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
Conclusion: PDC can extract more dipeptide information. PDC-Ensemble is a useful tool for the studies of protein remote