SSTMProt, a point mutation sensitive tool to combine results and to predict the consensus sequence, and secondary structure of transmembrane proteins

• Autorzy: Jastrzębski J. P.
• Języki publikacji: EN

Abstrakty

EN

The efficiency of predictions of protein secondary structures can be increased by treating this process as a cycle of steps, where each step is an approach to the single natural event in folding process (model and simulation of successive events). The set of simulation steps qualifies reliability of in silico simulation of single steps, allowing to verify the correctness of each step as well as to retain sensitivity in case of a single amino acidic substitution. For this purpose the three-part algorithm (SSTMProt) has been designed. This algorithm combines the results of known methods of prediction of proteins secondary structure. Furthermore, the efficiency of this algorithm has been verified using the models received from the RCSB PDB (the Research Collaboratory for Structural Bioinformatics – Protein Data Base; http://www.rcsb.org). The accuracy of known methods has been compared with the accuracy of designed algorithms. The accuracy has been tested by the comparison of true secondary structure with predicted secondary structure of a given protein. The results of accuracy test has been presented as percentage values of similarity between both secondary structures: predicted structure using known method vs. true structure and predicted structure using designed method vs. true structure. The results demonstrate 20-30% higher accuracy of prediction for designed algorithms then for adequate known methods. The test of sensitivity has been done for proteins of a very conservative and stable structure (subunits of bovine cytochrome c oxidase and bacterial ATP-ase, bovine rhodopsin and human hemoblobin as a globular but alpha-helical protein). The influence of a single amino acid substitution on a resulted secondary structure predicted by SSTMProt algorithms has been examined. The repeatability of elaborated algorithms is 100% and each of all 12 tested combinations of methods were sensitive on a single amino acid substitution. All tests have been done for 10 models of native forms of proteins of known structure (models downloaded from the RCSB PDB 1HBB, 1HBS, 1OCC, 1U17, 1C17) and over 500 modified models; 30 known methods of prediction of secondary structure of proteins and 40 combinations of these methods included in three versions of elaborated algorithms have been examined for each protein model.

Słowa kluczowe

EN

secondary structure; structure prediction; transmembrane protein; bioinformatics; sensitivity; algorithm

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2011
• Tom: Vol. 7, no. 4
• Strony: 67--78
• Opis fizyczny: Bibliogr. 48 poz., rys., tab., wykr.

Twórcy

autor

Jastrzębski J. P.

• Department of Plant Physiology and Biotechnology, Faculty of Biology, University of Warmia and Mazury in Olsztyn, ul. Oczapowskiego 1A p.113, 10-719 Olsztyn / Poland

