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Deep Learning for Prediction and Optimization of Fast-Flow Peptide Synthesis
ACS Central Science ( IF 18.2 ) Pub Date : 2020-11-12 , DOI: 10.1021/acscentsci.0c00979
Somesh Mohapatra 1 , Nina Hartrampf 2 , Mackenzie Poskus 2 , Andrei Loas 2 , Rafael Gómez-Bombarelli 1 , Bradley L. Pentelute 2, 3, 4, 5
Affiliation  

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.

中文翻译:

深度学习用于快速流肽合成的预测和优化

多肽的化学合成涉及在固定的固体支持物上逐步形成酰胺键。有效整合每个氨基酸到生长链中所需的高产量通常受到序列依赖性事件(如聚集)的影响。在这里,我们对通过自动快速流动肽合成仪进行的35 427个单独的芴基甲氧基羰基(Fmoc)脱保护反应收集的紫外可见(UV-vis)分析数据进行了深度学习。这些时间分辨的UV-vis脱保护迹线的积分,高度和宽度间接地允许分析树脂上的酰胺重复迭代循环。该计算模型将氨基酸和肽序列的结构表示映射到实验合成参数,并预测脱保护反应的结果,误差小于6%。我们的深度学习方法可实现具有实验意义的计算设计,以预测Fmoc脱保护效率并最小化聚集事件,从而为实时优化流动肽合成奠定基础。
更新日期:2020-12-23
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