Which cryptic sites are ligandable, and why?

Many interesting proteins have flat, featureless surfaces, lacking the deep pockets in which small molecules usually bind. But structures can be deceptive: crevasses can open unexpectedly, revealing “cryptic sites” for ligands. Or not – just because a site is available does not mean it is ligandable (able to bind to ligands with high affinity). A new (open access) paper in Drug Disc. Today by Sandor Vajda and collaborators at Boston University and Stony Brook University asks “which cryptic sites are feasible for drug targets?” (Sandor presented some of this at FBLD 2024 last month.)

 

To get started, the researchers turned to the aptly named CryptoSite, a previously published list of 93 proteins where unexpected pockets had been found. Each protein has at least two published crystal structures, one in the apo form and one with a ligand bound to the (no longer) cryptic pocket. Cryptic sites form primarily through two mechanisms. In the first, amino acid side chains move aside, opening a pocket. In the second, larger motions occur in protein loops or secondary structural elements, such as alpha helices, creating pockets.

 

Of the 18 cases for which cryptic sites formed primarily through the movement of side chains, ten had published affinities for the ligands, and all of these were weak, with the best being low micromolar. In contrast, of the 27 cryptic sites created by loop movements for which affinity information was available, all but two were nanomolar binders. From this evidence, the researchers suggest that cryptic sites formed only by the motion of side chains are not sufficient to support high affinity ligands. Why?

 

The researchers note that side chain motions occur very rapidly, on a timescale of 10^-11 to 10^-10 seconds, much faster than ligand binding, which at its fastest is 10^-8 seconds. Thus, “a fast-moving side chain that spends a substantial fraction of time in the pocket interacting with the other residues competes with ligands for binding and, hence, acts as a competitive inhibitor.” This intuitive picture is supported in the paper by mathematical simulations.

 

In contrast, loop movements occur on 10^-9 to 10^-6 second timescales, while the movements of secondary structure elements are even slower. Thus, a ligand could bind while the cryptic site is open, and, like a wrench in a machine, keep it open.

 

This finding is important. As the researchers point out, the molecular dynamics calculations frequently used to find cryptic pockets are typically run at short timescales likely to miss loop movements. Other computational methods used to assess ligandability may also suffer; the researchers note that their program FTMap, which we’ve written about here and here, overestimates the ligandability of cryptic sites created by side chain movements.

 

Of course, just because a cryptic site is created by loop movements does not mean it is ligandable, as we discussed for interleukin-1β. And the researchers acknowledge that covalent inhibitors might be able to take advantage of less traditionally ligandable sites, cryptic or otherwise. Certainly this has been the case for KRAS. I’m confident that many more examples will be forthcoming.

Fragments vs LpxC revisited

Back in 2020 we described fragment-derived inhibitors of the highly conserved bacterial enzyme LpxC, which is essential for biosynthesis of the outer membrane in Gram-negative bacteria. In a recent (open access) paper in J. Med. Chem., a different group consisting of Ralph Holl and collaborators at Universität Hamburg and several other academic centers describe a new series.

 

The researchers started with compound 9, a molecule they had previously discovered. The substrate for LpxC is a rather large small molecule called (UDP)-3-O-[(R)-3-hydroxymyristoyl]-N-acetylglucosamine. Compound 9 does not occupy the UDP-binding site, so the researchers initially tried building towards it with a series of simple linkers connected to a phenyl group. The (S) enantiomers tended to be more active than the (R)-enantiomers, and the most potent was compound (S)-13a, which showed sub-micromolar activity against LpxC from E. coli as well as P. aeruginosa in an enzymatic assay. (For simplicity only the E. coli data are shown here.)

 

Seeking to improve affinity, the researchers screened 650 fragments in pools of five against LpxC in the presence of compound 9 using STD NMR and WaterLOGSY. After deconvolution, this led to 97 hits. STD-based epitope mapping, which we wrote about here, was used to prioritize fragments likely to have a single, well-defined binding mode, culling the number to 19. Finally, NMR-ILOE experiments (see here) suggested that nine of this set bound in close proximity to compound 9, while the other ten did not. Four of these fragments, including the simple indole F3, were then linked to compound 9 at various positions. This is akin to SAR by NMR, but with less information about the relative binding modes so more trial and error is necessary.