Bibliografia

• 1. Weissman J.S., Kim P.S.: Reexamination of the folding of BPTI: predominance of native intermediates. Science 1991, 253 (5026): 1386-1393.
• 2. Radford S.E., Dobson C.M., Evans P.A: The folding of hen lysozyme involves partially structured intermediates and multiple pathways. Nature 1992, 358(6384): 302-307.
• 3. Kanaya E., Ishihara K., Tsunasawa S., Nokihara K., Kikuchi M.: Indication of possible post-translational formation of disulphide bonds in the beta-sheet domain of human lysozyme. Biochem J. 1993, 292(2):469-476.
• 4. Clarke D.T., Doig A.J., Stapley B.J., Jones G.R.: The alpha-helix folds on the millisecond time scale. Proc Natl Acad Sci U S A 1999, 96(13):7232-7237.
• 5. Protein Structure Prediction, http://cmgm.stanford.edu/WWW/www_predict.html
• 6. Hovijitra N.: Computational methods for predicting transmembrane alpha helices. Computational Molecular Biology Final Project. Stanford Uniwersity, Biochemistry BioMedical Informatics 2002, 218-231.
• 7. Frishman D., Argos P.: Seventy-five percent accuracy in protein secondary structure prediction. Proteins: Structure, Function, and Genetics 1997, 27(3): 329-335.
• 8. Garnier J., Osguthorpe D.J., Robson B.: Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. Journal of Molecular Biology 1978, 120: 97-120.
• 9. Garnier J, Gibrat JF, Robson B: GOR method for predicting protein secondary structure from amino acid sequence. Methods in Enzymology 1996, 266:540-53.
• 10. Gibrat J.F., Garnier J., Robson B.: Further developpments of protein secondary structure prediction using information theory. Journal of Molecular Biology 1987, 198:425-443.
• 11. Sen T.Z., Jernigan R.L., Garnier J., Kloczkowski A.: GOR V server for protein secondary structure prediction. Bioinformatics 2005, 21(11):2787–2788.
• 12. Kloczkowski A., Ting K.L., Jernigan R.L., Garnier J.: Combining the GORV Algorithm With Evolutionary Information for Protein Secondary Structure Prediction FromAmino Acid Sequence. Proteins: Structure, Function and Genetics 2002, 49:154–166.
• 13. Thompson M.J. ,Goldstein R.A.: Predicting protein secondary structure with probabilistic schemata of evolutionarily derived information. Protein Science 1997, 6(9):1963–1975.
• 14. Deléage G., Combet C., Blanchet C., Geourjon C.: ANTHEPROT: An integrated protein sequence analysis software with client/server capabilities. Computers in Biology and Medicine 2001, 31(4):259-267.
• 15. Kyte J., Doolittle R.F.: A simple method for displaying the hydrophobic character of a protein. Journal of Molecular Biology 1982, 157:105-132.
• 16. von Heijne G.: Membrane protein structure prediction. Hydrophobicity analysis and the positive-inside rule. Journal of Molecular Biology 1992, 8:249-254.
• 17. Graff D.K., Pastrana-Rios B., Venyaminov S.Y., Prendergast F.G.: The Effects of Chain Length and Thermal Denaturation on Helix-Forming Peptides: A Mode-Specific Analysis Using 2D FT-IR. Journal of the American Chemical Society 1997, 119(46):11282-11294.
• 18. Klein C.T., Mayer B., Kohler G., Wolschann P.: Influence of solvation on helix formation of poly-alanine studied by multiple annealing simulations. Journal of Molecular Structure: THEOCHEM 1996, 370(1):33-43.
• 19. Hendry.P., Mccall M.J., Lockett T.J.: Influence of helix length on cleavage efficiency of hammerhead ribozymes. Australian Journal of Chemistry 2005, 58(12):851-858.
• 20. Bertaud J., Hester J., Jimenez D.D., Buehler M.J.: Energy landscape, structure and rate effects on strength properties of alpha-helical proteins. Journal of Physics: Condensed Matter 2010, 22(3):035102.
• 21. Stryer L.: Biochemia. Warszawa: Wydawnictwo Naukowe PWN; 1999.
• 22. Bowie J.U.: Helix packing in membrane proteins. J. Mol. Biol. 1997, 272:780-789.
• 23. Honnappa S., Gouveia S.M., Weisbrich A., Damberger F.F., Bhavesh N.S., Jawhari H., Grigoriev I, van Rijssel FJA, Buey RM, Lawera A, Jelesarov I, Winkler FK, Wuthrich,.K., Akhmanova A., Steinmetz M.O.: An EB1-Binding Motif Acts as a Microtubule Tip Localization Signal. Cell (Cambridge ,Mass.) 2009, 138:366-376.
• 24. Yang Y., Gourinath S., Kovacs M., Nyitray L., Reutzel R., Himmel D.M., O`Neall-Hennessey E., Reshetnikova L., Szent-Gyorgyi A.G., Brown J.H., Cohen C.: Rigor-like structures from muscle myosins reveal key mechanical elements in the transduction pathways of this allosteric motor. Structure 2007, 15:553-564.
