Sequence-Structure-Binding Relationships Reveal Adhesion Behavior of the Car9 Solid-Binding Peptide: An Integrated Experimental and Simulation Study.


Journal

Journal of the American Chemical Society
ISSN: 1520-5126
Titre abrégé: J Am Chem Soc
Pays: United States
ID NLM: 7503056

Informations de publication

Date de publication:
05 02 2020
Historique:
pubmed: 15 1 2020
medline: 15 1 2020
entrez: 15 1 2020
Statut: ppublish

Résumé

Solid-binding peptides (SBPs) recognizing inorganic and synthetic interfaces have enabled a broad range of materials science applications and hold promise as adhesive or morphogenetic control units that can be genetically encoded within desirable or designed protein frameworks. To date, the underlying relationships governing both SBP-surface and SBP-SBP interactions and how they give rise to different adsorption mechanisms remain unclear. Here, we combine protein engineering, surface plasmon resonance characterization, and molecular dynamics (MD) simulations initiated from Rosetta predictions to gain insights on the interplay of amino acid composition, structure, self-association, and adhesion modality in a panel of variants of the Car9 silica-binding peptide (DSARGFKKPGKR) fused to the C-terminus of superfolder green fluorescent protein (sfGFP). Analysis of kinetics, energetics, and MD-predicted structures shows that the high-affinity binding of Car9 to the silanol-rich surface of silica is dominated by electrostatic contributions and a spectrum of several persistent interactions that, along with a high surface population of bound molecules, promote cooperative interactions between neighboring SBPs and higher order structure formation. Transition from cooperative to Langmuir adhesion in sfGFP-Car9 variants occurs in concert with a reduction of stable surface interactions and self-association, as confirmed by atomic force microscopy imaging of proteins exhibiting the two different binding behaviors. We discuss the implications of these results for the de novo design of SBP-surface binding systems.

Identifiants

pubmed: 31934768
doi: 10.1021/jacs.9b11617
doi:

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

2355-2363

Auteurs

Shuai Zhang (S)

Physical Sciences Division, Physical and Computational Sciences Directorate , Pacific Northwest National Laboratory , Richland , Washington 99352 , United States.

James J De Yoreo (JJ)

Physical Sciences Division, Physical and Computational Sciences Directorate , Pacific Northwest National Laboratory , Richland , Washington 99352 , United States.

Classifications MeSH