Transcription factors are central regulators of gene expression and transcription that critically steer cell development, differentiation, and death. Numerous diseases, such as neurodegenerative disorders, diabetes, and cancer, are associated with the deregulation of transcriptional networks. Except for ligand-activated nuclear receptors, direct modulation of transcription factor function by small-molecule compounds is still widely regarded as “impossible”. Drug targeting approaches mostly address protein-protein interfaces with essential co-factors, transcription factor dimerization partners, chaperone proteins, or proteins that regulate subcellular shuttling (Fig. 1), and only a few studies are known to be devoted to compounds that directly interfere with DNA binding. Alternative strategies represent DNA-intercalating, alkylating, or DNA-groove-binding compounds that either block transcription factor-binding or change the 3D-conformation of the consensus DNA strand.
Recently, much interest has been focused on advances in targeting transcription factors with small molecules, the challenges that are related to the complex function and regulation of these proteins, and also possible future directions and applications of transcription factor drug targeting [1,2]. Several small molecules were found to inhibit transcription factor activity (including the STAT family, NF-κB, and Myc) in cell culture, and in some cases, in vivo .
To support the transcription-related drug discovery, Life Chemicals has designed a dedicated set of over 9,200 transcription-related screening compounds for high-throughput screening.
The compound selection can be customized based on your requirements, cherry picking is available.
Please, contact us at email@example.com for any additional information and price quotations.
Figure 1. Biological interfaces for transcription factor (TF) inhibition. Picture source: Hagenbuchner J., Ausserlechner M.J., 2016 .
First, a reference set of known modulators of transcription-associated proteins was obtained from the ChEMBL database (over 46,990 pre-filtered compounds with more than 120,000 activity entries). Next, the 2D fingerprint similarity search against the Life Chemicals proprietary HTS Compound Collection was performed with the Tanimoto index ≥ 0.8 (limit of 10 analogs max per reference compound), resulting in almost 23,700 transcription-related molecules (this extended compound set is available on request). Then the filtration on reported activity values was performed (< 10 uM) followed by in-house medchem filters (excluding PAINS, toxic and reactive compounds) to result in the final compound set of over 9,200 drug-like screening compounds.
The search was performed against the following transcription-related protein targets (Fig. 1):
- Adenosine A3 receptor
- Estrogen receptor alpha
- Nuclear receptor ROR-gamma
- Cellular tumor antigen p53
- Smoothened homolog
- Histamine H3 receptor
- Peroxisome proliferator-activated receptor alpha
- Nuclear receptor ROR-alpha
- Androgen Receptor
- Transforming protein RhoA
- Transcription factor GATA-4
- Hypoxia-inducible factor 1 alpha
- Krueppel-like factor 10
- Human immunodeficiency virus type 1 Tat protein
- Vitamin D receptor
- G-protein coupled receptor 35
- C-C chemokine receptor type 1
- Proto-oncogene c-JUN
- Signal transducer and activator of transcription 1-alpha/beta
- Catenin beta-1
- Signal transducer and activator of transcription 3
- Progesterone receptor
- Transcriptional activator protein lasR
- Aryl hydrocarbon receptor
- Peroxisome proliferator-activated receptor delta
- Sphingosine 1-phosphate receptor Edg-6
- Mothers against decapentaplegic homolog 3
- Nuclear receptor subfamily 1 group D member 1
- TGF-beta receptor type I
- Nucleotide-binding oligomerization domain-containing protein 1
- Retinoic acid receptor beta
- Heat shock factor protein 1
- Estrogen-related receptor alpha
- Cannabinoid CB1 receptor
- Peroxisome proliferator-activated receptor gamma
- Estrogen receptor beta
- Zinc finger protein GLI1
All results could be traced back to the targets or assays indicated in the column “Target type” or “Description” within the corresponding SDfile. Cherry-picking is available. Custom compound selection based on specific parameters can be performed on request.
Fig. 1. Compound distribution by target (Nuclear receptor ROR (a,b,g) and Nucleic Acid are not shown here).
- Hagenbuchner J., Ausserlechner M.J. Targeting transcription factors by small compounds—Current strategies and future implications. Biochemical Pharmacology, 2016; Volume 107: Pages 1-13. https://doi.org/10.1016/j.bcp.2015.12.006.
- Chen A., Koehler A.N. Transcription Factor Inhibition: Lessons Learned and Emerging Targets. Trends in molecular-medicine, 2020; Volume 26,5: Pages 508-518. https://doi.org/10.1016/j.molmed.2020.01.004.