## 3 Results
### Experiment II: Identification of DNA-Binding Proteins  
Numerous biological mechanisms depend on nucleic acid-protein interactions. The first step for understanding these mechanisms is to identify the interacting molecules. There are different strategies for determining DNA sequences that bind specifically to a known protein. However, it is difficult to accurately identify DNA-binding proteins [50]. Existing experimental techniques have low practical value due to time consumption and expensive costs [51]. Therefore, developing an efficient computational approach for identifying DNA-binding proteins is becoming increasingly important. In this section, we explore the application of the G-PseAAC to the identification of DNA-binding proteins. The parameters \(\lambda\), \(w_{1}\), and \(w_{2}\) used here are the same as those determined in Section 3.1. The dataset used here is taken from [51]. Itsoriginal version was created in 2009 by [PERSON] et al. [52], in which the DNA-binding proteins are extracted from the Pfam database [53] with keywords of \"DNA-binding domain\" and pairwise sequence identity cutoff of 25%, while the non DNA-binding domains are randomly selected from Pfam protein families that are unrelated to the DNA-binding protein family. [PERSON] et al. [51] removed some sequences from the original dataset, and its current version is composed of 1585 protein sequences. This benchmark dataset contains 770 DNA-binding proteins and 815 non DNA-binding proteins, which form the positive sample set and negative sample set, respectively. We randomly divide the 770 DNA-binding proteins into two parts, one has 410 sequences and the other 360 sequences. Also, we randomly select 410 and 405 sequences from the 815 non DNA-binding proteins, respectively. We conduct two sets of data. Set I contains 410 DNA-binding proteins and 410 non DNA-binding proteins. This set serves as a training set. The remaining protein sequences (360 DNA-binding proteins and 405 non DNA-binding proteins) form Set II, which serves as a test set. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**NO.** & **Accession Number** & **Virus Name/Strain** & **Abbreviation** \\ \hline
56 & \(\mathrm{ALZ74433}\) & MERS coronavirus & Hu-France-FRA2 \\
57 & \(\mathrm{ALJ54502}\) & MERS coronavirus & Hu-Qunfidhah-KSA-Rs1338 \\
58 & \(\mathrm{AKN24821}\) & MERS coronavirus & KFMC-1 \\
59 & \(\mathrm{AKN24830}\) & MERS coronavirus & KFMC-7 \\
60 & \(\mathrm{ALJ76282}\) & MERS coronavirus & Hu/Taif, KSA-2083 \\
61 & \(\mathrm{ALJ76281}\) & MERS coronavirus & Hu/Taif, KSA-5920 \\
62 & \(\mathrm{ALJ54493}\) & MERS coronavirus & Hu/Makkah-KSA-728 \\
63 & \(\mathrm{ALB08267}\) & MERS coronavirus & KOREA/Seoul/014-1 \\
64 & \(\mathrm{ALB08278}\) & MERS coronavirus & KOREA/Seoul/014-2 \\
65 & \(\mathrm{ALK69641}\) & MERS coronavirus & D2731.3 \\
66 & \(\mathrm{AKQ21055}\) & MERS coronavirus & ADPCA-HKU1 \\
67 & \(\mathrm{AKQ21064}\) & MERS coronavirus & ADPCA-HKU2 \\
68 & \(\mathrm{AKQ21073}\) & MERS coronavirus & ADPCA-HKU3 \\
69 & \(\mathrm{ALA50001}\) & MERS coronavirus & camel/Taif/T68 \\
70 & \(\mathrm{ALA50012}\) & MERS coronavirus & camel/Taif/T89 \\
71 & \(\mathrm{ALT66813}\) & MERS coronavirus & Jordan\_1 \\
72 & \(\mathrm{ALT66802}\) & MERS coronavirus & Jordan\_10 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Figure 3: The relationship tree of 72 coronavirus spike proteins. Support vector machine (SVM) is employed as the classifier, and its implementation is based on the package LIBSVM (a Library for Support Vector Machines) v3.17 [54], which is open sourced and can be freely downloaded from [[http://www.csie.ntu.edu.tw/~cjlin/libsvm](http://www.csie.ntu.edu.tw/~cjlin/libsvm)]([http://www.csie.ntu.edu.tw/~cjlin/libsvm](http://www.csie.ntu.edu.tw/~cjlin/libsvm)). There are four types of kernel functions in LIBSVM: linear kernel; polynomial kernel; radial basis function (RBF) kernel; and sigmoid kernel. Among them, the RBF kernel is deemed a reasonable first choice [55].