Richard D. Taylor

A review of protein-small molecule docking methods

The binding of small molecule ligands to large protein targets is central to numerous biological processes. The accurate prediction of the binding modes between the ligand and protein, (the docking problem) is of fundamental importance in modern structure-based drug design. An overview of current docking techniques is presented with a description of applications including single docking experiments and the virtual screening of databases.

The nature of X-ray spectral variability in Seyfert galaxies

We use a model-independent technique to investigate the nature of the 2–15 keV X-ray spectral variability in four Seyfert galaxies and distinguish between spectral pivoting and the two-component model for spectral variability. Our analysis reveals conclusively that the softening of the X-ray continuum with increasing flux in MCG?6-30-15 and NGC 3516 is a result of summing two spectral components: a soft varying component (SVC) with spectral shape independent of flux and a hard constant component (HCC). In contrast, the spectral variability in NGC 4051 can be well described by simple pivoting of one component, together with an additional hard constant component. The spectral variability model for NGC 5506 is ambiguous, due to the smaller range of fluxes sampled by the data. We investigate the shape of the hard spectral component in MCG?6-30-15 and find that it appears similar to a pure reflection spectrum, but requires a large reflected fraction (R> 3). We briefly discuss physical interpretations of the different modes of spectral variability.

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.

Efficient generalized Born models for Monte Carlo simulations

The Generalized Born Surface Area theory (GBSA) has become a popular method to model the solvation of biomolecules. While efficient in the context of molecular dynamics simulations, GBSA calculations do not integrate well with Monte Carlo simulations because of the nonlocal nature of the Generalized Born energy. We present a method by which Monte Carlo Generalized Born simulations can be made seven to eight times faster on a protein-ligand binding free energy calculation with little or no loss of accuracy. The method can be employed in any type of Monte Carlo or Hybrid Monte Carlo-molecular dynamics simulation and should prove useful in numerous applications.

The parameterization and validation of generalized born models using the pairwise descreening approximation

Generalized Born Surface Area (GBSA) models for water using the Pairwise Descreening Approximation (PDA) have been parameterized by two different methods. The first method, similar to that used in previously reported parameterizations, optimizes all parameters against the experimental free energies of hydration of organic molecules. The second method optimizes the PDA parameters to compensate only for systematic errors of the PDA. The best models are compared to Poisson–Boltzmann calculations and applied to the computation of potentials of mean force (PMFs) for the association of various molecules. PMFs present a more rigorous test of the ability of a solvation model to correctly reproduce the screening of intermolecular interactions by the solvent, than its accuracy at predicting free energies of hydration of small molecules. Models derived with the first method are sometimes shown to fail to compute accurate potentials of mean force because of large errors in the computation of Born radii, while no such difficulties are observed with the second method. Furthermore, accurate computation of the Born radii appears to be more important than good agreement with experimental free energies of solvation. We discuss the source of errors in the potentials of mean force and suggest means to reduce them. Our findings suggest that Generalized Born models that use the Pairwise Descreening Approximation and that are derived solely by unconstrained optimization of parameters against free energies of hydration should be applied to the modeling of intermolecular interactions with caution.