PseDNA‐Pro: DNA‐Binding Protein Identification by Combining Chou’s PseAAC and Physicochemical Distance Transformation

Abstract

Identification of DNA-binding proteins is an important problem in biomedical research as DNA-binding proteins are crucial for various cellular processes. Currently, the machine learning methods achieve the-state-of-the-art performance with different features. A key step to improve the performance of these methods is to find a suitable representation of proteins. In this study, we proposed a feature vector composed of three kinds of sequence-based features, including overall amino acid composition, pseudo amino acid composition (PseAAC) proposed by Chou and physicochemical distance transformation. These features not only consider the sequence composition of proteins, but also incorporate the sequence-order information of amino acids in proteins. The feature vectors were fed into Support Vector Machine (SVM) for DNA-binding protein identification. The proposed method is called PseDNA-Pro. Experiments on stringent benchmark datasets and independent test datasets by using the Jackknife test showed that PseDNA-Pro can achieve an accuracy of higher than 80 %, outperforming several state-of-the-art methods, including DNAbinder, DNA-Prot, and iDNA-Prot. These results indicate that the combination of various features for DNA-binding protein prediction is a suitable approach, and the sequence-order information among residues in proteins is relative for discrimination. For practical applications, a web-server of PseDNA-Pro was established, which is available from http://bioinformatics.hitsz.edu.cn/PseDNA-Pro/.

Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information

“…In this subsection, the performance of Local-DPP is evaluated on the benchmark dataset PDB1075, and compared with the performances of several state-of-the-art predictors; namely, iDNA-Prot|dis [28], iDNA-Prot [24], DNA-Prot [19], PseDNA-Pro [27], DNAbinder [20], iDNAPro-PseAAC [25], and Kmer1+ACC [11]. The predictive results of the jackknife validation test are presented in Table 3.…”

“…To further improve DNA-binding protein prediction from the PseAAC vector, they also combined the PseAAC with a physicochemical distance transformation [27]. Besides PseAAC, DNA-binding proteins are represented by other commonly used sequence-based features, such as physicochemical properties [7,27,39,47], amino acid composition [7,42,49], autocross-covariance transformation [11,12], dipeptide composition [12,32], and other hybrid features [25]. Kumar et al [20] newly incorporated A C C E P T E D M A N U S C R I P T evolutionary information into sequence-based methods.…”

Using weighted features to predict recombination hotspots in Saccharomyces cerevisiae

“…As deletion of redundant and noisy features might be important especially when reduction of feature dimensionality is required, we shall develop a method in this regard. 15 As demonstrated in a series of publications (42, [81][82][83][84][85], user-friendly and publicly accessible web-servers can greatly facilitate relevant investigators, we shall make efforts in our future work to provide a web-server for the prediction method presented in this study.…”

A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen–Shannon Divergence

“…Until now, several groups have published different studies based on either experimental or computational identification of DNA-binding proteins [1,[6][7][8][9][10][11] as well as residues in these proteins [12][13][14][15][16][17][18][19][20][21][22][23]. However, the usage of experimental approaches for the determination of binding sites is still challenging since they are often demanding, relatively expensive, and time-consuming.…”