Discrimination of Psychrophilic and Mesophilic Proteins Using Random Forest Algorithm

Abstract

Psychrophilic organisms are those organisms which thrive at very low temperatures. In order to carry out the normal physiological and biochemical functions, these organisms produces psychrophilic proteins that have evolved through a vast amount of physicochemical adaptations at the sequence and structural levels. Our study is focussed on selecting suitable classification algorithm and appropriate input features for better discrimination of psychrophilic protein sequences from mesophilic protein sequences. We have used amino acid composition and hydrophobic residue patterns as input features and found Random Forest algorithm, a recently developed ensemble machine learning technique for better discriminating between mesophilic and psychrophilic proteins. A balanced dataset with 6000 mesophilic and 6000 psychrophilic sequences for training, and with 8432 psychrophilic and 3169 mesophilic sequences for testing was created and used for experiments. Discrimination using only the statistically significant amino acids taken from previous literature was also experimented. For the first time 70.3% testing accuracy is being reported with 71.3% correctly predicted psychrophilic and 67. % correctly predicted mesophilic proteins.