Identification of Consensus Glycosaminoglycan Binding Strings in Proteins

Heparin is a member of the negatively charged glycosaminoglycan family which is present in the extracellular matrix of all eukaryotic cells. Heparin-binding proteins (HBPs) play an important role in various biological processes like cell signaling, development, and angiogenesis. Interestingly, despite their wide array of functions, very little knowledge exists on the precise structural determinants that govern the specificity of the heparin-protein interaction(s). In this context, a critical survey of the presence of heparin-binding amino acid sequence(s) is a pre-requisite for the development of therapeutic principles against heparin protein-mediated diseases. Many proteins have been shown in vivo to bind to heparin. Based on previous structural studies on heparin-binding proteins (HBPs), nineteen consensus heparin-binding motifs have been identified. The length of these strings varies between five and eight amino acids. These heparin-binding motifs were found to contain a pattern of basic natural amino acids (B= lysine/arginine/histidine) spread between the remaining 17 natural amino acids (X). In the present study, a novel search algorithm has been developed to identify the frequency of occurrence of heparin-binding motifs in 200 proteins which have been experimentally identified to have a strong heparin-binding affinity. The list includes the 23 fibroblast growth factors that bind strongly to heparin, whose sequences were obtained from the UniProt Knowledgebase. Results reveal that the HBPs contain multiple putative heparin-binding motifs. Motifs such as, XBXBX, XBXXBX, and XBXXXBX are most preferred in the heparin-binding proteins. Lysine and arginine are the most preferred basic amino acids (B) in the HBPs. Interestingly, hydrophobic amino acids methionine and isoleucine rarely occur in the heparin-binding motifs. In our opinion, results of our study will enable the design of novel heparin-binding peptides and peptoids which can potentially inhibit heparin me- iated pathogenesis. In addition, the algorithm developed in this study can also be of significant use to identify new heparin-binding proteins that have still not been annotated in the human genome databank.