A comparative analysis of dipeptides distribution in eukaryotes and prokaryotes by statistical mechanics.

Previous researches have shown that relevant words to the main topic of a given text have a long-range correlation in their spatial distribution, while common words are randomly distributed. Inspired by the features of human-written texts, in the present study, we have used non-extensive statistical mechanics for ranking dipeptides in proteomes of prokaryotes and eukaryotes. The ranking is performed based on the correlation between the words' spatial distributions. It is found that some representative dipeptides have higher values of the non-extensivity parameter, and the majority of dipeptides are randomly distributed in their respective proteomes. Interestingly, the number of random-distributed dipeptides in evolutionary higher organisms is greater than those in prokaryotes. It concluded that reduction in the number of clustered dipeptides; provide a more effective energy optimization to overcome energetic challenges in more complex organisms. Also the reduction of the dipeptides clustering in the proteome of eukaryotes could be effectively compensated by higher clustering of CG dinucleotide at the genome level, that are used to control and optimize the gene expression. • In linguistics, keywords of a given text are clustered. • Cystein has the highest clustering level in proteomes. • Number of clustered dipeptides in eukaryotes is lower than those in prokaryotes. • Increasing the information entropy leads to energy optimization in eukaryotes. [ABSTRACT FROM AUTHOR]