The Relation between Indel Length and Functional Divergence: A Formal Study

  • Raheleh Salari
  • Alexander Schönhuth
  • Fereydoun Hormozdiari
  • Artem Cherkasov
  • S. Cenk Sahinalp
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5251)


Although insertions and deletions (indels) are a common type of evolutionary sequence variation, their origins and their functional consequences have not been comprehensively understood. There is evidence that, on one hand, classical alignment procedures only roughly reflect the evolutionary processes and, on the other hand, that they cause structural changes in the proteins’ surfaces.

We first demonstrate how to identify alignment gaps that have been introduced by evolution to a statistical significant degree, by means of a novel, sound statistical framework, based on pair hidden Markov models (HMMs). Second, we examine paralogous protein pairs in E. coli, obtained by computation of classical global alignments. Distinguishing between indel and non-indel pairs, according to our novel statistics, revealed that, despite having the same sequence identity, indel pairs are significantly less functionally similar than non-indel pairs, as measured by recently suggested GO based functional distances. This suggests that indels cause more severe functional changes than other types of sequence variation and that indel statistics should be taken into additional account to assess functional similarity between paralogous protein pairs.


Alignment statistics Deletions Insertions GO Pair Hidden Markov Models 


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  1. 1.
    Altschul, S.F., Gish, W.: Local alignment statistics. Methods in Enzymology 266, 460–480 (1996)CrossRefGoogle Scholar
  2. 2.
    Benner, S.A., Cohen, M.A., Gonnet, G.H.: Empirical and structural models for insertions and deletions in the divergent evolution of proteins. Journal of Molecular Biology 229, 1065–1082 (1993)CrossRefGoogle Scholar
  3. 3.
    Chan, S.K., Hsing, M., Hormozdiari, F., Cherkasov, A.: Relationship between insertion/deletion (indel) frequency of proteins and essentiality. BMC Bioinformatics 8, 227 (2007)CrossRefGoogle Scholar
  4. 4.
    Chang, M.S.S., Benner, S.A.: Empirical analysis of protein insertions and deletions determining parameters for the correct placement of gaps in protein sequence alignments. Journal of Molecular Biology 341, 617–631 (2004)CrossRefGoogle Scholar
  5. 5.
    Cherkasov, A., Lee, S.J., Nandan, D., Reiner, N.E.: Large-scale survey for potentially targetable indels in bacterial and protozoan proteins. Proteins 62, 371–380 (2005)CrossRefGoogle Scholar
  6. 6.
    Cherkasov, A., Nandan, D., Reiner, N.E.: Selective targetting of indel-inferred differences in 3D structures of highly homologous proteins. Proteins: Structure, Function and Bioinformatics 58, 950–954 (2005)CrossRefGoogle Scholar
  7. 7.
    Couto, F.M., Silva, M.J., Coutinho, P.M.: Measuring semantic similarity between Gene Ontology terms. Data & Knowledge Engineering 61, 137–152 (2007)CrossRefGoogle Scholar
  8. 8.
    Dembo, A., Karlin, S.: Strong limit theorem of empirical functions for large exceedances of partial sums of i.i.d. variables. Annals of Probability 19, 1737–1755 (1991)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Denver, D.R., Morris, K., Lynch, M., Thomas, W.K.: High mutation rate and predominance of insertions in the Caenorhabditis elegans nuclear genome. Nature 430, 679–682 (2004)CrossRefGoogle Scholar
  10. 10.
    Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biological sequence analysis. Cambridge University Press, Cambridge (1998)zbMATHGoogle Scholar
  11. 11.
    Fechteler, T., Dengler, U., Schomburg, D.: Prediction of protein three-dimensional structures in insertion and deletion regions: a procedure for searching data bases of representative protein fragments using geometric scoring criteria. Journal of Molecular Biology 253, 114–131 (1995)CrossRefGoogle Scholar
  12. 12.
    Gerlt, J.A., Babbitt, P.C.: Can sequence determine function? Genome Biology 1(5), reviews0005.1-0005.10 (2000)Google Scholar
  13. 13.
    Gotoh, O.: An improved algorithm for matching biological sequences. Journal of Molecular Biology 162, 705–708 (1982)CrossRefGoogle Scholar
  14. 14.
    Gu, X., Li, W.-H.: The size distribution of insertions and deletions in human and rodent pseudogenes suggests the logarithmic gap penalty for sequence alignment. Journal of Molecular Evolution 40, 464–473 (1995)CrossRefGoogle Scholar
  15. 15.
    Hsiao, W.W.L., Ung, K., Aeschliman, D., Bryan, J., Finlay, B.B., Brinkman, F.S.L.: Evidence of a large novel gene pool associated with prokaryotic genomic islands. PLoS Genetics 1, e62 (2005)CrossRefGoogle Scholar
