Deep Learning for Prediction and Optimization of Fast-Flow Peptide Synthesis
Name
acscentsci.0c00979.pdf
Description
Published version
Size
1.7 MB
Format
Adobe PDF
Checksum (MD5)
21eafde60f0d64a30afd730ba1e52fef
Author(s)
Mohapatra, Somesh
Hartrampf, Nina
Poskus, Mackenzie
Loas, Andrei
Gómez-Bombarelli, Rafael
Pentelute, Bradley L
Date Issued
2020
Journal
ACS Central Science
Publisher
American Chemical Society (ACS)
Citation
Mohapatra, Somesh, Hartrampf, Nina, Poskus, Mackenzie, Loas, Andrei, Gómez-Bombarelli, Rafael et al. 2020. "Deep Learning for Prediction and Optimization of Fast-Flow Peptide Synthesis." ACS Central Science, 6 (12).
Version
Final published version
Abstract
© 2020 American Chemical Society. 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.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Chemistry
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1021/ACSCENTSCI.0C00979
