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An Integrated Approach for Microprotein Identification and Sequence Analysis
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BioSemAF-BiLSTM: a protein sequence feature extraction framework based on semantic and evolutionary information.

Zihan Zhang1, Yixuan Wang2

  • 1Haide College, Ocean University of China, Qingdao, China.

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|October 3, 2025
PubMed
Summary

A new deep learning model, BioSemAF-BiLSTM, accurately predicts S-sulfenylation sites. This computational tool aids in understanding protein function and disease relevance, overcoming experimental limitations.

Keywords:
Kolmogorov complexityadaptive fusionbidirectional LSTM neural networkbioinformation encodinginformation losspost-translational modificationprotein sequence embeddingsulfenylation site

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Biology

Background:

  • S-sulfenylation is a crucial post-translational modification impacting protein function, redox signaling, and cellular homeostasis.
  • Identifying S-sulfenylation sites is vital for biological and disease-related research.
  • Experimental methods for S-sulfenylation site detection are often time-consuming and costly.

Purpose of the Study:

  • To develop an accurate deep learning-based computational framework for predicting S-sulfenylation sites.
  • To integrate evolutionary and semantic features for enhanced prediction performance.
  • To provide a robust tool for post-translational modification site prediction.

Main Methods:

  • Proposed BioSemAF-BiLSTM framework utilizing fastText for subword embeddings and PSSMs for evolutionary features.
  • Employed a bidirectional long short-term memory (BiLSTM) network to capture long-range dependencies.
  • Incorporated an adaptive feature fusion module for refined feature interaction and evaluated feature sufficiency using a sequence compression-based measure.

Main Results:

  • The BioSemAF-BiLSTM model achieved 89.32% accuracy on an independent test dataset.
  • Demonstrated significant outperformance compared to conventional machine learning and state-of-the-art deep learning methods.
  • Showcased improved sensitivity and specificity in S-sulfenylation site prediction.

Conclusions:

  • The developed framework offers a powerful computational tool for predicting S-sulfenylation sites.
  • Effectively bridges the gap between bioinformatics and deep learning applications.
  • Facilitates a deeper understanding of the biological significance of S-sulfenylation in health and disease.