FDS: flexible ligand and receptor docking with a continuum solvent model and soft-core energy function.
The docking of flexible small molecule ligands to large flexible protein targets is addressed in this article using a two-stage simulation-based method. The methodology presented is a hybrid approach where the first component is a dock of the ligand to the protein binding site, based on deriving sets of simultaneously satisfied intermolecular hydrogen bonds using graph theory and a recursive distance geometry algorithm. The output structures are reduced in number by cluster analysis based on distance similarities. These structures are submitted to a modified Monte Carlo algorithm using the AMBER-AA molecular mechanics force field with the Generalized Born/Surface Area (GB/SA) continuum model. This solvent model is not only less expensive than an explicit representation, but also yields increased sampling. Sampling is also increased using a rotamer library to direct some of the protein side-chain movements along with large dihedral moves. Finally, a softening function for the nonbonded force field terms is used, enabling the potential energy function to be slowly turned on throughout the course of the simulation. The docking procedure is optimized, and the results are presented for a single complex of the arabinose binding protein. It was found that for a rigid receptor model, the X-ray binding geometry was reproduced and uniquely identified based on the associated potential energy. However, when side-chain flexibility was included, although the X-ray structure was identified, it was one of three possible binding geometries that were energetically indistinguishable. These results suggest that on relaxing the constraint on receptor flexibility, the docking energy hypersurface changes from being funnel-like to rugged. A further 14 complexes were then examined using the optimized protocol. For each complex the docking methodology was tested for a fully flexible ligand, both with and without protein side-chain flexibility. For the rigid protein docking, 13 out of the 15 test cases were able to find the experimental binding mode; this number was reduced to 11 for the flexible protein docking. However, of these 11, in the majority of cases the experimental binding mode was not uniquely identified, but was present in a cluster of low energy structures that were energetically indistinguishable. These results not only support the presence of a rugged docking energy hypersurface, but also suggest that it may be necessary to consider the possibility of more than one binding conformation during ligand optimization.
Taylor
Richard D
RD
Department of Chemistry, University of Southampton, Highfield, Southampton, SO17 1BJ, UK.
Jewsbury
Philip J
PJ
Essex
Jonathan W
JW
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D002352:Carrier Proteins / Q000737:chemistry; D003198:Computer Simulation; D018360:Crystallography, X-Ray; D029968:Escherichia coli Proteins / Q000737:chemistry; D006860:Hydrogen Bonding; D008024:Ligands; D008958:Models, Molecular; D015394:Molecular Structure; D009010:Monte Carlo Method; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D013816:Thermodynamics
D000596:Amino Acids; C016242:AraF protein, E coli; D002352:Carrier Proteins; D029968:Escherichia coli Proteins; D008024:Ligands; D011506:Proteins