The researchers dissect three non-nucleoside reverse transcriptase inhibitors (NNRTIs) into a total of 21 commercially available “fragments”. Each of these was then tested for binding using SPR (see also this paper for a detailed account of how they perform these screens, and this one for discovery of new fragments against this target). If the binding energies of the fragments were evenly distributed across the entire parent NNTRIs, most of the fragments would be predicted to be sub-millimolar. In fact, most of them were much worse: only 9 showed any evidence at for binding, and only 3 were fragment-sized (the other six had molecular weights above 300 Da).
This sort of result – that fragments of larger molecules bind less effectively than predicted – has now been seen several times, and the researchers asked why. One issue is that when a molecule binds to a protein it loses translational and rotational entropy, and this imposes an energetic cost. This “fee” is, unfortunately, hard to estimate, and complicated by the fact that there may be further energetic costs if the protein itself is flexible (as in the case of HIV-1 RT). The authors provide a nice review of the literature, where values range from 2.5 to a whopping 16 kcal/mol (see here for more discussion on this). When they (admittedly arbitrarily) subtracted 7.0 kcal/mol, the agreement between expected and observed binding of their fragments improved.
However, as the researchers acknowledge, this model still assumes that the binding energy is equally distributed over the entire parent molecule – in other words, it ignores the existence of hot spots. The fact that hot spots exist probably accounts for the decrease in maximum observed ligand efficiency with an increase in the number of heavy atoms:
Once [the hot spot] is occupied, larger molecules need also to interact with other parts of the ligand binding pocket. Hence, a decrease in ligand efficiency will be observed for larger molecules.True, and to complicate things even more, different proteins will have hot spots of different sizes and “temperature” – or perhaps none at all. This variation calls into question the utility of using notions such as fit quality or %LE, which attempt to normalize ligand efficiency for the size of the ligand. The problem is that different proteins are likely to have different maximal affinity ligands; kinases tend to have high-affinity binding sites where high ligand efficiency can be achieved, while for protein-protein interactions the ligand binding site is likely to be larger and the ligand efficiencies lower. Thus, one-size fits all metrics could prove too stringent – or not stringent enough.