CoChat
Sign Up
Cited by 459Protein Kinase Regulation and GTPase SignalingProtein Tyrosine PhosphatasesComputational Drug Discovery Methods

The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.

Drugging an undruggable pocket on KRAS

Open access
Kessler, Dirk, Gmachl, Michael, Mantoulidis, Andreas, Martin, Laetitia J., Zoephel, Andreas, Mayer, Moriz, Gollner, Andreas, Covini, David, Fischer, Silke, Gerstberger, Thomas, Gmaschitz, Teresa, Goodwin, Craig, Greb, Peter, Häring, Daniela, Hela, Wolfgang, Hoffmann, Johann, Karolyi-Oezguer, Jale, Knesl, Petr, Kornigg, Stefan, Koegl, Manfred, Kousek, Roland, Lamarre, Lyne, Moser, Franziska, Munico-Martinez, Silvia, Peinsipp, Christoph, Phan, Jason, Rinnenthal, Jörg, Sai, Jiqing, Salamon, Christian, Scherbantin, Yvonne, Schipany, Katharina, Schnitzer, Renate, Schrenk, Andreas, Sharps, Bernadette, Siszler, Gabriella, Sun, Qi, Waterson, Alex, Wolkerstorfer, Bernhard, Zeeb, Markus, Pearson, Mark, Fesik, Stephen W., McConnell, Darryl B.·2019·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.1904529116PMC PMC6689897
Open this paper in CoChat — free accountOpen PDF

Abstract

The 3 human RAS genes, KRAS, NRAS, and HRAS, encode 4 different RAS proteins which belong to the protein family of small GTPases that function as binary molecular switches involved in cell signaling. Activating mutations in RAS are among the most common oncogenic drivers in human cancers, with KRAS being the most frequently mutated oncogene. Although KRAS is an excellent drug discovery target for many cancers, and despite decades of research, no therapeutic agent directly targeting RAS has been clinically approved. Using structure-based drug design, we have discovered BI-2852 (1), a KRAS inhibitor that binds with nanomolar affinity to a pocket, thus far perceived to be “undruggable,” between switch I and II on RAS; 1 is mechanistically distinct from covalent KRAS G12C inhibitors because it binds to a different pocket present in both the active and inactive forms of KRAS. In doing so, it blocks all GEF, GAP, and effector interactions with KRAS, leading to inhibition of downstream signaling and an antiproliferative effect in the low micromolar range in KRAS mutant cells. These findings clearly demonstrate that this so-called switch I/II pocket is indeed druggable and provide the scientific community with a chemical probe that simultaneously targets the active and inactive forms of KRAS.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1073/pnas.1904529116

