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Twist Bioscience HQ
681 Gateway Blvd
South San Francisco, CA 94080
Better AI Begins at the Bench
Using Gene Fragments and Pooled DNA, A-Alpha Bio generates massive, high-quality wet-lab datasets to train its AlphaBind AI model, enabling the rapid and simultaneous optimization of complex therapeutic antibody properties in a single, data-driven step.
A-Alpha Bio has created an AI and synthetic biology-enabled workflow to enable rapid discovery and optimization of therapeutic antibodies. For each project, their AI model is tailored to provide focused predictions that match the unique therapeutic requirements. To do that, they first need a wet-lab-validated training dataset that captures the vast sequence space of specific antibody-target interactions. Generating this data requires precise, unbiased DNA libraries of hundreds of thousands of sequences, supplied by Twist Bioscience.
Covered in this Case Study
Learn how sequential optimization of properties like affinity and developability creates a "knot" of interdependencies that increases timelines and uncertainty.
Discover how precise, bias-free DNA libraries are essential for generating the large-scale, validated training data needed for predictive protein engineering.
Examine how the AlphaSeq platform uses a modified yeast surface-display system to measure millions of protein-protein interactions simultaneously.
Analyze how the AlphaBind model uses high-quality experimental data to predict binding landscapes for variants up to 20 mutations away from the parental antibody.
Results are specific to the institution where they were obtained and may not reflect the results achievable at other institutions. For research use only, not for use in diagnostic procedures.
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