CFI protein engineering with Machine Learning

KEYWORDS: Protein engineering  | Machine Learning & AI | Immunology


The complement factor I (CFI) protein plays an important role in the regulation of the body’s immune response. Our customer wanted to assess how changes in amino acid sequences impact the behavior and properties of the protein. By translating these insights into a predictive approach, they wanted to streamline the experimental process.


We applied optimized Machine Learning models that can predict protein activity from sequence information using a previously established set of variants with assay results. This enabled us to feature important analyses to identify residues responsible for changes in protein activity. In the protein engineering process, we utilized the 3D crystal structure of the protein to generate additional variants based on the spatial distances between functionally important sites.

As a result, we were able to predict CFI protein activity from its sequence information and we gained additional insight into novel mutants with increased activity.


The customer received from us a large set of variant candidates with improved protease activity to be validated in their follow-up experiments. 

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