into the machine-readable GlycoCT format and into SNFG (Symbol Nomenclature For Glycan) images and (iii) convert their sequences into a format processed by a builder generating three-dimensional structures of polysaccharides based on a repertoire of conformations experimentally validated by data extracted from crystallized GAG–protein complexes. We have developed a converter to automatically translate the GlycoCT code of a GAG sequence into the input file required to construct a three-dimensional model .
From the analysis of the 3D structures of GAG-protein complexes, we could identify the signatures of the patterns of amino-acids most closely involved in the interactions with the sulfate groups. The significance of such signatures has been corroborated by the calculations of the electrostatic surfaces of the proteins. This agreement opens the road to implementing pattern searches in protein-binding GAG sequences.
While strengthening the implementation of GAGs in the field of glycobioinformatics, these new developments are a step forward towards bridging glycomics with interatomics .
- O. CLERC, J. MARIETHOZ, A. RIVET, F. LISACEK, S. PEREZ & S. RICARD-BLUM (2018). A pipeline to translate glycosaminoglycan sequences into 3D modles. Application to the exploration of glycosaminoglycan conformational space. Glycobiology, 29, 36-44
- M. DENIAUD, O. CLERC, S.D. VALLET, A. NABA, A. RIVET, S. PEREZ, N. THIERRY-MIEG & S. RICARD-BLUM (2019). MatrixDB; integration of new data with a focus on glycosaminoglycan interactions, Nucleic Acid Research, 47, D376-D381