Identifying ligand-binding pockets in RNA, computationally and experimentally

Most drugs bind to proteins, but RNA provides many interesting targets. Unfortunately, finding drug-like small molecules that bind to RNA is difficult. A new paper in Proc. Nat. Acad. Sci. USA from Kevin Weeks and colleagues at University of North Carolina Chapel Hill provides tools to do so.

 

RNA presents several challenges for drug discovery. First, there are far fewer high-resolution structures than there are for proteins. This is in part due to the second challenge: RNA strands are often wriggly, able to form multiple conformations. And finally, RNA is highly charged and more polar than most proteins, so there are fewer opportunities for the hydrophobic interactions that often provide significant affinity in protein-ligand complexes.

 

These challenges have not deterred intrepid investigators: Practical Fragments first wrote about targeting RNA with fragments way back in 2009. However, examples of high-affinity ligands remain elusive, and in 2023 I wondered whether “most RNA is truly undruggable.”

 

The latest paper leaves me more optimistic. It describes a computational approach to find small-molecule binding sites in RNA. The researchers started with an open-source tool called fpocket, which was built for proteins. The fpocket program places virtual spheres all around a biomolecule, where each sphere contacts the center of four atoms. The size of each sphere depends on local curvature, and clusters of spheres define pockets.

 

To benchmark fpocket on RNA, the researchers first constructed a curated database of drug-like ligands bound to RNA. Of 538 RNA-ligand structures solved at the fairly low bar of ˂ 3.5 Å resolution, only 48 ligands were deemed drug-like by the quantitative estimate of drug-likeness (QED) score. (Although the QED score may be overly restrictive, and many approved drugs have low QED scores, setting a strict threshold means that any pockets identified are likely to be particularly attractive.)

 

Using default (protein-appropriate) parameters, fpocket identified just 63% of known ligand-binding sites in RNA, vs 83% for proteins. Worse, many predicted RNA pockets probably aren’t actually ligandable because they are too exposed to solvent. By tweaking parameters, the researchers improved performance of the program for RNA to 92%, and they also identified several attractive pockets that had previously been missed.

 

When the researchers applied the reparametrized program, redubbed fpocketR, to two bacterial ribosomes, they found several dozen pockets in each, including known antibiotic-binding sites. To assess whether the new pockets could bind fragments, they used an experimental approach called Frag-MaP, which uses fully functionalized fragment (FFF) probes containing a variable fragment, a photoreactive diazirine, and an alkyne. Treating bacterial cells with these FFF probes in the presence of UV light crosslinks them to nearby RNA. Crosslinked probes can then be isolated using click chemistry with the alkyne, and RNA sequencing reveals the sites of modification. Impressively, 89% of ligand binding sites found in the Frag-MaP experiments were predicted by fpocketR.

 

In another validation experiment, fpocketR identified pockets where 7 out of 17 antibiotics bind to bacterial ribosomes. Notably, all but one of the undetected pockets bind antibiotics such as aminoglycosides that don’t appear conventionally drug-like and indeed are not orally bioavailable.

 

Continuing to apply fpocketR to more RNAs led to the identification of dozens of new pockets. Interestingly, most of these pockets occur in complex RNA structures, such as multi-helix junctions or pseudoknots, rather than simpler structures such as bulges and consecutive loops. This could explain the paucity of fragment hits in a study we highlighted in 2023, which focused on simple loops.

 

Now that we know where to find attractive ligand-binding pockets in RNA, hopefully we will be more successful finding high-affinity ligands.