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ProG-SOL: Predicting Protein Solubility Using Protein Embeddings and Dual-Graph Convolutional Networks.

Gen Li¹, Ning Zhang², Long Fan¹

¹Production and R&D Center I of LSS, GenScript (Shanghai) Biotech Co., Ltd., Shanghai 200131, China.

|February 10, 2025

Summary

We developed ProG-SOL, a novel dual-graph convolutional network, to accurately predict protein solubility. This method improves upon existing techniques for both classification and regression tasks in protein engineering.

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Area of Science:

Biophysics
Protein Engineering
Computational Biology

Background:

Protein solubility is a critical biophysical property for biochemical engineering applications.
Existing protein solubility prediction methods struggle with generalization and regression tasks.
Improved prediction accuracy is needed for effective protein engineering.

Purpose of the Study:

To develop an advanced method for predicting protein solubility.
To enhance the generalization performance of solubility prediction models.
To improve solubility prediction for both classification and regression.

Main Methods:

Developed ProG-SOL, a sequence-based dual-graph convolutional network.
Utilized both protein pretrained graphs and protein evolutionary graphs.
Evaluated the model on independent test sets for classification and regression.

Main Results:

ProG-SOL demonstrated superior performance compared to existing methods.
Achieved improved classification and regression results on independent test sets.
The model framework shows potential for predicting other protein properties.

Conclusions:

ProG-SOL offers a significant advancement in protein solubility prediction.
The dual-graph convolutional network approach enhances accuracy and generalization.
The method has broad applicability in protein engineering and related fields.