Prediction of DNA-Binding Protein from Profile-Based Hidden Markov Model Feature

Abstract

In protein structure prediction problem, DNA-binding protein identification plays a significant role in various processes like transcription, DNA replication, DNA recombination, repair and modification. It has been a very active area of research to find an effective way to solve DNA-binding protein problem. The experimental methods that are employed for protein structure prediction are quite expensive and time consuming. Most of the methods have yielded better results in extracting evolutionary features. Recently, hidden Markov model (HMM) has been employed to extract features with remarkable increase in the efficiency for identification of DNA-binding proteins. In this study, a novel system based on HMM profile has been proposed to capture distant homology and evolutionary information. The experiments using the HMM profile model have been carried out on independent dataset with prediction accuracy 72% prediction accuracy, which is 3% better compared to the previous attempts. In this study, a comprehensive analysis of HMM profile model is conducted and the results are compared with other traditional models based on performances indices.