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Tasmin, M.

Publications and source records attributed to Tasmin, M..

3 recordsLinked to original sources
bioRxiv2026

FARM: Forecasting Antibiotic Resistance in Mycobacterium tuberculosis using biophysics and machine learning

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.

bioinformatics↗
bioRxiv2026

BIG-TB: A benchmark for evaluating prediction and interpretability of sequence-based machine learning using *Mycobacterium tuberculosis* genomes

Foundation models aim to learn useful representations of biological sequences. However, the applicability of these representations for a wide range of tasks, including phenotype prediction and variant discovery, is still in question, in large part due to the relatively small set of benchmark tasks. To this end, we present the Benchmarks for Interpretable prediction from Genomes of Tuberculosis, BIG-TB. We curate over 17,000 genomes with high-quality short read sequencing data and experimentally measured antibiotic resistance phenotypes, combined with a curated list of canonical resistance-conferring variants, and provide these data in an ML-ready format. BIG-TB defines two tasks for interrogating the utility of foundation models: (1) predictive performance of antibiotic resistance phenotypes, and (2) attribution of predictions to known resistance-conferring variants from an expert-curated dataset. Using our benchmark, we show that DNA-based foundation models do not yet outperform simple machine learning baselines (mean test AUC = 0.888 vs. 0.846 for best CNN variant vs. best DNABERT variant across drugs). Our benchmark also supports protein-based models, where performance is worse than DNA-based models due to the loss of representation of non-coding variants, but where foundation models are more competitive with simple ML. We show that the models with highest predictive performance do not necessarily perform the best at canonical resistance variant discovery - indicating that in many cases the improved performance may be due to non-causal associations between variants and phenotype. Finally, we show that the choice of embedding representation has a major impact on foundation model performance, and that representations that average over sequence position perform poorly at both prediction and canonical resistance variant discovery. Overall, BIG-TB provides a new type of benchmark for foundation models of biological sequences, facilitating comparison of representations across multiple tasks. Code available at https://github.com/SAGE-Lab-UMass/Big-TB-benchmark.

bioinformatics↗
bioRxiv2025

The structural context of mutations in proteins predicts their effect on antibiotic resistance

In Mycobacterium tuberculosis, a prevalent and deadly pathogen, resistance to antibiotics evolves primarily through non-synonymous mutations in proteins. Sequence-based analyses are currently used to understand the genetic basis of antibiotic resistance, either via genotype-phenotype association, or via signals of convergent evolution. These methods focus on primary sequence and often neglect other biological signals such as protein structural information. We hypothesize that integrating the structural context of mutations improves the prediction of effects on function and phenotype. We curate high confidence structural annotations for the M. tuberculosis proteome from 1,371 crystallography and 2,316 AlphaFold predictions, and combine the structures with mutations from over 31,000 clinical M. tuberculosis isolates. We demonstrate that mutations in proteins known to cause resistance are clustered in 3D space, even in proteins where inactivating mutations at any position are thought to cause resistance. We develop a statistic to search the M. tuberculosis proteome for signal of clustered mutations, finding over 450 proteins that display this signal, many of which have a known relationship with antibiotic resistance. We show that a supervised classifier trained on 3D distance to known resistance sites alone has an F1 score of 94.6% at classifying mutations as resistance-conferring across proteins. This work demonstrates that protein structure provides useful information for categorizing which variants may cause antibiotic resistance, even when the majority of structures are AI-predicted.

bioinformatics↗