Skip to main content

Compressing Proteomes: The Relevance of Medium Range Correlations


We study the nonrandomness of proteome sequences by analysing the correlations that arise between amino acids at a short and medium range, more specifically, between amino acids located 10 or 100 residues apart; respectively. We show that statistical models that consider these two types of correlation are more likely to seize the information contained in protein sequences and thus achieve good compression rates. Finally, we propose that the cause for this redundancy is related to the evolutionary origin of proteomes and protein sequences.



  1. 1.

    Wootton JC: Non-globular domains in protein sequences: automated segmentation using complexity measures. Computers & Chemistry 1994, 18(3):269-285.

    Article  MATH  Google Scholar 

  2. 2.

    Blaisdell BE: A prevalent persistent global nonrandomness that distinguishes coding and non-coding eucaryotic nuclear DNA sequences. Journal of Molecular Evolution 1983, 19(2):122-133. 10.1007/BF02300750

    Article  MathSciNet  Google Scholar 

  3. 3.

    Almirantis Y, Provata A: An evolutionary model for the origin of non-randomness, long-range order and fractality in the genome. BioEssays 2001, 23(7):647-656. 10.1002/bies.1090

    Article  Google Scholar 

  4. 4.

    Weiss O, Jiménez-Montaño MA, Herzel H: Information content of protein sequences. Journal of Theoretical Biology 2000, 206(3):379-386. 10.1006/jtbi.2000.2138

    Article  Google Scholar 

  5. 5.

    Nevill-Manning CG, Witten IH: Protein is incompressible. Proceedings of the Data Compression Conference (DCC '99), Snowbird, Utah, USA, March 1999 257-266.

    Google Scholar 

  6. 6.

    Matsumoto T, Sadakane K, Imai H: Biological sequence compression algorithms. Genome Informatics 2000, 11: 43-52.

    Google Scholar 

  7. 7.

    Cao MD, Dix TI, Allison L, Mears C: A simple statistical algorithm for biological sequence compression. Proceedings of the Data Compression Conference (DCC '07), Snowbird, Utah, USA, March 2007 43-52.

    Google Scholar 

  8. 8.

    Hategan A, Tabus I: Protein is compressible. Proceedings of the 6th Nordic Signal Processing Symposium (NORSIG '04), Espoo, Finland, June 2004 192-195.

    Google Scholar 

  9. 9.

    Adjeroh D, Nan F: On compressibility of protein sequences. Proceedings of the Data Compression Conference (DCC '06), Snowbird, Utah, USA, March 2006 422-434.

    Google Scholar 

  10. 10.

    Sampath G: A block coding method that leads to significantly lower entropy values for the proteins and coding sections of Haemophilus influenzae . Proceedings of the IEEE Bioinformatics Conference (CSB '03), Stanford, Calif, USA, August 2003 287-293.

    Google Scholar 

  11. 11.

    Shannon CE: A mathematical theory of communication. Bell System Technical Journal 1948, 27: 379-423 and 623–656.

    Article  MathSciNet  MATH  Google Scholar 

  12. 12.

    Cleary J, Witten I: Data compression using adaptive coding and partial string matching. IEEE Transactions on Communications 1984, 32(4):396-402.

    Article  Google Scholar 

  13. 13.

    Willems FMJ, Shtarkov YM, Tjalkens TJ: The context-tree weighting method: basic properties. IEEE Transactions on Information Theory 1995, 41(3):653-664. 10.1109/18.382012

    Article  MATH  Google Scholar 

  14. 14.

    Integr8 web portal2006. []

  15. 15.

    Abel J: The data compression resource on the internet.2005. []

    Google Scholar 

  16. 16.

    Orengo CA, Thornton JM: Protein families and their evolution—a structural perspective. Annual Review of Biochemistry 2005, 74: 867-900. 10.1146/annurev.biochem.74.082803.133029

    Article  Google Scholar 

  17. 17.

