4.7 Article

Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening

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Many bioactive peptides have therapeutic effects on complicated diseases, and deep learning can be used to generate potentially bioactive peptides, similar to the generation of de novo chemical compounds. Our work focuses on generating de novo peptides using the LSTM_Pep model and fine-tuning it for specific therapeutic benefits. We have utilized the Antimicrobial Peptide Database to generate various potential de novo peptides and developed the DeepPep model for rapid screening. Overall, this research demonstrates the potential of deep learning-based methods and pipelines to generate bioactive peptides and showcases the role of artificial intelligence in peptide discovery.
Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such as antiviral, antibacterial, anticancer, etc. It is possible to generate a large number of potentially bioactive peptides using deep learning in a manner analogous to the generation of de novo chemical compounds using the acquired bioactive peptides as a training set. Such generative techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. Despite the limited availability of deep learning-based peptide-generating models, we have built an LSTM model (called LSTM_Pep) to generate de novo peptides and fine-tuned the model to generate de novo peptides with specific prospective therapeutic benefits. Remarkably, the Antimicrobial Peptide Database has been effectively utilized to generate various kinds of potential active de novo peptides. We proposed a pipeline for screening those generated peptides for a given target and used the main protease of SARSCOV-2 as a proof-of-concept. Moreover, we have developed a deep learning-based protein-peptide prediction model (DeepPep) for rapid screening of the generated peptides for the given targets. Together with the generating model, we have demonstrated that iteratively fine-tuning training, generating, and screening peptides for higher-predicted binding affinity peptides can be achieved. Our work sheds light on developing deep learning-based methods and pipelines to effectively generate and obtain bioactive peptides with a specific therapeutic effect and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.

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