Prediction of protein disordered regions in a protein sequence based on gap-constraint subsequence patterns

The disordered region is an important protein structure which contains much information about protein function. Until now, the prediction of protein disordered region is also much popular task. In this paper, we proposed a new approach to predict protein disordered regions in a protein sequence using the gap-constraint subsequence patterns mining and association rule mining. At first, the gap-constraint frequent subsequences are generated by Gap-BIDE algorithm in two classes, disordered sequence and ordered sequence. Based on these frequent subsequences, we calculated the conditional probability of disordered subsequence patterns in both classes and classify the candidates into the class which has the higher conditional probability. Finally, we used the disordered/ordered subsequence patterns which we generate to search the disordered regions in a protein sequence. In the experiment, we used the CASP 9 and Disprot 5.7 dataset as test data and the performance is higher than other methods.