When do ligands change their binding modes?

Elaborating a fragment to improve its affinity relies on the assumption that the fragment will maintain its position and orientation during optimization. Although this is usually the case, exceptions are common, and when flips go unrecognized the resulting SAR can be confusing. Is it possible to predict which ligands are most likely to change their binding mode? This question is addressed in a recent J. Med. Chem. paper by Shipra Malhotra and John Karanicolas of the Fox Chase Cancer Center and the University of Kansas.

The researchers scoured the Protein Data Bank (PDB) to find pairs of molecules bound to the same protein where one ligand was a substructure of the other. (In most cases these were not actually from fragment-based efforts, and the two structures were often solved by different research groups.) This generated 297 pairs of crystal structures. Computational and manual analyses revealed 41 instances (14%) in which the larger ligand had a significantly different binding mode than the smaller ligand. Careful inspection revealed that these observations were probably not due to crystallographic artifacts or differences in experimental conditions. The researchers then examined well over a dozen parameters to look for correlations with changes in binding mode.

Size matters: for the 73 rule-of-three compliant smaller ligands, the binding modes were not conserved in the larger ligands 23% of the time. Binding modes changed 30% of the time when the smaller ligand was ~100 Da, but only 5% of the time when the smaller ligand was ~400 Da.

Potency also matters: as might be expected, weaker ligands were statistically less likely to preserve their binding mode. (Of course, as the researchers observe, potency often correlates with size.) More polar ligands, as assessed by clogP, were also less likely to maintain their binding modes.

Looking beyond molecular properties to those of the initial complex, ligands binding to a small pocket were less likely to maintain their binding modes. Also, ligands for which a large amount of solvent-accessible surface area was buried upon binding to the protein were more likely to maintain their binding modes.

Many other properties showed no statistically significant correlation with binding modes. These included ligand efficiency, fraction of the ligand buried, and various descriptions of the protein binding site, such as hydrophobicity and the fraction of polar or aromatic amino acid residues.

The open-access hot-spot finding software FTMap has previously been used to assess when ligands change their conformation, and it performed well on this set of molecules, although as it requires structures of both the larger and smaller ligands it has limited predictive value. The researchers also introduced another computational tool, RMAC (RMSD after Minimization of the Aligned Complex) which did even better.

This paper is a fun read, and there’s lots more than can be summarized here, including detailed analyses of specific examples. The researchers have done a great job collecting and synthesizing a huge amount of data. Admirably, all of the calculated properties are available in the supporting information. Of course – in view of the title of this blog – we have to ask, how practical is it? For any given fragment to lead program, it is still impossible to predict whether the binding mode will shift. But if you’ve got a particularly small, weak fragment binding to a little dimple on a protein surface, you should probably expect surprises.