肽
自编码
药物发现
计算生物学
环肽
计算机科学
生物
人工智能
化学
人工神经网络
生物化学
作者
Zhipeng Wu,Yejian Wu,Zhu C,Xinyi Wu,Silong Zhai,Xinqiao Wang,Zhihao Su,Hongliang Duan
标识
DOI:10.1021/acs.jcim.3c01385
摘要
The development of potentially active peptides for specific targets is critical for the modern pharmaceutical industry's growth. In this study, we present an efficient computational framework for the discovery of active peptides targeting a specific pharmacological target, which combines a conditional variational autoencoder (CVAE) and a classifier named TCPP based on the Transformer and convolutional neural network. In our example scenario, we constructed an active cyclic peptide library targeting interleukin-17C (IL-17C) through a library-based in vitro selection strategy. The CVAE model is trained on the preprocessed peptide data sets to generate potentially active peptides and the TCPP further screens the generated peptides. Ultimately, six candidate peptides predicted by the model were synthesized and assayed for their activity, and four of them exhibited promising binding affinity to IL-17C. Our study provides a one-stop-shop for target-specific active peptide discovery, which is expected to boost up the process of peptide drug development.
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