Display of a naïve affibody library on staphylococci for selection of binders by means of flow cytometry sorting.

Within the field of combinatorial protein engineering there is a great demand for robust high-throughput selection platforms that allow for unbiased protein library display, affinity-based screening, and amplification of selected clones. We have previously described the development of a staphylococcal display system used for displaying both alternative-scaffolds and antibody-derived proteins. In this study, the objective was to generate an improved expression vector for displaying and screening a high-complexity naïve affibody library, and to facilitate downstream validation of isolated clones. A high-affinity normalization tag, consisting of two ABD-moieties, was introduced to simplify off-rate screening procedures. In addition, the vector was furnished with a TEV protease substrate recognition sequence upstream of the protein library which enables proteolytic processing of the displayed construct for improved binding signal. In the library design, 13 of the 58 surface-exposed amino acid positions were selected for full randomization (except proline and cysteine) using trinucleotide technology. The genetic library was successfully transformed to Staphylococcus carnosus cells, generating a protein library exceeding 109 members. De novo selections against three target proteins (CD14, MAPK9 and the affibody ZEGFR:2377) were successfully performed using magnetic bead-based capture followed by flow-cytometric sorting, yielding affibody molecules binding their respective target with nanomolar affinity. Taken together, the results demonstrate the feasibility of the staphylococcal display system and the proposed selection procedure to generate new affibody molecules with high affinity.

A new prodrug form of Affibody molecules (pro-Affibody) is selectively activated by cancer-associated proteases.

Affinity proteins have advanced the field of targeted therapeutics due to their generally higher specificity compared to small molecular compounds. However, side effects caused by on-target binding in healthy tissues are still an issue. Here, we design and investigate a prodrug strategy for improving tissue specificity of Affibody molecules in future in vivo studies. The prodrug Affibody (pro-Affibody) against the HER2 receptor was constructed by fusing a HER2-specific Affibody (ZHER2) to an anti-idiotypic Affibody (anti-ZHER2). The linker was engineered to comprise a substrate peptide for the cancer-associated matrix metalloprotease 1 (MMP-1). The hypothesis was that the binding surface of ZHER2 would thereby be blocked from interacting with HER2 until the substrate peptide was specifically hydrolyzed by MMP-1. Binding should thereby only occur where MMP-1 is overexpressed, potentially decreasing on-target toxicities in normal tissues. The pro-Affibody was engineered to find a suitable linker and substrate peptide, and the different constructs were evaluated with a new bacterial display assay. HER2-binding of the pro-Affibody was efficiently masked and proteolytic activation of the best variant yielded over 1,000-fold increase in apparent binding affinity. Biosensor analysis revealed that blocking of the pro-Affibody primarily affected the association phase. In a cell-binding assay, the activated pro-Affibody targeted native HER2 on cancer cells as opposed to the non-activated pro-Affibody. We believe this prodrug approach with proteolytic activation is promising for improving tissue specificity in future in vivo targeting applications and can hopefully be extended to other Affibody molecules and similar affinity proteins as well.

Protease substrate profiling using bacterial display of self-blocking affinity proteins and flow-cytometric sorting.

Proteases are involved in fundamental biological processes and are important tools in both biotechnological and biomedical research. An important property of proteases is to discriminate among potential substrates. Here, a new method for substrate profiling of proteases is presented. The substrates are displayed between two anti-idiotypic affinity domains on the Gram-positive bacterium Staphylococcus carnosus. The first domain functions as a reporter tag and has affinity for a labeled reporter protein, whereas the second domain blocks the reporter tag from interacting with the reporter protein. Site-specific proteolysis of the substrate results in release of the blocking domain, enabling the reporter tag to bind the labeled reporter protein. Proteolysis is therefore reflected in reporter binding, which is quantified by flow cytometry. First, the method with tobacco etch virus protease (TEVp) is evaluated and then the substrate preference of matrix metalloprotease-1 (MMP-1) is determined using two libraries of around three million substrates each. Identified substrate peptides contained the previously reported motif (PXXXHy ) and on-cell determination of apparent kcat /KM revealed that the enriched substrate peptides are hydrolyzed six to eight-fold more efficiently than a previously reported substrate peptide. The method thus works as intended and the authors believe it has potential as an efficient tool for substrate profiling.

Flow-cytometric screening of aggregation-inhibitors using a fluorescence-assisted intracellular method.

Aggregation of misfolded peptides and proteins is a key event in several neurodegenerative diseases. Suggested treatments of such disorders aim to inhibit the initial aggregation process. Here, we have developed an intracellular, function-based screening method, intended for isolation of aggregation-inhibitors from combinatorial protein libraries by flow-cytometric cell sorting. The method is based on fusion of aggregation-prone peptides to a fluorescent protein, functioning as a solubility reporter. Co-expression of a protein-based aggregation-inhibitor should prevent aggregation and thus increase the whole-cell fluorescence. We evaluated the method using the aggregation-prone Alzheimer's-related amyloid-ß (Aß) peptide in fusion to green-fluorescent protein (GFP), and an Aß aggregation-inhibiting Affibody molecule. To adapt the method for library applications, the inhibitor was linked to an mCherry reporter for normalization of protein expression levels. We found that aggregation propensity correlates with fluorescence intensity, as co-expression of the Affibody-inhibitor increased the whole-cell fluorescence relative to a non-inhibitor. Employing improved cultivation parameters, we furthermore demonstrated efficient rescue from aggregation of an α-synuclein-derived protein using a different type of aggregation-inhibitor. Importantly, we also showed that the Aß aggregation-inhibiting Affibody molecule could be isolated from a 1:10,000 background of non-inhibitors, with around 3,500-fold enrichment, in one cycle of fluorescence-based cell sorting. In conclusion, our new method represents a promising approach for generation of novel protein-based aggregation-inhibitors.

A protease substrate profiling method that links site-specific proteolysis with antibiotic resistance.

Proteases are involved in many biological processes and have become important tools in biomedical research and industry. Technologies for engineering and characterization of, for example, proteolytic activity and specificity are essential in protease research. Here, we present a novel method for assessment of site-specific proteolysis. The assay utilizes plasmid-encoded reporters that, upon processing by a co-expressed protease, confer antibiotic resistance to bacteria in proportion to the cleavage efficiency. We have demonstrated that cells co-expressing cleavable reporters together with tobacco etch virus protease (TEVp) could be discriminated from cells with non-cleavable reporters by growth in selective media. Importantly, the resistance to antibiotics proved to correlate with the substrate processing efficiency. Thus, by applying competitive growth of a mock library in antibiotic-containing medium, we could show that the substrate preferred by TEVp was enriched relative to less-efficient substrates. We believe that this simple methodology will facilitate protease substrate identification, and hold great promise for directed evolution of proteases and protease recognition sequences towards improved or even new functionality.