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A High Performing Tool for Residue Solvent Accessibility Prediction

  • Conference paper
Information Technology in Bio- and Medical Informatics (ITBAM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6865))

Abstract

Many efforts were spent in the last years in bridging the gap between the huge number of sequenced proteins and the relatively few solved structures. Relative Solvent Accessibility (RSA) prediction of residues in protein complexes is a key step towards secondary structure and protein-protein interaction sites prediction. With very different approaches, a number of software tools for RSA prediction have been produced throughout the last twenty years. Here, we present a binary classifier which implements a new method mainly based on sequence homology and implemented by means of look-up tables. The tool exploits residue similarity in solvent exposure pattern of neighboring context in similar protein chains, using BLAST search and DSSP structure. A two-state classification with 89.5% accuracy and 0.79 correlation coefficient against the real data is achieved on a widely used dataset.

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References

  1. Jones, S., Thornton, J.M.: Analysis of Protein-Protein Interaction Sites Using Surface Patches. J. Mol. Biol. 272, 132–143 (1997)

    Google Scholar 

  2. Wako, H., Blundell, T.L.: Use of Amino Acid Environment-Dependent Substitution Tables and Conformational Propensities in Structure Prediction from Aligned Sequences of Homologous Proteins. I. Solvent accessibility classes. J. Mol. Biol. 238, 682–692 (1994)

    Article  Google Scholar 

  3. Chakrabarti, P., Janin, J.: Dissecting Protein-Protein Recognition Sites. Proteins 47, 334–343 (2002)

    Article  Google Scholar 

  4. Rost, B., Sander, C.: Conservation and Prediction of Solvent Accessibility in Protein Families. Proteins 20, 216–226 (1994)

    Article  Google Scholar 

  5. Carugo, O.: Predicting Residue Solvent Accessibility From Protein Sequence by Considering the Sequence Environment. Protein Eng. 13, 607–609 (2000)

    Article  Google Scholar 

  6. Naderi-Manesh, H., Sadeghi, M., Arab, S., Moosavi Movahedi, A.A.: Prediction of Protein Surface Accessibility with Information Theory. Proteins 42, 452–459 (2001)

    Article  Google Scholar 

  7. Thompson, M.J., Goldstein, R.A.: Predicting Solvent Accessibility: Higher Accuracy Using Bayesian Statistics and Optimized Residue Substitution Classes. Proteins 25, 38–47 (1996)

    Article  Google Scholar 

  8. Gianese, G., Bossa, F., Pascarella, S.: Improvement in Prediction of Solvent Accessibility by Probability Profiles. Protein Eng. 16, 987–992 (2003)

    Article  Google Scholar 

  9. Holbrook, S.R., Muskal, S.M., Kim, S.H.: Predicting Surface Exposure of Amino Acids from Protein Sequences. Protein Eng. 3, 659–665 (1990)

    Article  Google Scholar 

  10. Rost, B., Sander, C.: Combining Evolutionary Information and Neural Networks to Predict Protein Secondary Structure. Proteins 19, 55–72 (1994)

    Article  Google Scholar 

  11. Ahmad, S., Gromiha, M.M.: NETASA: Neural Network Based Prediction of Solvent Accessibility. Bioinformatics 18, 819–824 (2002)

    Article  Google Scholar 

  12. Pollastri, G., Baldi, P., Fariselli, P., Casadio, R.: Prediction of Coordination Number and Relative Solvent Accessibility in Proteins. Proteins 47, 142–153 (2002)

    Article  Google Scholar 

  13. Adamczak, R., Porollo, A., Meller, J.: Accurate Prediction of Solvent Accessibility Using Neural Networks Based Regression. Proteins 56, 753–767 (2004)

    Article  Google Scholar 

  14. Garg, A., Kaur, H., Raghava, G.P.S.: Real Value Prediction of Solvent Accessibility in Proteins Using Multiple Sequence Alignment and Secondary Structure. Proteins 61, 318–324 (2005)

