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Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation. (English) Zbl 1400.92417
Summary: In this paper, we intend to predict protein structural classes (\(\alpha\), \(\beta\), \(\alpha+\beta\), or \(\alpha/\beta\)) for low-homology data sets. Two data sets were used widely, 1189 (containing 1092 proteins) and 25PDB (containing 1673 proteins) with sequence homology being 40% and 25%, respectively. We propose to decompose the chaos game representation of proteins into two kinds of time series. Then, a novel and powerful nonlinear analysis technique, recurrence quantification analysis (RQA), is applied to analyze these time series. For a given protein sequence, a total of 16 characteristic parameters can be calculated with RQA, which are treated as feature representation of protein sequences. Based on such feature representation, the structural class for each protein is predicted with Fisher’s linear discriminant algorithm. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies with step-by-step procedure are 65.8% and 64.2% for 1189 and 25PDB data sets, respectively. With one-against-others procedure used widely, we compare our method with five other existing methods. Especially, the overall accuracies of our method are 6.3% and 4.1% higher for the two data sets, respectively. Furthermore, only 16 parameters are used in our method, which is less than that used by other methods. This suggests that the current method may play a complementary role to the existing methods and is promising to perform the prediction of protein structural classes.

MSC:
92D20 Protein sequences, DNA sequences
91A80 Applications of game theory
68T05 Learning and adaptive systems in artificial intelligence
62F40 Bootstrap, jackknife and other resampling methods
62P10 Applications of statistics to biology and medical sciences; meta analysis
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