Efficient recovery of multispecific biologics requires optimized engineering and purification processes. We developed an integrated small-scale purification and analytical QC platform enabling in-process prediction of purification strategies for complex modalities. Upstream of wet-lab validation, we deploy AI/ML models to predict molecular attributes from sequence, including expression titer and chain pairing. These approaches link sequence design, assembly prediction, and purification strategy selection to accelerate lead triaging and delivery of complex formats.

Voltage-gated calcium (CaV) and sodium (NaV) channels are essential regulators of cellular excitability but remain challenging to study because of their structural complexity and multi-subunit organization. We developed a modular platform using cell-free protein expression coupled with co-translational nanodisc assembly to reconstruct functional human ion channel systems without living cells. The platform enables rapid synthesis, membrane insertion, and functional characterization of CaV and NaV channels in defined lipid environments, supporting analysis of channel regulation, pharmacological modulation, and signaling networks. This synthetic approach provides a scalable foundation for mechanistic studies, high-throughput drug screening, medical countermeasure development, and programmable bioelectronic technologies.

The proliferation of AI has made it easier than ever to design proteins for improved activity, stability, or other characteristics. However, there is a bottleneck bringing these designs into the real world. Comprehensive integration of AI in areas like drug development, protein engineering and mutagenesis studies demand an equally rapid downstream screening platform. Opera Bioscience has developed a high-throughput protein screening platform harnessing a bacterial secretion host to produce high-purity recombinant proteins. Opera incorporates automation-capable purification screens and analytical workflows to enable rapid optimization from screening to scaling protein manufacturing.

Despite rapid advances in AI-driven protein design, a critical gap remains between predictive models and experimental reality. This talk presents an actionable framework for integrating experimental datasets with sequence-and structure-based predictive tools to guide designs for proteins, binding domains, and CAR-T constructs. Case studies will highlight how combining experimental and computational datasets improves insights, outcomes and decision-making.

Better protein expression models require data that is clean, quantitative, and learnable. Flock Bio builds high-complexity DNA libraries through optimized design, cloning, barcoding, transformation, and NGS QC workflows that reduce bias, dropouts, frameshifts, chimeras, and sequencing artifacts. We use multi-agent AI workflows to accelerate library design, identify manufacturability risks, and interpret NGS data. However, AI cannot rescue flawed inputs: better libraries produce better data, and better data produces better models.