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

05 August 2022, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Rapid and in-depth exploration of the chemical space of high molecular weight synthetic polypeptides via the ring-opening polymerization (ROP) of N-carboxyanhydride (NCA) is a viable approach towards protein mimics and functional biomaterials. The traditional synthetic workflow, however, has been labour-intensive and with limited throughput. Here, we develop an efficient chemistry for the high throughput diversification of polypeptides based on a click-like reaction between selenolate and various electrophiles in aqueous solutions. Importantly, the platform is amenable to automation, which allows rapid generation of up to 1200 homopolypeptides or random heteropolypeptides (RHP) within one day. With the assistance of machine learning, iterative exploration of the RHP library efficiently and effectively identifies candidates with improved glutathione peroxidase-like activity from complex chemical space of which we have little prior knowledge. This automated and high-throughput platform provides potential solutions to unmet challenges such as de novo design of artificial enzyme, biomacromolecule delivery, and understanding of intrinsically disordered proteins.

Keywords

N-carboxyanhydrides
Polypeptides
High throughput synthesis
Machine Learning
GPx mimics

Supplementary materials

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