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2016 | OriginalPaper | Buchkapitel

eQuant - A Server for Fast Protein Model Quality Assessment by Integrating High-Dimensional Data and Machine Learning

verfasst von : Sebastian Bittrich, Florian Heinke, Dirk Labudde

Erschienen in: Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery

Verlag: Springer International Publishing

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Abstract

In molecular biology, reliable protein structure models are essential in order to understand the functional role of proteins as well as diseases related to them. Structures are derived by complex and resource-demanding experiments, whereas in silico structure modeling and refinement approaches are established to cope with experimental limitations. Nevertheless, both experimental and computational methods are prone to errors. In consequence, small local regions or even the whole tertiary structure can be unreliable or erroneous, leading the researcher to formulate false hypotheses and draw false conclusions.

Here, we present eQuant, a novel and fast model quality assessment program (MQAP) and server. By utilizing a hybrid approach of established MQAPs in combination with machine learning techniques, eQuant achieves more homogeneous assessments with less uncertainty compared to other established MQAPs. For normal sized protein structures, computation requires less than ten seconds, making eQuant one of the fastest MQAPs available. The eQuant server is freely available at https://biosciences.hs-mittweida.de/equant/.

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Vorheriges Kapitel TripleID: A Low-Overhead Representation and Querying Using GPU for Large RDFs

Nächstes Kapitel Evaluation of Descriptor Algorithms of Biological Sequences and Distance Measures for the Intelligent Cluster Index (ICIx)

One Ångström corresponds to a length of 0.1 nm.

Altschul, S.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25(17), 3389–3402 (1997)CrossRef

Ambrish, R., Kucukural, A., Zhang, Y.: I-TASSER: a unified platform for automated protein structure and function prediction. Nucleic Acids Res. 5(4), 725–738 (2010)

Anfinsen, C.B.: Principles that govern the folding of protein chains. Science 181(4096), 223–230 (1973)CrossRef

Arnold, K., Bordoli, L., Kopp, J., Schwede, T.: The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics 22(2), 195–201 (2006)CrossRef

Bahar, I., Rader, A.J.: Coarse-grained normal mode analysis in structural biology. Bioinformatics 15(5), 586–592 (2005)

Bastolla, U.: Detecting selection on protein stability through statistical mechanical models of folding and evolution. Bioinformatics 4(1), 291–314 (2014)

Benkert, P., Biasini, M., Schwede, T.: Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 27(3), 343–350 (2011)CrossRef

Benkert, P., Kunzli, M., Schwede, T.: QMEAN server for protein model quality estimation. Nucleic Acids Res. 37(Web Server), W510–W514 (2009)CrossRef

Benkert, P., Schwede, T., Tosatto, S.: QMEANclust: estimation of protein model quality by combining a composite scoring function with structural density information. Bioinformatics 9(1), 35 (2009)

10.

Benkert, P., Tosatto, S.E., Schomburg, D.: QMEAN: a comprehensive scoring function for model quality assessment. Bioinformatics 71(1), 261–277 (2008)

11.

Berjanskii, M., Liang, Y., Zhou, J., Tang, P., Stothard, P., Zhou, Y., Cruz, J., MacDonell, C., Lin, G., Lu, P., et al.: PROSESS: a protein structure evaluation suite and server. Nucleic Acids Res. 38(Web Server), W633–W640 (2010)CrossRef

12.

Bhattacharya, A., Tejero, R., Montelione, G.T.: Evaluating protein structures determined by structural genomics consortia. Bioinformatics 66(4), 778–795 (2006)

13.

Biasini, M.: Pv-WebGL-based protein viewer (2014)

14.

Biasini, M., Bienert, S., Waterhouse, A., Arnold, K., Studer, G., Schmidt, T., Kiefer, F., Cassarino, T.G., Bertoni, M., Bordoli, L., Schwede, T.: SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 42(W1), W252–W258 (2014)CrossRef

15.

Blundell, T., et al.: Structural biology and bioinformatics in drug design: opportunities and challenges for target identification and lead discovery. Bioinformatics 361(1467), 413–423 (2006)

16.

