Skip to main content
Log in

Prediction of parathyroid hormone signalling potency using SVMs

  • Published:
Molecules and Cells

Abstract

Parathyroid hormone is the most important endocrine regulator of calcium concentration. Its N-terminal fragment (1–34) has sufficient activity for biological function. Recently, site-directed mutagenesis studies demonstrated that substitutions at several positions within shorter analogues (1–14) can enhance the bioactivity to greater than that of PTH (1–34). However, designing the optimal sequence combination is not simple due to complex combinatorial problems. In this study, support vector machines were introduced to predict the biological activity of modified PTH (1–14) analogues using mono-substituted experimental data and to analyze the key physicochemical properties at each position that correlated with bioactivity. This systematic approach can reduce the time and effort needed to obtain desirable molecules by bench experiments and provide useful information in the design of simpler activating molecules.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Aurora, R., and Rose, G.D. (1998). Helix capping. Protein Sci. 7, 21–38.

    CAS  PubMed  Google Scholar 

  • Barazza, A., Wittelsberger, A., Fiori, N., Schievano, E., Mammi, S., Toniolo, C., Alexander, J.M., Rosenblatt, M., Peggion, E., and Chorev, M. (2005). Bioactive N-terminal undecapeptides derived from parathyroid hormone: the role of alpha-helicity. J. Pept. Res. 65, 23–35.

    Article  CAS  PubMed  Google Scholar 

  • Bhasin, M., and Raghava, G.P. (2004). Analysis and prediction of affinity of TAP binding peptides using cascade SVM. Protein Sci. 13, 596–607.

    Article  CAS  PubMed  Google Scholar 

  • Bhaskaran, R., and Ponnuswamy, P.K. (1988). Positional flexibilities of amino acid residues in globular proteins. Int. J. Peptide Protein Res. 32, 241–255.

    CAS  Google Scholar 

  • Bisello, A., Adams, A.E., Mierke, D.F., Pellegrini, M., Rosenblatt, M., Suva, L.J., and Chorev, M. (1998). Parathyroid hormone-receptor interactions identified directly by photocross-linking and molecular modeling studies. J. Biol. Chem. 273, 22498–22505.

    Article  CAS  PubMed  Google Scholar 

  • Chorev, M. (2002). Parathyroid hormone 1 receptor: insights into structure and function. Receptors Channels 8, 219–242.

    Article  CAS  PubMed  Google Scholar 

  • Crammer, K., and Singer, Y. (2001). On the algorithm implementation of multi-class SVM. J.M.L.R. 2, 265–292.

    Article  Google Scholar 

  • Cristianini, N., and Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods (New York: Cambridge University Press).

    Google Scholar 

  • Dubey, A., Realff, M.J., Lee, J.H., and Bommarium, A.S. (2004). Support vector machines for learning to identify the critical position of a protein. J. Theor. Biol. 234, 351–361.

    Article  Google Scholar 

  • Jin, L., Briggs, S.L., Chandrasekhar, S., Chirgadze, N.Y., Clawson, D.K., Schevitz, R.W., Smiley, D.L., Tashjian, A.H., and Zhang, F. (2000). Crystal structure of human parathyroid hormone 1-34 at 0.9-A resolution. J. Biol. Chem. 275, 27238–27244.

    CAS  PubMed  Google Scholar 

  • Joachims, T. (1999). Making large-scale SVM learning practical. In B. Scholkopf, ed., Advances in kernel methods - support vector learning, (Cambridge, Massachusetts: The MIT Press).

    Google Scholar 

  • Karle, I.L., and Balaram, P. (1990). Structural characteristics of alpha-helical peptide molecules containing Aib residues. Biochemistry 29, 6747–6756.

    Article  CAS  PubMed  Google Scholar 

  • Kawashima, S., Ogata, H., and Kanehisa, M. (1999). AAindex: amino acid index database. Nucleic Acids Res. 27, 368–369.

    Article  CAS  PubMed  Google Scholar 

  • Klein, P., Kanehisa, M., and DeLisi, C. (1984). Prediction of protein function from sequence properties. Discriminant analysis of a data base. Biochim. Biophys. Acta 787, 221–226.

    CAS  PubMed  Google Scholar 

  • Kowalczyk, W., Prahl, A., Dawidowska, O., Derdowska, I., Sobolewski, D., Hartrodt, B., Neubert, K., Slaninova, J., and Lammek, B. (2005). The influence of 1-aminocyclopentane-1-carboxylic acid at position 2 or 3 of AVP and its analogues on their pharmacological properties. J. Pept. Sci. 11, 584–588.

    Article  CAS  PubMed  Google Scholar 

  • Krigbaum, W.R., and Komoriya, A. (1979). Local interactions as a structure determinant for protein molecules: II. Biochim. Biophys. Acta 576, 204–248.

    CAS  PubMed  Google Scholar 

  • Levitt, M. (1976). A simplified representation of protein conformations for rapid simulation of protein folding. J. Mol. Biol. 104, 59–107.

    Article  CAS  PubMed  Google Scholar 

  • Luck, M.D., Carter, P.H., and Gardella, T.J. (1999). The (1-14) fragment of parathyroid hormone (PTH) activates intact and amino-terminally truncated PTH-1 receptors. Mol. Endocrinol. 13, 670–680.

