The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Utilization of an Optimized AlphaFold Protein Model for Structure-Based Design of a Selective HDAC11 Inhibitor with Anti-neuroblastoma Activity
Open accessPreprint — published as 10.1002/ardp.202400486 · Paper page
Abstract
AlphaFold is an artificial intelligence approach for predicting the 3D structures of proteins with atomic accuracy. One challenge that limits the use of AlphaFold models for drug discovery is the correct prediction of folding in the absence of ligands and cofactors, which compromises their direct use. We have previously described the optimization and use of the HDAC11-AlphaFold model for the docking of selective inhibitors such as FT895 and SIS17. Based on the predicted binding mode of FT895 in the optimized HDAC11 AlphaFold model, a new scaffold for HDAC11 inhibitors was designed, and the resulting compounds were tested in vitro against various HDAC isoforms. Compound 5a proved to be the most active compound with an IC50 of 365 nM and was able to selectively inhibit HDAC11. 5a also showed promising activity with an EC50 of 3.6 µM on neuroblastoma cells. Furthermore, we supported our study by comparative docking and MD simulations.
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.
Integrity signals
Registry
Cites
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.
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
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Flagged first, then by citations
Metadata
- Authors
- Baselious, F., Hilscher, S., Hagemann, S., Tripathee, S., Robaa, D., Barinka, C., Hüttelmaier, S., Schutkowski, M., & Sippl, W.
- Affiliations
- Martin Luther University Halle-Wittenberg (DE)
- Czech Academy of Sciences
- Corresponding author
- Sippl, Wolfgang
- ORCID
- Venue
- Archiv der Pharmazie
- Publisher
- American Chemical Society (ACS)
- Published
- 2024-01-09
- Volume
- 357
- Issue
- 10
- Pages
- e2400486-e2400486
- ISSN
- 0365-6233 (ISSN-L)
- OpenAlex
- W4400576372
- Cited by
- 16
- References
- 1
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 2
- FWCI
- 2.16
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Archiv der Pharmazie (publishedVersion, cc-by-nc-nd)
- ChemRxiv (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (471614207, 469954457)
- Grantová Agentura České Republiky (RVO 86652036, GA24-12155S)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Histone Deacetylase Inhibitors Research
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Enzyme function and inhibition
- Keywords
- HDAC11, histone deacetylase inhibitors, structure-based drug design, neuroblastoma, molecular docking, molecular dynamics simulation, scaffold optimization, HDAC isoforms
- MeSH terms
- Antineoplastic Agents, Artificial Intelligence, Dose-Response Relationship, Drug, Histone Deacetylases, Humans, Neuroblastoma, Structure-Activity Relationship, Drug Design, Molecular Structure, Cell Line, Tumor, Molecular Dynamics Simulation, Histone Deacetylase Inhibitors, Molecular Docking Simulation
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