Skip to content
naulon

No retraction, correction or concern found when last checked on Oct 17, 2026.

Maximum Allowed Solvent Accessibilites of Residues in Proteins

Matthew Z. Tien, Austin G. Meyer, Dariya K. Sydykova and 2 more

PLoS ONE · 2013 · CC BY

doi:10.1371/journal.pone.0080635

Abstract

The relative solvent accessibility (RSA) of a residue in a protein measures the extent of burial or exposure of that residue in the 3D structure. RSA is frequently used to describe a protein's biophysical or evolutionary properties. To calculate RSA, a residue's solvent accessibility (ASA) needs to be normalized by a suitable reference value for the given amino acid; several normalization scales have previously been proposed. However, these scales do not provide tight upper bounds on ASA values frequently observed in empirical crystal structures. Instead, they underestimate the largest allowed ASA values, by up to 20%. As a result, many empirical crystal structures contain residues that seem to have RSA values in excess of one. Here, we derive a new normalization scale that does provide a tight upper bound on observed ASA values. We pursue two complementary strategies, one based on extensive analysis of empirical structures and one based on systematic enumeration of biophysically allowed tripeptides. Both approaches yield congruent results that consistently exceed published values. We conclude that previously published ASA normalization values were too small, primarily because the conformations that maximize ASA had not been correctly identified. As an application of our results, we show that empirically derived hydrophobicity scales are sensitive to accurate RSA calculation, and we derive new hydrophobicity scales that show increased correlation with experimentally measured scales.

Full text

Its licence does not allow us to show the full text. Read it at PubMed Central:

Read at PubMed Central

References 40

  1. 1.

    Chothia C (1976) The nature of the accessible and buried surfaces in proteins. J Mol Biol: 1–14.

    Not in our library10.1016/0022-2836(76)90191-1

  2. 2.

    Rose GD, Geselowitz AR, Lesser GJ, Lee RH, Zehfus MH (1985 ) Hydrophobicity of amino acid residues in globular proteins. Science 229: 834–838.

    Not in our library10.1126/science.4023714

  3. 3.

    Miller S, Janin J, Lesk AM, Chothia C (1987 ) Interior and surface of monomeric proteins. J Mol Biol 196: 641–656.

    Not in our library10.1016/0022-2836(87)90038-6

  4. 4.

    Moelbert S, Emberly E, Tang C (2004) Correlation between sequence hydrophobicity and surface-exposure pattern of database proteins. Prot Sci: 752–762.

    Not in our library10.1110/ps.03431704

  5. 5.

    Shaytan AK, Shaitan KV, Khokhlov AR (2009 ) Solvent accessible surface area of amino acid residues in globular proteins: Correlations of apparent transfer free engergies with experimental hydrophobicity scales. Biomacromolecules 10: 1224–1237.

    Not in our library10.1021/bm8015169

  6. 6.

    Goldman N, Thorne JL, Jones DT (1998 ) Assessing the impact of secondary structure and solvent accessibility on protein evolution. Genetics 149: 445–458.

    Not in our library10.1093/genetics/149.1.445

  7. 7.

    Bloom JD, Drummond DA, Arnold FH, Wilke CO (2006 ) Structural determinants of the rate of protein evolution in yeast. Mol Biol Evol 23: 1751–1761.

    Not in our library10.1093/molbev/msl040

  8. 8.

    Franzosa EA, Xia Y (2009 ) Structural determinants of protein evolution are context-sensitive at the residue level. Mol Biol Evol 26: 2387–2395.

    Not in our library10.1093/molbev/msp146

  9. 9.

    Zhou T, Weems M, Wilke CO (2009 ) Translationally optimal codons associate with structurally sensitive sites in proteins. Mol Biol Evol 26: 1571–1580.

    Not in our library10.1093/molbev/msp070

  10. 10.

    Franzosa EA, Xia Y (2012 ) Independent effects of protein core size and expression on structure-evolution relationships at the residue level. PLoS One 7: e46602.

    Not in our library10.1371/journal.pone.0046602

  11. 11.

    Scherrer MP, Meyer AG, Wilke CO (2012 ) Modeling coding-sequence evolution within the context of residue solvent accessibility. BMC Evol Biol 12: 179.

    Not in our library10.1186/1471-2148-12-179

  12. 12.

    Meyer AG, Wilke CO (2012) Integrating sequence variation and protein structure to identify sites under selection. Mol Biol Evol.

    Not in our library10.1093/molbev/mss217

  13. 13.

    Contant GC, Stadler PF (2009 ) Solvent exposure imparts similar selective pressures across a range of yeast proteins. Mol Biol Evol 26: 1155–1161.

    Not in our library10.1093/molbev/msp031

  14. 14.

    Rost B, Sander C (1994 ) Conservation and prediction of solvent accessibility in protein families. Proteins 20: 216–226.

    Not in our library10.1002/prot.340200303

  15. 15.

    Pollastri G, Baldi P, Fariselli P, Casadio R (2002 ) Prediction of coordination number and relative solvent accessibility in proteins. Proteins 47: 142–153.

    Not in our library10.1002/prot.10069

  16. 16.

    Kim H, Park H (2004 ) Prediction of protein relative solvent accessibility with support vector machines and long-range interaction 3D local descriptor. Proteins 54: 557–562.

