Sino Biological launched the 4RISE high-throughput wet-lab validation service on Friday to test AI-designed protein binders.
The company said that the service addresses the experimental bottleneck following computational design, noting that artificial intelligence generates candidates that still require physical production and target-binding verification. According to Sino Biological, the workflow delivers sequence-to-AI-ready binding data in as little as three days.
According to Sino Biological, the XPressMAX cell-free protein synthesis platform synthesizes recombinant proteins in three hours. Success rates for established workflows exceed 99 percent according to company data.
Binding measurements use Carterra and Biacore surface plasmon resonance alongside Octet bio-layer interferometry. The workflow provides real-time API access to structured target-binding data for downstream research.
The platform supports miniproteins, VHHs, scFvs, Fabs, enzymes, and cytokines through cell-free expression. Formats requiring mammalian production utilize HEK293 and CHO transient expression systems, though company project data show HEK293 expression succeeded for 50 to 60 percent of candidates compared to over 99 percent using cell-free methods.
The service page notes that standalone cell-free expression starts at $47 per sequence. Rapid Hit Discovery begins at $67 per sequence for crude-supernatant screening, while Comparative Binder Screening starts at $112 per sequence using purified candidates.
Affinity and Kinetic Characterization starts at $142 per sequence for multi-concentration analysis. Specialized Binder Expression and Validation utilizes custom quotes for mammalian expression formats.
Beyond binding, the service includes over 20 physicochemical developability assays, more than 1,000 protein bioactivity assays, and over 10 cell-based functional assays. Sino Biological supported more than 200 AI-designed enzymes across three design rounds for Tencent AI Lab to optimize protein-design frameworks.
Published studies in journals such as Science and Nature Communications have utilized 4RISE wet-lab validation for de novo binder design and antibody discovery. Company case studies include evaluating more than 2,000 de novo-designed scFv and VHH sequences.
“AI-designed molecules need reliable wet-lab experimental evidence to demonstrate their potential,” said Dr. Rob Burgess, Chief Business Officer at Sino Biological US. “Our integrated capabilities provide the high-quality data researchers need to evaluate candidates quickly, shorten design cycles, and turn promising discoveries into real-world applications.”
Sino Biological reported that the service acts as an experimental-feedback layer for iterative design improvements. The service targets researchers seeking to shorten the validation cycle for computationally generated protein candidates.
