Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-24T04:37:13.904Z Has data issue: false hasContentIssue false
  • English
  • Français

CLP-based protein fragment assembly*

Published online by Cambridge University Press:  09 July 2010

ALESSANDRO DAL PALÙ ,
AGOSTINO DOVIER ,
FEDERICO FOGOLARI  and
ENRICO PONTELLI
ALESSANDRO DAL PALÙ
Affiliation:
Department of Mathematics, University of Parma, Italy
AGOSTINO DOVIER
Affiliation:
Department of Mathematics and Computer Science, University of Udine, Italy
FEDERICO FOGOLARI
Affiliation:
Department of Biomedical Sciences, University of Udine, Italy
ENRICO PONTELLI
Affiliation:
Department of Computer Science, New Mexico State University, NM, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

The paper investigates a novel approach, based on Constraint Logic Programming (CLP), to predict the 3D conformation of a protein via fragments assembly. The fragments are extracted by a preprocessor—also developed for this work—from a database of known protein structures that clusters and classifies the fragments according to similarity and frequency. The problem of assembling fragments into a complete conformation is mapped to a constraint solving problem and solved using CLP. The constraint-based model uses a medium discretization degree Cα-side chain centroid protein model that offers efficiency and a good approximation for space filling. The approach and adapts existing energy models to the protein representation used and applies a large neighboring search strategy. The results shows the feasibility and efficiency of the method. The declarative nature of the solution allows to include future extensions, e.g., different size fragments for better accuracy.

Keywords

constraint logic programmingprotein fragment assembly
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 10 , Special Issue 4-6: 26th International Conference on Logic Programming , July 2010 , pp. 709 - 724
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Backofen, R. and Will, S. 2006. A constraint-based approach to fast and exact structure prediction in three-dimensional protein models. Constraints 11, 1, 5–30.CrossRefGoogle Scholar
Barahona, P. and Krippahl, L. 2008. Constraint programming in structural bioinformatics. Constraints 13, 1–2, 3–20.CrossRefGoogle Scholar
Ben-David, M., Noivirt-Brik, O., Paz, A., Prilusky, J., Sussman, J. L. and Levy, Y. 2009. Assessment of CASP8 structure predictions for template free targets. Proteins: Structure, Function, and Bioinformatics 77, S9, 50–65.CrossRefGoogle ScholarPubMed
Berrera, M., Molinari, H. and Fogolari, F. 2003. Amino acid empirical contact energy definitions for fold recognition in the space of contact maps. BMC Bioinformatics 4, 8.CrossRefGoogle ScholarPubMed
Crescenzi, P., Goldman, D., Papadimitriou, C., Piccolboni, A. and Yannakakis, M. 1998. On the complexity of protein folding (extended abstract). In STOC '98: Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing. ACM, New York597–603.CrossRefGoogle Scholar
Dal Palù, A., Dovier, A. and Fogolari, F. 2004. Constraint logic programming approach to protein structure prediction. BMC Bioinformatics 5, 186.CrossRefGoogle ScholarPubMed
Dal Palù, A., Dovier, A. and Pontelli, E. 2007. A constraint solver for discrete lattices, its parallelization, and application to protein structure prediction. Software: Practice and Experience 37, 13, 1405–1449.Google Scholar
Dal Palù, A., Dovier, A. and Pontelli, E. 2010. Computing approximate solutions of the protein structure determination problem using global constraints on discrete crystal lattices. International Journal of Data Mining and Bioinformatics 4, 1, 1–20.CrossRefGoogle ScholarPubMed
Dotu, I., Cebrián, M., Hentenryck, P. and Clote, P. 2008. Protein structure prediction with large neighborhood constraint programming search. In CP '08: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming. LNCS, vol. 5202. Springer, Berlin, 82–96.CrossRefGoogle Scholar
Fogolari, F., Pieri, L., Dovier, A., Bortolussi, L., Giugliarelli, G., Corazza, A., Esposito, G., and Viglino, P. 2007. Scoring predictive models using a reduced representation of proteins: model and energy definition. BMC Structural Biology 7, 15.CrossRefGoogle ScholarPubMed
Levinthal, C. 1968. Are there pathways in protein folding? Journal of Chemical Physics 65, 44–45.Google Scholar
Lovell, S., Davis, I., Arendall, W., de Bakker, P., Word, J., Prisant, M., Richardson, J. and Richardson, D. 2003. Structure validation by cα geometry: φ, ψ and cβ deviation. Proteins 50, 437–450.CrossRefGoogle ScholarPubMed
Raman, S., Vernon, R., Thompson, J., Tyka, M., Sadreyev, R., Pei, J., Kim, D., Kellogg, E., DiMaio, F., Lange, O., Kinch, L., Sheffler, W., Kim, B.-H., Das, R., Grishin, N. V. and Baker, D. 2009. Structure prediction for casp8 with all-atom refinement using rosetta. Proteins 77, S9, 89–99.CrossRefGoogle ScholarPubMed
Shaw, P. 1998. Using constraint programming and local search methods to solve vehicle routing problems. In CP '98: Proceedings of the 14th International Conference on Principles and Practice of Constraint Programming. LNCS, vol. 1520. Springer, 417–431.Google Scholar
Wu, S., Skolnick, J. and Zhang, Y. 2007. Ab initio modeling of small proteins by iterative tasser simulations. BMC Biology 5, 17.CrossRefGoogle ScholarPubMed
Zemla, A. 2003. LGA: A method for finding 3D similarities in protein structures. Nucleic Acids Research 31, 13, 3370–3374.CrossRefGoogle ScholarPubMed