Covalent fragments vs chikungunya nsP2

Perhaps because it sounds like “chicken,” when I first heard of chikungunya I thought it was a joke. But there’s nothing funny about a disease whose name comes from a word meaning “to become contorted,” referring to contortion caused by pain, which can last for months. The mosquito-borne alphavirus was first identified in 1952 in West Africa, introduced to the Americas in 2013, and is now spreading rapidly worldwide. There is no specific treatment. In three recent papers, a large group of researchers mostly from the Structural Genomics Consortium take the first steps towards one.

 

Like many viruses, the chikungunya genome encodes polyproteins that are cleaved by viral proteases, in this case a domain of the nonstructural protein 2 (nsP2). This cysteine protease is essential for viral replication, and the three papers collectively describe finding and exploring selective probes against it.  

 

In Proc. Nat. Acad. Sci. USA, Kenneth Pearce (University of North Carolina at Chapel Hill) and collaborators describe a screen of 6120 covalent fragments from Enamine against this target. Compounds were preincubated in a FRET-based functional assay at 20 µM for 30 minutes, resulting in 153 hits that inhibited activity by at least 50%. 43 of these were repurchased for full-dose response curves, and 20 of these had IC₅₀ values < 20 µM. Of these, compound RA-0002034 was the most potent, with IC₅₀ = 180 nM.

The proper way to assess irreversible covalent inhibitors is not the time-dependent IC₅₀, but rather the (theoretically) invariant k_inact/K_I ratio. The researchers measured this for the best hits and found the value for RA-0002034 to be 6400 M^-1s^-1, which is not far below that for the approved covalent drug sotorasib for its target.

 

Mass spectrometry experiments after tryptic digestion revealed the compound binds to the catalytic cysteine of nsP2, as expected, and not to other cysteines. RA-0002034 contains a potentially reactive vinyl sulfone warhead, but the half-life against the biologically relevant nucleophile glutathione is a respectable 130 minutes. A screen against 13 other cysteine proteases was also quite clean, as was chemoproteomic profiling in human cells.

 

The compound was also tested in cellular viral replication assays and found to be remarkably potent, with a low nanomolar EC₅₀ value. Encouragingly, it was also potent against three other alphaviruses, Ross River virus, Venezuelan Equine Encephalitis virus, and Mayaro virus.

 

RA-0002034 appears to be an attractive chemical probe for exploring the biology of chikungunya. Best practices are to also have an inactive control molecule, and the researchers made a substitution off the central pyrazole ring to produce RA-0003161, which is 500-fold less active.

 

The paper includes some SAR-by-catalog, and the chemistry is more extensively explored in an open-access J. Med. Chem. paper by Timothy Willson (UNC Chapel Hill) and collaborators. Although no crystal structures of the compounds bound to nsP2 were available, the researchers used modeling to guide modification of all portions of the molecule. The most potent molecule was 8d, which is slightly more active than RA-0002034. Also, methyl substitution near the electrophilic center is tolerated, which could improve stability, as seen with the covalent WRN inhibitor from Vividion which we wrote about here.

 

One annoying feature of RA-0002034 is its tendency to cyclize to inactive compound 2, a process explored in an open-access Pharmaceuticals paper by Timothy Willson and collaborators. This occurs even at neutral pH. However, replacing the central pyrazole with an isoxazole (compound 10) fixes this problem.

 

Collectively these three publications provide new insights and tools for investigating chikungunya. RA-0002034 is a far more attractive starting point than a molecule Teddy described on Practical Fragments back in 2015. The pharmacokinetics of RA-0002034 need to be improved before in vivo experiments are warranted, but this seems achievable, and I look forward to watching this story develop.

Poll results: fragment finding methods and structural information needed for fragment-to-lead efforts

Our most recent poll asked about fragment finding methods. The poll ran from September 21 through November 8 and received 135 responses from 20 countries. Two thirds of these were from the US, about 12% were from the UK, 4% from Germany, 3% from the Netherlands, and 2% from Australia.

 

The first question asked how much structural information you need to begin optimizing a fragment. In contrast to 2017, when we first asked this question, crystallography has significantly increased at the expense of the other choices. 

 

 

I confess to being surprised, as I expected that by now people would be more comfortable beginning optimization in the absence of structural information, an approach that has been quite successful as discussed in a 2019 open-access Cell Chemical Biology review by Ben Davis, Wolfgang Jahnke, and me. Perhaps the increasing speed and accessibility of new methods has so lowered the bar to getting crystal structures that people have the luxury of waiting. Of course, with an online poll there is always the risk that many respondents from the same organization may skew the results.

 

The second question asked which methods you use to find and validate fragments. This is the fifth time we’ve run this poll, starting in 2011. As with our first question, X-ray crystallography came out on top, with nearly 80% of respondents choosing it. This was followed by SPR, at 67%, and thermal shift and ligand-detected NMR, each around 55%. 

 

 

Functional screening was used by nearly half of respondents, with computational methods, protein-detected NMR, and literature starting points used by around a third. Mass spectrometry and ITC were each used by slightly more than a quarter of respondents.

 

For the first time we asked about cryo-EM, and nearly 20% of respondents reported using this technique.

 

MST and affinity-based methods each came in at 13%, with just 4% of respondents using BLI, and 5 individual respondents using other methods. I’d be curious to know what these are.

 

The average respondent reported using just over 5 different techniques, which is down slightly from 6 in 2019 but up from 4 in 2016. Using multiple orthogonal methods is clearly well established as best practice, even if the precise number varies.

 

How do these results compare with your own practices?