HPClas: A data-driven approach for identifying halophilic proteins based on catBoost

Halophilic proteins possess unique structural properties and show high stability under extreme conditions. This distinct characteristic makes them invaluable for application in various aspects such as bioenergy, pharmaceuticals, environmental clean-up, and energy production. Generally, halophilic proteins are discovered and characterized through labor-intensive and time-consuming wet lab experiments. In this study, we introduce the Halophilic Protein Classifier (HPClas), a machine learning-based classifier developed using the catBoost ensemble learning technique to identify halophilic proteins. Extensive in silico calculations were conducted on a large public dataset of 12,574 samples and HPClas achieved an area under the receiver operating characteristic curve (AUROC) of 0.844 on an independent test set of 200 samples. The source code and curated dataset of HPClas are publicly available at . In conclusion, HPClas can be explored as a promising tool to aid in the identification of halophilic proteins and accelerate their application in different fields. A method based on prediction of proteins secreted by extreme halophilic bacteria was used to successfully extract a large number of halophilic proteins. Using these data, we have trained an accurate halophilic protein classifier that could determine whether an input protein is halophilic with a high accuracy of 84.5%. This work could not only promote the exploration and mining of halophilic proteins in nature but also provide guidance for the generation of mutant halophilic enzymes.