The role of water in mediating protein-ligand interactions has now been well-recognized by both experimentalists and modelers. However it's been relatively recently that modelers have actually started taking the unique roles that water plays into account. While the role of water in bridging ligand and protein atoms is obvious, a more subtle but crucial role of water is to fill up hydrophobic pockets in proteins. Such waters can be very unhappy in such pockets because of both unfavourable entropy (not much movement) and enthalpy (inability to form a full complement of 4 hydrogen bonds). If one can design a ligand that will displace such waters, significant gains in affinity would be obtained. One docking approach that does take such properties of waters into consideration is Schrodinger's Glide, with a recent paper attesting to the importance of such a method for Factor Xa inhibitors.
Clearly the exclusion of water molecules during docking and virtual screening (VS) will hamper enrichment factors, namely how well you can rank actives above inactives. Now a series of experiments from Brian Shoichet's group illustrates the benefits of including waters in active sites when doing virtual screening. These experiments seem to work in spite of two approximations that should have posed significant problems, but surprisingly did not.
To initiate the experiments, the authors chose a set of 24 targets and their corresponding ligands from their well-known DUD ligand set. This is a VS data set in which ligands are distinguished by topology but not by physical properties such as size and lipophilicity. This feature makes sure that ligands aren't trivially distinguished by VS methods on the basis of such properties alone. Importantly, the complexes were chosen so that the waters in them are bridging waters with at least two hydrogen bonds to the protein, and not waters which simply occupy hydrophobic pockets. Note that this would exclude a lot of important cases where affinity comes from displacement of such waters.
Now for the approximations. Firstly, the authors treated each water molecule separately in multiple configurations. They then scored the docked ligands against each such configuration as well as the rest of the protein. The waters were treated as either "on" or "off", that is, either displaced or not displaced. Whether to keep a water or not depended on whether the score improved or not when it was displaced by a ligand. The best scored ligands were then selected and figured high on the enrichment curve. This is a significant approximation because the assumption here is that every water contributes to ligand binding affinity independently of the other waters. While this would be true in certain cases, there is no reason to assume that it would generally hold.
The second approximation was even more important and startling. All the waters were regarded as energetically equivalent. From our knowledge of protein-ligand interactions, we know that the reason why evaluating waters in protein active sites is such a tricky business is precisely because each water has a different energetic profile. In fact the Factor Xa study cited above takes this profile into consideration. Without such an analysis it would be difficult to tell the medicinal chemist which part of the molecule to modify to get the best binding affinity from water displacement.
The most important benefit of this approximate approach was a linear increase in computational time instead of an exponential one. This was clearly because of the separate-water configuration approximation. The calculation of individual water free energies would also have added to this time.
In spite of these crucial approximations, the results indicate that the ability to distinguish actives from inactives was considerably improved for 12 out of 24 targets. This is not saying much, but even 50% sounds like a lot in the face of such approximations. Clearly an examination of the protein active site will also help to evaluate which cases will benefit, but it will also naturally depend on the structure of the ligand.
For now, this is an encouraging result and indicates that this approach could be implemented in virtual screening. There are probably very few cases where docking accuracy decreases when waters are included. With the sparse increases in computational time, this would be a quick and dirty but viable approach for virtual screening.
Reference: Niu Huang, Brian K. Shoichet (2008). Exploiting Ordered Waters in Molecular Docking Journal of Medicinal Chemistry, 51 (16), 4862-4865 DOI: 10.1021/jm8006239