    Not in our library10.1002/prot.10602

  17. 17.

    Adamczak R, Porollo A, Meller J (2004 ) Accurate prediction of solvent accessibility using neural networks-based regression. Proteins: Structure, Function, and Bioinformatics 56: 753–767.

    Not in our library10.1002/prot.20176

  18. 18.

    Nguyen MN, Rajapakse JC (2005 ) Prediction of protein relative solvent accessibility with a two-stage SVM approach. Proteins 59: 30–37.

    Not in our library10.1002/prot.20404

  19. 19.
  20. 20.

    Singh H, Ahmad S (2009 ) Context dependent reference states of solvent accessibility derived from native protein sturctures and assessed by predictability analysis. BMC Struct Biol 9: 25.

    Not in our library10.1186/1472-6807-9-25

  21. 21.

    Levy ED (2010 ) A simple definition of structural regions in proteins and its use in analyzing interface evolution. J Mol Biol 403: 660–670.

    Not in our library10.1016/j.jmb.2010.09.028

  22. 22.

    Cheng J, Sweredoski MJ, Baldi P (2006 ) DOMpro: protein domain prediction using profiles, secondary structure, relative solvent accessibility, and recursive neural networks. Data Mining and Knowledge Discovery 13: 1–10.

    Not in our library

  23. 23.

    Chen H, Zhou HX (2005 ) Prediction of solvent accessibility and sites of deleterious mutations from protein sequence. Nucl Acids Res 33: 3193–3199.

    Not in our library10.1093/nar/gki633

  24. 24.

    Wang G, Dunbrack RL (2003 ) PISCES: a protein sequence culling server. Bioinformatics 19: 1589–1591.

    Not in our library10.1093/bioinformatics/btg224

  25. 25.

    Morris AL, MacArthur MW, Hutchinson EG, Thornton JM (1992 ) Stereochemical quality of protein structure coordinates. Proteins 12: 345–364.

    Not in our library10.1002/prot.340120407

  26. 26.

    Wolfenden R, Anderson L, Cullis PM, Southgate CCB (1981) Affinities of amino acid side chains for solvent water. Biochemistry: 849–855.

    Not in our library10.1021/bi00507a030

  27. 27.

    Kyte J, Doolittle RF (1982 ) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157: 105–132.

    Not in our library10.1016/0022-2836(82)90515-0

  28. 28.

    Fauchere JL, Pliska VE (1983 ) Hydrophobic parameters of amino-acid side chains from the partitioning of N-acetyl-amino-acid amides. Eur J Med Chem 18: 369–375.

    Not in our library

  29. 29.

    Radzicka A, Wolfenden R (1988 ) Comparing the polarities of the amino acids: side-chain distribution coefficients between the vapor phase, cyclohexane, 1-octanol, and neutral aqueous solution. Biochem 27: 1664–1670.

    Not in our library

  30. 30.

    MacCallum JL, Bennett WFD, Tieleman DP (2007 ) Partitioning of amino acid side chains into lipid bilayers: results from computer simulations and comparison to experiment. J Gen Physiol 129: 371–377.

    Not in our library10.1085/jgp.200709745

  31. 31.

    Moon CP, Fleming KG (2011 ) Side chain hydrophobicity scales derived from transmembrane protein folding into lipid bilayers. Proc Natl Acad Sci USA 108: 10174–10177.

    Not in our library10.1073/pnas.1103979108

  32. 32.

    Wimley WC, Creamer TP, White SH (1996 ) Solvation energies of amino acid side chains and backbone in a family of host-guest pentapeptides. Biochem 35: 5109–5124.

    Not in our library10.1021/bi9600153

  33. 33.

    Ahmad S, Gromiha MM, Sarai A (2003 ) Real-value prediction of solvent accessibility from amino acid sequence. Proteins 50: 629–635.

    Not in our library10.1002/prot.10328

  34. 34.

    Kabsch W, Sander C (1983 ) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22: 2577–2637.

    Not in our library10.1002/bip.360221211

  35. 35.

    Lee B, Richards FM (1971 ) The interpretation of protein structures: estimation of static accessibility. J Mol Biol 55: 379–400.

    Not in our library10.1016/0022-2836(71)90324-x

  36. 36.

    Kawashima S, Kanehisa M (2000 ) AAindex: amino acid index database. Nucleic Acids Res 28: 374.

    Not in our library10.1093/nar/28.1.374

  37. 37.

    Tomii K, Kanehisa M (1996 ) Analysis of amino acid indices and mutation matrices for sequence comparison and structure prediction of proteins. Protein Eng 9: 27–36.

    Not in our library10.1093/protein/9.1.27

  38. 38.

    Kawashima S, Pokarowski P, Pokarowska M, Kolinski A, Katayama T, et al (2008 ) AAindex: amino acid index database, progress report 2008. Nucleic Acids Res 36: D202–D205.

    Not in our library10.1093/nar/gkm998

  39. 39.

    Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993 ) PROCHECK – a program to check the stereochemical quality of protein structures. J App Cryst 26: 283–291.

    Not in our library

  40. 40.

    Tien MZ, Sydykova DK, Meyer AG, Wilke CO (2013 ) PeptideBuilder: a simple Python library to generate model peptides. PeerJ 1: e80.

    Not in our library10.7717/peerj.80

Cited by papers we hold 7

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.