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Accessible surface area from NMR chemical shifts

Journal of Biomolecular NMR volume 62, pages 387–401 (2015)Cite this article

Abstract

Accessible surface area (ASA) is the surface area of an atom, amino acid or biomolecule that is exposed to solvent. The calculation of a molecule’s ASA requires three-dimensional coordinate data and the use of a “rolling ball” algorithm to both define and calculate the ASA. For polymers such as proteins, the ASA for individual amino acids is closely related to the hydrophobicity of the amino acid as well as its local secondary and tertiary structure. For proteins, ASA is a structural descriptor that can often be as informative as secondary structure. Consequently there has been considerable effort over the past two decades to try to predict ASA from protein sequence data and to use ASA information (derived from chemical modification studies) as a structure constraint. Recently it has become evident that protein chemical shifts are also sensitive to ASA. Given the potential utility of ASA estimates as structural constraints for NMR we decided to explore this relationship further. Using machine learning techniques (specifically a boosted tree regression model) we developed an algorithm called “ShiftASA” that combines chemical-shift and sequence derived features to accurately estimate per-residue fractional ASA values of water-soluble proteins. This method showed a correlation coefficient between predicted and experimental values of 0.79 when evaluated on a set of 65 independent test proteins, which was an 8.2 % improvement over the next best performing (sequence-only) method. On a separate test set of 92 proteins, ShiftASA reported a mean correlation coefficient of 0.82, which was 12.3 % better than the next best performing method. ShiftASA is available as a web server (http://shiftasa.wishartlab.com) for submitting input queries for fractional ASA calculation.

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References

  • Adamczak R, Porollo A, Meller J (2004) Accurate prediction of solvent accessibility using neural networks-based regression. Proteins Struct Funct Bioinform 56(4):753–767

    Article  Google Scholar 

  • Ahmad S, Gromiha MM (2002) NETASA: neural network based prediction of solvent accessibility. Bioinformatics 18(6):819–824

    Article  Google Scholar 

  • Ahmad S, Gromiha MM, Sarai A (2003) Real value prediction of solvent accessibility from amino acid sequence. Proteins Struct Funct Bioinform 50(4):629–635

    Article  Google Scholar 

  • Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25(17):3389–3402

    Article  Google Scholar 

  • Avbeli F, Kocjan D, Baldwin RL (2004) Protein chemical shifts arising from alpha-helices and beta-sheets depend on solvent exposure. Proc Natl Acad Sci USA 101(50):17394–17397

    ADS  Article  Google Scholar 

  • Benkert P, Tosatto SC, Schomburg D (2008) QMEAN: a comprehensive scoring function for model quality assessment. Proteins Struct Funct Bioinform 71(1):261–277

    Article  Google Scholar 

  • Berjanskii MV, Wishart DS (2005) A simple method to predict protein flexibility using secondary chemical shifts. J Am Chem Soc 127(43):14970–14971

    Article  Google Scholar 

  • Berjanskii MV, Wishart DS (2013) A simple method to measure protein side-chain mobility using NMR chemical shifts. J Am Chem Soc 135(39):14536–14539

    Article  Google Scholar 

  • Biswas KM, DeVido DR, Dorsey JG (2003) Evaluation of methods for measuring amino acid hydrophobicities and interactions. J Chromatogr A 1000(1):637–655

    Article  Google Scholar 

  • Chen H, Zhou HX (2005) Prediction of solvent accessibility and sites of deleterious mutations from protein sequence. Nucleic Acids Res 33(10):3193–3199

    ADS  Article  Google Scholar 

  • Chothia C (1976) The nature of the accessible and buried surfaces in proteins. J Mol Biol 105(1):1–12

    Article  Google Scholar 

  • Croy CH, Koeppe JR, Bergqvist S, Komives EA (2004) Allosteric changes in solvent accessibility observed in thrombin upon active site occupation. Biochemistry 43(18):5246–5255

    Article  Google Scholar 

  • Eisenberg D, Weiss RM, Terwilliger TC (1984) The hydrophobic moment detects periodicity in protein hydrophobicity. Proc Natl Acad Sci 81(1):140–144

    ADS  Article  Google Scholar 

  • Eisenhaber F, Argos P (1993) Improved strategy in analytic surface calculation for molecular systems: handling of singularities and computational efficiency. J Comput Chem 14(11):1272–1280

