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ProtLoc-GRPO: Cell line-specific subcellular localization prediction using a graph-based model and reinforcement

Shuai Zeng1, Weinan Zhang1, Chaohan Li2

  • 1Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri; Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri.

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Summary

This study introduces ProtLoc-GRPO, a novel reinforcement learning method that optimizes protein-protein interaction networks for accurate cell line-specific subcellular localization prediction, improving accuracy by 7%.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Cell Biology

Background:

  • Subcellular localization prediction is vital for understanding protein functions and cellular dynamics.
  • Cell line-specific localization is influenced by tissue and cell type, requiring tailored prediction methods.
  • Existing protein-protein interaction (PPI) networks often contain errors, limiting the accuracy of subcellular localization predictions.

Purpose of the Study:

  • To develop an enhanced method for predicting cell line-specific subcellular localization using protein-protein interaction (PPI) networks.
  • To improve prediction accuracy by optimizing the structure of PPI networks through a novel reinforcement learning approach.

Main Methods:

  • Proposed ProtLoc-GRPO, a reinforcement learning approach utilizing Group Relative Policy Optimization (GRPO).
  • Optimized PPI network structure by ranking and retaining informative PPI edges to maximize macro-F1 score.
  • Evaluated method robustness across various edge pruning rates and compared against conventional pruning strategies.

Main Results:

  • Achieved a 7% improvement in macro-F1 score for cell line-specific subcellular localization prediction compared to baseline methods.
  • Demonstrated consistent performance improvement over existing approaches across different edge pruning rates.
  • Established the first sequence-based study for cell line-specific protein subcellular localization prediction.

Conclusions:

  • ProtLoc-GRPO effectively enhances subcellular localization prediction accuracy by refining PPI network structures.
  • The GRPO framework shows promise for application in graph-based bioinformatics tasks.
  • This work advances the field by providing a robust, sequence-based method for predicting dynamic, cell line-specific protein localization.