Bowie, J., Luthy, R., Eisenberg, D.: A method to identify protein sequences that fold into a known three-dimensional structure. Science 253(5016), 164–170 (1991)CrossRef

17.

Bradley, P., Malmström, L., Qian, B., Schonbrun, J., Chivian, D., Kim, D., Meiler, J., Misura, K., Baker, D.: Free modeling with Rosetta in CASP6. Science 61(S7), 128–134 (2005)

18.

Bryll, R., Gutierrez-Osuna, R., Quek, F.: Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Science 36(6), 1291–1302 (2003)MATH

19.

Domingues, F., Lackner, P., Andreeva, A., Sippl, M.J.: Structure-based evaluation of sequence comparison and fold recognition alignment accuracy. Science 297(4), 1003–1013 (2000)

20.

Dressel, F., Marsico, A., Tuukkanen, A., Schroeder, M., Labudde, D.: Understanding of SMFS barriers by means of energy profiles. In: Proceedings of German Conference on Bioinformatics, pp. 90–99 (2007)

21.

Eisenberg, D., Lüthy, R., Bowie, J.U.: Verify3D: assessment of protein models with three-dimensional profiles. Science 277, 396–404 (1997)

22.

Elofsson, A., Le Grand, S.M., Eisenberg, D.: Local moves: an efficient algorithm for simulation of protein folding. Science 23(1), 73–82 (1995)

23.

Engh, R.A., Huber, R.: Accurate bond and angle parameters for x-ray protein structure refinement. Science 47(4), 392–400 (1991)

24.

Fersht, A.: Structure and Mechanism in Protein Science: A Guide to Enzyme Catalysis and Protein Folding, 3rd edn. W H Freeman & Co, New York (1995)

25.

Forster, M.J.: Molecular modelling in structural biology. Science 33(4), 365–384 (2002)MathSciNet

26.

Frank, E., Hall, M., Trigg, L., Holmes, G., Witten, I.H.: Data mining in bioinformatics using Weka. Bioinformatics 20(15), 2479–2481 (2004)CrossRef

27.

Fujiwara, T.M., Bichet, D.G.: Molecular biology of hereditary diabetes insipidus. Bioinformatics 16(10), 2836–2846 (2005)

28.

Go, N., Noguti, T., Nishikawa, T.: Dynamics of a small globular protein in terms of low-frequency vibrational modes. Bioinformatics 80(12), 3696–3700 (1983)

29.

Grabowski, M., Chruszcz, M., Zimmerman, M.D., Kirillova, O., Minor, W.: Benefits of structural genomics for drug discovery research. Bioinformatics 9(5), 459–474 (2009)

30.

Guex, N., Peitsch, M.C., Schwede, T.: Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: a historical perspective. Bioinformatics 30(S1), S162–S173 (2009)

31.

Haas, J., Roth, S., Arnold, K., Kiefer, F., Schmidt, T., Bordoli, L., Schwede, T.: The protein model portal – a comprehensive resource for protein structure and model information. Database 2013, bat031 (2013)CrossRef

32.

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The Weka data mining software. Bioinformatics 11(1), 10 (2009)

33.

Heinke, F., Labudde, D.: Membrane protein stability analyses by means of protein energy profiles in case of nephrogenic diabetes insipidus. Bioinformatics 2012, 1–11 (2012)MATH

34.

Heinke, F., Schildbach, S., Stockmann, D., Labudde, D.: eProS-a database and toolbox for investigating protein sequence-structure-function relationships through energy profiles. Bioinformatics 41(D1), D320–D326 (2013)

35.

A Highsoft Solutions: Highcharts JS (2012)

36.

Holland, R.C.G., Down, T.A., Pocock, M., Prlic, A., Huen, D., James, K., Foisy, S., Drager, A., Yates, A., Heuer, M., et al.: BioJava: an open-source framework for bioinformatics. Bioinformatics 24(18), 2096–2097 (2008)CrossRef

37.

Holmes, G., Donkin, A., Witten, I.: Weka: a machine learning workbench. In: Proceedings of ANZIIS 94 - Australian New Zealand Intelligent Information Systems Conference, pp. 357–361 (1994)

38.

