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A Deep-Learning-Based Server SecEff-Pred for Predicting Signal Peptide Secretion Efficiency to Improve Protein

Hui Sun1, Xiangbo Meng1, Linhao Meng1

  • 1Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, College of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, P. R. China.

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|February 19, 2026
PubMed
Summary

A new tool, SecEff-Pred, accurately predicts signal peptide (SP) efficiency in Bacillus subtilis protein production. This computational method enhances protein secretion by identifying effective signal peptides for industrial applications.

Keywords:
Bacillus subtilisdeep learningsecretion efficiencysignal peptide

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Molecular Biology

Background:

  • Signal peptides (SP) are essential for protein secretion in Bacillus species, impacting production efficiency.
  • Current computational tools lack reliability in predicting signal peptide secretion efficiency.
  • Accurate prediction of SP efficiency is crucial for optimizing recombinant protein production.

Purpose of the Study:

  • To develop a novel computational tool, SecEff-Pred, for predicting signal peptide efficiency in Bacillus subtilis.
  • To enhance the accuracy of signal peptide efficiency prediction using advanced machine learning techniques.
  • To provide a reliable web server for researchers working on protein secretion in Bacillus species.

Main Methods:

  • Development of SecEff-Pred, a web server utilizing an ESM-2 based predictor.
  • Implementation of an innovative data simulation strategy and a multitask learning framework.
  • Validation of SecEff-Pred using reporter proteins like phospholipase D (PLD).

Main Results:

  • SecEff-Pred achieved high prediction accuracies for various proteins: 85.59% for α-amylase, 81.58% for alkaline xylanase, and 74.68% for cutinase.
  • Validation with PLD showed an overall prediction accuracy of 72% for signal peptide efficiency.
  • The tool demonstrated 80% accuracy for efficient SPs and 62.50% for inefficient SPs.

Conclusions:

  • SecEff-Pred is a powerful and accurate computational tool for predicting signal peptide efficiency in Bacillus subtilis.
  • The developed tool can significantly guide and optimize protein secretion processes in B. subtilis.
  • SecEff-Pred offers a reliable solution for enhancing protein production through improved signal peptide selection.