Classify a Protein Domain Using SVM Sigmoid Kernel

Protein domains are discrete portion of protein sequence that can fold independently with their own function. Protein domain classification is important for multiple reasons, which including determines the protein function in order to manufacture new protein with new function. However, there are several issues that need to be addressed in protein domain classification which include increasing domain signal and accurate classify to their category. Therefore, to overcome this issue, this paper proposed a new approach to classify protein domain from protein subsequences and protein structure information using SVM sigmoid kernel. The proposed method consists of three phases: Data generating, creating sequence information and classification. The data generating phase selects potential protein and generates clear domain information. The creating sequence information phase used several calculations to generate protein structure information in order to optimize the domain signal. The classification phase involves SVM sigmoid kernel and performance evaluation. The performance of the approach method is evaluated in terms of sensitivity and specificity on single-domain and multiple-domain using dataset SCOP 1.75. The result on SVM sigmoid kernel shown higher accuracy compare with single neural network and double neural network for single and multiple domain prediction. This proposed approach develops in order to solve the problem of coincidently group into both categories either single or multiple domain. This method showed an improvement of classification in term of sensitivity, specificity and accuracy.