NDLI: Word Decoding of Protein Amino Acid Sequences with Availability Analysis: A Linguistic Approach

Content Provider	PubMed Central
Author	Motomura, Kenta Fujita, Tomohiro Tsutsumi, Motosuke Satsuki, Kikuzato Nakamura, Morikazu Otaki, Joji M.
Editor	Altmann, Eduardo G.
Copyright Year	2012
Abstract	The amino acid sequences of proteins determine their three-dimensional structures and functions. However, how sequence information is related to structures and functions is still enigmatic. In this study, we show that at least a part of the sequence information can be extracted by treating amino acid sequences of proteins as a collection of English words, based on a working hypothesis that amino acid sequences of proteins are composed of short constituent amino acid sequences (SCSs) or “words”. We first confirmed that the English language highly likely follows Zipf's law, a special case of power law. We found that the rank-frequency plot of SCSs in proteins exhibits a similar distribution when low-rank tails are excluded. In comparison with natural English and “compressed” English without spaces between words, amino acid sequences of proteins show larger linear ranges and smaller exponents with heavier low-rank tails, demonstrating that the SCS distribution in proteins is largely scale-free. A distribution pattern of SCSs in proteins is similar among species, but species-specific features are also present. Based on the availability scores of SCSs, we found that sequence motifs are enriched in high-availability sites (i.e., “key words”) and vice versa. In fact, the highest availability peak within a given protein sequence often directly corresponds to a sequence motif. The amino acid composition of high-availability sites within motifs is different from that of entire motifs and all protein sequences, suggesting the possible functional importance of specific SCSs and their compositional amino acids within motifs. We anticipate that our availability-based word decoding approach is complementary to sequence alignment approaches in predicting functionally important sites of unknown proteins from their amino acid sequences.
Related Links	http://dx.doi.org/10.1371/journal.pone.0050039
Starting Page	50039
File Format	PDF
ISSN	19326203
e-ISSN	19326203
Journal	PLoS ONE
Issue Number	11
Volume Number	7
Language	English
Publisher	Public Library of Science
Publisher Date	2012-11-21
Access Restriction	Open
Rights Holder	Public Library of Science
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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