Protein Remote Homology Detection by Combining Pseudo Dimer Composition with an Ensemble Learning Method

Background: With the development of the next generation sequencing technique in biology, more and more protein sequence data is generated exponentially. However, the protein structure data grows slowly. The gap between them is growing large. The protein remote homology detection becomes an important and intense research problem. Objective: Although several methods have been reported to tackle this problem, their performance is still too low to be used for real world application. Therefore, it is necessary and urgent to characterize protein sequences from a new perspective so as to improve the predictive performance of protein remote homology detection. Method: In this study, we proposed a new feature of proteins called Pseudo Dimer Composition (PDC). A new computational method for protein remote homology detection called PDC-Ensemble was constructed by combining PDC via an ensemble learning approach. Result: Experimental results on a public benchmark dataset showed that the performance of PDC-Ensemble outperformed other sequence-based methods, and is highly comparable with some state-of-the-art predictors in the field of protein remote homology detection. Conclusion: PDC can extract more dipeptide information. PDC-Ensemble is a useful tool for the studies of protein remote homology detection.