AI PROTEIN EXPRESSION
Don’t let wet lab bottlenecks choke your model.
AI-driven discovery depends on experimental data. However, generating this data through traditional wet lab processes is slow and limits iteration speed.
Design cycles are constrained by:
- Limited throughput of expression systems
- Delays in testing and validation
- Incomplete exploration of sequence space
This creates the “lab-in-the-loop” bottleneck, where model performance is restricted by the speed of data generation.
ALiCE® ENABLES RAPID PRODUCTION OF FULL LENGTH PROTEINS
- Expression in as little as 6 hours
- Binding analysis without purification
- Compatibility with automated workflows
This allows rapid design-build-test-learn cycles and accelerates model development.
PROOF
CASE 1: AI-designed VHH sequences
22 de novo VHH sequences failed in cell-based systems and other cell-free systems. Using ALiCE®, 19 of 22 exceeded the required 3 µM yield threshold, demonstrating improved tolerance for non-natural sequences.
CASE 2: IgG panel characterisation
A panel of IgG constructs was expressed and analysed directly in crude reaction mixtures. Binding affinities (KD) matched purified benchmarks, eliminating the need for purification steps.
IMPACT
- Increased experimental throughput
- Better coverage of design space
- Faster model iteration and convergence
APPLICATION
ALiCE® can be accessed as a service or implemented directly within laboratory workflows.
Fast. Functional. Scalable.