Antibiotic-resistant tuberculosis remains a major public health challenge, and rapid diagnosis of resistant infections based on genomic markers holds promise for improving time to effective treatment. However, the vast majority of clinically observed variants in resistance-associated genes remain of uncertain significance, limiting the utility of predictors. Here we develop a multimodal forecasting framework, FARM (Forecasting Antibiotic Resistance in Mycobacterium tuberculosis) to determine whether a newly observed mutation in a resistance gene may indeed cause resistance. Our framework combines structural context, biophysical energy features, protein language model features, and mutational AAIndex physicochemical descriptors. Using 345 labeled mutations from the World Health Organization 2021 catalogue, we train interpretable models that distinguish resistance-associated from non-resistance-associated variants with holdout AUCs of 0.843–0.943. In a novel temporal evaluation of 62 mutations reclassified after the training data was released, the selected Combined model achieved 80.7% recall of resistant reclassifications (resistant-class F1=86.8; AUC=0.735). Applied to 4,525 current uncertain-significance mutations, the framework prioritizes 696 candidate resistance mutations, including genes associated with the new antibiotics bedaquiline, delamanid, and pretomanid. These forecasts are intended to support future catalogue updates and experimental follow-up.
Mahbuba Tasmin, Shrishti Barethiya, Yu Wang et al.· bioRxiv· 0 citations
Investigating the conformational dynamics of intrinsically disordered proteins (IDPs) is essential to understanding how their structural heterogeneity underlies function and how their dysregulation contributes to diseases. Here, we utilized an MspA nanopore-based approach for studying the conformational dynamics and interactions of IDPs at the single-molecule level. The platform was demonstrated using the intrinsically disordered transactivation domain of tumor suppressor p53 (p53-TAD), one of the important proteins in cancer biology. We showed that MspA can stably capture p53-TAD and resolve up to six distinct current states with frequent interconversions, revealing a rich conformational landscape. The nanopore also detected the effect of a cancer-associated double mutational variant, N29K/N30D. Combining experiments with steered molecular dynamics simulations, we showed that the mutant sampled compact conformational states more frequently than wild type, consistent with previous NMR studies. Importantly, the MspA platform enabled direct monitoring of E3 ligase MDM2 binding to p53-TAD and resolved how this interaction is inhibited by anti-cancer compound epigallocatechin gallate (EGCG). Notably, EGCG stabilizes one of the six states sampled by p53-TAD, providing a mechanistic explanation for its inhibitory effect. Together, these findings demonstrate the promise of the nanopore platform for label-free monitoring of IDP conformational dynamics, modulation, binding and inhibition at single-molecule resolution.
David DeCoeur, Samantha A Schultz, Jianhan Chen et al.· bioRxiv· 0 citations
The spatial distribution of small molecules within cells shapes their biological activity, yet these distributions are generally assumed to be governed passively by reaction-driven electrochemical gradients. Here we show that aminoglycoside antibiotics actively control their own subcellular organization by undergoing phase separation with RNAs. Combining in vitro reconstitution, bacterial assays, and molecular dynamics simulations, we discovered that aminoglycosides coacervate with RNA through multivalent electrostatic interactions, displacing and releasing RNA-bound Mg2+. This condensate-dependent Mg2+ release remodels the cytosolic labile Mg2+ pool and activates magnesium signaling. This effect dampens the magnesium-starvation regulation, sustains ribosome activity, and shifts the cellular electrochemical state, promoting bacterial fitness. Because condensation occurs only above a defined concentration threshold, it generates a non-monotonic dose-response in which higher antibiotic concentrations paradoxically enhance bacterial survival. This antibiotic condensate-dependent Mg2+ signaling confers tolerance to multiple ribosome-targeting antibiotics simultaneously even in cells lacking resistance gene, while condensate dissolution restores antibiotic efficacy. Our findings establish antibiotic-driven phase separation as a previously unrecognized mechanism to encode cellular signaling and identify antibiotic condensates as a distinct functional unit underlying drug tolerance.
Yuefeng Ma, Wen Yu, E. Moon et al.· bioRxiv· 0 citations
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