Cites

51 of 51 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1. Fragments identified from 2 separate fragment screens. (A) Representative indole and benzimidazole fragments identified from the fragment screens. (B) The binding mode of indole 5 in GDP-KRAS (Protein Data Bank [PDB] ID code 4EPV) showing the H bond between the indole NH and the side chain of D54. Indole 5 shown in yellow, water molecules shown in red. Arrow indicates the strategy of forming an additional charge−charge interaction with the side chain of D54 from the indole 2 position.
Fig. 1. Fragments identified from 2 separate fragment screens. (A) Representative indole and benzimidazole fragments identified from the fragment screens. (B) The binding mode of indole 5 in GDP-KRAS (Protein Data Bank [PDB] ID code 4EPV) showing the H bond between the indole NH and the side chain of D54. Indole 5 shown in yellow, water molecules shown in red. Arrow indicates the strategy of forming an additional charge−charge interaction with the side chain of D54 from the indole 2 position.CC BY · PubMed Central
Fig. 2. Analysis of protein pockets and X-ray structure of 15 in GCP-KRASG12D. A Computer Atlas of Surface Topography of proteins (48) analysis of pockets in proteins for calculating the solvent-accessible surface area (Area SA) and volume (Volume SA) with a radius probe of 1.4 Å showing differences in pocket size for (A) BRD4-BD1 (PDB ID code 5M39) as an example of a highly druggable pocket and (B) KRAS G12D (PDB ID code 6GJ5) with a very small volume. Calculated pockets are shown in red. Insets show ligand binding in the respective pocket, for comparison reasons. (C) Polar interactions formed by 15 to T74, D54, and a conserved water. The ideal position of E37 for introducing a further polar interaction is highlighted with the red dotted line (PDB ID code 6GJ5). (D) Comparison of SI/II-pocket in GDP (PDB ID code 4EPV) and GTP-KRAS showing the significantly reduced pocket size in GTP-KRAS. The switch I and switch II regions are colored dark green and dark red in GDP-KRAS and light green and light red for GTP-KRAS, respectively.
Fig. 2. Analysis of protein pockets and X-ray structure of 15 in GCP-KRASG12D. A Computer Atlas of Surface Topography of proteins (48) analysis of pockets in proteins for calculating the solvent-accessible surface area (Area SA) and volume (Volume SA) with a radius probe of 1.4 Å showing differences in pocket size for (A) BRD4-BD1 (PDB ID code 5M39) as an example of a highly druggable pocket and (B) KRAS G12D (PDB ID code 6GJ5) with a very small volume. Calculated pockets are shown in red. Insets show ligand binding in the respective pocket, for comparison reasons. (C) Polar interactions formed by 15 to T74, D54, and a conserved water. The ideal position of E37 for introducing a further polar interaction is highlighted with the red dotted line (PDB ID code 6GJ5). (D) Comparison of SI/II-pocket in GDP (PDB ID code 4EPV) and GTP-KRAS showing the significantly reduced pocket size in GTP-KRAS. The switch I and switch II regions are colored dark green and dark red in GDP-KRAS and light green and light red for GTP-KRAS, respectively.CC BY · PubMed Central
Fig. 3. X-ray, biophysical and cellular data for 18. (A) Chemical structure of 18. (B) X-ray structure of 18 in GCP-KRASG12D, highlighting the polar interactions formed with D54, T74, and E37 (PDB ID code 6GJ6). (C) ITC dose titration curve for 18 and GCP-KRASG12D. (D) Meso Scale Discovery analysis of pERK levels in NCI-H358 cells after 2-h treatment of 18.
Fig. 3. X-ray, biophysical and cellular data for 18. (A) Chemical structure of 18. (B) X-ray structure of 18 in GCP-KRASG12D, highlighting the polar interactions formed with D54, T74, and E37 (PDB ID code 6GJ6). (C) ITC dose titration curve for 18 and GCP-KRASG12D. (D) Meso Scale Discovery analysis of pERK levels in NCI-H358 cells after 2-h treatment of 18.CC BY · PubMed Central
Fig. 4. GCP-KRASG12D X-ray structures of compound 22 and BI-2852 (1). (A) Chemical structure of 22. (B) Overlay of the binding modes of 18 and 22. The relative orientation of 18 and E37 in the X-ray with KRASG12D is depicted in yellow. The binding mode of 22, E37, and T74 are depicted in green. Dotted mesh depicts the van der Waals radii around 22, showing overlap with 3 waters from the X-ray structure of 18 (PDB ID code 6GJ8). (C) Chemical structure of 1. (D) X-ray structure of 1 in GCP-KRASG12D, highlighting the polar interactions formed with D54, T74, S39, and E37 (PDB ID code 6GJ7). The racemate 23 was used for soaking, and eutomer 1 was crystallized.
Fig. 4. GCP-KRASG12D X-ray structures of compound 22 and BI-2852 (1). (A) Chemical structure of 22. (B) Overlay of the binding modes of 18 and 22. The relative orientation of 18 and E37 in the X-ray with KRASG12D is depicted in yellow. The binding mode of 22, E37, and T74 are depicted in green. Dotted mesh depicts the van der Waals radii around 22, showing overlap with 3 waters from the X-ray structure of 18 (PDB ID code 6GJ8). (C) Chemical structure of 1. (D) X-ray structure of 1 in GCP-KRASG12D, highlighting the polar interactions formed with D54, T74, S39, and E37 (PDB ID code 6GJ7). The racemate 23 was used for soaking, and eutomer 1 was crystallized.CC BY · PubMed Central
Fig. 5. Biochemical assay dose–response curves for BI-2852 (1), distomer 44, and ARS-1620. (A) KRAS cycle is depicted with KRAS in Channing Der‘s “beating heart of cancer” orientation switching between its “off state” with the nucleotide GDP bound (red surface) and its “on state” with GCP bound (green surface). The 4 PPI intervention points in the KRAS cycle are shown. (i) The interaction between GDP-KRAS and the catalytic site of its GEF; here SOS is depicted. (ii) GTP-KRAS binding to the allosteric site of SOS which constitutes the feed forward loop. (iii) GTP-KRAS binding to downstream effectors; CRAF (in blue) is shown as an example. (iv) GTP-KRAS binding to GAPs. KRAS is depicted in gray, with the switch I region colored in orange and the switch II region in brown. (B−E) Biochemical assay dose–response curves for 1 (red), distomer 44 (green), and ARS-1620 (blue) for (B) GDP-KRASG12C::SOS1 alpha screen assay, (C) GTP-KRASG12C::CRAF alpha screen assay, (D) GTP-KRASG12C::SOS1 alpha assay, and (E) GTP-KRASG12C::PI3Kα alpha screen assay. All values shown are normalized to DMSO (= 100%) for better comparability. Error bars indicated show the SD of duplicates measured. Shown are representative examples of multiple repetitions with identical results. (F and G) The ability of test compounds to affect SOScat-catalyzed nucleotide exchange on RAS was assessed at several concentrations. The addition of SOScat and excess GTP (at 120 s) initiates the exchange of labeled boron-dipyrromethene-GDP (BODIPY-GDP) already loaded into RAS. The BODIPY-GDP to GTP exchange mediated by SOScat (black curve) is observed as a decrease in relative fluorescence units (RFUs) over time. While the negative control distomer 44 (G) shows no effect, increasing concentrations of BI-2852 (1) (F) show a slower decrease in RFU over time, representing a slower exchange rate. The highest concentrations show full inhibition of SOScat-mediated exchange, matching the rate in the absence of SOScat (red curve).
