Timmers, L. F. S. M. | bioRxiv Science

bioRxiv2023

Identification and characterization of new structural scaffolds modulating the activity of Mycobacterium tuberculosis dihydroneopterin aldolase (FolB) in vitro

Antifolates were among the first broad-spectrum compounds used as antimycobacterial agents and can still be of use when no other therapeutic options are available. The discovery of compounds targeting this essential pathway could lead to new therapeutic agents to treat tuberculosis (TB). In particular, the enzyme required for the conversion of 7,8-dihydroneopterin (DHNP) to 6-hydroxymethyl-7,8-dihydropterin (HP) and glycolaldehyde (GA) in the folate pathway (MtbFolB, a dihydroneopterin aldolase - DHNA, EC 4.1.2.25), has received little attention as a potential drug target. Here, we conducted a small-scale diversity screening to identify MtbFolB inhibitors using a microplate-based enzyme inhibition assay. About 6,000 compounds were assembled for the screening and 19 hits were identified, spanning 5 independent clusters. These compounds were tested in dose-response studies and active compounds selected for kinetic inhibition and time-dependent inhibition studies, leading to compounds with IC50 values ranging from 2.6 to 47 {micro}M. A preliminary structure activity analysis was performed, revealing that bi-sulfonamide compounds could be explored for further optimizations. Docking studies highlighted two modes of binding for pyrazol-3-one compounds and, for the sulfonamide series, indicated several interactions with the catalytic Tyrosine-54 (Tyr54D) and Lysine-99 (Lys99A) residues of MtbFolB. The sulfonamide compound 13 represents the first identified compound directed against MtbFolB with an antimycobacterial activity.

biochemistry↗

bioRxiv2023

Development of QSAR Models to Identify Mycobacterium tuberculosis enoyl-ACP-reductase Enzyme Inhibitors

Tuberculosis is a global concern due to its high prevalence in developing countries and the ability of mycobacteria to develop resistance to current treatment regimens. In this project, we propose the use of QSAR (Quantitative Structure-Activity Relationships) modeling as a means to identify and evaluate the inhibitory activity of candidate molecules for molecular improvement stages and/or in vitro assays. This approach allows for in silico estimation, reducing research time and costs. To achieve this, we utilized the SAR (Structure-Activity Relationships) study conducted by He, Alian, and Montellano (2007), which focused on a series of arylamides tested as inhibitors of the enzyme enoyl-ACP-reductase (InhA) in Mycobacterium tuberculosis. We developed both the Hansh-Fujita (classical) and CoMFA (Comparative Molecular Field Analysis) QSAR models. The classical QSAR model produced the most favorable statistical results using Multiple Linear Regression (MLR). It achieved an internal validation correlation factor R2 of 0.9012 and demonstrated predictive quality with a Stone-Geisser indicator Q2 of 0.8612. External validation resulted in a correlation factor R2 of 0.9298 and Q2 of 0.720, indicating a highly predictive mathematical model. The CoMFA Model obtained a Q2 of 0.6520 in internal validation, enabling the estimation of energy fields around the molecules. This information is crucial for molecular improvement efforts. We constructed a library of small molecules, analogous to those used in the SAR study, and subjected them to the classic QSAR function. As a result, we identified ten molecules with high estimated biological activity. Molecular docking analysis suggests that these ten analogs, identified by the classical QSAR model, exhibit favorable estimated free energy of binding. In conclusion, the QSAR methodology proves to be an efficient and effective tool for searching and identifying promising drug-like molecules.

bioinformatics↗