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Optimized deep intelligent parameter efficient fine-tuning protein language model system for predicting

Anurag Singh1, P K Singh2, Rohit Kumar Tiwari2

  • 1Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology (MMMUT), Gorakhpur, UP, India. gbtuanurag@gmail.com.

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Summary

A new Termite Life Cycle Boltzmann Machine (TLCBM) with Low-Rank Adaptation (LoRA) accurately predicts protein dephosphorylation sites. This advanced protein language model enhances understanding of cellular signaling and protein function.

Keywords:
Dephosphorylation site predictionLow-Rank Adaptation techniqueParameter efficient fine tuningProtein language model

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Bioinformatics

Background:

  • Protein dephosphorylation is vital for cellular regulation.
  • Predicting dephosphorylation sites is key to understanding protein function and signaling.
  • Current methods struggle with flexibility and complex protein sequence dependencies.

Purpose of the Study:

  • To develop a novel, flexible, and accurate method for predicting protein dephosphorylation sites.
  • To improve the understanding of protein function and cellular signaling pathways.

Main Methods:

  • Developed a Termite Life Cycle Boltzmann Machine (TLCBM) integrated with Low-Rank Adaptation (LoRA).
  • Utilized a sliding window approach to capture local and long-range sequence contexts.
  • Focused on predicting phosphorylation sites for tyrosine (Y), serine (S), and threonine (T) residues.

Main Results:

  • Achieved high predictive performance: accuracy (0.92-0.96), MCC (0.91-0.94), specificity (0.92-0.97), sensitivity (0.93-0.95), and ROC_AUC (0.95-0.98).
  • Demonstrated a 3% performance improvement over traditional models.
  • TLCBM-LoRA effectively learns high-dimensional sequence representations.

Conclusions:

  • TLCBM-LoRA offers a flexible and powerful tool for protein sequence analysis.
  • The model significantly enhances the recognition of dephosphorylation sites.
  • This approach advances the study of protein dephosphorylation and cellular signaling.