ICDMO's recombinant protein expression services deliver high-quality proteins for structural biology, drug target validation, antibody development, enzyme engineering, and biopharmaceutical manufacturing. Five orthogonal expression platforms operate in parallel, enabling our scientists to match the optimal system to each target based on size, glycosylation, disulfide bonds, and required quantity.
Our pipeline integrates codon optimization, vector engineering, fermentation or cell culture scale-up, and multi-step chromatography into a seamless one-stop service. Every batch undergoes rigorous QC before delivery.
Each system is maintained by dedicated protein scientists with platform-specific expertise.
Note: All services are for research use only unless specified as GMP. Contact us for IND-enabling or clinical manufacturing documentation.
The results below were published by independent groups and are cited to show what these computational approaches have achieved in the literature. They are not ICDMO project results, and outcomes vary substantially between targets, datasets and programmes. We do not promise comparable results for any particular project.
An active-learning workflow optimising five interacting active-site residues improved the yield of a desired non-native cyclopropanation product from 12% to 93% in three rounds of laboratory experimentation. The design principle is the one we apply: computation chooses which small set of variants to make, and measured results retrain the model between rounds.
Nature Communications, 2025 ↗A machine-learning-guided enzyme engineering campaign evaluated 1,217 variants across 10,953 reactions to build predictive models for amide synthetases. Model-selected variants showed 1.6- to 42-fold improved activity relative to the parent enzyme across nine pharmaceutical targets, illustrating both the scale of data that makes models predictive and the spread of outcomes that remains.
Nature Communications, 2024 ↗Recent design methods report nanomolar binder hit rates between roughly 17% and 82% across evaluated targets, while independent evaluations of earlier pipelines on other targets report designs failing on expression, non-specific binding or undetectable affinity. The honest reading is that de novo binder design now works, that success is strongly target-dependent, and that panels must be sized accordingly rather than on a best-case figure.
Comparative design studies, bioRxiv, 2025 ↗Solubility and expression prediction rank constructs and identify likely problems; they do not guarantee yield. Expression outcomes remain substantially empirical and depend on host strain, induction conditions, media and scale. We therefore design several constructs to be tested in parallel rather than a single one. Expression, purification and analytics are executed through qualified partner laboratories.
Protein scientists respond within 24 hours with a feasibility assessment and detailed proposal.
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