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CD47Binder: Identify CD47 Binding Peptides by Combining Next-Generation Phage Display Data and Multiple Peptide
Bowen Li1, Heng Chen2, Jian Huang3
1Medical College, Guizhou University, Huaxi District, Guiyang, 550025, Guizhou, China.
Abstract:
CD47/SIRPα pathway is a new breakthrough in the field of tumor immunity after PD-1/PD-L1. While current monoclonal antibody therapies targeting CD47/SIRPα have demonstrated some anti-tumor effectiveness, there are several inherent limitations associated with these formulations. In the paper, we developed a predictive model that combines next-generation phage display (NGPD) and traditional machine learning methods to distinguish CD47 binding peptides. First, we utilized NGPD biopanning technology to screen CD47 binding peptides. Second, ten traditional machine learning methods based on multiple peptide descriptors and three deep learning methods were used to build computational models for identifying CD47 binding peptides. Finally, we proposed an integrated model based on support vector machine. During the five-fold cross-validation, the integrated predictor demonstrated specificity, accuracy, and sensitivity of 0.755, 0.764, and 0.772, respectively. Furthermore, an online bioinformatics tool called CD47Binder has been developed for the integrated predictor. This tool is readily accessible on http://i.uestc.edu.cn/CD47Binder/cgi-bin/CD47Binder.pl .
Insights
Researchers developed a predictive model using next-generation phage display and machine learning to identify CD47 binding peptides for cancer immunotherapy, overcoming limitations of current antibody therapies targeting the CD47/SIRPα pathway.
Area of Science:
- Immunology
- Computational Biology
- Biotechnology
Background:
- The CD47/SIRPα pathway is a critical target in tumor immunity, emerging after PD-1/PD-L1.
- Current monoclonal antibody therapies for CD47/SIRPα face limitations.
- Peptide-based therapeutics offer potential alternatives with distinct advantages.
Purpose of the Study:
- To develop a predictive computational model for identifying CD47 binding peptides.
- To overcome limitations of existing CD47-targeting antibody therapies.
- To create an accessible bioinformatics tool for CD47 peptide prediction.
Main Methods:
- Utilized next-generation phage display (NGPD) for screening CD47 binding peptides.
- Employed ten traditional machine learning methods and three deep learning methods for model development.
- Integrated multiple peptide descriptors and a support vector machine for the final predictive model.
Main Results:
- The integrated predictive model achieved high performance metrics: 0.755 specificity, 0.764 accuracy, and 0.772 sensitivity via five-fold cross-validation.
- Successfully screened CD47 binding peptides using NGPD biopanning.
- Developed an online bioinformatics tool, CD47Binder, for predicting CD47 binding peptides.
Conclusions:
- The developed integrated model effectively predicts CD47 binding peptides.
- This approach offers a promising strategy for developing novel CD47-targeting cancer immunotherapies.
- The CD47Binder tool provides a valuable resource for researchers in the field.
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