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

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.

Search for Human-Specific Proteins Based on Availability Scores of Short Constituent Sequences: Identification of a WRWSH Protein in Human Testis

“…There are some noteworthy recent studies that encourage this line of approach: for example, nonrandom distributions of 5-aa SCS are demonstrated in the current proteome databases [38], confirming the previous finding that biological bias occurs in protein coding [28,29]. Among these existing studies, our approach is operationally one of the simplest, and it emphasizes analogies between languages and protein sequences [32,33]. Encouragingly, linguistic aspects of proteins have been noted in other studies [48,49].…”

“…The advantage of the alignment-free approach is that any collections of proteins can be compared quantitatively. Although various types of alignment-free approaches have been developed [24,25], including our previous attempts to use membrane topology [26] and a self-organizing map [27], the alignment-free approach in the present study is based on the "availability" (frequency bias) of short constituent sequences (SCSs) of amino acids (aa) in proteins [28][29][30][31][32][33]. The length of SCSs can be 2 aa (doublet), 3 aa (triplet), 4 aa (quartet), 5 aa (pentat), and more in a given protein.…”

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics

“…Much like humans adopt languages to communicate, biological organisms use sophisticated languages to convey information within and between cells. Inspired by this conceptual analogy, we adopt existing methods in natural language processing (NLP) to gain a deeper understanding of the "language of life" with the ultimate goal to discover functions encoded within biological sequences [1][2][3][4].…”

Protein Word Detection using Text Segmentation Techniques

“…They call the short consequent sequences (SCS) present in protein sequences as words and use availability scores to assess the biological usage bias of SCS. Our approach of using MDL for segmentation is interesting in that it does not require prior fixing of word length as in (Motomura et al, 2012), (Motomura et al, 2013).…”