Abstract: Disclosed are systems and methods that provide a framework for machine-assisted discovery of Chondroitinase ABC (ChABC) complexes towards sustained neural regeneration. In some embodiments, the framework may leverage a determination of a diverse set of tailor-made random copolymers that complex and stabilize ChABC at physiological temperature. The copolymer designs, which are based on chain length and/or composition of the copolymers, may be identified using an active machine learning paradigm, which involves, but is not limited to, copolymer synthesis, testing for ChABC thermostability upon copolymer complexation, Gaussian Process Regression modeling and Bayesian optimization. Copolymers are synthesized by automated PET-RAFT, and thermostability of ChABC may be assessed by retained enzyme activity (REA) after a predefined interval of hours at 37° C.

Abstract: A method includes receiving one input feature and one output feature of importance of polymers used to stabilize a protein. A set of polymers from a library are identified based on the input feature and the output feature of importance that are applied to a machine learning model. Data for each polymer in the library includes features for each polymer and reagents for stabilizing the protein. Each polymer in the identified set is used to stabilize samples of the protein in well plates in a well plate array based on the reagents from the library data in the identified set. A score for each sample of the protein is assigned by comparing the measured output feature from the well plates corresponding to the identified set to the output feature of importance. The samples of the protein are identified having scores higher than a predefined threshold.