Application of Spark in the discovery of potent SOS1 inhibitors that block RAS activation via disruption of the RAS–SOS1 interaction

Bayer AG recently published an interesting paper on the discovery of potent and selective SOS1 inhibitors that block RAS activation via disruption of the RAS–SOS1 interaction.

They report using Spark™, Cresset’s bioisostere replacement tool, to rationally design linkers between active hit compounds from fragment screen and HTS. Herein, we review this interesting application of Spark to a ligand joining experiment that led to the discovery of BAY-293, a potent and selective inhibitor of KRAS-SOS1 interaction.

Fragment screen

Bayer decided on a two-pronged approach, running a HTS campaign and a fragment screen in parallel. The fragment screen, with a library of 3,000 fragments, led to the identification of fragments which bind to and can induce a conformational change at the KRAS-SOS1 protein-protein interaction site. By triggering a rotation of the Phe890 side chain, they open a new subpocket adjacent to the main binding pocket.

F1 was chosen as the starting point for further optimization. The crystal structure of F1 in complex with SOS1 (PDB: 6EPM, Figure 1) shows that the phenyl moiety makes a π–π interaction with Phe890 (Phe-out). The aminomethyl moiety forms hydrogen bonds to Asp887 and the backbone carbonyl of Tyr884 and makes an additional cation–π interaction with the Tyr884 side chain.

Figure 1. X-ray crystal structure of F1 complexed with KRASG12C–SOS1cat (PDB: 6EPM).

HTS and initial optimization

HTS of a Bayer library of 3 million compounds led to the identification of compound 1 (IC50 320 nM, Figure 2).

Figure 2. Compound 1 (HTS hit: IC50 320 nM).

Replacement of the naphthyl moiety in compound 1 with a pyrazolylphenyl group resulted in compound 17 (Figure 3), showing a good potency on SOS1 (IC50 140 nM) and improved aqueous solubility.

In terms of interactions with SOS1 (PDB: 5OVF, Figure 3), the quinazoline scaffold of compound 17 is sandwiched between His905 and Tyr884 (π–π stacking). The pyrazolylphenyl moiety occupies a hydrophobic pocket composed of Leu901 and Phe890 (Phe-in) and makes a T-stacking interaction with the side chain of Tyr884. The pyrazole moiety makes a water-bridged H-bond to Glu902. The central aniline NH makes a H-bond with the side chain of Asn879.

Figure 3: X-ray crystal structure of compound 17 in the SOS1SB active site (PDB: 5OVF).

Linking of the fragment and HTS hits with Spark

As the fragment screen identified a new subpocket that was not yet addressed by the HTS hits, scientists at Bayer attempted a ligand joining approach using Spark to try and further improve potency by combining both ligand series. A superimposition of the crystal structures of compound 17 (PDB: 5OVF) and F1 (PDB: 6EPM), shown in Figure 4 – A, suggests that this approach is feasible.

After cutting out the overlapping aryl groups of compounds 17 and F1, the authors performed ligand joining experiments with Spark (Figure 4 – B, C) to identify appropriate linkers that could correctly orientate both the tetrahydrocyclopenta[c]pyrazole moiety of F1 ­­­­and the aminoquinazoline group of 17 within the active site of SOS1. Among the top-scoring linkers suggested by Spark, the thiophene linker was chosen, synthesized, and found to be active.

Figure 4: Ligand-joining approach of fragment and HTS hits. A) Superimposition of the crystal structures of F1 bound to KRASG12C_SB–SOS1cat (SOS1 in gray, F1 and Phe890 with carbon atoms in magenta) and 17 bound to SOS1SB (17 and Phe890 shown; carbon atoms in green). B) Schematic depiction of the merging approach. C):  Representation of F1 and 17 with respective fields from Cresset.

Further optimization and discovery of BAY-293

Optimization of the hybrid hits containing the thiophene linker identified by Spark by introducing polar moieties (such as OH, NH2) that mimic the hydrogen bond interactions of the amino side chain of F1, and trigger the Phe-out conformation, led to the discovery of the optimized compound 23 (BAY-293, IC50 21nM). As shown in Figure 5, the side chain amino group of compound 23 interacts with SOS1 by making H-bond interactions with Asp887 and Tyr884 backbone carbonyl, as well as a cation-π interaction with the Tyr884 side chain.

