Advancing fragments without high-resolution structural information remains a challenge scientists often choose not to take on, according to our poll last year. But for many appealing targets, such as membrane proteins, structural information is difficult to obtain. In a new paper in J. Med. Chem., Peter Kolb and collaborators at Philipps-University Marburg and Vrije Universiteit Brussel describe a computational strategy.
The approach, called “growing via merging”, starts with a core fragment that binds to a target, in this case the β2-adrenergic receptor (β2AR). Ideally this interaction is structurally characterized, but if not a model can suffice. Here, the researchers started with five fragments they had previously discovered. All of these had in common a lipophilic core with a primary or secondary amine appendage; this is a known pharmacophore for β2AR, so modeling could be used to orient the fragments.
Next, this core fragment is derivatized in silico with other fragments using a selection of 58 common reactions. Since all five fragments contained an amine, reductive amination was used here. A set of nearly 19,000 fragment-sized aldehydes and ketones was extracted from the ZINC database and computationally transformed into amines – as if they were reacted with one of the core fragments. These were then docked into the receptor, and those that did not overlap with the core fragments and also placed the amine near the amine of the core fragment were kept for further analysis.
The top 500-scoring fragments were then “reacted” – again in silico – with the core fragments and again docked. Eight of these were actually synthesized and tested for binding, of which four had higher affinity than the initial fragments. The best, compound 11, showed a 40-fold boost in affinity over its starting fragment.