 

Among the roughly two dozen molecules made, compound (S)-13j was the most potent against LpxC, with low nanomolar activity. This compound (and several others) also showed antibacterial activity against E. coli and several other strains of Gram-negative bacteria. In vitro stability studies of compound (S)-13j were promising, though the researchers noted the need for improvement. And, since the molecule contains a hydroxamic acid moiety potentially capable of binding to multiple metalloproteins, it was tested against a handful of mammalian zinc-dependent enzymes and shown to be nearly inactive.

 

Compound (S)-13j is 15-fold more potent than the simple phenyl analog (S)-13a, and molecular modeling suggested this may be due to a hydrogen bond from the protein to the indole NH. Although one could argue that it would have been possible to arrive at compound (S)-13j using standard medicinal chemistry starting from (S)-13a, this may have taken longer without knowledge of the indole fragment. Whether or not the molecules advance further, this is a nice example of using fragment screening to find a second-site binder to improve affinity of an existing lead.

Fragments vs KAT6A: ligand efficiency in action

Epigenetic writers such as histone acetyltransferases (HATs) control gene expression, which often goes awry in cancer. The gene encoding the lysine acetyltransferase KAT6A, for example, is amplified in multiple types of cancer, and its overexpression is associated with poor clinical outcomes for patients with ER⁺ breast cancer. A recent paper in Bioorg. Med. Chem. Lett. from Andrew Buesking and colleagues at Prelude Therapeutics reports a new series of inhibitors.

 

The researchers started by considering previously reported molecules such as PF-9363. Recognizing the importance of the central sulfonamide, they generated a library of 150 fragments containing this core and screened them in a biochemical assay. Taking a similar approach as other researchers reporting on a different epigenetic target we discussed last year, the Prelude team explicitly used ligand efficiency to call hits, setting the cutoff at > 0.3 kcal per mol per heavy atom.

 

A direct deconstruction of PF-9363 led to compound 6, but, seeking something novel, the researchers were drawn to compound 8, with similar ligand efficiency. Modifications were made to both of the terminal aromatic groups, leading to compound 13, which was co-crystallized with KAT6A. This led to further structure-guided modifications, with compound 25 being the most potent.

 

Unfortunately, further improvements in potency were not forthcoming, and the ligand efficiency had dropped steadily from both the initial fragment as well as PF-9363. The researchers conclude by stating that they “decided to pursue alternative approaches.” Still, this paper is a nice, concise example of using metrics both for pursuing a chemical series and, ultimately, discontinuing it.

Discovery on Target 2024

Last week Boston hosted CHI's 22^nd Annual Discovery on Target. With dozens of talks spread across seven or eight concurrent tracks over three days, and an additional day of pre-conference symposia, I’ll just touch on a few themes.

 

Computational Approaches

Artificial intelligence and machine learning were well represented. Brandon White described an ML model built at Axiom to predict liver toxicity, responsible for a quarter of clinical trial failures. As we noted last week, good ML models require lots of data, and Axiom has tested 50,000 small molecules in primary human hepatocytes from multiple donors using assays including high-content imaging. Just input a chemical structure and the model will predict toxicity. When run against the FDA’s database of drug-induced liver injury, the model performed with 74% sensitivity and 97% specificity, and even gave good dose predictions.

 

Woody Sherman (Psivant) laid out a series of “grand challenges for computers in drug discovery.” This is the working title for a publication he is spearheading to focus attention on key problems. They fall into five categories: chemistry (including synthesis, stability, and covalency), structure predictions (including protein-ligand structures, dynamics, and cryptic pockets), energetics (including affinity, selectivity, and kinetics), ADME (including everything from solubility and aggregation to bioavailability), and pharmacology (including toxicity). A sixth category, human considerations (including intellectual property and interpreting experimental data), is also being considered.

 

The success of AlphaFold to predict protein structures shows what computers can achieve, but in that case the effort was enabled by massive amounts of high-quality public data in the Protein Data Bank. Few of these challenges can draw on anything approaching the PDB. Indeed, even parameters as seemingly simple as solubility can change dramatically depending on crystal form and subtle changes to pH.