Jones, D.T., Taylort, W.R., Thornton, J.M.: A new approach to protein fold recognition. Nature 358(6381), 86–89 (1992)CrossRef

39.

Kaiser, F., Eisold, A., Bittrich, S., Labudde, D.: Fit3D - a web application for highly accurate screening of spatial residue patterns in protein structure data. Bioinformatics 32(5), 792–794 (2015)CrossRef

40.

Kaiser, F., Eisold, A., Labudde, D.: A novel algorithm for enhanced structural motif matching in proteins. Nature 22(7), 698–713 (2015)MathSciNet

41.

Ho, T.K.: The random subspace method for constructing decision forests. Nature 20(8), 832–844 (1998)

42.

Kang, J., Lemaire, H., Unterbeck, A., Salbaum, J.M., Masters, C.L., Grzeschik, K.H., Multhaup, G., Beyreuther, K., Müller-Hill, B.: The precursor of Alzheimer’s disease amyloid A4 protein resembles a cell-surface receptor. Nature 325(6106), 733–736 (1987)CrossRef

43.

Kendrew, J.C., Bodo, G., Dintzis, H.M., Parrish, R.G., Wyckoff, H., Phillips, D.C.: A three-dimensional model of the myoglobin molecule obtained by x-ray analysis. Nature 181(4610), 662–666 (1958)CrossRef

44.

Kryshtafovych, A., Barbato, A., Fidelis, K., Monastyrskyy, B., Schwede, T., Tramontano, A.: Assessment of the assessment: evaluation of the model quality estimates in CASP10. Nature 82, 112–126 (2014)

45.

Kryshtafovych, A., Monastyrskyy, B., Fidelis, K.: CASP prediction center infrastructure and evaluation measures in CASP10 and CASP ROLL. Nature 82, 7–13 (2014)

46.

Kuntz, I.D.: Structure-based strategies for drug design and discovery. Science 257(5073), 1078–1082 (1992)CrossRef

47.

Laskowski, R., Rullmann, J., MacArthur, M., Kaptein, R., Thornton, J.M.: AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J. Biomol. NMR 8(4), 477–486 (1996)CrossRef

48.

Laskowski, R.A., MacArthur, M.W., Moss, D.S., Thornton, J.M.: PROCHECK: a program to check the stereochemical quality of protein structures. Science 26(2), 283–291 (1993)

49.

Lüthy, R., Bowie, J.U., Eisenberg, D.: Assessment of protein models with three-dimensional profiles. Nature 356(6364), 83–85 (1992)CrossRef

50.

Marrin, C.: WebGL Specification. Khronos WebGL Working Group (2011)

51.

McGuffin, L.J., Buenavista, M.T., Roche, D.B.: The ModFOLD4 server for the quality assessment of 3D protein models. Nature 41(W1), W368–W372 (2013)

52.

Melo, F., Devos, D., Depiereux, E., Feytmans, E.: ANOLEA: a WWW server to assess protein structures. Nature 5, 187–190 (1997)

53.

Melo, F., Feytmans, E.: Novel knowledge-based mean force potential at atomic level. Nature 267(1), 207–222 (1997)

54.

Melo, F., Feytmans, E.: Assessing protein structures with a non-local atomic interaction energy. Nature 277(5), 1141–1152 (1998)

55.

Noguchi, T.: PDB-REPRDB: a database of representative protein chains from the Protein Data Bank (PDB). Nature 29(1), 219–220 (2001)MathSciNet

56.

Oostenbrink, C., Villa, A., Mark, A.E., van Gunsteren, W.F.: A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6. Nature 25(13), 1656–1676 (2004)

57.

Panov, P., Dzeroski, S.: Combining bagging and random subspaces to create better ensembles. In: Berthold, M., Shawe-Taylor, J., Lavrač, N. (eds.) IDA 2007. LNCS, vol. 4723, pp. 118–129. Springer, Heidelberg (2007)CrossRef

58.

Prlic, A., et al.: BioJava: an open-source framework for bioinformatics in 2012. Bioinformatics 28(20), 2693–2695 (2012)CrossRef

59.

