ProtSolM: Protein Solubility Prediction with Multi-modal Features

TL;DR

ProtSolM is a deep learning model that combines multi-modal features and large datasets to improve the accuracy of protein solubility prediction, advancing computational methods in protein engineering.

Contribution

It introduces a novel multi-modal deep learning approach that integrates physicochemical, sequence, and structural data, trained on the largest solubility dataset to date.

Findings

Achieved state-of-the-art performance on multiple evaluation metrics.

Demonstrated significant improvement over existing methods.

Provided a comprehensive leaderboard for protein solubility prediction.

Abstract

Understanding protein solubility is essential for their functional applications. Computational methods for predicting protein solubility are crucial for reducing experimental costs and enhancing the efficiency and success rates of protein engineering. Existing methods either construct a supervised learning scheme on small-scale datasets with manually processed physicochemical properties, or blindly apply pre-trained protein language models to extract amino acid interaction information. The scale and quality of available training datasets leave significant room for improvement in terms of accuracy and generalization. To address these research gaps, we propose \sol, a novel deep learning method that combines pre-training and fine-tuning schemes for protein solubility prediction. ProtSolM integrates information from multiple dimensions, including physicochemical properties, amino acid sequences, and protein backbone structures. Our model is trained using \data, the largest solubility dataset that we have constructed. PDBSol includes over $60,000$ protein sequences and structures. We provide a comprehensive leaderboard of existing statistical learning and deep learning methods on independent datasets with computational and experimental labels. ProtSolM achieved state-of-the-art performance across various evaluation metrics, demonstrating its potential to significantly advance the accuracy of protein solubility prediction.