Deep Learning for Prediction and Optimization of Fast-Flow Peptide Synthesis

0301 basic medicine Chemistry 03 medical and health sciences QD1-999 01 natural sciences 0104 chemical sciences
DOI: 10.1021/acscentsci.0c00979 Publication Date: 2020-11-12T17:53:35Z
ABSTRACT
Chemical synthesis of polypeptides involves stepwise formation of amide bonds on an immobilized solid support. The high yields required for efficient incorporation of each individual amino acid in the growing chain are often impacted by sequence-dependent events such as aggregation. Here we apply deep learning over ultraviolet-visible (UV-Vis) analytical data collected from 35,485 individual fluorenylmethyloxycarbonyl (Fmoc) deprotection reactions performed with an automated fast-flow peptide synthesizer. The integral, height and width of these time-resolved UV-Vis deprotection traces indirectly allow for analysis of the iterative amide coupling cycles on resin. The computational model maps structural representations of amino acids and peptide sequences to experimental synthesis parameters and predicts the outcome of deprotection reactions with less than 4% error. Our deep learning approach enables experimentally-aware computational design for prediction of Fmoc deprotection efficiency and minimization of aggregation events, building the foundation for real-time optimization of peptide synthesis in flow.
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