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CD47Binder: Identify CD47 Binding Peptides by Combining Next-Generation Phage Display Data and Multiple Peptide Descriptors

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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.

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All the raw data required for the research can be obtained through supplementary materials or CD47Binder website.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant numbers: 62261006, 62263003, 62071099, and 61901130), Science and Technology Department of Guizhou Province (Grant numbers: [2020]1Y407, ZK [2022]-general-056, and ZK [2022]-general-038), Health Commission of Guizhou Province (Grant Number: gzwkj2022-473) and Guizhou University (Grant number: [2020]5). At the same time, thanks for the computing support of the State Key Laboratory of Public Big Data, Guizhou University.

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Authors and Affiliations

  1. Medical College, Guizhou University, Huaxi District, Guiyang, 550025, Guizhou, China

    Bowen Li, Heng Chen & Bifang He

  2. School of Life Science and Technology, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 6173001, Sichuan, China

    Jian Huang

  3. State Key Laboratory of Public Big Data, Guizhou University, Huaxi District, Guiyang, 550025, Guizhou, China

    Bifang He

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Contributions

Conceptualization, HC, JH and BH; Methodology, HC, JH and BH; Software, BL, HC, JH and BH; Validation, BL; Formal analysis, BL; Investigation, BL; Resources, BL; Data curation, BL; Writing – original draft, BL; Writing – review & editing, BL, HC and BH; Visualization, BL; Supervision, HC, JH and BH; Project administration, HC, JH and BH; Funding acquisition, HC, JH and BH All authors have read and agreed to the published version of the manuscript.

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Correspondence to Heng Chen, Jian Huang or Bifang He.

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Li, B., Chen, H., Huang, J. et al. CD47Binder: Identify CD47 Binding Peptides by Combining Next-Generation Phage Display Data and Multiple Peptide Descriptors. Interdiscip Sci Comput Life Sci 15, 578–589 (2023). https://doi.org/10.1007/s12539-023-00575-x

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