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Computational design of model scaffold for anion recognition based on the `(CNN)-N-alpha' motif

Sheet, Tridip and Ghosh, Suvankar and Pal, Debnath and Banerjee, Raja (2017) Computational design of model scaffold for anion recognition based on the `(CNN)-N-alpha' motif. In: BIOPOLYMERS, 108 (1, SI).

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Official URL: http://doi.org/10.1002/bip.22921

Abstract

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.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the WILEY, 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
Department/Centre: Division of Interdisciplinary Research > Supercomputer Education & Research Centre
Depositing User: Id for Latest eprints
Date Deposited: 16 Sep 2017 05:49
Last Modified: 16 Sep 2017 05:49
URI: http://eprints.iisc.ac.in/id/eprint/57838

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