bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2020.07.30.228726

Repurposing Novobiocin for activity against latency associated Mycobacterium tuberculosis drug target nicotinate-nucleotide adenylyltransferase (Rv2421c)

Cloete, R.·Shahbaaz, M.·Grobbelaar, M.·Sampson, S. L.·Christoffels, A.
View original source ↗Export metadata ↓

Abstract

Nicotinamide-nucleotide adenylyl transferase (Rv2421c) was selected as a potential drug target, because it has been shown, in vitro, to be essential for Mycobacterium tuberculosis growth. It is conserved between mycobacterium species, is up-regulated during dormancy, has a known 3D crystal structure and has no known human homologs. A model of Rv2421c in complex with nicotinic acid adenine dinucleotide and magnesium ion was constructed and subject to virtual ligand screening against the Prestwick Chemical Library and the ZINC database, which yielded 155 potential hit molecules. 3D-QSAR studies of the 155 drug molecules indicated five compounds with similar inhibitory efficiencies compared to known inhibitors of Rv2421c. Molecular docking validation and molecular dynamics simulation analysis of the top five compounds indicated that the identified inhibitor molecules bind to Rv2421c with comparable efficiency as the substrate DND. Subsequent in vitro testing of the five compounds identified Novobiocin sodium salt with activity against Mycobacterium tuberculosis at 50 M, 25M and weakly at 10M concentrations. Although, Novobiocin salt targets Mycobacterium tuberculosis DNA gyrase B our studies suggest that it has the potential to be repurposed to inhibit Rv2421c. Subsequent in silico structural analysis of known Novobiocin sodium salt derivatives against Rv2421c suggest promising alternatives for the treatment of Mycobacterium tuberculosis. Author SummaryRv2421c has been shown to be essential for Mycobacterium tuberculosis growth, shares no homology to known proteins in the human host, is conserved between various Mycobacterium species, is up-regulated during the non-replicative metabolic growth phase, making it an attractive drug target. It has a known 3D structure which has been exploited to screen for putative compounds within the Prestwick chemical library and ZINC database, resulting in the successful identification of 155 candidate compounds. Thereafter 3D-QSAR, molecular docking and molecular dynamics simulation studies were used to prioritize five potential compounds. Of the five compounds tested in vitro, only one, a Novobiocin disodium salt, showed activity against Mycobacterium tuberculosis at 50, 25 and weakly at 10 M concentrations. Novobiocin is known to target Mycobacterium tuberculosis DNA gyrase B, but emerging resistance stimulated us to seek derivatives to target Rv2421c as alternatives for the treatment of Mycobacterium tuberculosis. Docking studies supported the higher binding affinities of Novobiocin derivatives to Rv2421c compared to DNA gyrase B. Future studies will involve testing these Novobiocin derivatives for activity against Mycobacterium tuberculosis.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Cloete, R., Shahbaaz, M., Grobbelaar, M., Sampson, S. L., Christoffels, A.. 2020-07-30. Repurposing Novobiocin for activity against latency associated Mycobacterium tuberculosis drug target nicotinate-nucleotide adenylyltransferase (Rv2421c). https://doi.org/10.1101/2020.07.30.228726

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

RoDPAA: A Robust Dissimilarity-Based Principal Angle Analysis Framework for Multi-View Microbiome Data Integration

Multiview microbiome studies profile the same participants across body sites or molecular layers, but integration is hampered by sparse, compositional data, modest sample sizes and methods that recognize only exact sharing. We present RoDPAA (Robust Dissimilarity-based Principal Angle Analysis), a framework that represents each view through any data-appropriate dissimilarity, such as Bray--Curtis, without log-ratio transformation or likelihood assumptions. RoDPAA aligns view-specific signal subspaces through principal angles, captures complete and partial sharing, and tests by permutation how many components are shared, using only eigendecomposition and little tuning. In Gaussian simulations, RoDPAA matched JIVE and MOFA given true ranks, ran faster and alone recovered the shared dimension in every replicate. In two inflammatory bowel disease (IBD) cohorts with sparse, zero-inflated data, Bray--Curtis-based RoDPAA recovered shared structure that was stable to prevalence filtering, whereas log-ratio-based alternatives were not. Its leading microbiome--metabolome factors, learned without disease labels, were the only ones associated with IBD.

bioinformatics↗
bioRxiv2026

Deducing splicing events from transcript sets with splicelogic

Long-read RNA sequencing captures a greater proportion of full-length transcripts, enabling more accurate isoform identification, discovery of novel isoforms, and more precise differential transcript usage analysis. However, comparing transcripts, whether between conditions or between sets such as novel and annotated isoforms, yields complex isoform changes that are hard to manipulate, summarize, and visualize. This is compounded by vertical integration of some tools, where downstream analysis depends on the specific output of a particular upstream tool. To address this gap, we have developed splicelogic, an R/Bioconductor package that makes transcript-set comparisons biologically interpretable by turning them into discrete splicing events, such as exon skipping, alternative splice sites, intron retention, and alternative transcription start and end sites. We designed splicelogic to integrate easily with existing RNA-seq workflows and tools. The splicelogic functions are modular, readable, and extensible, making it easy for users to adapt core logic to their own custom scientific questions or novel splicing definitions. By obtaining interpretable splicing events from transcript changes, splicelogic enables downstream analyses, including annotation of RNA-binding protein recognition sites, alongside summary statistics and visualization. The splicelogic package is publicly available and distributed through Bioconductor.

bioinformatics↗
bioRxiv2026

Contrastive Learning Improves Generalization on M2OR Olfactory Binding Dataset

Olfaction-based biosensors stand to have profound impacts in fields such as medical diagnostics, national security, and drug discovery. A promising mode of vapor phase biodetection mimics volatile organic compound-olfactory receptor (VOC-OR) interactions with target-specific protein or peptide probes; however, lack of experimental coverage remains a core bottleneck. Current computational approaches aim to improve prediction of VOC-OR binding to reduce experimental burden, and current machine learning approaches fail to demonstrate a level of generalization that addresses current engineering challenges. Here, using the Molecular to Olfactory Dataset (M2OR) and Insect Olfactory Binding Protein Database (iOBPdb) datasets, data partitioning methods and their performance are evaluated on a neural network and contrastive learning framework on such VOC-OR binding datasets, varying: model training, architecture, and structure of the input data distribution. It was found that holding out certain chemical or proteomic motifs from the training data diminished model performance with a two layer neural network model. The model was able to generalize to state of the art performance (0.88 AUCROC) on every data split by applying a contrastive learning objective defined by shared molecular constituents and a stochastic sampler. This suggests the contrastive objective provides a strong inductive bias for neural networks to predict novel VOC-OR pairs. These findings highlight that model performance on pairwise biological interaction datasets can be inflated through identity leakage from constituent overlap, and emphasize the need for more robust evaluation protocols. Furthermore, we show contrastive regularization mitigates dataset degeneracy through enforcement of compatibility in the models latent geometry that may extend to other binder target-datasets where identity leakage confounds evaluation.

bioinformatics↗