Fig. 5. Biochemical assay dose–response curves for BI-2852 (1), distomer 44, and ARS-1620. (A) KRAS cycle is depicted with KRAS in Channing Der‘s “beating heart of cancer” orientation switching between its “off state” with the nucleotide GDP bound (red surface) and its “on state” with GCP bound (green surface). The 4 PPI intervention points in the KRAS cycle are shown. (i) The interaction between GDP-KRAS and the catalytic site of its GEF; here SOS is depicted. (ii) GTP-KRAS binding to the allosteric site of SOS which constitutes the feed forward loop. (iii) GTP-KRAS binding to downstream effectors; CRAF (in blue) is shown as an example. (iv) GTP-KRAS binding to GAPs. KRAS is depicted in gray, with the switch I region colored in orange and the switch II region in brown. (B−E) Biochemical assay dose–response curves for 1 (red), distomer 44 (green), and ARS-1620 (blue) for (B) GDP-KRASG12C::SOS1 alpha screen assay, (C) GTP-KRASG12C::CRAF alpha screen assay, (D) GTP-KRASG12C::SOS1 alpha assay, and (E) GTP-KRASG12C::PI3Kα alpha screen assay. All values shown are normalized to DMSO (= 100%) for better comparability. Error bars indicated show the SD of duplicates measured. Shown are representative examples of multiple repetitions with identical results. (F and G) The ability of test compounds to affect SOScat-catalyzed nucleotide exchange on RAS was assessed at several concentrations. The addition of SOScat and excess GTP (at 120 s) initiates the exchange of labeled boron-dipyrromethene-GDP (BODIPY-GDP) already loaded into RAS. The BODIPY-GDP to GTP exchange mediated by SOScat (black curve) is observed as a decrease in relative fluorescence units (RFUs) over time. While the negative control distomer 44 (G) shows no effect, increasing concentrations of BI-2852 (1) (F) show a slower decrease in RFU over time, representing a slower exchange rate. The highest concentrations show full inhibition of SOScat-mediated exchange, matching the rate in the absence of SOScat (red curve).CC BY · PubMed Central
Fig. 6. Cellular data for BI-2852 (1), distomer 44, and ARS-1620. (A) Western blot of pERK levels versus total ERK and alpha-tubulin under high serum conditions in NCI-H358 cells upon increasing doses of 1 and a fixed concentration of ARS-1620 (Upper) and G-LISA assay measuring GTP-RAS levels under high serum conditions in NCI-H358 cells upon increasing doses of 1 and a fixed concentration of ARS-1620 (Lower). (B) GTP-RAS levels measured with a G-LISA assay in NCI-H358 cells starved for 24 h in low serum conditions (−EGF), followed by 2-h treatment with increasing concentrations of 1 or 20 µM treatment of ARS-1620 and then EGF addition. For quantification, RAS GTP levels without EGF and in the presence of DMSO were set to 1 (A and B). (C) pERK and pAKT levels in NCI-H358 cells starved for 24 h in low serum conditions (−EGF), followed by 2-h treatment with increasing concentrations of 1 (red), 44 (green), and ARS-1620 (blue); then EGF addition were quantified. DMSO-treated samples after EGF stimulation were set to 100%. (D) GTP-RAS levels measured with a G-LISA assay in NCI-H358 cells with treatment of 50 µM compound 1, 20 µM ARS-1620 and simultaneous treatment of 1 and ARS-1620. For quantification, RAS GTP levels in the presence of DMSO were set to 1. (E) pERK dose–response curves for 1 (red), 44 (green), and ARS-1620 (blue) in NCI-H358 cells under high serum conditions. (F) Antiproliferative dose–response curves for NCI-H358 cells in soft agar and low serum conditions for 1 (red) and 44 (blue). DMSO-treated samples were set to 100% (E and F). Error bars indicate SDs. Indicated experiments were performed 2 or more times with similar results.
Fig. 6. Cellular data for BI-2852 (1), distomer 44, and ARS-1620. (A) Western blot of pERK levels versus total ERK and alpha-tubulin under high serum conditions in NCI-H358 cells upon increasing doses of 1 and a fixed concentration of ARS-1620 (Upper) and G-LISA assay measuring GTP-RAS levels under high serum conditions in NCI-H358 cells upon increasing doses of 1 and a fixed concentration of ARS-1620 (Lower). (B) GTP-RAS levels measured with a G-LISA assay in NCI-H358 cells starved for 24 h in low serum conditions (−EGF), followed by 2-h treatment with increasing concentrations of 1 or 20 µM treatment of ARS-1620 and then EGF addition. For quantification, RAS GTP levels without EGF and in the presence of DMSO were set to 1 (A and B). (C) pERK and pAKT levels in NCI-H358 cells starved for 24 h in low serum conditions (−EGF), followed by 2-h treatment with increasing concentrations of 1 (red), 44 (green), and ARS-1620 (blue); then EGF addition were quantified. DMSO-treated samples after EGF stimulation were set to 100%. (D) GTP-RAS levels measured with a G-LISA assay in NCI-H358 cells with treatment of 50 µM compound 1, 20 µM ARS-1620 and simultaneous treatment of 1 and ARS-1620. For quantification, RAS GTP levels in the presence of DMSO were set to 1. (E) pERK dose–response curves for 1 (red), 44 (green), and ARS-1620 (blue) in NCI-H358 cells under high serum conditions. (F) Antiproliferative dose–response curves for NCI-H358 cells in soft agar and low serum conditions for 1 (red) and 44 (blue). DMSO-treated samples were set to 100% (E and F). Error bars indicate SDs. Indicated experiments were performed 2 or more times with similar results.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 51 references

