DNA-binding proteins (DNA-BPs) play an important role in many biological processes.
Now next-generation sequencing technologies are widely used to obtain genome of many organisms.
Consequently, identification of DNA-BPs accurately and rapidly will provide significant helps in
annotation of genomes. Chaos game representation (CGR) can reveal the information hidden in protein
sequences. Furthermore, fractal dimensions are a vital index to measure compactness of complex and
irregular geometric objects. In this research, in order to extract the intrinsic correlation with DNAbinding
property from protein sequence, CGR algorithm and fractal dimension, together with amino
acid composition are applied to formulate the protein samples. Here we employ the random forest as
the classifier to predict DNA-BPs based on sequence-derived features with amino acid composition and fractal dimension.
This resulting predictor is compared with three important existing methods DNA-Prot, iDNA-Prot and DNAbinder in the
same datasets. On two benchmark datasets from DNA-Prot and iDNA-Prot, the average accuracies (ACC) achieve
82.07%, 84.91% respectively, and average Matthew's correlation coefficients (MCC) achieve 0.6085, 0.6981 respectively.
The point to point comparisons demonstrate that our fractal approach shows some improvements.