mRNA display allows production and selection of vast macrocyclic peptide libraries.  Here we present a novel strategy for making target class-selective mRNA display libraries by using N-terminal selective cyclization chemistry to allow post-translational chemical derivatization of internal cysteines. We thus install analogs of dimethyl lysine (KMe2) in selections against epigenetic targets UHRF1 and RBBP7. We further expand this methodology by combining it with a novel, late-stage barcoding strategy that allows rapid preparation of focused, bespoke mRNA display libraries for hit-to-lead optimization. This approach highlights how combined library diversification methods can enhance mRNA display for discovering potent binders to protein targets.

Phage display has become a cornerstone of peptide discovery, enabling the identification of high-affinity binders against a wide array of targets. However, binding affinity alone is not a reliable predictor of therapeutic success. Enhancing the translational potential of peptides requires addressing critical factors such as off-target effects, biodistribution, and pharmacokinetics early in the discovery process. By employing selection strategies under physiologically relevant conditions, we can prioritize candidates with optimized therapeutic profiles, leading to more effective peptide-based therapies.

Hydrolases are enzymes that often play pathogenic roles in diseases such as cancer, asthma, arthritis, atherosclerosis, and infection by pathogens. Probes that allow dynamic monitoring of their activity can be used as diagnostic and imaging agents, as well as for identification of enzymes as drug leads. I will describe efforts using phage display, mRNA display, and high-throughput fragment screening to identify selective covalent-binding probes for diverse protein targets.

We have integrated genetically encoded noncanonical amino acids (ncAAs) into the phage display technique to facilitate simultaneous cyclization of phage-displayed peptides and use the chemical handles in ncAAs as anchors for active sites of protein targets for directed ligand evolution and enrichment. To achieve the full potential of this novel technique, we have also developed chemical linkers that conjugate to an N-terminal cysteine and an internal cysteine for ligation of phage-displayed peptides. These techniques have been successfully applied for the identification of peptide ligands for epigenetic protein targets, including SIRT2, ENL, HDAC2, and BRD9. 

Affinity selection on random peptide libraries, coupled with next-generation sequencing, yields high-throughput yet sparse data, which we use to train biophysical models that predict SH2 domain binding free energy and c-Src kinase efficiency over the full theoretical sequence space. Our model predictions are validated against biophysical measurements of synthesized peptides. This unbiased approach enables scalable, accurate prediction of protein functional properties, supporting more effective identification and optimization of drug candidates.

The integration of automation creates a scalable, dynamic workflow enabling hundreds of antibodies to be produced weekly. These advancements have fundamentally transformed biotherapeutic discovery, delivering remarkable gains in both speed and reproducibility within screening workflows. Recent advancements in automated cloning strategies—combined with next-generation sequencing for rapid clone validation—enable the parallel construction and verification of hundreds of expression vectors with high efficiency. Coupled with scalable, automated protein production methods, this provides a robust framework supporting high-throughput discovery pipelines tailored to the needs of emerging artificial intelligence and machine learning applications. This work presents comprehensive, end-to-end automated workflows encompassing both cloning and protein purification, and examines the progression from modular system components to fully integrated solutions that advance the frontiers of biologics research.

At the Institute for Protein Innovation, we’ve developed a scalable antibody discovery platform capable of identifying and validating antibodies against hundreds of protein targets annually. Traditional automation approaches often become bottlenecks that are rigid, over-specialized, or single points of failure. In this talk, we introduce our fleet-based automation strategy, designed to provide flexibility, built-in redundancy, and rapid deployment across discovery and validation workflows. By standardizing core equipment and protocols across multiple systems, we've created an automation platform that aligns with our scientists' needs, grows with platform demand, and minimizes operational downtime.

• Larger culture volumes for harder to express biologics challenge plate formats
• Gaps in off-the-shelf instrumentation for mid-scale production
• Mild elution conditions lessen recovery in HTP
• When one-size-fits-most is no longer applicable
• How to address high-throughput protein polishing

Targeting sperm-specific proteins for non-hormonal contraception poses unique protein science challenges due to poor annotation, a lack of close homologs, and a scarcity of model systems. We sought to address this with high-throughput protein production using bacterial, insect, and mammalian systems. Our scalable workflow includes parallel construct design, tiered purification, real-time QC, and ongoing AI and automation integration, advancing best practices for tackling difficult drug-discovery targets.

Multispecific antibodies present challenges in production due to product-related impurities. Large Molecule Research has developed a uHPLC workflow that enables high-throughput characterization of molecules using 1mL or less of supernatant. The automated process delivers accurate, purification-based titer measurements and integrated product quality data. This multi-dimensional analysis enables the development of downstream processing methods based on analytical-scale HIC and IEX chromatography prior to harvesting, saving significant time and resources.

High-throughput Screening is vital in drug discovery, using robotic liquid handlers for assays and sample screening. Traditional instruments are costly, complex, and require extensive training. Opentrons’ OT-2 offers a low-cost (<$10,000), medium-throughput alternative running on Python, enhancing flexibility and ease of use. Two assays (PicoGreen for DNA, Bradford for protein) showed OT-2’s accuracy to be comparable to Tecan EVO systems, though it lacks a 96-channel pipette, crash detection, and has limited deck space. Overall, OT-2 is a cost-effective, open-source tool ideal for early-stage development and method transfer.

Comprising a robotic arm, ten unique instruments, and a purpose-built, safety-focused enclosure, the Ada work cell enables functional testing of large molecules spanning a breadth of modalities. Leveraging customized software and real-time LIMS integration, the work cell supports advanced ADC and T-cell engager mediated cytotoxicity and immunophenotyping assays. This innovative platform accelerates discovery and drives advancements at the forefront of complex biologics research and development.

The complexity of mammalian cell culture and the heterogeneity of large molecule products have historically limited the application of robotic automation platforms in production and characterization to mainly either early stages for small-quantity, stage-gate quality material, or later stages for industrializing specific task accomplishments. Here, we reveal our next-generation automation strategy to bridge the gap and prepare large-quantity of high-quality material, solving an essential need for more complex biologics modalities.