Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 292

The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.

“Inverse Drug Discovery” Strategy To Identify Proteins That Are Targeted by Latent Electrophiles As Exemplified by Aryl Fluorosulfates

Open access
Mortenson, David E., Brighty, Gabriel J., Plate, Lars, Bare, Grant, Chen, Wentao, Li, Suhua, Wang, Hua, Cravatt, Benjamin F., Forli, Stefano, Powers, Evan T., Sharpless, K. Barry, Wilson, Ian A., Kelly, Jeffery W.·2018·Journal of the American Chemical Society
DOI 10.1021/jacs.7b08366
Open this paper in CoChat — free account

Abstract

Drug candidates are generally discovered using biochemical screens employing an isolated target protein or by utilizing cell-based phenotypic assays. Both noncovalent and covalent hits emerge from such endeavors. Herein, we exemplify an "Inverse Drug Discovery" strategy in which organic compounds of intermediate complexity harboring weak, but activatable, electrophiles are matched with the protein(s) they react with in cells or cell lysate. An alkyne substructure in each candidate small molecule enables affinity chromatography-mass spectrometry, which produces a list of proteins that each distinct compound reacts with. A notable feature of this approach is that it is agnostic with respect to the cellular proteins targeted. To illustrate this strategy, we employed aryl fluorosulfates, an underexplored class of sulfur(VI) halides, that are generally unreactive unless activated by protein binding. Reversible aryl fluorosulfate binding, correct juxtaposition of protein side chain functional groups, and transition-state stabilization of the S(VI) exchange reaction all seem to be critical for conjugate formation. The aryl fluorosulfates studied thus far exhibit chemoselective reactivity toward Lys and, particularly, Tyr side chains, and can be used to target nonenzymes (e.g., a hormone carrier or a small-molecule carrier protein) as well as enzymes. The "Inverse Drug Discovery" strategy should be particularly attractive as a means to explore latent electrophiles not typically used in medicinal chemistry efforts, until one reacts with a protein target of exceptional interest. Structure-activity data can then be used to enhance the selectivity of conjugate formation or the covalent probe can be used as a competitor to develop noncovalent drug candidates. Here we use the "Inverse Drug Discovery" platform to identify and validate covalent ligands for 11 different human proteins. In the case of one of these proteins, we have identified and validated a small-molecule probe for the first time.

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 · 10.1021/jacs.7b08366

Cites

66 of 66 references verified0 citation errors · 0 not checkable

Statistics

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

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

Cited by

See all 292

Metadata

Authors
Mortenson, D. E., Brighty, G. J., Plate, L., Bare, G., Chen, W., Li, S., Wang, H., Cravatt, B. F., Forli, S., Powers, E. T., Sharpless, K. B., Wilson, I. A., & Kelly, J. W.
Venue
Journal of the American Chemical Society
Publisher
American Chemical Society (ACS)
Published
2017-12-21
Volume
140
Issue
1
Pages
200-210
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
DOI
10.1021/jacs.7b08366
OpenAlex
W2781191952
Cited by
292
References
66
FWCI
9.84
Type
journal article
Language
en
Open access
green

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

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.