A High-Throughput Platform for Efficient Exploration of Polypeptides Chemical Space via Automation and Machine Learning

Guangqi Wu, Connor W. Coley, Hua Lu

Keywords: Polypeptide, Protein-like materials, High-throughput synthesis, Bayesian optimization, random copolymer

TL;DR: An efficient platform for the discovery of new polypeptide materials via automation and machine learning

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. 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. With the assistance of automation and machine learning, iterative exploration of the random heteropolypeptides (RHPs) library efficiently and effectively identifies hit materials from a model system of which we have little prior knowledge. This automated and high-throughput platform provides a useful interface between wet and dry experiment, which would accelerate the discovery of new polypeptide materials for unmet challenges such as de novo design of artificial enzyme, biomacromolecule delivery, and understanding of intrinsically disordered proteins.

Paper Track: Papers

Submission Category: AI-Guided Design, Automated Chemical Synthesis