Boqiang Tu (LU & ESVELT LABS)

An Ultra-high-throughput Method for Measuring Biomolecular Activities

Large datasets of biomolecular activities are crucial for protein engineering, yet their scarcity due to limited experimental throughput hampers progress. We introduce Direct High-throughput Activity Recording and Measurement Assay (DHARMA), an innovative method enabling ultra-high-throughput measurement of biomolecular activities. DHARMA employs molecular recording techniques to link activity directly to editing rates of DNA segments contiguous with the coding sequence of biomolecule of interest. Leveraging a Bayesian inference-based denoising model, we mapped the fitness landscape of TEV protease across 160,000 variants. Using these datasets, we benchmarked popular protein models and showed the impact of data size on model performance. We also developed circuit self-optimization strategies and demonstrated DHARMA's capability to measure a wide range of biomolecular activities. DHARMA represents a leap forward, offering the machine learning community unparalleled datasets for accurate protein fitness prediction and enhancing our understanding of sequence-to-function relationships.

Lauren Duhamel (WITTRUP LAB)

Engineering Strategies to Modulate CD45 for Cancer Immunotherapy

Over the last decade, cancer immunotherapy has revolutionized cancer treatment by targeting and modulating signaling receptors present on lymphocytes. To expand upon this success, we explore CD45, a receptor protein tyrosine phosphatase and a critical regulator in leukocyte signaling pathways, as a novel cancer immunotherapy target. We leverage multimeric protein engineering approaches to design a therapeutic capable of modulating CD45 activity and assess its ability to drive anticancer immune responses in preclinical models.