Figure 5. X-ray crystal structure of compound 23 in the SOS1 active site (PDB: 5OVI).

Further screening and antiproliferative data proved that compound 23 (BAY-293) is a potent and selective inhibitor of KRAS-SOS1 interaction and indicates that inhibition of GEFs may represent a viable approach for targeting RAS-driven tumors.


This paper by Bayer shows the utility of the Spark approach. As well as being a superb bioisostere finder, Spark’s advanced capabilities include easy-to-use methods for water replacement, macrocyclization, fragment growing and fragment linking. In this case Spark was vital in suggesting how to join two disparate molecules occupying different parts of a protein binding site, transferring SAR from one series to another.

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May 2017 newsletter

Last chance to register for The Cresset User Group Meeting

Register by 16th June to secure your place at our 15th Anniversary User Group Meeting.

  • June 29th, Scientific program
    • Official launch of Flare, our new structure-based design application
    • Update on science and ligand-based design applications
    • Customers share their experience of Cresset technology
    • Limited accommodation available at The Møller Centre
  • June 30th, Hands-on software workshops. Limited spaces remain for the following workshops:
    • Introduction to Flare for computational chemists
    • Introduction to Flare for medicinal and synthetic chemists
    • 3D-QSAR
    • Torch

Water stability is key to designing novel patentable chemistry

Read about how Cresset Discovery Services informed the design of novel ligands for a customer target, which led to patent.

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What can Torch do for you that TorchLite can’t?

We hope you enjoy using TorchLite, our freeware 3D molecule viewer, editor and design tool, but did you know how much more you can do with the full power of Torch?

Web clips

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  • Structural changes and fields
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Electrostatics of heterocyclic rings and the Topliss tree

TorchLite has been used to illustrate 2D structures next to their 3D charge distribution patterns. Download the following illustrations:

  • Electrostatics of heterocyclic rings
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February 2017 newsletter

Case study: Conduct ligand-protein docking

Find out how we conducted ligand-protein docking to narrow down a 50k compound library to the best 1.5k compounds at a fraction of the cost of buying and screening the entire 50k library.

RDKit molecular simulations through OpenMM

Paolo Tosco describes how RDKit is used in Cresset software and presents preliminary work on boosting RDKit molecular simulations through OpenMM.

Web clip tips and tricks

  • Changing preferred alignments
  • Importing pre-existing conformations
  • Using Tags to organize data sets

See more tips and tricks.

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Fragment hopping with Blaze


In this case study we look at the performance of Blaze1 (our 3D ligand-based virtual screening (VS) application) on a fragment data set. Comparing the Blaze default settings with an alternative shape-only screen, we found good retrieval rates of known actives in both cases. The two methods are found to be surprisingly complementary as distinctly different hits are obtained from the different methods. Examination of the results shows that Blaze is capable of finding fragments that are similar to known actives as well as completely novel suggestions. Unlike many virtual screening methods, there is no evidence that the performance of Blaze decreases with smaller search queries.


In a recent paper Keserű and co-workers reported2 the results of a fragment screen for adrenergic α2c agonists. They used a combination of cell based wet screening and docking to a homology model to identify 17 novel hits with varying levels of inhibitory and agonist activity. The published structures are a valuable resource for exploring the performance of virtual screening methods on fragments. Fragment VS is known to be difficult: 2D fingerprints perform poorly on small molecules and there have been many publications on the difficulties of docking fragments compared to drug-sized molecules.

In this study we used one of the Keserű docking results as a query molecule for exploring the retrieval of the other published hits using two ligand-based virtual screening methods that are both available within Blaze, Cresset’s VS platform.


The published hits were uploaded to Blaze as a spike set. Where the stereochemistry was unknown we uploaded them with unspecified stereochemistry and relied on Blaze to enumerate chiral centres at the same time as it generated conformation populations. The final collection ‘Adrenergic_a2C_fragments’ was included in the search and Blaze monitored and reported the retrieval rates and enrichment factors automatically.