    Heringa J: The evolution and recognition of protein sequence repeats. Computers & Chemistry 1994, 18(3):233-243.

    Article  MATH  Google Scholar 

  18. 18.

    Andrade MA, Petosa C, O'Donoghue SI, Müller CW, Bork P: Comparison of ARM and HEAT protein repeats. Journal of Molecular Biology 2001, 309(1):1-18. 10.1006/jmbi.2001.4624

    Article  Google Scholar 

  19. 19.

    Kirkpatrick S, Gelatt CD Jr, Vecchi MP: Optimization by simulated annealing. Science 1983, 220(4598):671-680. 10.1126/science.220.4598.671

    Article  MathSciNet  MATH  Google Scholar 

  20. 20.

    Mirny LA, Shakhnovich EI: Universally conserved positions in protein folds: reading evolutionary signals about stability, folding kinetics and function. Journal of Molecular Biology 1999, 291(1):177-196. 10.1006/jmbi.1999.2911

    Article  Google Scholar 

  21. 21.

    Huynen MA, Stadler PF, Fontana W: Smoothness within ruggedness: the role of neutrality in adaptation. Proceedings of the National Academy of Sciences of the United States of America 1996, 93(1):397-401. 10.1073/pnas.93.1.397

    Article  Google Scholar 

  22. 22.

    Karlin S: Statistical signals in bioinformatics. Proceedings of the National Academy of Sciences of the United States of America 2005, 102(38):13355-13362. 10.1073/pnas.0501804102

    Article  Google Scholar 

  23. 23.

    Dill KA: Dominant forces in protein folding. Biochemistry 1990, 29(31):7133-7155. 10.1021/bi00483a001

    Article  Google Scholar 

  24. 24.

    Rost B: Did evolution leap to create the protein universe? Current Opinion in Structural Biology 2002, 12(3):409-416. 10.1016/S0959-440X(02)00337-8

    Article  MathSciNet  Google Scholar 

  25. 25.

    Rissanen J, Langdon GG Jr.: Arithmetic Coding. IBM Journal of Research and Development 1979, 23(2):149-162.

    Article  MathSciNet  MATH  Google Scholar 

  26. 26.

    Salzberg SL, Delcher AL, Kasif S, White O: Microbial gene identification using interpolated Markov models. Nucleic Acids Research 1998, 26(2):544-548. 10.1093/nar/26.2.544

    Article  Google Scholar 

  27. 27.

    Turutina VP, Laskin AA, Kudryashov NA, Skryabin KG, Korotkov EV: Identification of latent periodicity in amino acid sequences of protein families. Biochemistry (Moscow) 2006, 71(1):18-31. 10.1134/S0006297906010032

    Article  Google Scholar 

  28. 28.

    Korotkov EV, Korotkova MA: Enlarged similarity of nucleic acid sequences. DNA Research 1996, 3(3):157-164. 10.1093/dnares/3.3.157

    Article  Google Scholar 

  29. 29.

    Camproux AC, Tufféry P: Hidden Markov model-derived structural alphabet for proteins: the learning of protein local shapes captures sequence specificity. Biochimica et Biophysica Acta 2005, 1724(3):394-403.

    Article  Google Scholar 

  30. 30.

    Bentley SD, Parkhill J: Comparative genomic structure of prokaryotes. Annual Review of Genetics 2004, 38: 771-791. 10.1146/annurev.genet.38.072902.094318

    Article  Google Scholar 

  31. 31.

    Raes J, Korbel JO, Lercher MJ, von Mering C, Bork P: Prediction of effective genome size in metagenomic samples. Genome Biology 2007, 8(1):R10. 10.1186/gb-2007-8-1-r10

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Claudia Chica.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Benedetto, D., Caglioti, E. & Chica, C. Compressing Proteomes: The Relevance of Medium Range Correlations. J Bioinform Sys Biology 2007, 60723 (2007).

Download citation


  • Statistical Model
  • Protein Sequence
  • System Biology
  • Evolutionary Origin
  • Compression Rate