 

Because these computational challenges are so daunting, collecting them into one forum may prove salutary. And other categories may be worth including, such as target discovery. Woody is looking for co-authors, so reach out to him if you’re interested.

 

Covalent approaches

Covalent approaches to drug discovery have gone mainstream, at least if this conference is any indication. But they are not without risk: Doug Johnson (Biogen) described research implicating the piperidine acrylamide pharmacophore in approved BTK inhibitors with inhibition of ALDH1A1 and possible liver injury.

 

Several talks focused on methodologies. Alexander Federation (Talus) described data-independent acquisition (DIA) mass spectrometry methods, which can be more comprehensive than the more commonly used data-dependent acquisition (DDA) methods in identifying peptides in chemoproteomic studies, which we first discussed here. Talus is focused specifically on transcription factors.

 

As we noted earlier this year, Steve Gygi (Harvard) has been at the forefront of increasing the throughput of mass spectrometry methods, and he described how to increase the number of samples that can be analyzed simultaneously from 18 to 35. He also described two approaches, GoDig and CysDig, to look for up to 200 pre-specified proteins in a sample, ensuring identification of even low-abundance targets.

 

Turning to specific targets, Wai Cheung Adrian Chan described work done at Harvard to find covalent inhibitors against deubiquitinating enzymes (DUBs), reporting that screens of a small library of 178 covalent fragments in cell lysates found hits against several dozen DUBs. (We previously wrote about non-covalent USP7 inhibitors.)

 

Brooke Brauer described the optimization of a covalent inhibitor of Bfl-1 at AstraZeneca, an interesting oncology target. AZ has published some nice papers on this project which I’ll write about soon.

 

Last week we mentioned work Michelle Arkin and collaborators had done on 14-3-3 proteins, and Lynn McGregor described work done at Novartis on the same system. A screen of 6000 covalent compounds identified hits that modified a specific cysteine in 14-3-3 more rapidly in the presence of a peptide derived from the estrogen receptor. Stabilizing this interaction could be useful for treating certain cancers.

 

Not everyone is focused on cysteine: Andrea Zuhl described work done at Hyku Biosciences, which as the name suggests is targeting histidine, tyrosine, and lysine. This has necessitated building a fragment library of more than 6000 compounds, more than 70% of which are stable in buffer. Andrea presented one example targeting the catalytic lysine residue of the oncogenic ALK fusion protein, though the selectivity against other kinases was not disclosed.

 

All of these examples focused on covalent molecules in which the warhead is maintained during optimization. But as we first wrote about here, fully functionalized fragments (FFFs) contain a photoreactive moiety that reacts covalently with nearby proteins but is subsequently discarded. Sherry Niessen described how Belharra has industrialized this process by creating a library of about 11,000 FFF probes. Because of the low efficiency of protein crosslinking (typically <5%), most of the library consists of enantiomeric pairs to facilitate hit identification. Also, the average molecular weight of the library is around 350 Da, and these super-sized fragments tend to perform better than the strictly rule-of-three compliant molecules.

 

Covalent success stories

At least two presentations covered covalent fragment-based drug candidates. Shota Kikuchi (Vividion) described the discovery of VVD-214/RO7589831, a WRN inhibitor we wrote about earlier this year. As I speculated at the time, the cyclopropyl group was introduced to lower the reactivity of the vinyl sulfone warhead. Interestingly though, even early molecules were quite selective for WRN. Like sotorasib, binding is largely driven by the k_inact term of k_inact/K_i, again demonstrating that high reactivity for the target does not necessarily mean high chemical reactivity.

 

Finally, in his plenary keynote Steve Fesik (Vanderbilt University) covered multiple success stories, including the discovery of the KRAS^G12C inhibitor BI 1823911, which we wrote about here. Boehringer Ingelheim has since published molecules that hit multiple KRAS mutants as well as KRAS degraders, and Steve noted that all of these contain the same “squirrely-looking” fragment identified from SAR by NMR, an illustration of the power of fragment-based methods to explore new regions of chemical space.

 

I’ll close there, but please add your thoughts. There are is still at least one good conference coming up this year, and 2025 is quickly approaching.