NRBO-AGP: A Novel Feature Selection Approach for Accurate Protein Solubility Prediction

Abstract

Protein solubility determines how well a protein dissolves in an aqueous solution, and this property is a critical factor in the functional analysis of proteins and biotechnological applications. Accurately estimating solubility can provide significant advantages in areas such as protein engineering and drug discovery. This study proposes a new feature selection method, Newton-Raphson-based Optimization and Adaptive Gradient Perturbation (NRBO-AGP) for predicting protein solubility. The research combines the accuracy and speed of the Newton-Raphson method with the capacity of population-based optimization techniques to balance exploration and exploitation. Using 3144 protein sequences from the eSOL database, descriptor features were obtained for each protein, resulting in a dataset with 3104 features. The performance of NRBO-AGP was compared with eight different metaheuristic algorithms and evaluated using five regression models: MLP, AdaBoost, Gradient Boosting Trees, Random Forest, and Support Vector Regressor (SVR). The best results were obtained with the Gradient Boosting and Random Forest. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (𝑅2) metrics were used for performance evaluation. The results show that NRBO-AGP outperforms other metaheuristic algorithms in all regression models. The best results were achieved with Gradient Boosting and Random Forest, reaching MAE:0.0001 ± 0.0000, RMSE: 0.0008 ± 0.0000, and 𝑅2: 0.9908 ± 0.0005, and MAE: 0.0002 ± 0.0000, RMSE: 0.0025 ± 0.0000, and 𝑅2: 0.9908 ± 0.0005. These findings show that NRBO-AGP is an effective feature selection tool for predicting protein solubility. Multiple statistical analyses based on Friedman and Nemenyi tests show that the NBRO-AGP method exhibits statistically significant superior performance (𝑝 < 0.05) compared to other metaheuristic algorithms in MAE and RMSE metrics and also achieves the highest performance in the 𝑅2 score.