Journal of the Korean Society for Industrial and Applied Mathematics

  • 제15권3호
  • /
  • Pages.191-199
  • /
  • 2011
  • /
  • 1226-9433(pISSN)
  • /
  • 1229-0645(eISSN)
한국산업응용수학회 (Korean Society for Industrial and Applied Mathematics)
권호 표지
DOI QR코드

DOI QR Code

USING AN ABSTRACTION OF AMINO ACID TYPES TO IMPROVE THE QUALITY OF STATISTICAL POTENTIALS FOR PROTEIN STRUCTURE PREDICTION

  • Lee, Jin-Woo (DEPARTMENT OF MATHEMATICS, KYUNGWON UNIVERSITY)
  • 투고 : 2011.06.01
  • 심사 : 2011.08.18
  • 발행 : 2011.09.23
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, we adopt a position specific scoring matrix as an abstraction of amino acid type to derive two new statistical potentials for protein structure prediction, and investigated its effect on the quality of the potentials compared to that derived using residue specific amino acid identity. For stringent test of the potential quality, we carried out folding simulations of 91 residue A chain of protein 2gpi, and found unexpectedly that the abstract amino acid type improved the quality of the one-body type statistical potential, but not for the two-body type statistical potential which describes long range interactions. This observation could be effectively used when one develops more accurate potentials for structure prediction, which are usually involved in merging various one-body and many-body potentials.

키워드

참고문헌

  1. A. Neumaier, Molecular modeling of proteins and mathematical prediction of protein structure, SIAM Rev, 39 (1997), 407-460. https://doi.org/10.1137/S0036144594278060
  2. C.B. Anfinsen, Principles that govern the folding of protein chains, Science, 181 (1973), 223-230. https://doi.org/10.1126/science.181.4096.223
  3. R. Samudrala and J. Moult, An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction, J Mol Biol, 275 (1998), 895-916. https://doi.org/10.1006/jmbi.1997.1479
  4. M.J. Sippl, Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins, J Mol Biol, 213 (1990), 859-883. https://doi.org/10.1016/S0022-2836(05)80269-4
  5. H. Zhou and Y. Zhou, Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction, Protein science, 11 (2002), 2714-2726.
  6. T. Hamelryck, Potentials of mean force for protein structure prediction vindicated, formalized and generalized, PLoS ONE, 5 (2010), 1-11.
  7. M. Gribskov, A.D. McLachlan, and D. Eisenberg, Profile analysis: detection of distantly related proteins, Proc Natl Acad Sci, 84 (1987), 4355-8. https://doi.org/10.1073/pnas.84.13.4355
  8. Y. Zhang, Template-based modeling and free modeling by I-TASSER in CASP7, Proteins, 69(Suppl 8) (2007), 108-117. https://doi.org/10.1002/prot.21702
  9. D. Chandler, Interfaces and the driving force of hydrophobic assembly, Nature, 437 (2005), 640-647. https://doi.org/10.1038/nature04162
  10. T. Hamelryck, An amino aicd has two sides: a new 2D measure provides a different view of solvent exposure, Proteins, 59 (2005), 38-48. https://doi.org/10.1002/prot.20379
  11. G. Wang and R.L.Dunbrack, PISCES: a protein sequence culling server, Bioinformatics, 19 (2003), 1589- 1591. https://doi.org/10.1093/bioinformatics/btg224
  12. R. Das and D. Baker, Macromolecuar modeling with rosetta, Annu.Rev.Biochem., 77 (2008), 363-382. https://doi.org/10.1146/annurev.biochem.77.062906.171838
  13. J. Lee, H.A.Scheraga, and S.Rackovsky, New optimization method for conformational energy calculations on polypeptides: conformational space annealing, J. Comput. Chem., 18 (1997), 1222-1232. https://doi.org/10.1002/(SICI)1096-987X(19970715)18:9<1222::AID-JCC10>3.0.CO;2-7
  14. S-Y Kim, S.J. Lee, and J. Lee, Conformational space annealing and an off-lattice frustrated model protein, J. Chem. Phys., 119 (2003), 10274-10279. https://doi.org/10.1063/1.1616917
  15. J. Lee, K. Joo, S-Y Kim, and J. Lee, Re-examination of structure optimization of off-lattice protein AB models by Conformational space annealing, J. Comput. Chem., 29 (2008), 2479-2484. https://doi.org/10.1002/jcc.20995
  16. K. Joo, J. Lee, I. Kim, S.J.Lee, and J. Lee, Multiple sequence alignment by conformational space annealing, Biophysical J., 95 (2008), 4813-4819. https://doi.org/10.1529/biophysj.108.129684
  17. K. Joo, J. Lee, J-H Seo, K. Lee, B-G Kim, and J. Lee, All-atom chain-building by optimizing MODELLER energy function using conformational space annealing, Proteins., 75 (2009), 1010-1023. https://doi.org/10.1002/prot.22312
  18. DT Jones, Protein secondary structure prediction based on position-specific scoring matrices, J. Mol. Biol., 292 (1999), 195-202. https://doi.org/10.1006/jmbi.1999.3091