Skip to main content
SpringerLink
Log in

CONTRAlign: Discriminative Training for Protein Sequence Alignment

  • Conference paper
Research in Computational Molecular Biology (RECOMB 2006)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 3909))

Abstract

In this paper, we present CONTRAlign, an extensible and fully automatic framework for parameter learning and protein pairwise sequence alignment using pair conditional random fields. When learning a substitution matrix and gap penalties from as few as 20 example alignments, CONTRAlign achieves alignment accuracies competitive with available modern tools. As confirmed by rigorous cross-validated testing, CONTRAlign effectively leverages weak biological signals in sequence alignment: using CONTRAlign, we find that hydropathy-based features result in improvements of 5-6% in aligner accuracy for sequences with less than 20% identity, a signal that state-of-the-art hand-tuned aligners are unable to exploit effectively. Furthermore, when known secondary structure and solvent accessibility are available, such external information is naturally incorporated as additional features within the CONTRAlign framework, yielding additional improvements of up to 15-16% in alignment accuracy for low-identity sequences.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Rost, B.: Twilight zone of protein sequence alignments. Protein Eng. 12, 85–94 (1999)

    Article  Google Scholar 

  2. O’Sullivan, O., Suhre, K., Abergel, C., Higgins, D.G., Notredame, C.: 3DCoffee: combining protein sequences and structures within multiple sequence alignments. J. Mol. Biol. 340, 385–395 (2004)

    Article  Google Scholar 

  3. Shi, J., Blundell, T.L., Mizuguchi, K.: FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. J. Mol. Biol. 310, 243–257 (2001)

    Article  Google Scholar 

  4. Taylor, W.R., Orengo, C.A.: Protein structure alignment. J. Mol. Biol. 208, 1–22 (1989)

    Article  Google Scholar 

  5. Kabsch, W.: A discussion of the solution for the best rotation to relate two sets of vectors. Acta Crystallog Sect A 34, 827–828 (1978)

    Article  Google Scholar 

  6. Simossis, V.A., Kleinjung, J., Heringa, J.: Homology-extended sequence alignment. Nucleic Acids Res 33, 816–824 (2005)

    Article  Google Scholar 

  7. Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25, 3389–3402 (1997)

    Article  Google Scholar 

  8. Zhou, H., Zhou, Y.: SPEM: improving multiple sequence alignment with sequence profiles and predicted secondary structures. Bioinformatics 21, 3615–3621 (2005)

    Article  Google Scholar 

  9. Jones, D.T.: Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292, 195–202 (1999)

    Article  Google Scholar 

  10. Simossis, V.A., Heringa, J.: PRALINE: A multiple alignment toolbox that integrates homology-extended and secondary structure information. Nucleic Acids Res 33(Web Server issue), W289–W294 (2005)

    Google Scholar 

  11. Henikoff, S., Henikoff, J.G.: Amino acid substitution matrices from protein blocks. Proc. Nat. Acad. Sci. USA 89, 10915–10919 (1992)

    Article  Google Scholar 

  12. Vingron, M., Waterman, M.S.: Sequence alignment and penalty choice. Review of concepts, case studies and implications. J. Mol. Biol. 235, 1–12 (1994)

    Article  Google Scholar 

  13. Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI, pp. 1137–1145 (1995)

    Google Scholar 

  14. Raghava, G.P.S., Searle, S.M.J., Audley, P.C., Barber, J.D., Barton, G.J.: OXBench: A benchmark for evaluation of protein multiple sequence alignment accuracy. BMC Bioinformatics 4 (2003)

    Google Scholar 

  15. Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proc. 18th ICML, pp. 282–289 (2001)

    Google Scholar 

  16. Sha, F., Pereira, F.: Shallow parsing with conditional random fields (2003)

    Google Scholar 

  17. Durbin, R., Eddy, S.R., Krogh, A., Mitchison, G.: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, Cambridge (1999)

    Google Scholar 

  18. Altschul, S.F.: Amino acid substitution matrices from an information theoretic perspective. J. Mol. Biol. 219, 555–565 (1991)

    Article  Google Scholar 

  19. Holmes, I., Durbin, R.: Dynamic programming alignment accuracy. J. Comp. Biol. 5, 493–504 (1998)

    Article  Google Scholar 

  20. Do, C.B., Mahabhashyam, M.S., Brudno, M., Batzoglou, S.: PROBCONS: probabilistic consistency-based multiple sequence alignment. Genome Res 15, 330–340 (2005)

    Article  Google Scholar 

  21. Ng, A., Jordan, M.: On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. In: NIPS 14 (2002)

    Google Scholar 

  22. Thompson, J.D., Plewniak, F., Poch, O.: A comprehensive comparison of multiple sequence alignment programs. Nucleic Acids Res 27, 2682–2690 (1999)

    Article  Google Scholar 

  23. Edgar, R.C.: MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32, 1792–1797 (2004)

    Article  Google Scholar 

  24. McCallum, A., Bellare, K., Pereira, F.: A conditional random field for discriminatively-trained finite-state string edit distance. In: Proc. UAI (2005)

    Google Scholar 

  25. Bilenko, M., Mooney, R.J.: Alignments and string similarity in information integration: A random field approach. In: Proc. Dagstuhl Seminar on Machine Learning for the Semantic Web (2005)

