title: Protein hypersaline adaptation: insight from amino acids with machine learning algorithms.
abstract: Traditional bioinformatics methods performed systematic comparison between the halophilic proteins and their non-halophilic homologues, to investigate the features related to hypersaline adaptation. Therefore, proposing some quantitative models to explain the sequence-characteristic relationship of halophilic proteins might shed new light on haloadaptation and help to design new biocatalysts adapt to high salt concentration. Five machine learning algorithm, including three linear and two non-linear methods were used to discriminate halophilic and their non-halophilic counterparts and the prediction accuracy was encouraging. The best prediction reliability for halophilic proteins was achieved by artificial neural network and support vector machine and reached 80 %, for non-halophilic proteins, it was achieved by linear regression and reached 100 %. Besides, the linear models have captured some clues for protein halo-stability. Among them, lower frequency of Ser in halophilic protein has not been report before.
authors.affiliation: Department of Bioengineering and Biotechnology, Huaqiao University, Xiamen, 361021, Fujian, People's Republic of China. zhgyghh@hqu.edu.cn
authors.forename: Guangya
authors.initials: G
authors.lastname: Zhang
authors.forename: Huihua
authors.initials: H
authors.lastname: Ge
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D001185:Artificial Intelligence; D019295:Computational Biology; D025341:Principal Component Analysis; D011506:Proteins / Q000737:chemistry*; D012044:Regression Analysis; D012965:Sodium Chloride / Q000737:chemistry*
chemical_list: D000596:Amino Acids; D011506:Proteins; D012965:Sodium Chloride