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Tutorial section Hydropathy — A window on the evasion of water ‘Proteome’ is currently the word of choice to represent those functioning polypeptides that result from the translation of a gene transcript. Only a small number of the proteins that make up this group have been fully annotated, and researchers worldwide are working to increase this volume. Structure and function are closely related in terms of understanding what these proteins do and how they govern processes within an organism. In the absence of structural information derived from X-ray crystallography or other experimental means, bioinformatics has sought to make up the shortfall by enabling calculations of many parameters that would contribute to an understanding of the final structure of a protein. Hydropathy is one such parameter and is used to determine the hydrophobicity of various regions of the molecule. As a protein is created from the translation of an mRNA template it is subject to a variety of forces, enabling it to fold in a way relevant to its future function. In addition to the chaperone proteins known to aid this process, chemical attractions and interactions between molecules are all seen to influence the final product. One of these forces is the repulsion of water by various hydrophobic residues on the emerging polypeptide chain — indeed many chaperones also function using hydrophobic interactions. Hydrophilic residues are necessary to fill the void. For soluble, globular proteins in particular, the energy required to bury hydrophobic residues is one of the main driving forces behind the folding of the polypeptide molecule. Once folded, these hydrophobic interactions are instrumental in retaining the tertiary structure of the protein. Those regions that repel water are the hydrophobic ones and are calculated in terms of ‘hydropathy’ — the score generated for a specified region of the protein, taking both the number of hydrophobic and the number of hydrophilic residues into account. Hydrophobicity may be represented by a positive or a negative score depending on the experimental data used to determine it. Such hydrophobic regions may indicate a buried core within the protein structure, or perhaps a feature such as a transmembrane segment. Computational calculations of the hydropathic nature of a protein from its secondary structure may reveal areas of high hydrophobicity, hinting at possible structural features. The most common programs determine the hydrophobicity of a region based on the calculation of experimentally determined values for each residue — the hydropathy index. A window (a run of adjacent residues within the sequence) is selected — often by the user on the basis of the feature that is being studied — and the hydrophobicity scores for each residue within that window added together. Often the window size for these and many other programs using this technique is an odd number of residues. This is to offer an obvious mid-point within the window for a dot or plot to be displayed if required. The resulting window total is then divided by the number of residues within the window to create an average (mean). It is this average that is shown as the hydropathy score of that region of the sequence. Once the first hydropathy value has been registered, the window moves & HENRY STEWART PUBLICATIONS 1467-5463. B R I E F I N G S I N B I O I N F O R M A T I C S . VOL 4. NO 3. 279–282. SEPTEMBER 2003 279 Tutorial section along one and an average is calculated using the second set of adjacent residues, producing a second hydropathy score. This process is repeated until the entire window has moved from the N terminus to the C terminus, or between the specified regions of a sequence, creating a score for each window-sized section of the protein. Each of these scores is then plotted on a graph, which is displayed as the result of the calculation. As a general rule, the more positive the score, the greater the hydrophobic content of the window, however, some hydropathy indices represent the hydrophobic regions with a more negative score. Possibly the most commonly used hydropathy index is that derived from experiments by Kyte and Doolittle in 1982.1 Based on a mix of experimental work and