Prediction Model of Thermophilic Protein Based on Stacking Method

Background: Through the in-depth study of the thermophilic protein heat resistance principle, it is of great significance for people to deeply understand the folding, structure, function, and the evolution of proteins, and the directed design and modification of protein molecules in protein processing. Objective: Aiming at the problem of low accuracy and low efficiency of thermophilic protein prediction, a thermophilic protein prediction model based on the Stacking method is proposed. Methods: Based on the idea of Stacking, this paper uses five features extraction methods, including amino acid composition, g-gap dipeptide, encoding based on grouped weight, entropy density, and autocorrelation coefficient to characterize protein sequences for the selected standard data set. Then, the SVM based on the Gaussian kernel function is used to design the classification prediction model; by taking the prediction results of the five methods as the second layer input, the logistic regression model is used to integrate the experimental results to build a thermophilic protein prediction model based on the Stacking method. Results: The accuracy of the proposed method was found up to 93.75% when verified by the Jackknife method, and a number of performance evaluation indexes were observed to be higher than those of other models, and the overall performance better than that of most of the reported methods. Conclusion: The model presented in this paper has shown strong robustness and can significantly improve the prediction performance of thermophilic proteins.

Automated summary

A thermophilic protein prediction model based on the Stacking method was proposed in this study, achieving an accuracy of up to 93.75% when verified by the Jackknife method. The overall performance was better than most reported methods, showing strong robustness and significantly improving the prediction performance of thermophilic proteins.