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Systems and Synthetic Biology

, Volume 4, Issue 4, pp 271–280 | Cite as

Sequence signatures of allosteric proteins towards rational design

  • Saritha Namboodiri
  • Chandra Verma
  • Pawan K. Dhar
  • Alessandro Giuliani
  • Achuthsankar S. Nair
Research Paper

Abstract

Allostery is the phenomenon of changes in the structure and activity of proteins that appear as a consequence of ligand binding at sites other than the active site. Studying mechanistic basis of allostery leading to protein design with predetermined functional endpoints is an important unmet need of synthetic biology. Here, we screened the amino acid sequence landscape in search of sequence-signatures of allostery using Recurrence Quantitative Analysis (RQA) method. A characteristic vector, comprised of 10 features extracted from RQA was defined for amino acid sequences. Using Principal Component Analysis, four factors were found to be important determinants of allosteric behavior. Our sequence–based predictor method shows 82.6% accuracy, 85.7% sensitivity and 77.9% specificity with the current dataset. Further, we show that Laminarity-Mean-hydrophobicity representing repeated hydrophobic patches is the most crucial indicator of allostery. To our best knowledge this is the first report that describes sequence determinants of allostery based on hydrophobicity. As an outcome of these findings, we plan to explore possibility of inducing allostery in proteins.

Keywords

Allostery Recurrence Quantitative Analysis Sequence-based predictor Hydrophobicity 

Notes

Acknowledgments

This work was funded by Kerala Government grant for the Inter University Centre for Excellence. The authors would like to thank Prof. Anders Liljas for his valuable comments.

References

  1. Berg JM, Tymoczko JL, Stryer L (2002) Biochemistry, 5th edn. WH Freeman, New YorkGoogle Scholar
  2. Bruni R, Costantino A, Tritarelli E, Marcantonio C, Ciccozzi Rapicetta M, El Sawaf G, Giuliani A, Ciccaglione AR (2009) A computational approach identifies two regions of Hepatitis C Virus E1 protein as interacting domains involved in viral fusion process. BMC Struct Biol 9:48PubMedCrossRefGoogle Scholar
  3. Colafranceschi M, Colosimo A, Zbilut JP, Uversky VN, Giuliani A (2005) Structure-related statistical singularities along protein sequences: A correlation study. J Chem Inf Model 45:183–189PubMedCrossRefGoogle Scholar
  4. Daily MD, Gray JJ (2007) Local motions in a benchmark of allosteric proteins. Bioinform Proteins 67:385–399CrossRefGoogle Scholar
  5. Dima RI, Thirumalai D (2006) Determination of network of residues that regulate allostery in protein families using sequence analysis. Protein Sci 15:258–268PubMedCrossRefGoogle Scholar
  6. Eckmann JP, Oliffson Kamphorst S, Ruelle D (1987) Recurrence plots of dynamical systems. Europhys Lett 91:973–977CrossRefGoogle Scholar
  7. Ferreiro DU, Hegler JA, Komives EA, Wolynes PG (2011) On the role of frustration in the energy landscapes of allosteric proteins. PNAS published ahead of print January 27, 2011Google Scholar
  8. Gunasekaran K, Ma B, Nussinov R (2004) Is allostery an intrinsic property of all dynamic proteins? Proteins: structure. Funct Bioinform 57:433–443CrossRefGoogle Scholar
  9. Jernigan MR (1985) Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation. Macromolecules 18:534–552CrossRefGoogle Scholar
  10. Lockless SW, Wall MA, Ranganathan R (2002) Evolutionarily conserved networks of residues mediate allosteric communication in proteins. Nat Struct Biol 10:59–68Google Scholar
  11. Monod J, Changeux JP, Jacob F (1963) Allosteric proteins and cellular control systems. J Mol Biol 20:306–329CrossRefGoogle Scholar
  12. Porrello A, Soddu S, Zbilut JP, Crescenzi M, Giuliani A (2004) Discrimination of single amino acid mutations of the p53 protein by means of deterministic singularities of recurrence quantification analysis. Proteins Struct Funct Bioinform 55:743–755CrossRefGoogle Scholar
  13. Zbilut JP, Webber CL Jr (1992) Embeddings and delays as derived from quantification of recurrence plots. Phys Lett A 171:199–203CrossRefGoogle Scholar
  14. Zbilut JP, Colosimo A, Conti F, Colafranceschi M, Manetti C, Valerio MC, Webber CL Jr, Giuliani A (2003) Proteiin aggregation/folding: the role of deterministic singularities of sequence hydrophobicity as determined by nonlinear signal analysis of acylphosphatase and Aβ(1–40). Biophysical J 85:3544–3557CrossRefGoogle Scholar
  15. Zbilut JP, Giuliani A, Colosimo A, Mitchell JC, Colafrancesch M, Marwan N, Webber CL, Uversky V (2004) Charge and hydrophobicity patterning along the sequence predicts the folding mechanism and aggregation of proteins: a computational approach. J Proteome Res 3:1243–1253PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Saritha Namboodiri
    • 1
  • Chandra Verma
    • 2
  • Pawan K. Dhar
    • 3
  • Alessandro Giuliani
    • 4
  • Achuthsankar S. Nair
    • 1
  1. 1.State Inter University Centre of Excellence in BioinformaticsUniversity of KeralaThiruvananthapuramIndia
  2. 2.Bioinformatics Institute (BII)Buona VistaSingapore
  3. 3.Centre for Systems and Synthetic BiologyUniversity of KeralaThiruvananthapuramIndia
  4. 4.Environment and Health Deptartment, Istituto Superiore di SanitàRomeItaly

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