Polymer–protein hybrids are intriguing materials that can bolster protein stability in non‐native environments, thereby enhancing their utility in diverse medicinal, commercial, and industrial applications. One stabilization strategy involves designing synthetic random copolymers with compositions attuned to the protein surface, but rational design is complicated by the vast chemical and composition space. Here, a strategy is reported to design protein‐stabilizing copolymers based on active machine learning, facilitated by automated material synthesis and characterization platforms. The versatility and robustness of the approach is demonstrated by the successful identification of copolymers that preserve, or even enhance, the activity of three chemically distinct enzymes following exposure to thermal denaturing conditions. Although systematic screening results in mixed success, active learning appropriately identifies unique and effective copolymer chemistries for the stabilization of each enzyme. Overall, this work broadens the capabilities to design fit‐for‐purpose synthetic copolymers that promote or otherwise manipulate protein activity, with extensions toward the design of robust polymer–protein hybrid materials.
From protein science, it is well
understood that ordered folding
and 3D structure mainly arise from balanced and noncovalent polar
and nonpolar interactions, such as hydrogen bonding. Similarly, it
is understood that single-chain polymer nanoparticles (SCNPs) will
also compact and become more rigid with greater hydrophobicity and
intrachain hydrogen bonding. Here, we couple high throughput photoinduced
electron/energy transfer reversible addition–fragmentation
chain-transfer (PET-RAFT) polymerization with high throughput small-angle
X-ray scattering (SAXS) to characterize a large combinatorial library
(>450) of several homopolymers, random heteropolymers, block copolymers,
PEG-conjugated polymers, and other polymer-functionalized polymers.
Coupling these two high throughput tools enables us to study the major
influence(s) for compactness and flexibility in higher breadth than
ever before possible. Not surprisingly, we found that many were either
highly disordered in solution, in the case of a highly hydrophilic
polymer, or insoluble if too hydrophobic. Remarkably, we also found
a small group (9/457) of PEG-functionalized random heteropolymers
and block copolymers that exhibited compactness and flexibility similar
to that of bovine serum albumin (BSA) by dynamic light scattering
(DLS), NMR, and SAXS. In general, we found that describing a rough
association between compactness and flexibility parameters (R
g/R
h and Porod exponent,
respectively) with log P, a quantity that describes
hydrophobicity, helps to demonstrate and predict material parameters
that lead to SCNPs with greater compactness, rigidity, and stability.
Future implementation of this combinatorial and high throughput approach
for characterizing SCNPs will allow for the creation of detailed design
parameters for well-defined macromolecular chemistry.
Controlled/living radical polymerization (CLRP) techniques are widely utilized to synthesize advanced and controlled synthetic polymers for chemical and biological applications. While automation has long stood as a high‐throughput (HTP) research tool to increase productivity as well as synthetic/analytical reliability and precision, oxygen intolerance of CLRP has limited the widespread adoption of these systems. Recently, however, oxygen‐tolerant CLRP techniques, such as oxygen‐tolerant photoinduced electron/energy transfer–reversible addition–fragmentation chain transfer (PET–RAFT), enzyme degassing of RAFT (Enz‐RAFT), and atom‐transfer radical polymerization (ATRP), have emerged. Herein, the use of a Hamilton MLSTARlet liquid handling robot for automating CLRP reactions is demonstrated. Synthesis processes are developed using Python and used to automate reagent handling, dispensing sequences, and synthesis steps required to create homopolymers, random heteropolymers, and block copolymers in 96‐well plates, as well as postpolymerization modifications. Using this approach, the synergy between highly customizable liquid handling robotics and oxygen‐tolerant CLRP to automate advanced polymer synthesis for HTP and combinatorial polymer research is demonstrated.
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.
Structure−function relationships for multivalent polymer scaffolds are highly complex due to the wide diversity of architectures offered by such macromolecules. Evaluation of this landscape has traditionally been accomplished case-by-case due to the experimental difficulty associated with making these complex conjugates. Here, we introduce a simple dualwavelength, two-step polymerize and click approach for making combinatorial conjugate libraries. It proceeds by incorporation of a polymerization friendly cyclopropenone-masked dibenzocyclooctyne into the side chain of linear polymers or the α-chain end of star polymers. Polymerizations are performed under visible light using an oxygen tolerant porphyrin-catalyzed photoinduced electron/energy transfer-reversible addition− fragmentation chain-transfer (PET-RAFT) process, after which the deprotection and click reaction is triggered by UV light. Using this approach, we are able to precisely control the valency and position of ligands on a polymer scaffold in a manner conducive to high throughput synthesis.
In article number 2102101, using a combination of robotics and machine learning, Michael A. Webb, Adam J. Gormley, and co-workers design complex copolymers that thermostabilize chondroitinase ABC for sustained digestion of spinal cord injury scar tissue.
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