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. 1993 Sep;2(9):1502–1510. doi: 10.1002/pro.5560020915

Modeling of protein loops by simulated annealing.

V Collura 1, J Higo 1, J Garnier 1
PMCID: PMC2142460  PMID: 8401234

Abstract

A method is presented to model loops of protein to be used in homology modeling of proteins. This method employs the ESAP program of Higo et al. (Higo, J., Collura, V., & Garnier, J., 1992, Biopolymers 32, 33-43) and is based on a fast Monte Carlo simulation and a simulated annealing algorithm. The method is tested on different loops or peptide segments from immunoglobulin, bovine pancreatic trypsin inhibitor, and bovine trypsin. The predicted structure is obtained from the ensemble average of the coordinates of the Monte Carlo simulation at 300 K, which exhibits the lowest internal energy. The starting conformation of the loop prior to modeling is chosen to be completely extended, and a closing harmonic potential is applied to N, CA, C, and O atoms of the terminal residues. A rigid geometry potential of Robson and Platt (1986, J. Mol. Biol. 188, 259-281) with a united atom representation is used. This we demonstrate to yield a loop structure with good hydrogen bonding and torsion angles in the allowed regions of the Ramachandran map. The average accuracy of the modeling evaluated on the eight modeled loops is 1 A root mean square deviation (rmsd) for the backbone atoms and 2.3 A rmsd for all heavy atoms.

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Selected References

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  1. Bernstein F. C., Koetzle T. F., Williams G. J., Meyer E. F., Jr, Brice M. D., Rodgers J. R., Kennard O., Shimanouchi T., Tasumi M. The Protein Data Bank: a computer-based archival file for macromolecular structures. J Mol Biol. 1977 May 25;112(3):535–542. doi: 10.1016/s0022-2836(77)80200-3. [DOI] [PubMed] [Google Scholar]
  2. Bouzida D, Kumar S, Swendsen RH. Efficient Monte Carlo methods for the computer simulation of biological molecules. Phys Rev A. 1992 Jun 15;45(12):8894–8901. doi: 10.1103/physreva.45.8894. [DOI] [PubMed] [Google Scholar]
  3. Bruccoleri R. E., Haber E., Novotný J. Structure of antibody hypervariable loops reproduced by a conformational search algorithm. Nature. 1988 Oct 6;335(6190):564–568. doi: 10.1038/335564a0. [DOI] [PubMed] [Google Scholar]
  4. Chothia C., Lesk A. M. Canonical structures for the hypervariable regions of immunoglobulins. J Mol Biol. 1987 Aug 20;196(4):901–917. doi: 10.1016/0022-2836(87)90412-8. [DOI] [PubMed] [Google Scholar]
  5. Chothia C., Lesk A. M. The relation between the divergence of sequence and structure in proteins. EMBO J. 1986 Apr;5(4):823–826. doi: 10.1002/j.1460-2075.1986.tb04288.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Fine R. M., Wang H., Shenkin P. S., Yarmush D. L., Levinthal C. Predicting antibody hypervariable loop conformations. II: Minimization and molecular dynamics studies of MCPC603 from many randomly generated loop conformations. Proteins. 1986 Dec;1(4):342–362. doi: 10.1002/prot.340010408. [DOI] [PubMed] [Google Scholar]
  7. Foote J., Winter G. Antibody framework residues affecting the conformation of the hypervariable loops. J Mol Biol. 1992 Mar 20;224(2):487–499. doi: 10.1016/0022-2836(92)91010-m. [DOI] [PubMed] [Google Scholar]
  8. Garnier J., Levin J. M. The protein structure code: what is its present status? Comput Appl Biosci. 1991 Apr;7(2):133–142. doi: 10.1093/bioinformatics/7.2.133. [DOI] [PubMed] [Google Scholar]
  9. Higo J., Collura V., Garnier J. Development of an extended simulated annealing method: application to the modeling of complementary determining regions of immunoglobulins. Biopolymers. 1992 Jan;32(1):33–43. doi: 10.1002/bip.360320106. [DOI] [PubMed] [Google Scholar]
  10. Kabsch W., Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers. 1983 Dec;22(12):2577–2637. doi: 10.1002/bip.360221211. [DOI] [PubMed] [Google Scholar]
  11. Kirkpatrick S., Gelatt C. D., Jr, Vecchi M. P. Optimization by simulated annealing. Science. 1983 May 13;220(4598):671–680. doi: 10.1126/science.220.4598.671. [DOI] [PubMed] [Google Scholar]
  12. Martin A. C., Cheetham J. C., Rees A. R. Modeling antibody hypervariable loops: a combined algorithm. Proc Natl Acad Sci U S A. 1989 Dec;86(23):9268–9272. doi: 10.1073/pnas.86.23.9268. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Morris A. L., MacArthur M. W., Hutchinson E. G., Thornton J. M. Stereochemical quality of protein structure coordinates. Proteins. 1992 Apr;12(4):345–364. doi: 10.1002/prot.340120407. [DOI] [PubMed] [Google Scholar]
  14. Northrup S. H., McCammon J. A. Simulation methods for protein structure fluctuations. Biopolymers. 1980 May;19(5):1001–1016. doi: 10.1002/bip.1980.360190506. [DOI] [PubMed] [Google Scholar]
  15. Satow Y., Cohen G. H., Padlan E. A., Davies D. R. Phosphocholine binding immunoglobulin Fab McPC603. An X-ray diffraction study at 2.7 A. J Mol Biol. 1986 Aug 20;190(4):593–604. doi: 10.1016/0022-2836(86)90245-7. [DOI] [PubMed] [Google Scholar]
  16. Shenkin P. S., Yarmush D. L., Fine R. M., Wang H. J., Levinthal C. Predicting antibody hypervariable loop conformation. I. Ensembles of random conformations for ringlike structures. Biopolymers. 1987 Dec;26(12):2053–2085. doi: 10.1002/bip.360261207. [DOI] [PubMed] [Google Scholar]
  17. Shin J. K., Jhon M. S. High directional Monte Carlo procedure coupled with the temperature heating and annealing as a method to obtain the global energy minimum structure of polypeptides and proteins. Biopolymers. 1991 Feb 5;31(2):177–185. doi: 10.1002/bip.360310206. [DOI] [PubMed] [Google Scholar]
  18. Srinivasan S., March C. J., Sudarsanam S. An automated method for modeling proteins on known templates using distance geometry. Protein Sci. 1993 Feb;2(2):277–289. doi: 10.1002/pro.5560020216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Sutcliffe M. J., Haneef I., Carney D., Blundell T. L. Knowledge based modelling of homologous proteins, Part I: Three-dimensional frameworks derived from the simultaneous superposition of multiple structures. Protein Eng. 1987 Oct-Nov;1(5):377–384. doi: 10.1093/protein/1.5.377. [DOI] [PubMed] [Google Scholar]

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