    Article  CAS  PubMed  Google Scholar 

  • Martinez, W.L., and Martinez, A.R. (2002). Computational statistics handbook with MATLAB. (Boca Raton, London, New York, Washington D.C.: Chapman and Hall/CRC).

    Google Scholar 

  • Maxfield, F.R., and Scheraga, H.A. (1976). Status of empirical methods for the prediction of protein backbone topography. Biochemistry 15, 5138–5153.

    Article  CAS  PubMed  Google Scholar 

  • Meirovitch, H., Rackovsky, S., and Scheraga, H.A. (1980). Empirical studies of hydrophobicity: 1. Effect of protein size on the hydrophobic behavior of amino acids. Macromolecules 13, 1398–1405.

    Article  CAS  Google Scholar 

  • Mita, K., Ichimura, S., and Zama, M. (2004). Conformation of poly(L-homoarginine). Biopolylmers 19, 1123–1135.

    Article  Google Scholar 

  • Ponnuswamy, P.K., Prabhakaran, M., and Manavalan, P. (1980). Hydrophobic packing and spatial arrangement of amino acid residues in globular proteins. Biochim. Biophys. Acta 623, 301–316.

    CAS  PubMed  Google Scholar 

  • Potts, J.T. (2005). Parathyroid hormone: past and present. J. Endocrinol. 187, 311–325.

    Article  CAS  PubMed  Google Scholar 

  • Prabhakaran, M., and Ponnuswamy, P.K. (1982). Shape and surface features of globular proteins. Macromolecules 15, 314–320.

    Article  CAS  Google Scholar 

  • Rackovsky, S., and Scheraga, H.A. (1977). Hydrophobicity, hydrophilicity, and the radial and orientational distributions of residues in native proteins. Proc. Natl. Acad. Sci. USA 74, 5248–5251.

    Article  CAS  PubMed  Google Scholar 

  • Richardson, J.S., and Richardson, D.C. (1988). Amino acid preferences for specific locations at the ends of alpha helices. Science 240, 1648–1652.

    Article  CAS  PubMed  Google Scholar 

  • Rölz, C., Pellegrini, M., and Mierke, D.F. (1999). Molecular characterization of the receptor-ligand complex for parathyroid hormone. Biochemistry 38, 6397–6405.

    Article  PubMed  Google Scholar 

  • Sali, A., and Blundell, T.L. (1993). Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815.

    Article  CAS  PubMed  Google Scholar 

  • Sarda, D., Chua, G.H., Li, K.B., and Krishnan, A. (2005). pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties. BMC Bioinformatics 6, 152.

    Article  PubMed  Google Scholar 

  • Scholkopf, B.,and Smola, A.J. (2002). Learning with Kernels (Cambridge: The MIT Press).

    Google Scholar 

  • Shimizu, M., Potts, J.T., Jr., and Gardella, T.J. (2000). Minimiz. of parathyroid hormone. Novel amino-terminal parathyroid hormone fragments with enhanced potency in activating the type-1 parathyroid hormone receptor. J. Biol. Chem. 275, 21836–21843.

    Article  CAS  PubMed  Google Scholar 

  • Shimizu, M., Carter, P.H., Khatri, A., Potts, J.T., Jr., and Gardella, T.J. (2001). Enhanced activity in parathyroid hormone-(1-14) and -(1-11): novel peptides for probing ligand-receptor interactions. Endocrinology 142, 3068–3074.

    Article  CAS  PubMed  Google Scholar 

  • Supper, J. (2005). Predicting MHC class I binding peptides based on amino acid properties using decision trees and support vector machines (Tübingen: Department for Simulation of Biological Systems, University of Tübingen).

    Google Scholar 

  • Tanaka, S., and Scheraga, H.A. (1977). Statistical mechanical treatment of protein conformation. 5. A multistate model for specific-sequence copolymers of amino acids. Macromolecules 10, 9–20.

    Article  CAS  PubMed  Google Scholar 

  • Toschantaridis, I., Hofmann, T., Joachims, T., and Altun, Y. (2004). Support vector machine learning for interdependent and structured output spaces. (Banff, Canada: 21st International Conference on Machine Learning).

    Google Scholar 

  • Tsomaia, N., Pellegrini, M., Hyde, K., Gardella, T.J., and Mierke, D.F. (2004). Toward parathyroid hormone minimization: conformational studies of cyclic PTH(1-14) analogues. Biochemistry 43, 690–699.

    Article  CAS  PubMed  Google Scholar 

  • Vapnik, V.N. (1995). The Natural of Statistical Learning Theory (New York: Springer Verlag).

    Google Scholar 

  • Wilce, M.C., Aguilar, M.I., and Hearn, M.T. (1995). Physicochemical basis of amino acid hydrophobicity scales: evaluation of four new scales of amino acid hydrophobicity coefficient derived from RP-HPLC of peptides. Anal. Chem. 67, 1210–1219.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Weontae Lee or Dae Ryook Yang.

About this article

Cite this article

Yoo, A., Ko, S., Lim, SK. et al. Prediction of parathyroid hormone signalling potency using SVMs. Mol Cells 27, 547–556 (2009). https://doi.org/10.1007/s10059-009-0082-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10059-009-0082-3

Keywords

Navigation