literature searching it was discovered that protein residues buried within the fold corresponded well to regions of hydrophobicity, while those on the outside of the fold matched more hydrophilic residues. These observations were reflected in their hydropathy index that, owing to the nature of its conception, is best suited to research attempting to determine whether residues will become buried in the final protein fold. Although the window size can — and should — be altered depending on what the program is attempting to Table 1: Web-based applications useful for predicting hydropathy Location Experimental Default window size http://www.rfcgr.mrc.ac.uk/Software/EMBOSS/ EMBOSS application pepwindow http://www.rfcgr.mrc.ac.uk/Software/EMBOSS/ EMBOSS application pepinfo Kyte and Doolittle1 7 Kyte and Doolittle1 Sweet and Eisenberg7 Eisenberg et al.3 White and Whimley6 9 Eisenberg et al.3 10 Kyte and Doolittle1 Eisenberg et al.3 Engelman et al.5 Kyte and Doolittle1 Hopp and Woods8 Kyte and Doolittle1 Kyte and Doolittle1 Abraham and Leo9 Black and Mould10 Bull and Breese11 Chothia12 Eisenberg et al.3 Fauchere and Pliska13 Guy14 Hopp and Woods8 Janin15 Kyte and Doolittle1 Manavalan and Ponnuswamy16 Miyazawa and Jernigen17 Rao and Argos18 Rose et al.19 Roseman20 Sweet and Eisenberg21 Welling et al.22 Wolfenden et al.23 9;6;20 White & Whimley6 19 http://www.rfcgr.mrc.ac.uk/Software/EMBOSS/ EMBOSS application octanol http://www.rfcgr.mrc.ac.uk/Software/EMBOSS/ EMBOSS application hmoment GCG application PepPlot24 http://bioinformatics.weizmann.ac.il/hydroph/plot_hydroph.html http://fasta.bioch.virginia.edu/fasta/grease.htm http://www.bio.davidson.edu/courses/compbio/flc/home.html http://us.expasy.org/cgi-bin/protscale.pl?1/ http://blanco.biomol.uci.edu/mpex/ Produces interactive java based graphic. Requires Java Web Start to launch 280 & HENRY STEWART PUBLICATIONS 1467-5463. B R I E F I N G S I N B I O I N F O R M A T I C S . VOL 4. NO 3. 279–282. SEPTEMBER 2003 19 17 9 User-defined 9 Tutorial section characterise, it has been noted by experimentalists that a region of buried residues is best detected using a window size of 9 and a trans-membrane region with a window size of 19. These are commonly used as the defaults of hydropathy programs searching for these features. As an indicator of protein folding, hydropathy analysis has also been incorporated into some of the tertiary structure prediction programs. Discrimination of protein Secondary structure Class (DSC),2 for example, uses the Eisenberg3 hydropathy index, while TopPred4 employs the scale developed by Engelman and coworkers5 to classify residues involved in trans-membrane helices. Such programs have, however, given way to prediction software that makes use of newer analysis methods such as hidden Markov models or neural networking, where hydropathy is implicit in the data they use. Other hydropathy indices rely on values derived from different experiment, and it may make sense to employ one of these depending on the research being undertaken. White and Wimley,6 for example, looked at the free energy change between water–lipid interactions and also water–octanol interactions of the protein solution. They suggested that large differences between them indicate the presence of a trans-membrane region in the protein. As this work has been carried out specifically for this type of feature, it would be pertinent to use this information for any projects involving trans-membrane proteins. There are, however, a host of other indices derived from experiments using trans-membrane proteins. There are many applications that use the sliding window technique and a reference database of experimental hydrophobicity or hydrophilicity values to predict the hydropathy of a protein. In addition to those applications within the EMBOSS suite,25 others may be found on web servers across the world. Table 1 lists some of these. Lisa J. Mullan, MRC Rosalind Franklin Centre for Genome Research, Genome Campus, Hinxton, Cambridge CB10 1SB Tel: +44 (0) 1223 494500 Fax: +44 (0) 1223 494512 E-mail: [email protected] References 1. Kyte, J. and Doolittle, R. F. (1982), ‘A simple method for displaying the hydropathic character of a protein’, J. Mol. Biol., Vol. 157, pp. 105–132. 