• 25. Su J.Y., Hodges R.S., Kay C.M.: Effect of Chain Length on the Formation and Stability of Synthetic .alpha.-Helical Coiled Coils. Biochemistry 1994, 33(51):15501–15510.
• 26. Lau S.Y., Taneja A.K., Hodges R.S.: Synthesis of a model protein of defined secondary and quaternary structure. Effect of chain length on the stabilization and formation of two-stranded alpha-helical coiled-coils. The Journal of Biological Chemistry 1984, 259:13253-13261.
• 27. Hirokawa T., Boon-Chieng S., Mitaku S.: SOSUI: classification and secondary structure prediction system for membrane proteins. Bioinformatics 1998, 14: 378-379.
• 28. Mitaku S., Hirokawa T.: Physicochemical factors for discriminating between soluble and membrane proteins: hydrophobicity of helical segments and protein length. Protein Engineering 1999, 11:953-957.
• 29. Tsukihara T., Aoyama H., Yamashita E., Tomizaki T., Yamaguchi H., Shinzawa-Itoh K., Nakashima R., Yaono R., Yoshikawa S.: The whole structure of the 13-subunit oxidized cytochrome c oxidase at 2.8 A. Science 1996, 272:1136-1144.
• 30. Tsukihara T., Shimokata K., Katayama Y., Shimada H., Muramoto K., Aoyama H., Mochizuki M., Shinzawa-Itoh K., Yamashita E., Yao M., Ishimura Y., Yoshikawa S.: The low-spin heme of cytochrome c oxidase as the driving element of the proton-pumping process. Proc.Natl.Acad.Sci.Usa 2003, 100:15304-15309.
• 31. Rastogi V.K., Girvin M.E.: Structural changes linked to proton translocation by subunit c of the ATP synthase. Nature 1999, 402:263-268.
• 32. Okada T., Sugihara M., Bondar A.N., Elstner M., Entel P., Buss V.: The retinal conformation and its environment in rhodopsin in light of a new 2.2 A crystal structure. J. Mol. Biol. 2004, 342:571-583.
• 33. Kavanaugh J.S., Rogers P.H., Case D.A., Arnone A.: High-resolution X-ray study of deoxyhemoglobin Rothschild 37 beta Trp----Arg: a mutation that creates an intersubunit chloride-binding site. Biochemistry 1992, 31:4111-4121.
• 34. Padlan E.A., Love W.E.: Refined crystal structure of deoxyhemoglobin S. I. Restrained least-squares refinement at 3.0-A resolution. J. Biol. Chem. 1985, 260:8272-8279.
• 35. Cármenes R.S., Freije J.P., Molina M.M., Martín J.M.: Predict7, a program for protein structure prediction. Biochem. Biophys. Res. Commun. 1981, 159(2):687-93.
• 36. Clamp M., Cuff J., Searle S.M., Barton G.J.: The Jalview Java Alignment Editor. Bioinformatics 2004, 20:426-427.
• 37. Hall T.A.: BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl. Acids Symp. Ser. 1999, 41:95-98.
• 38. Phillips J.C., Braun R., Wang W., Gumbart J., Tajkhorshid E., Villa E., Chipot C., Skeel R.D., Kale L., Schulten S.: Scalable molecular dynamics with NAMD. Journal of Computational Chemistry 2005, 26:1781-1802.
• 39. Humphrey W., Dalke A., Schulten K.: VMD - Visual Molecular Dynamics, J. Molec. Graphics 1996, 14:33-38.
• 40. Guex N., Peitsch M.C.: SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modeling. Electrophoresis 1997, 18:2714-2723.
• 41. Sayle R., Milner-White E.J.: RasMol: Biomolecular graphics for all. Trends in Biochemical Sciences 1995, 20(9):374.
• 42. The PyMOL Molecular Graphics System, http://www.pymol.org.
• 43. PovRay: Persistence of Vision Raytracer, http://www.povray.org.
• 44. 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. 1997, 25:3389-3402.
• 45. 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. 1994, 22:4673-4680.
• 46. Attimonelli M., Accetturo M., Santamaria M., Lascaro D., Scioscia G., Pappada G., Russo L., Zanchetta L., Tommaseo-Ponzetta M.: HmtDB, a Human Mitochondrial Genomic Resource Based on Variability Studies Supporting Population Genetics and Biomedical Research. BMC Bioinformatics 2005, 6(4):S4.
• 47. Ruiz-Pesini E., Lott M.T., Procaccio V., Poole J., Brandon M.C., Mishmar D., Yi C., Kreuziger J., Baldi P., Wallace D.C.: An enhanced MITOMAP with a global mtDNA mutational phylogeny. Nucleic Acids Research 2007, 35:D823-D828.
• 48. Anderson S., Bankier A.T., Barrell B.G., de Bruijn M.H., Coulson A.R., Drouin J., Eperon I.C., Nierlich D.P., Roe B.A., Sanger F., Schreier P.H., Smith A.J., Staden R., Young I.G.: Sequence and organization of the human mitochondrial genome. Nature 1981, 290:457-65.