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