  16. 16.
    Karlin, S., Altschul, S.F.: Methods for assessing the statistic significance of molecular sequence features by using general scoring schemes. Proceedings of the National Academy of Sciences of the USA 87, 2264–2268 (1990)zbMATHCrossRefGoogle Scholar
  17. 17.
    Kondrashov, A.S., Rogozin, I.B.: Context of Deletions and Insertions in Human Coding Sequences. Human Mutation 23, 177–185 (2004)CrossRefGoogle Scholar
  18. 18.
    Lake, J.A., Riveral, M.C.: Horizontal gene transfer among genomes: The complexity hypothesis. Proceedings of the National Academy of Science 96(7), 3801–3806 (1999)CrossRefGoogle Scholar
  19. 19.
    Lord, P.W., Stevens, R.D., Brass, A., Goble, C.A.: Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation. Bioinformatics 19, 1275–1283 (2003)CrossRefGoogle Scholar
  20. 20.
    Lunter, G., Rocco, A., Mimouni, N., Heger, A., Caldeira, A., Hein, J.: Uncertainty in homology inferences: Assessing and improving genomic sequence alignment. Genome Research 18 (2007), doi:10.1101/gr.6725608Google Scholar
  21. 21.
    Nandan, D., Lopez, M., Ban, F., Huang, M., Li, Y., Reiner, N.E., Cherkasov, A.: Indel-based targeting of essential proteins in human pathogens that have close host orthologue(s): Discovery of selective inhibitors for Leishmania donovani elongation factor-1 − α. Proteins: Structure, Function and Bioinformatics 67, 53–67 (2007)CrossRefGoogle Scholar
  22. 22.
    Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology 48, 443–453 (1970)CrossRefGoogle Scholar
  23. 23.
    Pang, A., Smith, A.D., Nuin, P.A.S., Tillier, E.T.M.: SIMPROT: Using an empirically determined indel distribution in simulations of protein evolution. BMC Bioinformatics 6, 236 (2005)CrossRefGoogle Scholar
  24. 24.
    Pearson, W.R., Lipman, D.J.: Improved tools for biological sequence comparison. Proc. Natl. Acad. Sci. USA 85, 2444–2448 (1988)CrossRefGoogle Scholar
  25. 25.
    Pesquita, C., Faria, D., Bastos, H., Falco, A.O., Couto, F.M.: Evaluating GO-based semantic similarity measures. In: Proceedings of the 10th Annual Bio-Ontologies Meeting (Bio-Ontologies 2007) (2007)Google Scholar
  26. 26.
    Pipenbacher, P., Schliep, A., Schneckener, S., Schönhuth, A., Schomburg, D., Schrader, R.: ProClust: improved clustering of protein sequences with an extended graph-based approach. Bioinformatics 18(Supp.2), 182–191 (2002)Google Scholar
  27. 27.
    Peköz, E.A., Ross, S.M.: A simple derivation of exact reliability formulas for linear and circular consecutive-k-of-n F systems. Journal of Applied Probability 32, 554–557 (1995)zbMATHCrossRefMathSciNetGoogle Scholar
  28. 28.
    Qian, B., Goldstein, R.A.: Distribution of indel lengths. Proteins: Structure, Function and Bioinformatics 45, 102–104 (2001)CrossRefGoogle Scholar
  29. 29.
    Resnik, P.: Semantic similarity in a taxonomy: an information- based measure and its application to problems of ambiguity in natural language. Artificial Intelligence Research 11, 95–130 (1999)zbMATHGoogle Scholar
  30. 30.
    Rost, B.: Twilight zone of protein sequence alignments. Protein Engineering 12(2), 85–94 (1999)CrossRefMathSciNetGoogle Scholar
  31. 31.
    Schlicker, A., Domingues, F.S., Rahnenführer, J., Lengauer, T.: A new measure for functional similarity of gene products based on gene ontology. BMC Bioinformatics 7, 302 (2006)CrossRefGoogle Scholar
  32. 32.
    Sevilla, J.L., Segura, V., Podhorski, A., Guruceaga, E., Mato, J.M., Martnez-Cruz, L.A., Corrales, F.J., Rubio, A.: Correlation between gene expression and GO semantic similarity. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2(4), 330–338 (2005)CrossRefGoogle Scholar
  33. 33.
    The Gene Ontology Consortium. Gene Ontology: tool for the unification of biology. Nature Genetics 25, 25–29 (2000)Google Scholar
  34. 34.
    Thorne, J.L., Kishino, H., Felsenstein, J.: Inching toward reality: An improved likelihood model of sequence evolution. Journal of Molecular Evolution 34, 3–16 (1992)CrossRefGoogle Scholar
  35. 35.
    The UniProt Consortium. The Universal Protein Resource (UniProt). Nucleic Acids Res. 35, D193-D197 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Raheleh Salari
    • 1
    • 3
  • Alexander Schönhuth
    • 1
    • 3
  • Fereydoun Hormozdiari
    • 1
  • Artem Cherkasov
    • 2
  • S. Cenk Sahinalp
    • 1
  1. 1.School of Computing ScienceSimon Fraser UniversityBCCanada
  2. 2.Division of Infectious Diseases, Faculty of MedicineUniversity of British ColumbiaBCCanada
  3. 3.The authors contributed equally 

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