Publications

Angiotensin converting enzyme 2 (ACE2) works in the renin angiotensin aldosterone system to decrease circulating levels of angiotensin II by removing the C-terminal phenylalanine and converting it to angiotensin (1-7). In addition, ACE2 has received increased interest in research due to its role in COVID-19 pathogenesis, as the binding site and cell entry gate for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While ACE2 inhibitors have been primarily used as pharmacological tools to study the renin-angiotensin system, small molecule ACE2 enhancers (aka activators) are highly desired because of their hypothesized therapeutic potential. This study was designed to identify peptide-based enhancers of ACE2. First, binding of human recombinant ACE2 to all possible tripeptides composed of the 20 proteinogenic amino acids, was evaluated using a proprietary immunofluorescence-based peptide microarray. Binding of 6xHis-tagged ACE2 to the 8000 tripeptides immobilized on a microchip was evaluated at 10 µg/ml and 100 µg/ml concentrations of the peptidase using a DyLight680-conjugated anti-6xHis-tag antibody. Hemagglutinin (HA) immobilized on the microchip served as a positive control peptide in the microarray and it was tracked using a DyLight800-conjugated anti-HA antibody. The read-out was performed with an Innopsys InnoScan 710-IR Microarray Scanner at scanning gains of 50/10 (red/green). In the result of the microarray a number of tripeptides were identified as potential ACE2 binders. Among them, 22 tripeptides were selected to represent several the most pronounced binders as well as a number of structurally similar tripeptides that did not show appreciable binding to ACE2 to serve as negative control. The effect of the selected peptides (at 1, 10 and 100 µM) on activity of human recombinant ACE2 was tested in a continuous enzymatic assay using a fluorogenic substrate. Contrary to our expectation, none of the peptides affected the activity of ACE2 in a significant manner. These results suggest that the selected peptides do not alter activity of ACE2, but they do not exclude the possibility that some of the peptides may still bind to the peptidase. Our subsequent experiments will apply differential scanning fluorometry (DSF) to determine whether these peptides physically interact with recombinant ACE2.

Abstract Sustained drug delivery strategies have many potential benefits for treating a range of diseases, particularly chronic diseases that require treatment for years. For many chronic ocular diseases, patient adherence to eye drop dosing regimens and the need for frequent intraocular injections are significant barriers to effective disease management. Here, we utilize peptide engineering to impart melanin binding properties to peptide-drug conjugates to act as a sustained-release depot in the eye. We develop a super learning-based methodology to engineer multifunctional peptides that efficiently enter cells, bind to melanin, and have low cytotoxicity. When the lead multifunctional peptide (HR97) is conjugated to brimonidine, an intraocular pressure lowering drug that is prescribed for three times per day topical dosing, intraocular pressure reduction is observed for up to 18 days after a single intracameral injection in rabbits. Further, the cumulative intraocular pressure lowering effect increases ~17-fold compared to free brimonidine injection. Engineered multifunctional peptide-drug conjugates are a promising approach for providing sustained therapeutic delivery in the eye and beyond.

Design of peptide binders is an attractive strategy for targeting “undruggable” protein-protein interfaces. Current design protocols rely on the extraction of an initial sequence from one known protein interactor of the target protein, followed by in-silico or in-vitro mutagenesis-based optimization of its binding affinity. Wet lab protocols can explore only a minor portion of the vast sequence space and cannot efficiently screen for other desirable properties such as high specificity and low toxicity, while in-silico design requires intensive computational resources and often relies on simplified binding models. Yet, for a multivalent protein target, dozens to hundreds of natural protein partners already exist in the cellular environment. Here, we describe a peptide design protocol that harnesses this diversity via a machine learning generative model. After identifying putative natural binding fragments by literature and homology search, a compositional Restricted Boltzmann Machine is trained and sampled to yield hundreds of diverse candidate peptides. The latter are further filtered via flexible molecular docking and an in-vitro microchip-based binding assay. We validate and test our protocol on calcineurin, a calcium-dependent protein phosphatase involved in various cellular pathways in health and disease. In a single screening round, we identified multiple 16-length peptides with up to six mutations from their closest natural sequence that successfully interfere with the binding of calcineurin to its substrates. In summary, integrating protein interaction and sequence databases, generative modeling, molecular docking and interaction assays enables the discovery of novel protein-protein interaction modulators.

An array of 4000 defined and addressable tripeptides on a polymer-coated glass slide is used to synthesize molecularly imprinted polymer (MIP) nanoparticles. This work is undertaken to systematically probe the impact of the peptide sequence on the ability to generate affinity MIPs. The polymer affinity is assessed by measuring the fluorescence of bound MIP nanoparticles at each peptide spot on the surface after washing the array to remove any low-affinity polymer. The generic composition commonly used in the preparation of MIPs against proteins seems to be equally suitable for imprinting hydrophobic and hydrophilic tripeptides. The amino acids frequently contributing to the formation of high-affinity MIPs include T, F, D, N, Y, W, and P. The amino acids that rarely contribute to the formation of high-affinity interactions with MIPs are G, V, A, L, I, and M. These observations are confirmed by computational modeling. The basic technique proposed here may be applicable in optimizing polymer compositions for the production of high-affinity MIPs or, more specifically, for the selection of appropriate amino acid sequences when peptide epitopes are used instead of whole protein imprinting.