    Article  Google Scholar 

  15. Dor, O., Zhou, Y.: Real-SPINE: An Integrated System of Neural Networks for Real-value Prediction of Protein Structural Properties. Proteins 68, 76–81 (2007)

    Article  Google Scholar 

  16. Li, X., Pan, X.M.: New Method for Accurate Prediction of Aolvent Accessibility from Protein Sequence. Proteins 42, 1–5 (2001)

    Article  Google Scholar 

  17. Wang, J., Lee, H., Ahmad, S.: Prediction and Evolutionary Information Analysis of Protein Solvent Accessibility Using Multiple Linear Regression. Proteins 61, 481–491 (2005)

    Article  Google Scholar 

  18. Yuan, Z., Burrage, K., Mattick, J.S.: Prediction of Protein Solvent Accessibility Using Support Vector Machines. Proteins 48, 566–570 (2002)

    Article  Google Scholar 

  19. Nguyen, M., Rajapakse, J.: Prediction of Protein Relative Solvent Accessibility with a two-stage SVM Approach. Proteins 59, 30–37 (2005)

    Article  Google Scholar 

  20. Meshkin, A., Ghafuri, H.: Prediction of Relative Solvent Accesibility by Support Vector Regression and Best-First Method. EXCLI Journal 9, 29–38 (2010)

    Google Scholar 

  21. Wang, J.-Y., Ahmad, S., Gromiha, M.M., Sarai, A.: Look-up Tables for Protein Solvent Accessibility Prediction and Nearest Neighbor Effect Analysis. Biopolymers 75, 209–216 (2004)

    Article  Google Scholar 

  22. Chen, H., Zhou, H.X.: Prediction of Solvent Accessibility and Sites of Deleterious Mutations from Protein Sequence. Nucleic Acids Res. 33, 3193–3199 (2005)

    Article  Google Scholar 

  23. Chen, K., Kurgan, M., Kurgan, L.: Sequence Based Prediction of Relative Solvent Accessibility Using two-stage Support Vector Regression with Confidence Values. J. Biomed. Sci. Eng. 1, 1–9 (2008)

    Article  Google Scholar 

  24. Flores, T.P., Orengo, C.A., Moss, D.S., Thornton, J.M.: Comparison of Conformational Characteristics in Structurally Similar Protein Pairs. Protein Sci. 2, 1811–1826 (1993)

    Article  Google Scholar 

  25. Cuff, J.A., Barton, G.J.: Application of Multiple Sequence Alignments Profiles to Improve Protein Secondary Structure Prediction. Proteins 40, 502–511 (2000)

    Article  Google Scholar 

  26. Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000)

    Article  Google Scholar 

  27. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic Local Alignment Search Tool. J. Mol. Biol. 215, 403–410 (1990)

    Article  Google Scholar 

  28. Kabsch, W., Sander, C.: Dictionary of Protein Secondary Structure: Pattern Recognition of Hydrogen-Bonded and Geometrical Features. Biopolymers 22, 2577–2637 (1983)

    Article  Google Scholar 

  29. Chothia, C.: The Nature of the Accessible and Buried Surfaces in Proteins. J. Mol. Biol. 105, 1–12 (1976)

    Article  Google Scholar 

  30. Carugo, O.: Prediction of Polypeptide Fragments Exposed to the Solvent. Silico Biology 3, 35 (2003)

    Google Scholar 

  31. Palmieri, L., Federico, M., Leoncini, M., Montangero, M.: Sequence-Based Prediction of Solvent Accessibility in Proteins. University of Modena and Reggio Emilia, M2CSC doctoral research school, internal report (2009)

    Google Scholar 

  32. Rose, G.D., Geselowitz, A.R., Lesser, G.J., Lee, R.H., Zehfus, M.H.: Hydrophobicity of Amino Acid Residues in Globular Proteins. Science 229, 834–838 (1985)

    Article  Google Scholar 

  33. Ahmad, S., Gromiha, M.M., Sarai, A.: Real Value Prediction of Solvent Accessibility from Amino Acid Sequence. Proteins 50, 629–635 (2003)

