High-Throughput Screening of Streptavidin-Binding Proteins in Self-Assembled Solid Films for Directed Evolution of Materials.

Evolution has shaped the development of proteins with an incredible diversity of properties. Incorporating proteins into materials is desirable for applications including biosensing; however, high-throughput selection techniques for screening protein libraries in materials contexts is lacking. In this work, a high-throughput platform to assess the binding affinity for ordered sensing proteins was established. A library of fusion proteins, consisting of an elastin-like polypeptide block, one of 22 variants of rcSso7d, and a coiled-coil order-directing sequence, was generated. All selected variants had high binding in films, likely due to the similarity of the assay to magnetic bead sorting used for initial selection, while solution binding was more variable. From these results, both the assembly of the fusion proteins in their operating state and the functionality of the binding protein are key factors in the biosensing performance. Thus, the integration of directed evolution with assembled systems is necessary to the design of better materials.

Tuning Selective Transport of Biomolecules through Site-Mutated Nucleoporin-like Protein (NLP) Hydrogels.

Natural selective filtering systems (e.g., the extracellular matrix, nuclear pores, and mucus) separate molecules selectively and efficiently, and the detailed understanding of transport mechanisms exploited in these systems provides important bioinspired design principles for selective filters. In particular, nucleoporins consist of consensus repeat sequences that are readily utilized for engineering repeat proteins. Here, the consensus repeat sequence of Nsp1, a yeast nucleoporin, is polymerized to form a nucleoporin-like protein (NLP) and mutated to understand the effect of sequence on selective transport. The hydrophilic spacers of the NLPs were redesigned considering net charge, charge distribution, and polarity. Mutations were made near to and far from the FSFG interacting domain to explore the role of highly conserved residues as a function of spatial proximity. A nuclear transport receptor-cargo complex, nuclear transport factor 2-green fluorescent protein (NTF2-GFP), was used as a model for changes in transport. For mutations of the charged spacer, some mutations of highly conserved charged residues were possible without knocking out selective transport of the NTF2, but the formation of regions of clustered negative charge has an unfavorable effect on nuclear transporter permeation. Thus, positive net charge and alternating positive and negative charge within the hydrophilic spacer are advantageous for recognition and selective transport. In the polarity panel, mutations that increased the interaction between NTF2-GFP and the gel led to decreased permeation of the NTF2-GFP due to blocking of the interface and inability of the NTF2-GFP to transport into the gel. Therefore, these results provide a strategy for tuning selective permeability of biomolecules using the artificially designed consensus repeat-based hydrogels.

Canonicalizing BigSMILES for Polymers with Defined Backbones.

BigSMILES, a line notation for encapsulating the molecular structure of stochastic molecules such as polymers, was recently proposed as a compact and readable solution for writing macromolecules. While BigSMILES strings serve as useful identifiers for reconstructing the molecular connectivity for polymers, in general, BigSMILES allows the same polymer to be codified into multiple equally valid representations. Having a canonicalization scheme that eliminates the multiplicity would be very useful in reducing time-intensive tasks like structural comparison and molecular search into simple string-matching tasks. Motivated by this, in this work, two strategies for deriving canonical representations for linear polymers are proposed. In the first approach, a canonicalization scheme is proposed to standardize the expression of BigSMILES stochastic objects, thereby standardizing the expression of overall BigSMILES strings. In the second approach, an analogy between formal language theory and the molecular ensemble of polymer molecules is drawn. Linear polymers can be converted into regular languages, and the minimal deterministic finite automaton uniquely associated with each prescribed language is used as the basis for constructing the unique text identifier associated with each distinct polymer. Overall, this work presents algorithms to convert linear polymers into unique structure-based text identifiers. The derived identifiers can be readily applied in chemical information systems for polymers and other polymer informatics applications.

Extending BigSMILES to non-covalent bonds in supramolecular polymer assemblies.

As a machine-recognizable representation of polymer connectivity, BigSMILES line notation extends SMILES from deterministic to stochastic structures. The same framework that allows BigSMILES to accommodate stochastic covalent connectivity can be extended to non-covalent bonds, enhancing its value for polymers, supramolecular materials, and colloidal chemistry. Non-covalent bonds are captured through the inclusion of annotations to pseudo atoms serving as complementary binding pairs, minimal key/value pairs to elaborate other relevant attributes, and indexes to specify the pairing among potential donors and acceptors or bond delocalization. Incorporating these annotations into BigSMILES line notation enables the representation of four common classes of non-covalent bonds in polymer science: electrostatic interactions, hydrogen bonding, metal-ligand complexation, and π-π stacking. The principal advantage of non-covalent BigSMILES is the ability to accommodate a broad variety of non-covalent chemistry with a simple user-orientated, semi-flexible annotation formalism. This goal is achieved by encoding a universal but non-exhaustive representation of non-covalent or stochastic bonding patterns through syntax for (de)protonated and delocalized state of bonding as well as nested bonds for correlated bonding and multi-component mixture. By allowing user-defined descriptors in the annotation expression, further applications in data-driven research can be envisioned to represent chemical structures in many other fields, including polymer nanocomposite and surface chemistry.

Enhanced Conductivity via Homopolymer-Rich Pathways in Block Polymer-Blended Electrolytes.

The optimization of ionic conductivity and lithium-ion battery stability can be achieved by independently tuning the ion transport and mechanical robustness of block polymer (BP) electrolytes. However, the ionic conductivity of BP electrolytes is inherently limited by the covalent attachment of the ionically conductive block to the mechanically robust block, among other factors. Herein, the BP electrolyte polystyrene-block-poly(oligo-oxyethylene methacrylate) [PS-b-POEM] was blended with POEM homopolymers of varying molecular weights. The incorporation of a higher molecular weight homopolymer additive (α > 1 state) promoted a "dry brush-like" homopolymer distribution within the BP self-assembly and led to higher lithium salt concentrations in the more mobile homopolymer-rich region, increasing overall ionic conductivity relative to the "wet brush-like" (α < 1 state) and unblended composites, where α is the molecular weight ratio between the POEM homopolymer and the POEM block in the copolymer. Neutron and X-ray reflectometry (NR and XRR, respectively) provided additional details on the lithium salt and polymer distributions. From XRR, the α > 1 blends showed increased interfacial widths in comparison to their BP (unblended) or α < 1 counterparts because of the more central distribution of the homopolymer. This result, paired with NR data that suggested even salt concentrations across the POEM domains, implied that there was a higher salt concentration in the homopolymer POEM-rich regions in the dry brush blend than in the wet brush blend. Furthermore, using 7Li solid-state nuclear magnetic resonance spectroscopy, we found a temperature corresponding to a transition in lithium mobility (T Li mobility) that was a function of blend type. T Li mobility was found to be 39 °C above T g in all cases. Interestingly, the ionic conductivity of the blended BPs was highest in the α > 1 composites, even though these composites had higher T gs than the α < 1 composites, demonstrating that homopolymer-rich conducting pathways formed in the α > 1 assemblies had a larger influence on conductivity than the greater lithium ion mobility in the α < 1 blends.