Machine‐Assisted Discovery of Chondroitinase ABC Complexes toward Sustained Neural Regeneration

Abstract

Among the many molecules that contribute to glial scarring, chondroitin sulfate proteoglycans (CSPGs) are known to be potent inhibitors of neuronal regeneration. Chondroitinase ABC (ChABC), a bacterial lyase, degrades the glycosaminoglycan (GAG) side chains of CSPGs and promotes tissue regeneration. However, ChABC is thermally unstable and loses all activity within a few hours at 37 °C under dilute conditions. To overcome this limitation, the discovery of a diverse set of tailor-made random copolymers that complex and stabilize ChABC at physiological temperature is reported. The copolymer designs, which are based on chain length and composition of the copolymers, are identified using an active machine learning paradigm, which involves iterative 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 is assessed by retained enzyme activity (REA) after 24 h at 37 °C. Significant improvements in REA in three iterations of active learning are demonstrated while identifying exceptionally high-performing copolymers. Most remarkably, one designed copolymer promotes residual ChABC activity near 30%, even after one week and notably outperforms other common stabilization methods for ChABC. Together, these results highlight a promising pathway toward sustained tissue regeneration.

A graph representation of molecular ensembles for polymer property prediction

“…1–10 This versatility is due to the wide range of properties achievable by tuning a polymer's chemical composition and architecture. The identification of novel copolymers for the delivery of therapeutics cargos, 11–20 or for energy harvesting and storage, 6,21–25 are examples of active areas of research that rely on the availability of a broad range of polymer chemistries.…”

“…Machine learning (ML) is now playing a significant role in supporting the discovery and synthesis of new functional organic molecules with specialized applications, 20,26,27 thanks to its ability to capture subtle chemical patterns when enough data is available. The field of polymer informatics has also attracted increasing attention, with a number of studies demonstrating the use of ML for the prediction of thermal, 28–35 thermodynamic, 28,36–38 electronic, 39–44 optical, 41,45,46 and mechanical 41,47 properties of polymers and copolymers.…”

Machine learning strategies for the structure-property relationship of copolymers

“…They assumed all copolymers to be random copolymers; thus, only monomer composition information is included in ML models without considering their sequences. Based on random copolymers and homopolymers, Hanaoka (2020) , Leibfarth et al., ( Reis et al., 2021 ), Kosuri et al. (2022) , Shi et al.…”

A High-Throughput Platform for Efficient Exploration of Functional polypeptides Chemical Space

“…38,39 Boyer et al used photo-induced electron transfer-reversible additionfragmentation chain transfer (PET-RAFT) for the HTS of poly(acryl amide)s to explore polymers with protein binding 40 and antimicrobial activity 41 . More recently, Leibfarth and Gormley et al applied controlled radical polymerization in combination with automated synthesis and ML to identify polymers for enhanced magnetic resonance signals 18 and protein preservation 42,43 , respectively. As a complementary approach to the aforementioned parallel polymerization, post-polymerization modifications (PPM) 44,45 is also common for library generation.…”