Computational Design Of Model Scaffold For Anion Recognition Based On The '(Cnn)-N-Alpha' Motif

The 'novel phosphate binding 'CaNN' motif', consisting of three consecutive amino acid residues, usually occurs in the protein loop regions preceding a helix. Recent computational and complementary biophysical experiments on a series of chimeric peptides containing the naturally occurring 'CaNN' motif at the N-terminus of a designed helix establishes that the motif segment recognizes the anion (sulfate and phosphate ions) through local interaction along with extension of the helical conformation which is thermodynamically favored even in a context-free, nonproteinaceous isolated system. However, the strength of the interaction depends on the amino acid sequence/conformation of the motif. Such a locally-mediated recognition of anions validates its intrinsic affinity towards anions and confirms that the affinity for recognition of anions is embedded within the 'local sequence' of the motif. Based on the knowledge gathered on the sequence/structural aspects of the naturally occurring 'CaNN' segment, which provides the guideline for rationally engineering model scaffolds, we have modeled a series of templates and investigated their interactions with anions using computational approach. Two of these designed scaffolds show more efficient anion recognition than those of the naturally occurring 'CaNN' motif which have been studied. This may provide an avenue in designing better anion receptors suitable for various biochemical applications.