Intra- and Inter-Molecular Coevolution: The Case of HIV1 Protease and Reverse Transcriptase

  • Patrick Boba
  • Philipp Weil
  • Franziska Hoffgaard
  • Kay Hamacher
Part of the Communications in Computer and Information Science book series (CCIS, volume 127)


The stability, fold, and the function of proteins need to be maintained throughout the evolution of these molecules – inducing a selective pressure, that can be revealed in sequence data sets. The conservation of structure and function implies coevolution of amino acids within the protein. To understand such selective pressure in the evolution of the human immunodeficiency virus (HIV), we apply information theoretical measures to the two most important enzymes for the progression of viral infection: the reverse transcriptase and the protease. We computed the mutual information to derive insight into the selective pressure acting locally and globally on the enzymes. We found intra- and inter-protein co-evolution of residues in these enzymes and annotate important structural-evolutionary correlations. We discuss a signal indicating a potential co-evolution between the protease and the reverse transcriptase.


Mutual information HIV Molecular evolution Coevolution Sequence analysis 


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  1. 1.
    Tsygankov, A.Y.: Current developments in anti-HIV/AIDS gene therapy. Curr. Opin. Investig Drugs 10(2), 137–149 (2009)MathSciNetGoogle Scholar
  2. 2.
    Wlodawer, A., Erickson, J.: Structure-based inhibitors of HIV-1 protease. Annu. Rev. Biochem. 62(1), 543–585 (1993)CrossRefGoogle Scholar
  3. 3.
    Perelson, A.S., Neumann, A.U., Markowitz, M., Leonard, J., Ho, D.: HIV-1 dynamics in vivo: virion clearance rate, infected cell life-span, and viral generation time. Science 271, 1582–1586 (1996)CrossRefGoogle Scholar
  4. 4.
    Richman, D., Margolis, D., Delaney, M., Greene, W.C., Hazuda, D., Pomerantz, R.J.: The challenge of finding a cure for HIV infection. Science 323, 1304–1307 (2009)CrossRefGoogle Scholar
  5. 5.
    Rong, L., Gilchrist, M.A., Feng, Z., Perelson, A.S.: Modeling within-host HIV-1 dynamics and the evolution of drug resistance: Trade-offs between viral enzyme function and drug susceptibility. J. Theo. Biol. 247, 804–818 (2007)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Chen, L., Lee, C.: Distinguishing HIV-1 drug resistance, accessory, and viral fitness mutations using conditional selection pressure analysis of treated versus untreated patient samples. Biology Direct 1(1), 14 (2006)CrossRefGoogle Scholar
  7. 7.
    Trylska, J., Tozzini, V., Chang, C., McCammon, J.A.: HIV-1 protease substrate binding and product release pathways explored with coarse-grained molecular dynamics. Biophys. J. 92, 4179–4187 (2007)CrossRefGoogle Scholar
  8. 8.
    Hamacher, K., McCammon, J.A.: Computing the amino acid specificity of fluctuations in biomolecular systems. J. Chem. Theory Comput. 2(3), 873–878 (2006)CrossRefGoogle Scholar
  9. 9.
    Hamacher, K.: Relating sequence evolution of HIV1-protease to its underlying molecular mechanics. Gene 422, 30–36 (2008)CrossRefGoogle Scholar
  10. 10.
    Pan, C., Kim, J., Chen, L., Wang, Q., Lee, C.: The hiv positive selection mutation database. Nuc. Acids Res. 35(1), D371–D375 (2007)CrossRefGoogle Scholar
  11. 11.
    Chen, L., Perlina, A., Lee, C.J.: Positive Selection Detection in 40,000 Human Immunodeficiency Virus (HIV) Type 1 Sequences Automatically Identifies Drug Resistance and Positive Fitness Mutations in HIV Protease and Reverse Transcriptase. J. Virol. 78(7), 3722–3732 (2004)CrossRefGoogle Scholar