Cited by

Sorted by citations

Items 201–300 of 459

Related

See all

Metadata

Authors
Kessler, D., Gmachl, M., Mantoulidis, A., Martin, L. J., Zoephel, A., Mayer, M., Gollner, A., Covini, D., Fischer, S., Gerstberger, T., Gmaschitz, T., Goodwin, C., Greb, P., Häring, D., Hela, W., Hoffmann, J., Karolyi-Oezguer, J., Knesl, P., Kornigg, S., Koegl, M., Kousek, R., Lamarre, L., Moser, F., Munico-Martinez, S., Peinsipp, C., Phan, J., Rinnenthal, J., Sai, J., Salamon, C., Scherbantin, Y., Schipany, K., Schnitzer, R., Schrenk, A., Sharps, B., Siszler, G., Sun, Q., Waterson, A., Wolkerstorfer, B., Zeeb, M., Pearson, M., Fesik, S. W., & McConnell, D. B.
Affiliations
  • Boehringer Ingelheim (Austria) (AT)
  • Vanderbilt University (US)
  • Discovery Research, Boehringer Ingelheim Regional Center Vienna GmbH & Co KG, 1120 Vienna, Austria;
  • Boehringer Ingelheim (Germany) (DE)
Corresponding author
McConnell, Darryl B.
ORCID
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
2019-07-22
Volume
116
Issue
32
Pages
15823-15829
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.1904529116
PMC
PMC6689897
OpenAlex
W2963086398
Cited by
449
References
51
Citations by year
2026: 52 · 2025: 58 · 2024: 71 · 2023: 66 · 2022: 58 · 2021: 63 · 2020: 69 · 2019: 12
FWCI
17.03
Type
journal article
Language
en
Open access
Licence
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DORA PSI (Paul Scherrer Institute) (submittedVersion, cc-by)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Funders
  • U.S. Department of Energy (DE‐ AC02‐06CH11357, AC02‐06CH11357, 06CH11357, DE-AC02-)
  • Vanderbilt University
  • Paul Scherrer Institut
  • National Institutes of Health (DE-AC02–06CH11357)
  • Office of Science (AC02‐06CH11357, DE-AC02, 06CH11357, DE-AC02-06CH11357)
  • National Cancer Institute (P50A095103, DE-AC02-06CH11357, P50CA095103, RC2CA148375, P30CA068485)
  • Vanderbilt Institute of Chemical Biology, School of Medicine, Vanderbilt University (P30CA68485)
  • Argonne National Laboratory (DE- AC02-06-CH11357, DE-AC02, AC02–06CH11357, 06CH11357)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Kinase Regulation and GTPase Signaling
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Tyrosine Phosphatases
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Keywords
KRAS, Ras GTPase, structure-based drug design, oncogenic Ras mutations, antiproliferative activity, chemical probes
MeSH terms
Pharmaceutical Preparations, Guanosine Triphosphate, Humans, Models, Molecular, Proto-Oncogene Proteins p21(ras), Nanoparticles, Drug Discovery

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review