Crystallography first in fragment optimization: Binding-Site Purification of Actives (B-SPA)

At FBLD 2024, Frank von Delft (Diamond Light Source) announced the ambitious goal of taking a 100 µM binder to a 10 nM lead in less than a week for less than £1000. Fragment to lead optimization usually takes longer, as dozens or even hundreds of compounds need to be synthesized and tested. One way to speed things up is through “crude reaction screening,” otherwise known as “direct to biology,” in which unpurified reaction mixtures are tested directly. In a new (open-access) Angew. Chem. Int. Ed. paper, Frank, John Spencer, and collaborators at University of Oxford, University of Sussex, and Creoptix apply this approach to crystallographic screening.

 

The researchers were interested in the second bromodomain of Pleckstrin Homology Domain-Interacting Protein, or PHIP(2), an oncology target. As we discussed in 2016, they had previously run a crystallographic screen and identified multiple hits, including F709, which, despite having no measurable affinity, had good electron density and multiple vectors for optimization. Six separate libraries based on this fragment were constructed, with between 58 and 1024 targeted small-molecule products per library and up to four steps done without purification.

 

One challenge for crude reaction screening is assessing whether or not a reaction has actually generated product. Typically this is done by analytical liquid chromatography mass spectrometry (LCMS), but analyzing results manually is tedious. Fortunately academics have graduate students and postdocs, and it was presumably these intrepid souls who spent 17 days analyzing the 1876 small-molecule products attempted.

 

I can say from personal experience that spending hours perusing LCMS chromatograms is not enjoyable, so the researchers built an automated tool called MSCheck, which appears to be freely available here. This showed 83% agreement with the manually curated data, and even identified additional true positives that had been missed. All together 1077 of the reaction mixtures had the desired product, with success rates for the various libraries ranging from 39% to 97%.

 

The successful reactions were soaked into crystals and screened, and nearly 90% of these generated usable data. A total of 29 crystals had interpretable density in the ligand binding site: 7 were starting materials and 22 were desired products. Of the products, 19 bound with the piperazine core in a similar position as the initial fragment, while three bound in an alternate manner.

 

Of course, the whole point of this exercise is to find improved binders, so the researchers tested pure versions of each of the 22 crystallographic hits in two different assays. Only compound PHIP-Am1-20 had measurable affinity, with modest ligand efficiency.

This is not the first example of crude reaction screening by crystallography; we wrote about REFiL_x and a related technique in 2020. In one of those papers, the crude reaction mixtures were assessed by SPR as well as crystallography, which revealed that the crystallographic screen missed some binders, and there is no reason to think the same did not happen here. Indeed, molecules that bind tightly in a different conformation may be more likely to shatter the crystal lattice and thus go undetected.

 

The researchers state that for non-crystallographic crude reaction screening “only strong assay readouts are informative.” But is this bug, or a feature? A 2019 publication that used crude reaction screening to identify KRAS ligands (which I wrote about here) used an assay cascade to quickly select the most potent hits. Even the fastest crystallographic screens can’t compete with plate-based assays in terms of speed.

 

Perhaps PHIP(2) is a particularly challenging test case. As we discussed in 2022, multiple computational screens performed poorly in predicting crystallographic binding modes of ligands for this protein. But as I wrote at the time, it may be that many crystallographic ligands are just too weak to be useful.

 

Although there is a strong case for using crystallography first for finding fragments, I am not yet convinced the same applies for optimizing fragments.

From fragment to macrocyclic Ras inhibitors

At the Drug Discovery Chemistry meeting last month chemist John Taylor described efforts against the oncology target RAS. This story was recently published in J. Med. Chem. by John, Charles Parry, and a team of some three dozen collaborators at CRUK Scotland Institute, Novartis, and Frederick National Laboratory for Cancer Research.

 

Practical Fragments has highlighted multiple Ras efforts, including the development and approval of sotorasib, which inhibits the G12C mutant of KRAS. Sotorasib binds in the so-called switch II region, next to the site where the nucleotides GDP and GTP bind. Before the discovery of this site, researchers had identified fragments that bind to a different site, switch I-II. 

 

Most of the ligands that bind to either site only inhibit the off-form of Ras proteins, in which the proteins are bound to GDP. One mechanism of resistance for cancer cells is to increase the amount of protein in the active, or GTP-bound state. Thus, the researchers focused on the oncogenic G12D mutant of KRAS bound to a GTP analog and screened it against 656 fragments using SPR. Ligand-detected NMR confirmed five of the hits, including compound 5.

