Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/346130

Computational evidence of a new allosteric communication pathway between active sites and putative regulatory sites in the alanine racemase of Mycobacterium tuberculosis

Jyothikumar, J.·Chandani, S.·Tangirala, R.
View original source ↗Export metadata ↓

Abstract

Alanine racemase, a popular drug target from Mycobacterium tuberculosis, catalyzes the biosynthesis of D-alanine, an essential component in bacterial cell walls. With the help of elastic network models of alanine racemase from Mycobacterium tuberculosis, we show that the mycobacterial enzyme fluctuates between two undiscovered states--a closed and an open state. A previous experimental screen identified several drug-like lead compounds against the mycobacterial alanine racemase, whose inhibitory mechanisms are not known. Docking simulations of the inhibitor leads onto the mycobacterial enzyme conformations obtained from the dynamics of the enzyme provide first clues to a putative regulatory role for two new pockets targeted by the leads. Further, our results implicate the movements of a short helix, behind the communication between the new pockets and the active site, indicating allosteric mechanisms for the inhibition. Based on our findings, we theorize that catalysis is feasible only in the open state. The putative regulatory pockets and the enzyme fluctuations are conserved across several alanine racemase homologs from diverse bacterial species, mostly pathogenic, pointing to a common regulatory mechanism important in drug discovery.\n\nAuthor summaryIn spite of the discovery of many inhibitors against the TB-causing pathogen Mycobacterium tuberculosis, only a very few have reached the market as effective TB drugs. Most of the marketed TB drugs induce toxic side effects in patients, as they non-specifically target human cells in addition to pathogens. One such TB drug, D-cycloserine, targets pyridoxal phosphate moiety non-specifically regardless of whether it is present in the pathogen or the human host enzymes. D-cycloserine was developed to inactivate alanine racemase in TB causing pathogen. Alanine racemase is a bacterial enzyme essential in cell wall synthesis. Serious side effects caused by TB drugs like D-cycloserine, lead to patients non-compliance with treatment regimen, often causing fatal outcomes. Current drug discovery efforts focus on finding specific, non-toxic TB drugs. Through computational studies, we have identified new pockets on the mycobacterial alanine racemase and show that they can bind drug-like compounds. The location of these pockets away from the pyridoxal phosphate-containing active site, make them attractive target sites for novel, specific TB drugs. We demonstrate the presence of these pockets in alanine racemases from several pathogens and expect our findings to accelerate the discovery of non-toxic drugs against TB and other bacterial infections.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

BibTeXRIS

Jyothikumar, J., Chandani, S., Tangirala, R.. 2018-06-13. Computational evidence of a new allosteric communication pathway between active sites and putative regulatory sites in the alanine racemase of Mycobacterium tuberculosis. https://doi.org/10.1101/346130

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

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