Deep Learning for Prediction and Optimization of Fast-Flow Peptide Synthesis
The Pentelute Lab aims to invent new chemistry for the efficient and selective modification of proteins, to ‘hijack’ these biological machines for efficient drug delivery into cells and to create new machines to rapidly and efficiently manufacture peptides and proteins.
Pentelute Lab, Chemistry, MIT, Chemistry Department, Boston, Cambridge, Biology, Peptides, Peptide, Proteins, Science, Rapid, Brad Pentelute, Brad,
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Deep Learning for Prediction and Optimization of Fast-Flow Peptide Synthesis

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

ACS Cent. Sci. 2020, 6, 12, 2277–2286
Publication Date:November 12, 2020

Somesh Mohapatra, Nina Hartrampf, Mackenzie Poskus, Andrei Loas, Rafael Gómez-Bombarelli*, and Bradley L. Pentelute


The 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 35 427 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 6% 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.

2020, Publications