Blaze requires an active search query in its bioactive conformation. We wished to use one of the hits obtained from docking as a query to mimic the effect of a combined structure-based and ligand-based screen. Of the two hits, compound 10 is the more active and hence was chosen as the primary search query. The original paper details docked poses for both hits that they obtained but did not provide the coordinates. We were able to approximately reproduce the conformation for compound 10 from the published picture and used this as our query (Figure 1).

Figure 1.

We ran the Blaze experiment on the public Blaze server3 using Chembl compounds as decoys. We limited the search to compounds with 11 to 20 heavy atoms so as to retrieve fragment-like hits. Furthermore we applied a filter to ensure that all Chembl compounds retrieved contained a positive charge, as this is a dominant feature of the published hits. Note that all spike molecules were allowed to pass the filter even if they were not charged. It is possible that this reduced the retrieval rates as it applies a charge bias between spikes and decoys.

Two search methods were applied. In the first approach the default Blaze conditions were used to score hits. This uses a combination of electrostatic (field) similarity and an explicit shape similarity in equal measure. The second approach used only the shape similarity to score hits. In both cases we applied the highest level of calculation (‘simplex’) to the complete filtered dataset of 10,680 compounds that lie within the heavy atom limits.

Results and discussion

Performance metrics

The results from each experiment – ‘Blaze defaults’ and ‘Shape only’ are summarized in the table below. Note that the query molecule was present in the data set and was retrieved at position 1 in both cases.

Method ROC AUC BEDROC20 Spikes in top 5% First new chemotype
Shape only 0.746 0.245 5 compound_16
Blaze defaults 0.795 0.392 8 compound_11

On first pass the results from the two methods seem fairly similar, although the Blaze default search gets a slightly better early enrichment. However, as always, the devil is in the details. If we take the rank of each molecule in the dataset and compare them between the two methods we can see that they have a low correlation (Figure 2).

Figure 2: Rank of compounds using Shape only vs Blaze defaults: (a) zoomed to top 1000 results; (b) overall highlighting spike molecules in blue.

Figure 2 shows that the rank for any particular compound can be very different between the two methods. There are a few compounds at the top of the both lists that are retrieved by both methods, but using a typical cutoff of e.g., 1% of the database (106 compounds) then there are large numbers of compounds that are exclusive to each method. This applies to both the spikes and the decoys. Although the Blaze defaults perform better in this particular case, the Shape-only method still finds spikes in the top 2000 compounds that are missed by the Blaze defaults.

Fragment hopping

As well as the known ligands a number of new structures are returned that are pharmacophorically similar yet structurally very different to the known actives. Figure 2 shows some selected molecules from the top 35 results in the Blaze-default search. As can be seen, Blaze finds a very wide variety of different chemotypes with excellent shape and pharmacophoric matches to the query.

(a)blaze_a2c_results (b)blaze_a2c_results_overlaid
Figure 2 (a). Selected results from Blaze results; top row: CHEML117418 (rank 2); CHEMBL74933 (rank 5); CHEMBL306011 (rank 7); bottom row: CHEMBL361235 (rank 18); CHEMBL76048 (rank 20); CHEMBL1332489 (rank35). (b) The same results as (a), shown overlaid on the query molecule (shown in pink).

The amino acid moiety present in CHEMBL74933 is a common motif in the full list of top-ranked compounds and interestingly is also represented in the other docking hit found by the original authors. In addition, CHEMBL306011 is a close analogue of one of the more active fragments reported in the paper and is found by both methods along with a large number of structurally similar molecules.


This experiment shows that Blaze can produce excellent results for virtual screening of fragments. Indeed the enrichments are similar or greater than those that are obtained on larger molecules. The results also reinforce our contention that the Blaze hit lists are often complementary to those obtained using other methods, such as pure shape.

In this case, once the Keserë group had obtained their first few hits from docking, a Blaze search to expand the hit list around these would have provided a highly cost-effective alternative to performing a large physical screen.


2. E. Szőllősi, A. Bobok, L. Kiss, M. Vass, D. Kurkó, S. Kolok, A. Visegrády, G.M. Keserű; Bioorg. Med. Chem. 23 (2015) 3991–3999. doi: 10.1016/j.bmc.2015.01.013.