## 2 G-Protein Coupled Receptor  
G protein-coupled receptors (GPCR) exist on the surface of every cell [50]. GPCRs generate signals in cells to regulate key physiological processes such as hormone signalling, neurotransmission, cognition, vision, taste, pain perception, and others. It is also known as the seven transmembrane domain (7 TM) receptor, because there are seven transmembrane segments in which three loops are outside the cell, and three loops are inside the cell with the N-terminal position being inside the cell and the C-terminal being outside the cell. GPCR hierarchy consists of three levels, namely family, subfamily, and sub-subfamily. The family class consists of five classes, namely families A, B, C, D, and E. The subfamily class consists of 38 classes, and the sub-subfamily class consists of 87 classes. Since there are very complex relationships between classes, GPCR classification has proven difficult [51]. In addition, many protein sequences in the same family share homology with protein sequences in other families, which also increases the difficulty of classification [52]. GPCR classification depends on sequence order and includes structural, functional, and evolutionary characteristics, such as chemical and pharmacological factors [53]. GPCRs are thus one of the most challenging datasets to classify. Various feature representation methods have been used in past research for GPCR proteins. Ref. [3] used the amino acid composition (AAC), pseudo amino acid composition (PseAAC), and dipeptide composition (DC) feature representation methods for the classification of GPCR proteins. Ref. [54] used several indices related to the hydrophobicity of amino acids to implicitly describe the properties of protein sequences. They showed that combining three hydrophobic indices with a constrained Boltzmann machine (RBM) achieved high-performance results for class C GPCR classification with 94% accuracy. The Ngram representation method was used by [6; 55] to represent GPCR proteins. The Local Descriptor feature representation method has been used by [52]. This representation method works by grouping amino acids into three groups: hydrophobic, neutral, and polar. This method has yielded 87% to 90% accuracy for the GPCR protein hierarchy family level. Deep learning methods have been used for GPCR protein classification by [51; 55; 56; 57]. The feature representation method and deep learning design varied between these studies. Ref. [51] used a combination of the n-gram feature representation method, term frequency-inverse document frequency (TF-IDF), and deep learning. This study has obtained an accuracy of 98.5% at the family level, 98.1% at the subfamily level, and 96.4% at the sub-subfamily level. Ref. [57] have used the deep learning method with one-hot coding feature representation and achieved an accuracy of 97.17% for the family level, 86.82% for the subfamily level and 81.17% for the sub-subfamily level. Ref. [55] used 1-2-3-g feature representation in deep learning methods and achieved an accuracy of 99.2% for the family level, 99.2% for the subfamily level, and 98.15% for the sub-subfamily level. Ref. [56] has used 2-3-4 g feature representation together with deep learning and obtained an accuracy of 97.40% for the family level, 87.78% for the subfamily level, and 81.13% for the sub-subfamily level. However, the performance of these studies cannot be directly compared because studies used different GPCR datasets. Ref. [58] used the DWT method and selected the DWT Coiflet 4 family with three levels of decomposition to represent GPCR protein features for the levels of family A, B and C, subfamily A, as well as sub-subfamily Amine. This study found that the Coiflet 4 family type had the highest accuracies compared to other DWT families, which is 99.67% for the family level, 97.64% for the A subfamily, and 99.20% for the Amine sub-subfamily. Ref. [59] used Fourier transform for GPCR protein classification. That study produced an accuracy of 96.1% for the classification of GPCR proteins at the family level.