Relative accessible surface area

Relative accessible surface area or relative solvent accessibility (RSA) of a protein residue is a measure of residue solvent exposure. It can be calculated by formula:

[1]

where ASA is the solvent accessible surface area and MaxASA is the maximum possible solvent accessible surface area for the residue.[1] Both ASA and MaxASA are commonly measured in .

To measure the relative solvent accessibility of the residue side-chain only, one usually takes MaxASA values that have been obtained from Gly-X-Gly tripeptides, where X is the residue of interest. Several MaxASA scales have been published[1][2][3] and are commonly used (see Table).

ResidueTien et al. 2013 (theor.)[1]Tien et al. 2013 (emp.)[1]Miller et al. 1987[2]Rose et al. 1985[3]
Alanine129.0121.0113.0118.1
Arginine274.0265.0241.0256.0
Asparagine195.0187.0158.0165.5
Aspartate193.0187.0151.0158.7
Cysteine167.0148.0140.0146.1
Glutamate223.0214.0183.0186.2
Glutamine225.0214.0189.0193.2
Glycine104.097.085.088.1
Histidine224.0216.0194.0202.5
Isoleucine197.0195.0182.0181.0
Leucine201.0191.0180.0193.1
Lysine236.0230.0211.0225.8
Methionine224.0203.0204.0203.4
Phenylalanine240.0228.0218.0222.8
Proline159.0154.0143.0146.8
Serine155.0143.0122.0129.8
Threonine172.0163.0146.0152.5
Tryptophan285.0264.0259.0266.3
Tyrosine263.0255.0229.0236.8
Valine174.0165.0160.0164.5

In this table, the more recently published MaxASA values (from Tien et al. 2013[1]) are systematically larger than the older values (from Miller et al. 1987[2] or Rose et al. 1985[3]). This discrepancy can be traced back to the conformation in which the Gly-X-Gly tripeptides are evaluated to calculate MaxASA. The earlier works used the extended conformation, with backbone angles of and .[2][3] However, Tien et al. 2013[1] demonstrated that tripeptides in extended conformation fall among the least-exposed conformations. The largest ASA values are consistently observed in alpha helices, with backbone angles around and . Tien et al. 2013 recommend to use their theoretical MaxASA values (2nd column in Table), as they were obtained from a systematic enumeration of all possible conformations and likely represent a true upper bound to observable ASA.[1]

ASA and hence RSA values are generally calculated from a protein structure, for example with the software DSSP.[4] However, there is also an extensive literature attempting to predict RSA values from sequence data, using machine-learning approaches.[5] [6]


Prediction tools

Experimentally predicting RSA is an expensive and time consuming task. In recent decades, several computational methods have been introduced for RSA prediction.[7][8][9]

gollark: Real payment systems partly get around this by making the chip on the card itself do some cryptography, so it can't make payments without the card being physically there still, but I don't think there's actually anything other than trust, the law, and "security" through obscurity stopping a payment thing from deducting more money than it should?
gollark: Obviously that's not very good.
gollark: .
gollark: Example issue with the central version: you scan your card on a payment terminal to pay one currency unit. But it reads your card's data off, and can now just take as much money as it wants at any time
gollark: And that would... probably be worse than the central version.

References
  1. Tien, M. Z.; Meyer, A. G.; Sydykova, D. K.; Spielman, S. J.; Wilke, C. O. (2013). "Maximum allowed solvent accessibilites of residues in proteins". PLOS ONE. 8 (11): e80635. arXiv:1211.4251. Bibcode:2013PLoSO...880635T. doi:10.1371/journal.pone.0080635. PMC 3836772. PMID 24278298.
  2. Miller, S.; Janin, J.; Lesk, A. M.; Chothia, C. (1987). "Interior and surface of monomeric proteins". J. Mol. Biol. 196 (3): 641–656. doi:10.1016/0022-2836(87)90038-6. PMID 3681970.
  3. Rose, G. D.; Geselowitz, A. R.; Lesser, G. J.; Lee, R. H.; Zehfus, M. H. (1985). "Hydrophobicity of amino acid residues in globular proteins". Science. 229 (4716): 834–838. Bibcode:1985Sci...229..834R. doi:10.1126/science.4023714. PMID 4023714.
  4. Kabsch, W.; Sander, C. (1983). "Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features". Biopolymers. 22 (12): 2577–2637. doi:10.1002/bip.360221211. PMID 6667333.
  5. Hyunsoo, Kim; Haesun, Park (2003). "Prediction of Protein Relative Solvent Accessibility with Support Vector Machines and Long-range Interaction 3D Local Descriptor" (PDF). Retrieved 10 April 2015.
  6. Rost, Burkhard; Sander, Chris (1994). "Conservation and prediction of solvent accessibility in protein families". Proteins. 20 (3): 216–26. doi:10.1002/prot.340200303. PMID 7892171. Retrieved 10 April 2015.
  7. Kaleel, Manaz; Torrisi, Mirko; Mooney, Catherine; Pollastri, Gianluca (2019-09-01). "PaleAle 5.0: prediction of protein relative solvent accessibility by deep learning". Amino Acids. 51 (9): 1289–1296. doi:10.1007/s00726-019-02767-6. ISSN 1438-2199. PMID 31388850.
  8. Wang, Sheng; Li, Wei; Liu, Shiwang; Xu, Jinbo (2016-07-08). "RaptorX-Property: a web server for protein structure property prediction". Nucleic Acids Research. 44 (W1): W430–W435. doi:10.1093/nar/gkw306. ISSN 0305-1048. PMC 4987890. PMID 27112573.
  9. Magnan, Christophe N.; Baldi, Pierre (2014-09-15). "SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity". Bioinformatics. 30 (18): 2592–2597. doi:10.1093/bioinformatics/btu352. ISSN 1367-4803. PMC 4215083. PMID 24860169.
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