A Virtual Screen Discovers Novel, Fragment-Sized Inhibitors ofMycobacterium tuberculosisInhA

Abstract

Isoniazid (INH) is usually administered to treat latent Mycobacterium tuberculosis (Mtb) infections, and is used in combination therapy to treat active tuberculosis disease (TB). Unfortunately, resistance to this drug is hampering its clinical effectiveness. INH is a prodrug that must be activated by Mtb catalase peroxidase (KatG) before it can inhibit InhA (Mtb enoyl-acyl-carrier-protein reductase). Isoniazid-resistant cases of TB found in clinical settings usually involve mutations in or deletion of katG, which abrogate INH activation. Compounds that inhibit InhA without requiring prior activation by KatG would not be affected by this resistance mechanism and hence would display continued potency against these drug-resistant isolates of Mtb. Virtual screening experiments versus InhA in the GO Fight Against Malaria project (GO FAM) were designed to discover new scaffolds that display base stacking interactions with the NAD cofactor. GO FAM experiments included targets from other pathogens, including Mtb, when they had structural similarity to a malaria target. Eight of the sixteen soluble compounds identified by docking against InhA plus visual inspection were modest inhibitors and did not require prior activation by KatG. The best two inhibitors discovered are both fragment-sized compounds and displayed Ki values of 54 and 59 μM, respectively. Importantly, the novel inhibitors discovered have low structural similarity to known InhA inhibitors and, thus, help expand the number of chemotypes on which future medicinal chemistry efforts can be focused. These new fragment hits could eventually help advance the fight against INH-resistant Mtb strains, which pose a significant global health threat.

Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data

“…[50] These PCA studies were performed using established protocols. [73,74] Briefly, eight different interpretable molecular descriptors were calculated and utilized to characterize the chemical properties of each compound: ALogP, molecular weight, number of rings, number of aromatic rings, number of rotatable bonds, number of hydrogen bond donors, number of hydrogen bond acceptors, and molecular fractional polar surface area. Pipeline Pilot then combined these eight descriptors in different ways, with different weights on each term, to reduce the dimensionality of the data into three Principal Components that describe most of the variance in the compounds.…”

Computational protein–ligand docking and virtual drug screening with the AutoDock suite

“…Results of several similar studies on proteins and their substrates were reported in the following years 9–11 . We have also used the Autodock suite as a tool for drug design and virtual screening against a number of targets, including HIV protease 12–14 , tuberculosis targets 15,16 , and beta-secretase 17,18 . AutoLigand was used to identify druggable sites for stabilizing a protein-protein interface 19 and covalent docking was used to explore covalent inhibitors of protein tyrosine phosphatases 20 .…”

Charting a Path to Success in Virtual Screening

“…Automated docking of small molecules is applied in many drug design efforts, and due to the ubiquitous availability of large computational resources, high throughput virtual screenings are now routinely applied in campaigns to assess druggability of novel targets where often no binders are known. A large number of successful applications have been reported using a variety of docking techniques [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. However, despite the improvements in recent years, a number of issues remain unsolved.…”