An efficient mRNA display protocol yields potent bicyclic peptide inhibitors for FGFR3c: outperforming linear and monocyclic formats in affinity and stability.

Macrocyclization has positioned itself as a powerful method for engineering potent peptide drug candidates. Introducing one or multiple cyclizations is a common strategy to improve properties such as affinity, bioavailability and proteolytic stability. Consequently, methodologies to create large libraries of polycyclic peptides by phage or mRNA display have emerged, allowing the rapid identification of binders to virtually any target. Yet, within those libraries, the performance of linear vs. mono- or bicyclic peptides has rarely been studied. Indeed, a key parameter to perform such a comparison is to use a display protocol and cyclization chemistry that enables the formation of all 3 formats in equal quality and diversity. Here, we developed a simple, efficient and fast mRNA display protocol which meets these criteria and can be used to generate highly diverse libraries of thioether cyclized polycyclic peptides. As a proof of concept, we selected peptides against fibroblast growth factor receptor 3c (FGFR3c) and compared the different formats regarding affinity, specificity, and human plasma stability. The peptides with the best KD's and stability were identified among bicyclic peptide hits, further strengthening the body of evidence pointing at the superiority of this class of molecules and providing functional and selective inhibitors of FGFR3c.

Identification and engineering of potent cyclic peptides with selective or promiscuous binding through biochemical profiling and bioinformatic data analysis.

As our understanding of biological systems grows, so does the need to selectively target individual or multiple members of specific protein families in order to probe their function. Many targets of current biological and pharmaceutical interest are part of a large family of closely related proteins and achieving ligand selectivity often remains either an elusive or time-consuming endeavour. Cyclic peptides (CPs) occupy a key niche in ligand space, able to achieve high affinity and selectivity while retaining synthetic accessibility. De novo cyclic peptide ligands can be rapidly generated against a given target using mRNA display. In this study we harness mRNA display technology and the wealth of next generation sequencing (NGS) data generated to explore both experimental approaches and bioinformatic, statistical data analysis of peptide enrichment in cross-screen selections to rapidly generate high affinity CPs with differing intra-family protein selectivity profiles against fibroblast growth factor receptor (FGF-R) family proteins. Using these methods, CPs with distinct selectivity profiles can be generated which can serve as valuable tool compounds to decipher biological questions.

Computational design of N-linked glycans for high throughput epitope profiling.

Efficient identification of epitopes is crucial for drug discovery and design as it enables the selection of optimal epitopes, expansion of lead antibody diversity, and verification of binding interface. Although high-resolution low throughput methods like x-ray crystallography can determine epitopes or protein-protein interactions accurately, they are time-consuming and can only be applied to a limited number of complexes. To overcome these limitations, we have developed a rapid computational method that incorporates N-linked glycans to mask epitopes or protein interaction surfaces, thereby providing a mapping of these regions. Using human coagulation factor IXa (fIXa) as a model system, we computationally screened 158 positions and expressed 98 variants to test experimentally for epitope mapping. We were able to delineate epitopes rapidly and reliably through the insertion of N-linked glycans that efficiently disrupted binding in a site-selective manner. To validate the efficacy of our method, we conducted ELISA experiments and high-throughput yeast surface display assays. Furthermore, x-ray crystallography was employed to verify the results, thereby recapitulating through the method of N-linked glycans a coarse-grained mapping of the epitope.

An IgM-like inhalable ACE2 fusion protein broadly neutralizes SARS-CoV-2 variants.

Many of the currently available COVID-19 vaccines and therapeutics are not effective against newly emerged SARS-CoV-2 variants. Here, we developed the metallo-enzyme domain of angiotensin converting enzyme 2 (ACE2)-the cellular receptor of SARS-CoV-2-into an IgM-like inhalable molecule (HH-120). HH-120 binds to the SARS-CoV-2 Spike (S) protein with high avidity and confers potent and broad-spectrum neutralization activity against all known SARS-CoV-2 variants of concern. HH-120 was developed as an inhaled formulation that achieves appropriate aerodynamic properties for rodent and monkey respiratory system delivery, and we found that early administration of HH-120 by aerosol inhalation significantly reduced viral loads and lung pathology scores in male golden Syrian hamsters infected by the SARS-CoV-2 ancestral strain (GDPCC-nCoV27) and the Delta variant. Our study presents a meaningful advancement in the inhalation delivery of large biologics like HH-120 (molecular weight (MW) ~ 1000 kDa) and demonstrates that HH-120 can serve as an efficacious, safe, and convenient agent against SARS-CoV-2 variants. Finally, given the known role of ACE2 in viral reception, it is conceivable that HH-120 has the potential to be efficacious against additional emergent coronaviruses.

An integrated platform approach enables discovery of potent, selective and ligand-competitive cyclic peptides targeting the GIP receptor.

