Homology-based inference sets the bar high for protein function prediction

详细信息    查看全文

• 作者：Tobias Hamp (1)
  Rebecca Kassner (1)
  Stefan Seemayer (1)
  Esmeralda Vicedo (1)
  Christian Schaefer (1)
  Dominik Achten (1)
  Florian Auer (1)
  Ariane Boehm (1)
  Tatjana Braun (1)
  Maximilian Hecht (1)
  Mark Heron (1)
  Peter H?nigschmid (1)
  Thomas A Hopf (1)
  Stefanie Kaufmann (1)
  Michael Kiening (1)
  Denis Krompass (1)
  Cedric Landerer (1)
  Yannick Mahlich (1)
  Manfred Roos (1)
  Burkhard Rost (1) (2) (3)
  
• 刊名：BMC Bioinformatics
• 出版年：2013
• 出版时间：February 2013
• 年：2013
• 卷：14
• 期：3-supp
• 全文大小：1246KB
• 参考文献：1. Consortium TU: Ongoing and Future Developments at the Universal Protein Resource. / Nucleic Acids Research 2011, 39:D214-19. CrossRef
  2. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, / et al.: Gene Ontology: Tool for the Unification of Biology. The Gene Ontology Consortium. / Nat Genet 2000, 25:25-9. CrossRef
  3. Rentzsch R, Orengo CA: Protein function prediction--the power of multiplicity. / Trends Biotechnol 2009, 27:210-19. CrossRef
  4. Radivojac P, Clark WT, Friedberg I: A Large-scale Evaluation of Computational Protein Function Prediction. / Nature Methods 2012.
  5. Wass MN, Sternberg MJ: ConFunc--Functional Annotation in the Twilight Zone. / Bioinformatics 2008, 24:798-06. CrossRef
  6. Jones CE, Schwerdt J, Bretag TA, Baumann U, Brown AL: GOSLING: A Rule-Based Protein Annotator Using BLAST and GO. / Bioinformatics 2008, 24:2628-629. CrossRef
  7. Hawkins T, Luban S, Kihara D: Enhanced Automated Function Prediction Using Distantly Related Sequences and Contextual Association by PFP. / Protein Science 2006, 15:1550-556. CrossRef
  8. Martin DM, Berriman M, Barton GJ: GOtcha: A New Method for Prediction of Protein Function Assessed by the Annotation of Seven Genomes. / BMC Bioinformatics 2004, 5:178. CrossRef
  9. Chitale M, Hawkins T, Park C, Kihara D: ESG: Extended Similarity Group Method for Automated Protein Function Prediction. / Bioinformatics 2009, 25:1739-745. CrossRef
  10. Sokolov A, Ben-Hur A: Hierarchical Classification of Gene Ontology Terms Using the GOstruct Method. / Journal of Bioinformatics and Computational Biology 2010, 8:357-76. CrossRef
  11. Clark WT, Radivojac P: Analysis of Protein Function and its Prediction from Amino Acid Sequence. / Proteins 2011, 79:2086-096. CrossRef
  12. Altschul SF, Madden TL, Schaeffer AA, Zhang J, Zhang Z, / et al.: Gapped Blast and PSI-Blast: A New Generation of Protein Database Search Programs. / Nucleic Acids Res 1997, 25:3389-402. CrossRef
  13. Ming K, Witten I: Issues in Stacked Generalization. / Journal of Artificial Intelligence Research 1999, 10:271-80. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.16.1519 http://dl.acm.org/citation.cfm?id=1622868
  14. Eyrich VA, Przybylski D, Koh IY, Grana O, Pazos F, / et al.: CAFASP3 in the Spotlight of EVA. / Proteins 2003,53(Suppl 6):548-60. CrossRef
  15. Koh IY, Eyrich VA, Marti-Renom MA, Przybylski D, Madhusudhan MS, / et al.: EVA: Evaluation of Protein Structure Prediction Servers. / Nucleic Acids Research 2003, 31:3311-315. CrossRef
  16. Marti-Renom MA, Madhusudhan MS, Fiser A, Rost B, Sali A: Reliability of Assessment of Protein Structure Prediction Methods. / Structure 2002, 10:435-40. CrossRef
  
• 作者单位：Tobias Hamp (1)
  Rebecca Kassner (1)
  Stefan Seemayer (1)
  Esmeralda Vicedo (1)
  Christian Schaefer (1)
  Dominik Achten (1)
  Florian Auer (1)
  Ariane Boehm (1)
  Tatjana Braun (1)
  Maximilian Hecht (1)
  Mark Heron (1)
  Peter H?nigschmid (1)
  Thomas A Hopf (1)
  Stefanie Kaufmann (1)
  Michael Kiening (1)
  Denis Krompass (1)
  Cedric Landerer (1)
  Yannick Mahlich (1)
  Manfred Roos (1)
  Burkhard Rost (1) (2) (3)
  
  1. TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr. 3, 85748, Garching/Munich, Germany
  2. Institute of Advanced Study (TUM-IAS) Lichtenbergstr. 2a, 85748, Garching/Munich, Germany
  3. New York Consortium on Membrane Protein Structure (NYCOMPS) & Department of Biochemistry and Molecular Biophysics, Columbia University, 701 West, 168th Street, New York, NY, 10032, USA
  
• ISSN：1471-2105

文摘

Background Any method that de novo predicts protein function should do better than random. More challenging, it also ought to outperform simple homology-based inference. Methods Here, we describe a few methods that predict protein function exclusively through homology. Together, they set the bar or lower limit for future improvements. Results and conclusions During the development of these methods, we faced two surprises. Firstly, our most successful implementation for the baseline ranked very high at CAFA1. In fact, our best combination of homology-based methods fared only slightly worse than the top-of-the-line prediction method from the Jones group. Secondly, although the concept of homology-based inference is simple, this work revealed that the precise details of the implementation are crucial: not only did the methods span from top to bottom performers at CAFA, but also the reasons for these differences were unexpected. In this work, we also propose a new rigorous measure to compare predicted and experimental annotations. It puts more emphasis on the details of protein function than the other measures employed by CAFA and may best reflect the expectations of users. Clearly, the definition of proper goals remains one major objective for CAFA.