Evolution has developed a set of principles that determine feasible domain combinations analogous to grammar within natural languages. Treating domains as words and proteins as sentences, made up of words, we apply a linguistic approach to represent the human proteome as an n-gram network. Combining this with network theory and application, we explore the functional language and rules of the human proteome. Additionally, we explored subnetwork languages by focusing on reversible post-translational modifications (PTMs) systems that follow a reader-writer-eraser paradigm. We find that PTM systems appear to sample grammar rules near the onset of the system expansion, but then convergently evolve towards similar grammar rules, which stabilize during the post-metazoan switch. For example, reader and writer domains are typically tightly connected through shared n-grams, but eraser domains are almost always loosely or completely disconnected from readers and writers. Additionally, after grammar fixation, domains with verb-like properties, such as writers and erasers, never appear - consistent with the idea of natural grammar that leads to clarity and limits futile enzymatic cycles. Then, given how some cancer fusion genes represent the possibility for the emergence of novel language, we investigate how cancer fusion genes alter the human proteome n-gram network. We find most cancer fusion genes follow existing grammar rules. Collectively, these results suggest that n-gram based analysis of proteomes is a complement to the more direct protein-protein interaction networks. N-grams can capture abstract functional connections in a more fully described manner, limited only by the definition of domains within the proteome and not by the combinatorial challenge of capturing all protein interaction connections.