Research article

FlexOracle: predicting flexible hinges by identification of stable domains

Samuel C Flores^1,2 and Mark B Gerstein^2,3,4

¹Department of Physics, Yale University, Bass 432, 266 Whitney Ave. New Haven, CT 06520, USA

²Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Ave. New Haven, CT 06520, USA

³Department of Computer Science, Yale University, Bass 432, 266 Whitney Ave. New Haven, CT 06520, USA

⁴Computational Biology and Bioinformatics Program, Yale University, Bass 432, 266 Whitney Ave. New Haven, CT 06520, USA

author email corresponding author email

BMC Bioinformatics 2007, 8:215doi:10.1186/1471-2105-8-215

Published:	22 June 2007

Abstract

Background

Protein motions play an essential role in catalysis and protein-ligand interactions, but are difficult to observe directly. A substantial fraction of protein motions involve hinge bending. For these proteins, the accurate identification of flexible hinges connecting rigid domains would provide significant insight into motion. Programs such as GNM and FIRST have made global flexibility predictions available at low computational cost, but are not designed specifically for finding hinge points.

Results

Here we present the novel FlexOracle hinge prediction approach based on the ideas that energetic interactions are stronger within structural domains than between them, and that fragments generated by cleaving the protein at the hinge site are independently stable. We implement this as a tool within the Database of Macromolecular Motions, MolMovDB.org. For a given structure, we generate pairs of fragments based on scanning all possible cleavage points on the protein chain, compute the energy of the fragments compared with the undivided protein, and predict hinges where this quantity is minimal. We present three specific implementations of this approach. In the first, we consider only pairs of fragments generated by cutting at a single location on the protein chain and then use a standard molecular mechanics force field to calculate the enthalpies of the two fragments. In the second, we generate fragments in the same way but instead compute their free energies using a knowledge based force field. In the third, we generate fragment pairs by cutting at two points on the protein chain and then calculate their free energies.

Conclusion

Quantitative results demonstrate our method's ability to predict known hinges from the Database of Macromolecular Motions.