In any drug discovery effort, the identification of hits for further optimisation is of crucial importance. For peptide therapeutics, display technologies such as mRNA display have emerged as powerful methodologies to identify these desired de novo hit ligands against targets of interest. The diverse peptide libraries are genetically encoded in these technologies, allowing for next-generation sequencing to be used to efficiently identify the binding ligands. Despite the vast datasets that can be generated, current downstream methodologies, however, are limited by low throughput validation processes, including hit prioritisation, peptide synthesis, biochemical and biophysical assays. In this work we report a highly efficient strategy that combines bioinformatic analysis with state-of-the-art high throughput peptide synthesis to identify nanomolar cyclic peptide (CP) ligands of the human glucose-dependent insulinotropic peptide receptor (hGIP-R). Furthermore, our workflow is able to discriminate between functional and remote binding non-functional ligands. Efficient structure-activity relationship analysis (SAR) combined with advanced in silico structural studies allow deduction of a thorough and holistic binding model which informs further chemical optimisation, including efficient half-life extension. We report the identification and design of the first de novo, GIP-competitive, incretin receptor family-selective CPs, which exhibit an in vivo half-life up to 10.7 h in rats. The workflow should be generally applicable to any selection target, improving and accelerating hit identification, validation, characterisation, and prioritisation for therapeutic development.

A statistical, task-based evaluation method for three-dimensional x-ray breast imaging systems using variable-background phantoms.

PURPOSE: For the last few years, development and optimization of three-dimensional (3D) x-ray breast imaging systems, such as digital breast tomosynthesis (DBT) and computed tomography, have drawn much attention from the medical imaging community, either academia or industry. However, there is still much room for understanding how to best optimize and evaluate the devices over a large space of many different system parameters and geometries. Current evaluation methods, which work well for 2D systems, do not incorporate the depth information from the 3D imaging systems. Therefore, it is critical to develop a statistically sound evaluation method to investigate the usefulness of inclusion of depth and background-variability information into the assessment and optimization of the 3D systems. METHODS: In this paper, we present a mathematical framework for a statistical assessment of planar and 3D x-ray breast imaging systems. Our method is based on statistical decision theory, in particular, making use of the ideal linear observer called the Hotelling observer. We also present a physical phantom that consists of spheres of different sizes and materials for producing an ensemble of randomly varying backgrounds to be imaged for a given patient class. Lastly, we demonstrate our evaluation method in comparing laboratory mammography and three-angle DBT systems for signal detection tasks using the phantom's projection data. We compare the variable phantom case to that of a phantom of the same dimensions filled with water, which we call the uniform phantom, based on the performance of the Hotelling observer as a function of signal size and intensity. RESULTS: Detectability trends calculated using the variable and uniform phantom methods are different from each other for both mammography and DBT systems. CONCLUSIONS: Our results indicate that measuring the system's detection performance with consideration of background variability may lead to differences in system performance estimates and comparisons. For the assessment of 3D systems, to accurately determine trade offs between image quality and radiation dose, it is critical to incorporate randomness arising from the imaging chain including background variability into system performance calculations.

Evaluation of clinical full field digital mammography with the task specific system-model-based Fourier Hotelling observer (SMFHO) SNR.

PURPOSE: The purpose of this work is to evaluate the performance of the image acquisition chain of clinical full field digital mammography (FFDM) systems by quantifying their image quality, and how well the desired information is captured by the images. METHODS: The authors present a practical methodology to evaluate FFDM using the task specific system-model-based Fourier Hotelling observer (SMFHO) signal to noise ratio (SNR), which evaluates the signal and noise transfer characteristics of FFDM systems in the presence of a uniform polymethyl methacrylate phantom that models the attenuation of a 6 cm thick 20/80 breast (20% glandular/80% adipose). The authors model the system performance using the generalized modulation transfer function, which accounts for scatter blur and focal spot unsharpness, and the generalized noise power spectrum, both estimated with the phantom placed in the field of view. Using the system model, the authors were able to estimate system detectability for a series of simulated disk signals with various diameters and thicknesses, quantified by a SMFHO SNR map. Contrast-detail (CD) curves were generated from the SNR map and adjusted using an estimate of the human observer efficiency, without performing time-consuming human reader studies. Using the SMFHO method the authors compared two FFDM systems, the GE Senographe DS and Hologic Selenia FFDM systems, which use indirect and direct detectors, respectively. RESULTS: Even though the two FFDM systems have different resolutions, noise properties, detector technologies, and antiscatter grids, the authors found no significant difference between them in terms of detectability for a given signal detection task. The authors also compared the performance between the two image acquisition modes (fine view and standard) of the GE Senographe DS system, and concluded that there is no significant difference when evaluated by the SMFHO. The estimated human observer efficiency was 30 ± 5% when compared to the SMFHO. The results showed good agreement when compared to other model observers as well as previously published human observer data. CONCLUSIONS: This method generates CD curves from the SMFHO SNR that can be used as figures of merit for evaluating the image acquisition performance of clinical FFDM systems. It provides a way of creating an empirical model of the FFDM system that accounts for patient scatter, focal spot unsharpness, and detector blur. With the use of simulated signals, this method can predict system performance for a signal known exactly/background known exactly detection task with a limited number of images, therefore, it can be readily applied in a clinical environment.