Rapid Single-Shot Synthesis of the 214 Amino Acid-Long N-Terminal Domain of Pyocin S2.

The impermeable outer membrane of Pseudomonas aeruginosa is bypassed by antibacterial proteins known as S-type pyocins. Because of their properties, pyocins are investigated as a potential new class of antimicrobials against Pseudomonas infections. Their production and modification, however, remain challenging. To address this limitation, we employed automated fast-flow peptide synthesis for the rapid production of a pyocin S2 import domain. The N-terminal domain sequence (PyS2NTD) was synthesized in under 10 h and purified to yield milligram quantities of the desired product. To our knowledge, the 214 amino acid sequence of PyS2NTD is among the longest peptides produced from a "single-shot" synthesis, i.e., made in a single stepwise route without the use of ligation techniques. Biophysical characterization of the PyS2NTD with circular dichroism was consistent with the literature reports. Fluorescently labeled PyS2NTD binds to P. aeruginosa expressing the cognate ferripyoverdine receptor and is taken up into the periplasm. This selective uptake was validated with confocal and super resolution microscopy, flow cytometry, and fluorescence recovery after photobleaching. These modified, synthetic S-type pyocin domains can be used to probe import mechanisms of P. aeruginosa and leveraged to develop selective antimicrobial agents that bypass the outer membrane.

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

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.