Designing predictors of DNA-binding proteinsusing an efficient physicochemical propertymining method

DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Many researches mainly focused on prediction and analysis of protein binding sites in DNA. We are interested in predicting binding and non-binding proteins from protein sequences. Physicochemical properties are well recognized to be effective in designing various predictors for understanding the functions and characteristics of proteins. Generally, the domain knowledge of proteins to be analyzed from biologists is needed to select effective physicochemical properties. In this study, we propose an efficient method for designing predictors of binding and non-binding proteins using a set of informative physicochemical properties obtained from an inheritable bi-objective genetic algorithm without using the domain knowledge of binding and non-binding proteins. Three benchmark datasets were used to evaluate the proposed method using SVM and informative physicochemical properties as the features. The prediction accuracy of independent test is close to 80.0%. From the analysis of informative physicochemical properties, some knowledge of binding and non-binding proteins can be further investigated. The proposed physicochemical property mining method can be used conveniently as the core for designing predictors for various DNA-binding problems.