Sequence–Structure–Property Relationships of Recombinant Spider Silk Proteins: Integration of Biopolymer Design, Processing, and Modeling. (13th August 2012)

• Record Type: Journal Article
• Authors: Krishnaji, Sreevidhya Tarakkad; Bratzel, Graham; Kinahan, Michelle E.; Kluge, Jonathan A.; Staii, Cristian; Wong, Joyce Y.; Buehler, Markus J.; Kaplan, David L.
• Is Part Of: Advanced functional materials. Volume 23:Number 2(2013)
• Page Start: 241
• Page End: 253
• Publication Date: 2012-08-13
• Subjects: Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adfm.201200510
• Languages: English
• ISSNs: 1616-301X

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <p>The mechanical properties of spider silks drive interest as sources of new materials. However, there remains a lot to learn regarding the relationships between sequence, structure, and mechanical properties. In order to predict the types of sequence–functional relationships, synthesis–characterization–computation are integrated using recombinant spider silk‐like block copolymers. Two designs are studied, both with origins from the spider <italic>Nephila clavipes</italic>. These proteins are studied both experimentally and in silico to understand the relationships between sequence chemistry, processing, structure, and materials function. Films formed from the two proteins are thoroughly characterized. In parallel, molecular modeling is used to assess the propensity of the two sequences to form <italic>β</italic>‐sheets or crystalline structures. The results demonstrate that the modeling predicts the structural differences between the two silk‐like polymers and these features can also be related to differences in functional outcomes. With this example of relating sequence design (hydrophobic–hydrophilic domains), experiment (genetic design and synthesis), processing (film and fiber formation) and modeling (predictions of crystallinity), synergy among these methods is demonstrated for predictable material outcomes. This approach offers a robust discovery path when looking towards next generation approaches to targeted materials outcomes.</p> </abstract>