Literature DB >> 23977885

Molecular structure of RADA16-I designer self-assembling peptide nanofibers.

Ashley R Cormier1, Xiaodong Pang, Maxwell I Zimmerman, Huan-Xiang Zhou, Anant K Paravastu.   

Abstract

The designer self-assembling peptide RADA16-I forms nanofiber matrices which have shown great promise for regenerative medicine and three-dimensional cell culture. The RADA16-I amino acid sequence has a β-strand-promoting alternating hydrophobic/charged motif, but arrangement of β-strands into the nanofiber structure has not been previously determined. Here we present a structural model of RADA16-I nanofibers, based on solid-state NMR measurements on samples with different schemes for (13)C isotopic labeling. NMR peak positions and line widths indicate an ordered structure composed of β-strands. The NMR data show that the nanofibers are composed of two stacked β-sheets stabilized by a hydrophobic core formed by alanine side chains, consistent with previous proposals. However, the previously proposed antiparallel β-sheet structure is ruled out by measured (13)C-(13)C dipolar couplings. Instead, neighboring β-strands within β-sheets are parallel, with a registry shift that allows cross-strand staggering of oppositely charged arginine and aspartate side chains. The resulting structural model is compared to nanofiber dimensions observed via images taken by transmission electron microscopy and atomic force microscopy. Multiple NMR peaks for each alanine side chain were observed and could be attributed to multiple configurations of side chain packing within a single scheme for intermolecular packing.

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Year:  2013        PMID: 23977885      PMCID: PMC3946435          DOI: 10.1021/nn401562f

Source DB:  PubMed          Journal:  ACS Nano        ISSN: 1936-0851            Impact factor:   15.881


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6.  Mechanisms of the self-assembly of EAK16-family peptides into fibrillar and globular structures: molecular dynamics simulations from nano- to micro-seconds.

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Review 10.  Biomolecular Assemblies: Moving from Observation to Predictive Design.

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