structure and function. With the growing of number of proteins with known structure, the future prospect of structure-based encodings is considerable. Furthermore, the encodings reflecting function potentials may be more useful than others for protein function prediction; thus, exploring function-based encoding methods is a worthwhile topic. Third, the machine-learning encoding methods can be promising topics for future studies. As the amino acid encoding is an open problem, most encoding methods are based on an artificially defined basis, i.e. the physicochemical property encodings are constructed from protein fold-related properties observed by researchers, which will inevitably bring some unknown deviations. However, the machine-learning methods can avoid those artificial deviations by learning the amino acid encoding from biological data automatically. The protein sequences and natural languages share some similarities to a certain extent; for instance, the protein sequences can be comparable to sentences, and the amino acid or polypeptide chains can be comparable to words in languages. Considering that the word distributed representation has achieved comprehensive improved performances in natural language processing tasks, the protein sequences should also gain improvements by using the distributed representations of amino acids or n-gram amino acids. Some recent studies have demonstrated the potential of amino acid-distributed representations in protein family classification, disordered protein identification and protein functional property prediction, but most of these methods are concerned with the n-gram amino acid-distributed representations that cannot be directly used to predict the residue-level properties. Thus, residue-level distributed representations of amino acid is a topic that needs more attention.
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