Protein Remote Homology Detection (PRHD) is a concept that aims to discover remote evolutionary links between proteins. PRHD research is currently vital for assessing protein structures and function. A variety of computational approaches have been developed in recent decades to overcome this challenge which requires constant-width characteristics to specify the Protein Sequences (PSs). However, with only a rudimentary knowledge of proteins, identifying their discrimination characteristics is not an easy task. Therefore, a brief comparative review and comparison of different computation methods is essential for PRHD. In this paper, a review of various PRHD methods with the help of different computational methods is presented. In addition, their benefits and drawbacks are discussed in a tabular form. Lastly, the whole survey is summarized and future directions are suggested to improve the efficiency of protein classification based on amino acid sequences, especially with low sequence identity between proteins.

Keywords : Protein Remote Homology Detection (PRHD), Protein Networks, Fold Recognition, Machine Learning, Deep Learning