Ramachandran, G., Ramakrishnan, C., Sasisekharan, V.: Stereochemistry of polypeptide chain configurations. Bioinformatics 7(1), 95–99 (1963)

60.

Ray, A., Lindahl, E., Wallner, B.: Improved model quality assessment using ProQ2. BMC Bioinform. 13(1), 224 (2012)CrossRef

61.

Rose, P.W., et al.: The RCSB Protein Data Bank: new resources for research and education. Nucleic Acids Res. 41(Database issue), D475–D482 (2013)CrossRef

62.

Sadowski, M.I., Jones, D.T.: Benchmarking template selection and model quality assessment for high-resolution comparative modeling. Proteins: Struct. Funct. Bioinform. 69(3), 476–485 (2007)CrossRef

63.

Sali, A., Blundell, T.L.: Comparative protein modelling by satisfaction of spatial restraints. BMC Bioinform. 234(3), 779–815 (1993)

64.

Schulz, G.E., Schirmer, R.H.: Principles of Protein Structure, 5th edn. Springer, New York (1984)

65.

Schwede, T., et al.: Outcome of a workshop on applications of protein models in biomedical research. BMC Bioinform. 17(2), 151–159 (2009)MathSciNet

66.

Sippl, M.J.: Boltzmann’s principle, knowledge-based mean fields and protein folding. An approach to the computational determination of protein structures. J. Comput.-Aided Mol. Des. 7(4), 473–501 (1993)CrossRef

67.

Sippl, M.J.: Recognition of errors in three-dimensional structures of proteins. BMC Bioinform. 17(4), 355–362 (1993)

68.

Sippl, M.J.: Knowledge-based potentials for proteins. BMC Bioinform. 5(2), 229–235 (1995)

69.

Strandberg, B.: Chapter 1: building the ground for the first two protein structures: myoglobin and haemoglobin. J. Mol. Biol. 392(1), 2–10 (2009)CrossRef

70.

Surade, S., Blundell, T.L.: Structural biology and drug discovery of difficult targets: the limits of ligandability. BMC Bioinform. 19(1), 42–50 (2012)

71.

The UniProt Consortium: Activities at the universal protein resource (UniProt). Nucleic Acids Res. 42(Database issue), D191–D198 (2014)

72.

Verkhivker, G., Appelt, K., Freer, S., Villafranca, J.: Empirical free energy calculations of ligand-protein crystallographic complexes. I. Knowledge-based ligand-protein interaction potentials applied to the prediction of human immunodeficiency virus 1 protease binding affinity. Protein Eng. Des. Sel. 8(7), 677–691 (1995)CrossRef

73.

Webb, B., Sali, A.: Protein structure modeling with modeller. BMC Bioinform. 1137, 1–15 (2014)

74.

Whittle, P.J., Blundell, T.L.: Protein structure-based drug design. BMC Bioinform. 23, 349–375 (1994)

75.

Wiederstein, M., Sippl, M.J.: ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 35(Web Server), W407–W410 (2007)CrossRef

76.

Willard, L.: VADAR: a web server for quantitative evaluation of protein structure quality. BMC Bioinform. 31(13), 3316–3319 (2003)

77.

Wüthrich, K.: Protein structure determination in solution by nmr spectroscopy. BMC Bioinform. 265(36), 22059–22062 (1990)

78.

Zemla, A.: LGA: a method for finding 3D similarities in protein structures. BMC Bioinform. 31(13), 3370–3374 (2003)

79.

Zhao, N., Han, J.G., Shyu, C., Korkin, D.: Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning. PLoS Comput. Biol. 10(5), e1003592 (2014)CrossRef

Titel: eQuant - A Server for Fast Protein Model Quality Assessment by Integrating High-Dimensional Data and Machine Learning
verfasst von: Sebastian Bittrich
Florian Heinke
Dirk Labudde
Verlag: Springer International Publishing
Buch: Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery
Print ISBN: 978-3-319-34098-2

Electronic ISBN: 978-3-319-34099-9

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-34099-9_32

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