The Landscape of the Anti-Kinase Activity of the IDH1 Inhibitors

Isocitrate dehydrogenases constitute a class of enzymes that are crucial for cellular metabolism. The overexpression or mutation of isocitrate dehydrogenases are often found in leukemias, glioblastomas, lung cancers, and ductal pancreatic cancer among others. Mutation R132H, which changes the functionality of an enzyme to produce mutagenic 2-hydroxyglutarate instead of a normal product, is particularly important in this field. A series of inhibitors were described for these enzymes of which ivosidenib was the first to be approved for treating leukemia and bile duct cancers in 2018. Here, we investigated the polypharmacological landscape of the activity for known sulfamoyl derivatives that are inhibitors, which are selective towards IDH1 R132H. These compounds appeared to be effective inhibitors of several non-receptor kinases at a similar level as imatinib and axitinib. The antiproliferative activity of these compounds against a panel of cancer cells was tested and is explained based on the relative expression levels of the investigated proteins. The multitargeted activity of these compounds makes them valuable agents against a wide range of cancers, regardless of the status of IDH1

EMQIT: a machine learning approach for energy based PWM matrix quality improvement

Abstract Background Transcription factor binding affinities to DNA play a key role for the gene regulation. Learning the specificity of the mechanisms of binding TFs to DNA is important both to experimentalists and theoreticians. With the development of high-throughput methods such as, e.g., ChiP-seq the need to provide unbiased models of binding events has been made apparent. We present EMQIT a modification to the approach introduced by Alamanova et al. and later implemented as 3DTF server. We observed that tuning of Boltzmann factor weights, used for conversion of calculated energies to nucleotide probabilities, has a significant impact on the quality of the associated PWM matrix. Results Consequently, we proposed to use receiver operator characteristics curves and the 10-fold cross-validation to learn best weights using experimentally verified data from TRANSFAC database. We applied our method to data available for various TFs. We verified the efficiency of detecting TF binding sites by the 3DTF matrices improved with our technique using experimental data from the TRANSFAC database. The comparison showed a significant similarity and comparable performance between the improved and the experimental matrices (TRANSFAC). Improved 3DTF matrices achieved significantly higher AUC values than the original 3DTF matrices (at least by 0.1) and, at the same time, detected notably more experimentally verified TFBSs. Conclusions The resulting new improved PWM matrices for analyzed factors show similarity to TRANSFAC matrices. Matrices had comparable predictive capabilities. Moreover, improved PWMs achieve better results than matrices downloaded from 3DTF server. Presented approach is general and applicable to any energy-based matrices. EMQIT is available online at http://biosolvers.polsl.pl:3838/emqit . Reviewers This article was reviewed by Oliviero Carugo, Marek Kimmel and István Simon

Additional file 1: Table S1: of EMQIT: a machine learning approach for energy based PWM matrix quality improvement

The β values selected for final improved matrices. Figure S1. The p53 tetramer sequence logos. From top: improved 3DTF matrix, original 3DTF matrix and TRANSFAC matrix. Figure S2. The GABP tetramer sequence logos. From the top: the improved 3DTF matrix, the original 3DTF matrix and the V$GABP_B TRANSFAC matrix. Figure S3. The Erα sequence logos. From the top: the improved 3DTF matrix, the original 3DTF matrix and the V$ERALPHA_Q6_02 TRANSFAC matrix. Figure S4. The p50p50 sequence logos. From the top: the improved 3DTF matrix, the original 3DTF matrix and the V$P50P50_Q3 TRANSFAC matrix. Figure S5. The p50p65 sequence logos. From the top: the improved 3DTF matrix, the original 3DTF matrix and the V$P50RELAP65_Q5_01 TRANSFAC matrix. Figure S6. The HSF1 sequence logos. From the top: the improved 3DTF matrix, the original 3DTF matrix, and the V$HSF_Q6 V$HSF1_Q6_01 TRANSFAC matrix. Figure S7. Results of the improved matrix scan of 21 experimentally confirmed p53 binding sites for the MSS 0.8. Figure S8. Results of the improved matrix scan of 12 experimentally confirmed GABP binding sites for the MSS 0.8. Figure S9. Results of the improved matrix scan of 26 experimentally confirmed Erα binding sites for the MSS 0.8. Figure S10. Results of the improved matrix scan of 19 experimentally confirmed p50p50 binding sites for the MSS 0.8. Figure S11. Results of the improved matrix scan of 46 experimentally confirmed p50p65 binding sites for the MSS 0.8. Figure S12. Results of the improved matrix scan of 26 experimentally confirmed HSF1 binding sites for the MSS 0.8. (PDF 741 kb

Styrylquinazoline derivatives as ABL inhibitors selective for different DFG orientations

AbstractAmong tyrosine kinase inhibitors, quinazoline-based compounds represent a large and well-known group of multi-target agents. Our previous studies have shown interesting kinases inhibition activity for a series of 4-aminostyrylquinazolines based on the CP-31398 scaffold. Here, we synthesised a new series of styrylquinazolines with a thioaryl moiety in the C4 position and evaluated in detail their biological activity. Our results showed high inhibition potential against non-receptor tyrosine kinases for several compounds. Molecular docking studies showed differential binding to the DFG conformational states of ABL kinase for two derivatives. The compounds showed sub-micromolar activity against leukaemia. Finally, in-depth cellular studies revealed the full landscape of the mechanism of action of the most active compounds. We conclude that S4-substituted styrylquinazolines can be considered as a promising scaffold for the development of multi-kinase inhibitors targeting a desired binding mode to kinases as effective anticancer drugs

Mathematical modeling of regulatory networks of intracellular processes – Aims and selected methods

Regulatory networks structure and signaling pathways dynamics are uncovered in time- and resource consuming experimental work. However, it is increasingly supported by modeling, analytical and computational techniques as well as discrete mathematics and artificial intelligence applied to to extract knowledge from existing databases. This review is focused on mathematical modeling used to analyze dynamics and robustness of these networks. This paper presents a review of selected modeling methods that facilitate advances in molecular biology

Styrylquinazoline derivatives as ABL inhibitors selective for different DFG orientations

Among tyrosine kinase inhibitors, quinazoline-based compounds represent a large and well-known group of multi-target agents. Our previous studies have shown interesting kinases inhibition activity for a series of 4-aminostyrylquinazolines based on the CP-31398 scaffold. Here, we synthesised a new series of styrylquinazolines with a thioaryl moiety in the C4 position and evaluated in detail their biological activity. Our results showed high inhibition potential against non-receptor tyrosine kinases for several compounds. Molecular docking studies showed differential binding to the DFG conformational states of ABL kinase for two derivatives. The compounds showed sub-micromolar activity against leukaemia. Finally, in-depth cellular studies revealed the full landscape of the mechanism of action of the most active compounds. We conclude that S4-substituted styrylquinazolines can be considered as a promising scaffold for the development of multi-kinase inhibitors targeting a desired binding mode to kinases as effective anticancer drugs.</p