 

Two dimensional ¹H-¹⁵N HSQC NMR revealed that compound 5 binds in the switch I-II pocket; merging this with a literature fragment generated compound 6. SAR studies led to compound 11, which was characterized crystallographically bound to the protein. The structure suggested trying to make a salt bridge with an aspartic acid residue, leading to compound 13, with sub-micromolar affinity for the inactive form of the protein. A crystal structure of a related compound suggested the possibility of macrocylization, and this turned out to be successful, with compound 21 being the most potent. (All values shown here are determined by NMR or SPR on the G12D KRAS mutant bound to either GDP or the GTP analog GMPPMP.)

 

A number of different macrocycles were made and tested, and all of them were more potent against the inactive than the active form of KRAS. Crystal structures suggested that a glutamic acid side chain adopts a conformation in the the GTP-bound form of KRAS that impedes ligand interactions.

 

Interestingly though, building off the molecules in another direction led to the opening of a small subpocket that had not previously been reported in the literature. Exploiting this “interswitch” region led to compound 36, with a nearly 10-fold preference for the active form of KRAS.

 

Most of the macrocycles in both series were able to block nucleotide exchange in a biochemical assay, meaning they could prevent the exchange of GDP for GTP. A few of the compounds were tested in cell-based assays and could block binding between RAF and multiple Ras isoforms, including two mutants of KRAS as well as wild-type KRAS, HRAS, and NRAS.

 

Unfortunately, and not surprisingly given their high polar surface areas, the compounds had low permeability, high efflux, and high clearance in vitro. Mouse studies on one compound confirmed these liabilities in vivo.

 

Although the compounds could not be advanced, this is still a nice fragment to lead story. The fact that a new pocket could be identified despite so much previous effort on this target is a good reminder that no matter how much you know, there is always room for surprises.

Solving protein-ligand NMR structures without isotopic labeling

Last week we highlighted a protein-detected NMR method that does not require expensive and sometimes difficult isotopic labeling of proteins. However, while that approach is able to provide affinity information, it does not provide structural information. A new (open-access) paper in J. Am. Chem. Soc. by Roland Riek, Julien Orts, and collaborators at the Institute for Molecular Physical Science and the University of Vienna tackles this challenge.

 

The approach builds on NMR Molecular Replacement (NMR²), which we last wrote about here. In NMR², brute force calculations obviate the need for assigning individual NMR peaks to specific protein residues, thereby sidestepping considerable up-front effort. Most of the new paper focuses on applying NMR² to ligand discovery for the oncogenic G12V mutant of KRAS, which I’ll briefly summarize.

 

The researchers start by screening the 890-membered DSI-poised fragment library (in pools of six, with each fragment at 0.6 mM) against KRAS using ligand-detected STD NMR. This produced 133 hits, which were then retested at 1 mM each using [¹⁵N,¹H]-HSQC two-dimensional protein-observed NMR, invalidating about 30% of them. Dose-response titrations were performed on the top 13 hits; all of them were found to be weak binders, with at best low millimolar affinity. NMR² was then used to determine protein-ligand structures for some of these hits. That information guided the design of additional ligands, which had slightly higher affinities.

 

This thorough description of the NMR² workflow should be useful if you’re trying to do this at home. But what really caught my eye was a bit at the very end of the paper describing a new relaxation-filtered NOESY pulse sequence. Specifically, “an inversion recovery pulse block serves as a T₁ filter, followed by a perfect echo sequence and a CPMG without J-modulation, as a T₂ filter.” In essence, the experiment takes advantage of the fact that proteins relax more rapidly than small molecules, so NMR peaks coming from the protein are filtered out. But NMR peaks from protons in the ligand that are in close proximity to protons on methyl groups of the protein are observed, and the intensity of these peaks correlates with the distance between ligand and protein protons. Feeding these distance constraints into NMR² generates a three-dimensional structural model. The researchers compare models generated using NMR² on unlabeled KRAS to those generated using NMR² on labeled KRAS and show that they are roughly similar.

 

This is a neat approach, and it will be interesting to see whether it catches on. According to our poll last year ligand-detected NMR has fallen to fourth place among fragment-finding methods, and protein-detected NMR is in seventh place. Perhaps approaches like this and that described last week will usher in a new era of NMR for FBLD.