Information Systems Frontiers

, Volume 8, Issue 1, pp 47–57 | Cite as

Mutation Mining—A Prospector's Tale

  • Christopher J. O. BakerEmail author
  • René Witte


Protein structure visualization tools render images that allow the user to explore structural features of a protein. Context specific information relating to a particular protein or protein family is, however, not easily integrated and must be uploaded from databases or provided through manual curation of input files. Protein Engineers spend considerable time iteratively reviewing both literature and protein structure visualizations manually annotated with mutated residues. Meanwhile, text mining tools are increasingly used to extract specific units of raw text from scientific literature and have demonstrated the potential to support the activities of Protein Engineers.

The transfer of mutation specific raw-text annotations to protein structures requires integrated data processing pipelines that can co-ordinate information retrieval, information extraction, protein sequence retrieval, sequence alignment and mutant residue mapping. We describe the Mutation Miner pipeline designed for this purpose and present case study evaluations of the key steps in the process. Starting with literature about mutations made to protein families; haloalkane dehalogenase, bi-phenyl dioxygenase, and xylanase we enumerate relevant documents available for text mining analysis, the available electronic formats, and the number of mutations made to a given protein family. We review the efficiency of NLP driven protein sequence retrieval from databases and report on the effectiveness of Mutation Miner in mapping annotations to protein structure visualizations. We highlight the feasibility and practicability of the approach.


Text mining Protein structure annotation Protein mutation Data mining Haloalkane dehalogenase Biphenyl dioxygenase Xylanase 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Kawabata T, Ota M, Nishikawa K. The protein mutant database. Nucleaic Acid Research 1999;27(1).Google Scholar
  2. Rebholz-Schuhmann D, Kirsch H, Couto F. Facts from text-is text mining ready to deliver? PLos Biol 2005;3(2):e65.CrossRefGoogle Scholar
  3. Rebholz-Schuhmann D, Marcel S, Albert S, Tolle R, Casari G, Kirsch H. Automatic extraction of mutations from medline and cross-validation with OMIM. Nucleic Acids Res 2004;32(1):135–142.CrossRefGoogle Scholar
  4. Witte R, Baker CJO. Combining biological databases and text mining to support new bioinformatics applications, 10th international conference on applications of natural language to information systems. A. Montoyo et al. (eds.): NLDB 2005, Springer LNCS 3513, 2005;310–321.Google Scholar
  5. Baker CJO, Witte R. Enriching protein structure visualizations with mutation annotations obtained by text mining protein engineering literature. IBM technical report: TR-74.203-(1:47), (eds.), Julie Waterhouse, Daniel Zilio, TR74. 2004;203-8 (pp. 25–32) Chairs: I. Jurisica, J. Glasgow, R. Ng, H. Hoos.Google Scholar
  6. Gabdoulline RR, Hoffmann R, Leitner F, Wade RC. ProSAT: functional annotation of protein 3D structures. Bioinformatics 2003;19(13): 1723–1725.CrossRefGoogle Scholar
  7. Cunningham H, Maynard D, Bontcheva K, Tablan V. GATE: A framework and graphical development environment for robust NLP tools and applications. In: Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics 2002.Google Scholar
  8. Wheeler DL, Chappey C, Lash AE, et al. Database resources of the national center for biotechnology information. Nucleic Acids Res. 2000;28(1):10–4.CrossRefGoogle Scholar
  9. Marchler-Bauer A, Panchenko AR, Shoemaker BA, Thiessen PA, Geer LY, Bryant SH. CDD: A database of conserved domain alignments with links to domain three dimensional structures. Nucleic Acids Research 2002;30(1):281–283.CrossRefGoogle Scholar
  10. Thompson JD, Higgins DG, Gibson TJ. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, positions specific gap penalties and weight matrix choice. Nucleic Acids Research 1994;22(22):4673–4680.Google Scholar
  11. Eddy S, Birney E. HMMER user's guide: Biological sequence analysis using profile hidden Markov models (version 2.1.1). Washington University. 1998.
  12. Altschul SF, Gish W, Miller W, Meyers EW, Lipman DJ. Basic local alignment search tool. Journal of Molecular Biology 1990;215(3):403–310.CrossRefGoogle Scholar
  13. Deshpande N, Addess KJ, Bluhm WF. et al. The RCSB protein data bank: a redesigned query system and relational database based on the mmCIF schema describes the capabilities of the PDB Beta site. Nucl. Acids Res 2005;33:D233–D237.Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Department of Computer Science and Software EngineeringConcordia UniversityMontrealCanada
  2. 2.Institute for Program Structures and Data Organization (IPD)Universität Karlsruhe (TH)Germany

Personalised recommendations