2. King, R. D. and Sternberg, M. J. E. (1996), ‘Identification and application of the concepts important for accurate and reliable protein secondary structure predication’, Protein Sci., Vol. 5, pp. 2298–2310. 3. Eisenberg, D., Weiss, R. M. and Terwilliger, T. C. (1984), ‘The hydrophobic moment detects periodicity in protein hydrophobicity’, Proc. Natl Acad. Sci. USA, Vol. 81, pp. 140–144. 4. Heijne, G. von (1992), ‘Membrane protein structure prediction, hydrophobicity analysis and the positive-inside rule’, J. Mol. Biol., Vol. 225, pp. 484–494. 5. Engelman, D. M., Steitz, T. A. and Goldman, A. (1986), ‘Identifying nonpolar transbilayer helices in amino acid sequences of membrane proteins’, Annu. Rev. Biophys. Biophys. Chem., Vol. 15, pp. 321–353. 6. White, S. H. and Wimley, W. C. (1999), ‘Membrane protein folding and stability: physical principles’, Annu. Rev.Biophys. Biomol. Struct., Vol. 28, pp. 319–365. 7. Sweet, R. M. and Eisenberg, D. (1983), ‘Correlation of sequence hydrophobicities measures similarity in three-dimensional protein structure’, J. Mol. Biol., Vol. 171, pp. 479–488. 8. Hopp, T. P. and Woods, K. R. (1981), ‘Prediction of protein antigenic determinants from amino acid sequences’, Proc. Natl Acad. Sci. USA, Vol. 78, pp. 3824–3828. 9. Abraham, D. J. and Leo, A. J. (1987), ‘Extension of the fragment method to calculate amino acid zwitterions and side chain partition coefficients’, Proteins: Structure, Function Genet., Vol. 2, pp. 130–152. 10. Black, S. D. and Mould, D. R. (1991), ‘Development of hydrophobicity parameters to analyze proteins which bear post- or cotranslational modifications’, Anal. Biochem., Vol. 193, pp. 72–82. 11. Bull, H. B. and Breese, K. (1974), ‘Surface tension of amino acid solutions: A hydrophobicity scale of the amino acid & HENRY STEWART PUBLICATIONS 1467-5463. B R I E F I N G S I N B I O I N F O R M A T I C S . VOL 4. NO 3. 279–282. SEPTEMBER 2003 281 Tutorial section residues’, Arch. Biochem. Biophys., Vol. 161, pp. 665–670. 12. Chothia, C. (1976), ‘The nature of the accessible and buried surfaces in proteins’, J. Mol. Biol., Vol. 105, pp. 1–14. 13. Fauchere, J.-L. and Pliska, V. E. (1983), ‘Hydrophobic parameters of amino acid side chains from the partitioning of N-acetylamino-acid amides’, Eur. J. Med. Chem., Vol. 18, pp. 369–375. 14. Guy, H. R. (1985), ‘Amino acid side-chain partition energies and distribution of residues in soluble proteins’, Biophys J., Vol. 47, pp. 61–70. 15. Janin, J. (1979), ‘Surface and inside volumes in globular proteins’, Nature, Vol. 277, pp. 491–492. 16. Manavalan, P. and Ponnuswamy, P. K. (1978), ‘Hydrophobic character of amino acid residues in globular proteins’, Nature, Vol. 275, pp. 673–674. 17. Miyazawa, S. and Jernigen, R. L. (1985), ‘Estimation of effective interresidue contact energies from protein crystal structures: Quasichemical approximation’, Macromolecules, Vol. 18, pp. 534–552. 18. Rao, J. K. and Argos, P. (1986) ‘A conformational preference parameter to 282 predict helices in integral membrane proteins’, Biochim. Biophys. Acta, Vol. 869, pp. 197–214. 19. Rose, G. D., Geselowitz, A. R., Lesser, G. J. et al. (1985), ‘Hydrophobicity of amino acid residues in globular proteins’, Science, Vol. 229, pp. 834–838. 20. Roseman, M. A. (1988), ‘Hydrophilicity of polar amino acid side-chains is markedly reduced by flanking peptide bonds’, J. Mol. Biol., Vol. 200, pp. 513–522. 21. Sweet, R. M. and Eisenberg, D. (1983), see reference 7. 22. Welling, G. W., Weijer, W. J., Van der Zee, R. and Welling-Wester, S. (1985), ‘Prediction of sequential antigenic regions in proteins’, FEBS Lett., Vol. 188, pp. 215–218. 23. Wolfenden, R. V., Andersson, L., Cullis, P. M. and Southgate, C. C. F. (1981), ‘Affinities of amino acid side chains for solvent water’, Biochemistry, Vol. 20, pp. 849–855. 24. Gribskov, M., Burgess, R. R. and Devereux, J. (1986), ‘PEPPLOT, a protein secondary structure analysis program for the UWGCG sequence analysis software package’, Nucleic Acids Res., Vol. 14, pp. 327–334. 25. Rice, P., Longden, I. and Bleasby, A. (2000), ‘EMBOSS: The European Molecular Biology Open Software Suite’, Trends Genet., Vol. 16, pp. 276–277. & HENRY STEWART PUBLICATIONS 1467-5463. B R I E F I N G S I N B I O I N F O R M A T I C S . VOL 4. NO 3. 279–282. SEPTEMBER 2003