Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening

Zhang, Haiping1; Saravanan, Konda Mani4; Wei, Yanjie5; Jiao, Yang6; Yang, Yang7; Pan, Yi5,6; Wu, Xuli8; Zhang, John Z. H.1,2,3

2023-02-13

10.1021/acs.jcim.2c01485835J

Journal of Chemical Information and Modeling   影响因子和分区

1549-9596

1549-960X

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.

SCI

英语

其他

National Science Foundation of China["62106253","21933010","22250710136"] ; Shenzhen Science and Technology Program["JCYJ20220531102205012","GJHZ20210705141803010"] ; Key-Area Research and Development Program of Guangdong Province[2019B020213001] ; Research Funding for Innovation Project of Universities in Guangdong Province[2018KTSCX192] ; Shenzhen KQTD Project[KQTD20200820113106007] ; Research Funding of Shenzhen[JCYJ20200109114818703]

Pharmacology & Pharmacy ; Chemistry ; Computer Science

Chemistry, Medicinal ; Chemistry, Multidisciplinary ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications

WOS:000933584800001

AMER CHEMICAL SOC

Web of Science

1.Chinese Acad Sci, Shenzhen Inst Synthet Biol, Shenzhen Inst Adv Technol, Fac Synthet Biol, Shenzhen 518055, Guangdong, Peoples R China
2.East China Normal Univ, Shanghai 200062, Peoples R China
3.NYU Shanghai, Ctr Computat Chem, ECNU, Shanghai 200062, Peoples R China
4.Bharath Inst Higher Educ & Res, Dept Biotechnol, Chennai 600073, Tamil Nadu, India
5.Chinese Acad Sci, Shenzhen Inst Adv Technol, Ctr High Performance Comp, Joint Engn Res Ctr Hlth Big Data Intelligent Anal, Shenzhen 518055, Guangdong, Peoples R China
6.Chinese Acad Sci, Shenzhen Inst Adv Technol, Fac Comp Sci & Control Engn, Shenzhen 518055, Peoples R China
7.Southern Univ Sci & Technol, Shenzhen Peoples Hosp 3, Natl Clin Res Ctr Infect Dis,Hosp 2, Shenzhen Key Lab Pathogen & Immun,State Key Discip, Shenzhen 518112, Peoples R China
8.Shenzhen Univ, Sch Med, Shenzhen 518060, Guangdong, Peoples R China

Zhang, Haiping,Saravanan, Konda Mani,Wei, Yanjie,et al. Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening[J]. Journal of Chemical Information and Modeling,2023,63(3).

Zhang, Haiping.,Saravanan, Konda Mani.,Wei, Yanjie.,Jiao, Yang.,Yang, Yang.,...&Zhang, John Z. H..(2023).Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening.Journal of Chemical Information and Modeling,63(3).

Zhang, Haiping,et al."Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening".Journal of Chemical Information and Modeling 63.3(2023).