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Peptide Identification Using Tandem Mass Spectrometry01:33

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MCMFPP: A Multifunctional Peptides Prediction Method Based on Class Feature Enhancement and Classifier Fusion.

Jintao Zhao1, Henghui Fan2, Jiwei Fang1

  • 1College of Mathematics and System Sciences, Xinjiang University, Xinjiang, 830017, China.

Journal of Chemical Information and Modeling
|September 18, 2025
PubMed
Summary
This summary is machine-generated.

Developing accurate computational tools for peptide function prediction is crucial. The new MCMFPP method improves prediction accuracy for multifunctional therapeutic peptides (MFTP) by integrating sequence and class feature learning.

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Area of Science:

  • * Computational biology and bioinformatics.
  • * Drug discovery and development.
  • * Peptide science and therapeutics.

Background:

  • * Traditional wet-lab methods for peptide function prediction are time-consuming and costly.
  • * Existing computational tools struggle with data sparsity, imbalanced data, and feature representation for multifunctional therapeutic peptides (MFTP).
  • * Accurate MFTP prediction is essential for developing targeted peptide drugs.

Purpose of the Study:

  • * To develop an advanced computational method for accurate multifunctional peptide prediction.
  • * To address limitations of existing methods, including data sparsity and inadequate feature representation.
  • * To improve the efficiency and accuracy of identifying candidate peptides for therapeutic applications.

Main Methods:

  • * Introduction of two subclassifiers: Sequence Learning Feature Enhancement (SLFE) and Class Feature Enhancement Classifier (CFEC).
  • * SLFE utilizes the large language model ESMC to enhance sequence representation, particularly for tail-class data.
  • * CFEC improves class feature learning using single-function peptide samples and contrastive learning.
  • * Proposal of MCMFPP, a deep learning model integrating SLFE and CFEC predictions via weighted fusion.

Main Results:

  • * MCMFPP demonstrates superior performance compared to state-of-the-art methods in multifunctional peptide prediction.
  • * Achieved improvements include 3.1% in precision, 2.7% in coverage, 2.8% in accuracy, and 2.7% in absolute true rate.
  • * Reduced the absolute false rate by 0.3%.
  • * Enhanced prediction accuracy for challenging multifunctional peptide samples.

Conclusions:

  • * MCMFPP effectively overcomes the limitations of single-classifier approaches in MFTP prediction.
  • * The proposed method offers a valuable tool for efficient and accurate identification of multifunctional therapeutic peptides.
  • * This advancement can accelerate the development of novel peptide-targeted drugs.