    Article  Google Scholar 

  • Engelman DM, Steitz TA, Goldman A (1986) Identifying nonpolar transbilayer helices in amino acid sequences of membrane proteins. Annu Rev Biophys Biomol Struct 15(1):321–353

    Article  Google Scholar 

  • Frishman D, Argos P (1995) Knowledge-based protein secondary structure assignment. Proteins Struct Funct Bioinform 23(4):566–579

    Article  Google Scholar 

  • Garg A, Kaur H, Raghava GPS (2005) Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure. Proteins Struct Funct Bioinform 61(2):318–324

    Article  Google Scholar 

  • Han B, Liu Y, Ginzinger SW, Wishart DS (2011) SHIFTX2: significantly improved protein chemical shift prediction. J Biomol NMR 50(1):43–57

    Article  Google Scholar 

  • Holbrook SR, Muskal SM, Kim SH (1990) Predicting surface exposure of amino acids from protein sequence. Protein Eng 3(8):659–665

    Article  Google Scholar 

  • Hopp TP, Woods KR (1981) Prediction of protein antigenic determinants from amino acid sequences. Proc Natl Acad Sci 78(6):3824–3828

    ADS  Article  Google Scholar 

  • Huyghues-Despointes BM, Langhorst U, Steyaert J, Pace CN, Scholtz JM (1999) Hydrogen-exchange stabilities of RNase T1 and variants with buried and solvent-exposed Ala → Gly mutations in the helix. Biochemistry 38(50):16481–16490

    Article  Google Scholar 

  • Janin J (1979) Surface and inside volumes in globular proteins. Nature 277:491–492

    ADS  Article  Google Scholar 

  • Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12):2577–2637

    Article  Google Scholar 

  • Kyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157(1):105–132

    Article  Google Scholar 

  • Lavigne P, Willard L, Sykes BD, Bagu JR, Boyko R, Holmes CE (2000) Structure-based thermodynamic analysis of the dissociation of protein phosphatase-1 catalytic subunit and microcystin-LR docked complexes. Protein Sci 9(2):252–264

    Article  Google Scholar 

  • Lee B, Richards FM (1971) The interpretation of protein structures: estimation of static accessibility. J Mol Biol 55(3):379–400

    Article  Google Scholar 

  • Li X, Pan XM (2001) New method for accurate prediction of solvent accessibility from protein sequence. Proteins Struct Funct Bioinform 42(1):1–5

    Article  Google Scholar 

  • Manavalan P, Ponnuswamy PK (1978) Hydrophobic character of amino acid residues in globular proteins. Nature 275:673–674

    ADS  Article  Google Scholar 

  • Marsh JA (2013) Buried and accessible surface area control intrinsic protein flexibility. J Mol Biol 425:3250–3263

    Article  Google Scholar 

  • Mayrose I, Graur D, Ben-Tal N, Pupko T (2004) Comparison of site-specific rate-inference methods: Bayesian methods are superior. Mol Biol Evol 21:1781–1791

    Article  Google Scholar 

  • Myers JK, Nick PC, Martin SJ (1995) Denaturant m values and heat capacity changes: relation to changes in accessible surface areas of protein unfolding. Protein Sci 4(10):2138–2148

    Article  Google Scholar 

  • Naderi-Manesh H, Sadeghi M, Arab S, Moosavi MAA (2001) Prediction of protein surface accessibility with information theory. Proteins Struct Funct Bioinform 42(4):452–459

    Article  Google Scholar 

  • Nguyen MN, Rajapakse JC (2005) Prediction of protein relative solvent accessibility with a two-stage SVM approach. Proteins Struct Funct Bioinform 59(1):30–37

    Article  Google Scholar 

  • Ozenne V, Bauer F, Salmon L, Huang JR, Jensen MR, Segard S, Blackledge M (2012) Flexible-meccano: a tool for the generation of explicit ensemble descriptions of intrinsically disordered proteins and their associated experimental observables. Bioinformatics 28(11):1463–1470

    Article  Google Scholar 

  • Petersen B, Petersen TN, Andersen P, Nielsen M, Lundegaard C (2009) A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Struct Biol 9(1):51