    Article  Google Scholar 

  34. Brenner, S.E., Chothia, C., Hubbard, T.J.P.: PNAS 95, 6073–6078 (1998)

    Google Scholar 

  35. Blaber, M., Lindstrom, J.D., Gassner, N., Xu, J., Heinz, D.W., Matthews, B.W.: Energetic Cost and Structural Consequences of Burying a Hydroxyl Group within the Core of a Protein Determined from Ala–>Ser and Val–>Thr Substitutions in T4 lysozyme. Biochemistry 32, 11363–11373 (1993)

    Article  Google Scholar 

  36. Chen, Z.G., Stauffacher, C., Li, Y., Schmidt, T., Bomu, W., Kamer, G., Shanks, M., Lomonossoff, G., Johnson, J.E.: Protein-RNA Interactions in an Icosahedral Virus at 3.0 A Resolution. Science 245, 154–159 (1998)

    Article  Google Scholar 

  37. Sironi, L., Mapelli, M., Knapp, S., Antoni, A., Jeang, K.T., Musacchio, A.: Crystal Structure of the Tetrameric Mad1-Mad2 Core Complex: Implications of a ’Safety Belt’ Binding Mechanism for the Spindle Checkpoint. Embo. J. 21, 2496 (2002)

    Article  Google Scholar 

  38. Ficko-Blean, E., Gregg, K.J., Adams, J.J., Hehemann, J.H., Smith, S.J., Czjzek, M., Boraston, A.B.: Portrait of an Enzyme, a Complete Structural Analysis of a Multimodular beta-N-acetylglucosaminidase from Clostridium Perfringens. J. Biol. Chem. 284, 9876–9884 (2009)

    Article  Google Scholar 

  39. Rao, F.V., Dorfmueller, H.C., Villa, F., Allwood, M., Eggleston, I.M., Van Aalten, D.M.F.: Structural Insights into the Mechanism and Inhibition of Eukaryotic O-GlcNAc Hydrolysis. Embo. J. 25, 1569 (2006)

    Article  Google Scholar 

  40. Gibson, R.P., Turkenburg, J.P., Charnock, S.J., Lloyd, R., Davies, G.J.: Insights into Trehalose Synthesis Provided by the Structure of the Retaining Glucosyltransferase OtsA. Chem. Biol. 9, 1337 (2002)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, Università di Modena e Reggio Emilia, Italy

    Lorenzo Palmieri, Maria Federico, Mauro Leoncini & Manuela Montangero

  2. CNR, Istituto di Informatica e Telematica, Pisa, Italy

    Mauro Leoncini & Manuela Montangero

Authors
  1. Lorenzo Palmieri
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  2. Maria Federico
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  3. Mauro Leoncini
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  4. Manuela Montangero
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Ludwig-Maximilians-Universität, Oettingenstrasse 67, 80538, München, Germany

    Christian Böhm

  2. Department of Computer Science, San José State University, One Washington Square, 95192-0249, San José, CA, U.S.A.

    Sami Khuri

  3. Faculty of Electrical Engineering, Department of Cybernetics, Czech Technical University, Technicka 2, 166 27, Prague 6, Czech Republic

    Lenka Lhotská

  4. Dipartimento di Informatica, Università di Pisa, Italy

    Nadia Pisanti

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© 2011 Springer-Verlag Berlin Heidelberg

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Palmieri, L., Federico, M., Leoncini, M., Montangero, M. (2011). A High Performing Tool for Residue Solvent Accessibility Prediction. In: Böhm, C., Khuri, S., Lhotská, L., Pisanti, N. (eds) Information Technology in Bio- and Medical Informatics. ITBAM 2011. Lecture Notes in Computer Science, vol 6865. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23208-4_13

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  • DOI: https://doi.org/10.1007/978-3-642-23208-4_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23207-7

  • Online ISBN: 978-3-642-23208-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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