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Inducible LAP-tagged Stable Cell Lines for Investigating Protein Function, Spatiotemporal Localization and Protein Interaction Networks

Inducible LAP-tagged Stable Cell Lines for Investigating Protein Function, Spatiotemporal Localization and Protein Interaction Networks

Published on: December 24, 2016

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

Shuai Zeng^1,2, Weinan Zhang^1,2, Chaohan Li²

¹Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.

Biorxiv : the Preprint Server for Biology

|August 8, 2025

Summary

This summary is machine-generated.

We developed ProtLoc-GRPO, a novel reinforcement learning method, to improve cell line-specific protein subcellular localization prediction by optimizing protein-protein interaction networks. This approach enhances accuracy by refining network structures for better biological insights.

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Area of Science:

Bioinformatics
Computational Biology
Cellular and Molecular Biology

Background:

Subcellular localization prediction is vital for understanding protein functions and cellular dynamics.
Cell line-specific localization requires accurate protein-protein interaction (PPI) network information.
Existing PPI networks often contain errors, limiting prediction accuracy.

Purpose of the Study:

To develop a novel method for predicting cell line-specific subcellular localization.
To enhance prediction accuracy by optimizing the structure of PPI networks.
To apply reinforcement learning to improve graph-based bioinformatics tasks.

Main Methods:

Proposed ProtLoc-GRPO, a reinforcement learning approach using Group Relative Policy Optimization (GRPO).
Optimized PPI network structure by ranking and retaining informative PPI edges.
Evaluated performance based on macro-F1 score for cell line-specific subcellular localization.

Main Results:

Achieved a 7% improvement in macro-F1 score compared to baseline methods.
Demonstrated consistent performance across various edge pruning rates.
Outperformed conventional PPI network pruning strategies.

Conclusions:

ProtLoc-GRPO effectively enhances cell line-specific subcellular localization prediction.
This study is the first to predict cell line-specific protein localization using PPI network optimization.
This work pioneers the application of GRPO to graph-based bioinformatics problems.