    Article  Google Scholar 

  • Pollastri G, Baldi P, Fariselli P, Casadio R (2002) Prediction of coordination number and relative solvent accessibility in proteins. Proteins Struct Funct Bioinform 47(2):142–153

    Article  Google Scholar 

  • R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ISBN 3-900051-07-0. http://www.R-project.org

  • Richards FM (1974) The interpretation of protein structures: total volume, group volume distributions and packing density. J Mol Biol 82(1):1–14

    Article  Google Scholar 

  • Richards FM (1977) Areas, volumes, packing and protein structure. Annu Rev Biophys Bioeng 6:151–176

    Article  Google Scholar 

  • Ridgeway G (2007) Generalized boosted models: a guide to the GBM package. R package vignette. http://CRAN.R-project.org/package=gbm

  • Rost B, Sander C (1994) Conservation and prediction of solvent accessibility in protein families. Proteins Struct Funct Bioinform 20(3):216–226

    Article  Google Scholar 

  • Serpa JJ, Makepeace KA, Borchers TH, Wishart DS, Petrotchenko EV, Borchers CH (2014) Using isotopically-coded hydrogen peroxide as a surface modification reagent for the structural characterization of prion protein aggregates. J Proteomics 100:160–166

    Article  Google Scholar 

  • Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W et al (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7(1):539

    Article  Google Scholar 

  • Thompson MJ, Goldstein RA (1996) Predicting solvent accessibility: higher accuracy using Bayesian statistics and optimized residue substitution classes. Proteins Struct Funct Genet 25(1):38–47

    Article  Google Scholar 

  • Trevor H, Robert T, Friedman JJH (2001) The elements of statistical learning, vol 1. Springer, New York

    MATH  Google Scholar 

  • UniProt Consortium (2010) The universal protein resource (UniProt) in 2010. Nucleic Acids Res 38(Suppl 1):D142–D148

    Article  Google Scholar 

  • Valdar WSJ (2002) Scoring residue conservation. Proteins Struct Funct Bioinform 48(2):227–241

    Article  Google Scholar 

  • Vranken W, Rieping W (2009) Relationship between chemical shift value and accessible surface area for all amino acid atoms. BMC Struct Biol 9(1):20

    Article  Google Scholar 

  • Wagner M, Adamczak R, Porollo A, Meller J (2005) Linear regression models for solvent accessibility prediction in proteins. J Comput Biol 12(3):355–369

    Article  Google Scholar 

  • Wang Y, Jardetzky O (2002) Probability-based protein secondary structure identification using combined NMR chemical-shift data. Protein Sci 11(4):852–861

    Article  Google Scholar 

  • Wishart DS (2011) Interpreting protein chemical shift data. Prog Nucl Magn Reson Spectrosc 58(1):62–87

    Article  Google Scholar 

  • Wishart DS, Sykes BD (1994) Chemical shifts as a tool for structure determination. Methods Enzymol 239:363–392

    Article  Google Scholar 

  • Yuan Z, Huang B (2004) Prediction of protein accessible surface areas by support vector regression. Proteins Struct Funct Bioinform 57(3):558–564

    MathSciNet  Article  Google Scholar 

  • Zhang H, Neal S, Wishat DS (2003) RefDB: a database of uniformly referenced protein chemical shifts. J Biomol NMR 25:173–195

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Dr. Mark Berjanskii for his helpful suggestions in preparing the ShiftASA program. Financial support from the Natural Sciences and Engineering Research Council (NSERC), the Alberta Prion Research Institute (APRI) and PrioNet is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Computing Science, University of Alberta, Edmonton, Canada

    Noor E. Hafsa, David Arndt & David S. Wishart

  2. Department of Biological Sciences, University of Alberta, Edmonton, Canada

    David S. Wishart

Authors
  1. Noor E. Hafsa
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  2. David Arndt
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  3. David S. Wishart
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Correspondence to David S. Wishart.

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Hafsa, N.E., Arndt, D. & Wishart, D.S. Accessible surface area from NMR chemical shifts. J Biomol NMR 62, 387–401 (2015). https://doi.org/10.1007/s10858-015-9957-0

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Keywords

  • Nuclear magnetic resonance
  • Chemical-shifts
  • Machine learning
  • Accessible surface area
  • Protein