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Screening and Identification of Small Peptides Targeting Fibroblast Growth Factor Receptor2 using a Phage Display Peptide Library
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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.

Interdisciplinary Sciences, Computational Life Sciences
|June 30, 2023
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
CD47 binding peptideCD47/SIRPα pathwayMachine learningNGPDSVM

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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.