Proteins can be classified according to their evolutionary, structural, or functional relationships. A protein in the context of its family is much more informative than the single protein itself. For example, residues conserved across the family often indicate special functional roles. Two proteins classified in the same functional family may suggest that they share similar structures, even when their sequences do not have significant similarity.
There is no unique way to classify proteins into families. Boundaries between different families may be subjective. The choice of classification system depends in part on the problem; in general, the authors suggest looking into classification systems from different databases and comparing them. Three types of classification methods are widely adopted, based upon the similarity of sequence, structure, or function. Sequence-based methods are applicable to any proteins whose sequences are known, while structure-based methods are limited to the proteins of known structures, and function-based methods depend on the functions of proteins being annotated. Sequence- and structure-based classifications can be automated and are scalable to high-throughput data, whereas function-based classification is typically carried out manually. Structure- and function-based methods are more reliable, while sequence-based methods may result in a false positive result when sequence similarity is weak (i.e., two proteins are classified into one family by chance rather than by any biological significance). In addition, since protein structure and function are better conserved than sequence, two proteins having similar structures or similar functions may not be identified through sequence-based methods.
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