    Google Scholar 

  26. Vapnik, V.N.: Statistical Learning Theory. Wiley-Interscience, New York (1998)

    MATH  Google Scholar 

  27. Nocedal, J., Wright, S.J.: Numerical Optimization. Springer, Heidelberg (1999)

    Book  MATH  Google Scholar 

  28. Thompson, J.D., Higgins, D.G., Gibson, T.J.: CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties, and weight matrix choice. Nucleic Acids Res 22, 4673–4680 (1994)

    Article  Google Scholar 

  29. Krieger, E., Hooft, R.W.W., Nabuurs, S., Vriend, G.: PDBFinderII—a database for protein structure analysis and prediction (submitted, 2004)

    Google Scholar 

  30. Eyrich, V.A., Mart’i-Renom, M.A., Przybylski, D., Madhusudhan, M.S., Fiser, A., Pazos, F., Valencia, A., Sali, A., Rost, B.: EVA: continuous automatic evaluation of protein structure prediction servers. Bioinformatics 17, 1242–1243 (2001)

    Article  Google Scholar 

  31. Karchin, R., Cline, M., Mandel-Guttfreund, Y., Karplus, K.: Hidden markov models that use predicted local structure for fold recognition: alphabets of backbone geometry. Proteins: Structure, Function, and Genetics 51, 504–514 (2003)

    Article  Google Scholar 

  32. Thompson, J.D., Koehl, P., Ripp, R., Poch, O.: BAliBASE 3.0: latest developments of the multiple sequence alignment benchmark. Proteins 61, 127–136 (2005)

    Article  Google Scholar 

  33. Walle, I.V., Lasters, I., Wyns, L.: SABmark—a benchmark for sequence alignment that covers the entire known fold space. Bioinformatics 21, 1267–1268 (2005)

    Article  Google Scholar 

  34. Murzin, A.G., Brenner, S.E., Hubbard, T., Chothia, C.: SCOP: a structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol. 247, 536–540 (1995)

    Google Scholar 

  35. Pruitt, K.D., Tatusova, T., Maglott, D.R.: NCBI Reference Sequence project: update and current status. Nucleic Acids Res 31, 34–37 (2003)

    Article  Google Scholar 

  36. Mizuguchi, K., Deane, C.M., Blundell, T.L., Overington, J.P.: HOMSTRAD: a database of protein structure alignments for homologous familes. Protein Sci. 7, 2469–2471 (1998)

    Article  Google Scholar 

  37. Orengo, C.A., Michie, A.D., Jones, S., Jones, D.T., Swindells, M.B., Thornton, J.M.: CATH—a hierarchic classification of protein domain structures. Structure 5, 1093–1108 (1997)

    Article  Google Scholar 

  38. Katoh, K., Misawa, K., Kuma, K., Miyata, T.: MAFFT: a novel method for rapid multiple sequence alignment based on fast fourier transform. Nucleic Acids Res 30, 3059–3066 (2002)

    Article  Google Scholar 

  39. Katoh, K., Kuma, K., Toh, H., Miyata, T.: MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Res 33, 511–518 (2005)

    Article  Google Scholar 

  40. Notredame, C., Higgins, D., Heringa, J.: T-Coffee: a novel method for multiple sequence alignments. J. Mol. Biol. 302, 205–217 (2000)

    Article  Google Scholar 

  41. Heringa, J.: Local weighting schemes for protein multiple sequence alignment. Computers and Chemistry 26, 459–477 (2002)

    Article  Google Scholar 

  42. Edgar, R.C.: MUSCLE: low-complexity multiple sequence alignment with T-Coffee accuracy. In: ISMB/ECCB (2004)

    Google Scholar 

  43. Edgar, R.C.: Local homology recognition and distance measures in linear time using compressed amino acid alphabets. Nucleic Acids Res 32, 380–385 (2004)

    Article  Google Scholar 

  44. Collins, M.: Discriminative training methods for hidden markov models: theory and experiments with perceptron algorithms. In: EMNLP (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Stanford University, Stanford, CA, 94305, USA

    Chuong B. Do, Samuel S. Gross & Serafim Batzoglou

Authors
  1. Chuong B. Do
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samuel S. Gross
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Serafim Batzoglou
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology and Università di Padova,  

    Alberto Apostolico

  2. Topic Chairs, P.O. Box

    Concettina Guerra

  3. Center for Molecular Biology and Computer Sciecne Department, Brown University, 115 Waterman St., 02912, Providence, RI, USA

    Sorin Istrail

  4. University of California, San Diego, USA

    Pavel A. Pevzner

  5. Department of Molecular and Computational Biology, University of Southern California, 1050 Childs Way, 90089-2910, Los Angeles, CA, USA

    Michael Waterman

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Do, C.B., Gross, S.S., Batzoglou, S. (2006). CONTRAlign: Discriminative Training for Protein Sequence Alignment. In: Apostolico, A., Guerra, C., Istrail, S., Pevzner, P.A., Waterman, M. (eds) Research in Computational Molecular Biology. RECOMB 2006. Lecture Notes in Computer Science(), vol 3909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732990_15

Download citation

  • DOI: https://doi.org/10.1007/11732990_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33295-4

  • Online ISBN: 978-3-540-33296-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation