Discovery of Small-Molecule VapC1 Nuclease Inhibitors by Virtual Screening and Scaffold Hopping from an Atomic Structure Revealing Protein-Protein Interactions with a Native VapB1 Inhibitor

Deciphering the role of VapBC toxin-antitoxin systems in Mycobacterium tuberculosis stress adaptation

Mycobacterium tuberculosis (Mtb) harbors a high number of Toxin-Antitoxin (TA) systems, wherein half of them belong to virulence associated proteins B and C (VapBC) family that has a characteristic PilT N-terminus domain and ribonuclease activity. Functional insights into Mtb VapBC TA modules unraveled their role in adaptation to various host-mediated stressors, including oxidative/nitrosative, chemical and nutrient starvation as well as multidrug tolerance and establishment of persistence. To understand the intricacies of Mtb's pathogenesis, absolute cellular targets of 19 VapC(s) were determined. Some exhibit a shared ribonuclease activity, whereas others harbor tRNAse and 23S rRNA cleavage activity. The detailed functional characterization of VapBC4, VapBC12 and VapBC22, including in vivo deletion mutant studies revealed their role in Mtb's virulence/persistence. For example, the VapC22 mutant was attenuated for Mtb's growth in mice and elicited a decreased TH1 response, whereas mice infected with VapC12 mutant displayed a substantially higher bacillary load and pro-inflammatory response than the wild type, showing a hyper-virulent phenotype. Further experimental studies are needed to decode the functional role of VapBC systems and unravel their cellular targets. Taken together, Mtb VapBC TA systems seem to be promising drug targets owing to their key role in enduring stressors, antibiotic resistance and persistence.

Predicting the Likelihood of Molecules to Act as Modulators of Protein-Protein Interactions

Targeting protein-protein interactions (PPIs) by small molecule modulators (iPPIs) is an attractive strategy for drug therapy, and some iPPIs have already been introduced into the clinic. Blocking PPIs is however considered to be a more difficult task than inhibiting enzymes or antagonizing receptor activity. In this paper, we examine whether it is possible to predict the likelihood of molecules to act as iPPIs. Using our in-house iterative stochastic elimination (ISE) algorithm, we constructed two classification models that successfully distinguish between iPPIs from the iPPI-DB database and decoy molecules from either the Enamine HTS collection (ISE 1) or the ZINC database (ISE 2). External test sets of iPPIs taken from the TIMBAL database and decoys from Enamine HTS or ZINC were screened by the models: the area under the curve for the receiver operating characteristic curve was 0.85-0.89, and the Enrichment Factor increased from an initial 1 to as much as 66 for ISE 1 and 57 for ISE 2. Screening of the Enamine HTS and ZINC data sets through both models results in a library of ∼1.3 million molecules that pass either one of the models. This library is enriched with iPPI candidates that are structurally different from known iPPIs, and thus, it is useful for target-specific screenings and should accelerate the discovery of iPPI drug candidates. The entire library is available in Table S6.