  12. 12.
    Shannon, C.E.: Prediction and entropy of printed english. The Bell System Technical Journal 30, 50–64 (1951)zbMATHGoogle Scholar
  13. 13.
    Lund, O., Nielsen, M., Lundegaard, C., Brunak, C.K.S.: Immunological Bioinformatics. MIT Press, Cambridge (2005)zbMATHGoogle Scholar
  14. 14.
    Hamacher, K.: Information theoretical measures to analyze trajectories in rational molecular design. J. Comp. Chem. 28(16), 2576–2580 (2007)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Hamacher, K.: Protein domain phylogenies - information theory and evolutionary dynamics. In: Fred, A., Filipe, J., Gamboa, H. (eds.) BIOINFORMATICS 2010, pp. 114–122 (2010)Google Scholar
  16. 16.
    Pape, S., Hoffgaard, F., Hamacher, K.: Distance-dependent classification of amino acids by information theory. Proteins: Structure, Function, and Bioinformatics 78, 2322–2328 (2010)CrossRefGoogle Scholar
  17. 17.
    Boba, P., Weil, P., Hoffgaard, F., Hamacher, K.: Co-evolution in HIV enzymes. In: Fred, A., Filipe, J., Gamboa, H. (eds.) BIOINFORMATICS 2010, pp. 39–47 (2010)Google Scholar
  18. 18.
    Weil, P., Hoffgaard, F., Hamacher, K.: Estimating sufficient statistics in co-evolutionary analysis by mutual information. Computational Biology and Chemistry 33(6), 440–444 (2009)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Boba, P., Hamacher, K. (2009),
  20. 20.
    Press, W.H., et al.: Numerical Recipies in C. Cambridge University Press, Cambridge (1995)Google Scholar
  21. 21.
    Sarafianos, S.G., Das, K., Hughes, S.H., Arnold, E.: Taking aim at a moving target: designing drugs to inhibit drug-resistant hiv-1 reverse transcriptases. Current Opinion in Structural Biology 14(6), 716–730 (2004)CrossRefGoogle Scholar
  22. 22.
    Prajapati, D.G., Ramajayam, R., Yadav, M.R., Giridhar, R.: The search for potent, small molecule nnrtis: A review. Bioorganic & Medicinal Chemistry 17(16), 5744–5762 (2009)CrossRefGoogle Scholar
  23. 23.
    Yoshimura, K., Kato, R., Yusa, K., Kavlick, M.F., Maroun, V., Nguyen, A., Mimoto, T., Ueno, T., Shintani, M., Falloon, J., Masur, H., Hayashi, H., Erickson, J., Mitsuya, H.: JE-2147: A dipeptide protease inhibitor (PI) that potently inhibits multi-PI-resistant HIV-1. Proc. Natl. Acad. Sci. 96, 8675–8680 (1999)CrossRefGoogle Scholar
  24. 24.
    Reiling, K., Endres, N., Dauber, D., Craik, C., Stroud, R.: Anisotropic dynamics of the JE-2147-HIV protease complex: Drug resistance and thermodynamic binding mode examined in a 1.09 a structure. Biochemistry 41, 4582 (2002)CrossRefGoogle Scholar
  25. 25.
    Perryman, A.L., Lin, J.H., McCammon, J.A.: Restrained molecular dynamics simulations of hiv-1 protease: The first step in validating a new target for drug design. Biopolymers 82(3), 272–284 (2006)CrossRefGoogle Scholar
  26. 26.
    Stone, J.: An Efficient Library for Parallel Ray Tracing and Animation. Master’s thesis, Computer Science Department, University of Missouri-Rolla (April 1998)Google Scholar
  27. 27.
    Humphrey, W., Dalke, A., Schulten, K.: VMD – Visual Molecular Dynamics. Journal of Molecular Graphics 14, 33–38 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Patrick Boba
    • 1
  • Philipp Weil
    • 1
  • Franziska Hoffgaard
    • 1
  • Kay Hamacher
    • 1
  1. 1.Bioinformatics & Theoretical Biology GroupTechnische Universität DarmstadtGermany

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