Ribosomes can produce proteins in minutes and are largely constrained to proteinogenic amino acids. Here, we report highly efficient chemistry matched with an automated fast-flow instrument for the direct manufacturing of peptide chains up to 164 amino acids long over 327 consecutive reactions. The machine is rapid: Peptide chain elongation is complete in hours. We demonstrate the utility of this approach by the chemical synthesis of nine different protein chains that represent enzymes, structural units, and regulatory factors. After purification and folding, the synthetic materials display biophysical and enzymatic properties comparable to the biologically expressed proteins. High-fidelity automated flow chemistry is an alternative for producing single-domain proteins without the ribosome.
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.
<p>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.</p>
<p>Ribosomes produce most proteins of living cells in seconds. Here we report highly efficient chemistry matched with an automated fast-flow instrument for the direct manufacturing of peptide chains up to 164 amino acids over 328 consecutive reactions. The machine is rapid - the peptide chain elongation is complete in hours. We demonstrate the utility of this approach by the chemical synthesis of nine different protein chains that represent enzymes, structural units, and regulatory factors. After purification and folding, the synthetic materials display biophysical and enzymatic properties comparable to the biologically expressed proteins. High-fidelity automated flow chemistry is an alternative for producing single-domain proteins without the ribosome.<i></i></p>
<p>Ribosomes produce most proteins of living cells in seconds. Here we report highly efficient chemistry matched with an automated fast-flow instrument for the direct manufacturing of peptide chains up to 164 amino acids over 328 consecutive reactions. The machine is rapid - the peptide chain elongation is complete in hours. We demonstrate the utility of this approach by the chemical synthesis of nine different protein chains that represent enzymes, structural units, and regulatory factors. After purification and folding, the synthetic materials display biophysical and enzymatic properties comparable to the biologically expressed proteins. High-fidelity automated flow chemistry is an alternative for producing single-domain proteins without the ribosome.<i></i></p>
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