Abstract
Tuberculosis is a major global public health problem caused by Mycobacterium tuberculosis. Pyrazinamide (PZA), a key first-line drug, is activated to pyrazinoic acid, which inhibits Mycobacterium tuberculosis by targeting ribosomal protein S1 (RpsA) and blocking trans-translation. Deletion of alanine at position 438 of RpsA (RpsAΔ438) increases C-terminal flexibility, reduces drug binding, and confers PZA resistance. To identify novel inhibitors against this mutant, we developed a screening pipeline integrating Transformer-based virtual screening, molecular docking, Gaussian accelerated molecular dynamics simulations, and MM/GBSA free energy calculations. Compounds from the ChEMBL database were curated, labeled by MIC, and used to train Transformer-based models. The best-performing Transformer model outperformed Forest and XGBoost across multiple evaluation metrics, demonstrated an AUC of 0.844 and an accuracy of 0.772 on the test set, was applied to screen FDA-approved and traditional Chinese medicine libraries, yielding 317 and 1757 candidate compounds, respectively. Top-ranked hits were docked into RpsAΔ438 and evaluated using 400 ns molecular dynamics and Gaussian accelerated molecular dynamics simulations. Trajectory analyses revealed that compound Z2568748600 was the most promising candidate, exhibiting an RMSD of approximately 1.2-2.0 Å, an RMSF of 1.5-2.0 Å, and a radius of gyration of ~18.0 Å, forming long-lived hydrogen bonds with multiple residues, markedly suppressing large-scale motions, and effectively stabilizing the deletion-affected C-terminal domain. The MM/GBSA binding free energy was calculated to be -26.00 ± 3.50 kJ/mol. Moreover, in silico ADMET profiling confirmed its favorable drug-likeness and balanced safety profile, underscored by excellent cardiac safety and minimal cellular cytotoxicity. Overall, this integrated approach offers a promising strategy for discovering new agents against PZA-resistant Mycobacterium tuberculosis.Scientific contributionThis study established an integrated, systematic screening strategy targeting the drug-resistant RpsAΔ438 protein by coupling a Transformer-based deep learning framework for high-throughput virtual screening with subsequent molecular docking, Gaussian accelerated molecular dynamics simulations, MM/GBSA free energy calculations and ADMET prediction. This strategy enabled the efficient identification of potential inhibitors from expansive chemical space and allowed for the in-depth evaluation of compound interactions with the mutant protein. Among the screened candidates, Z2568748600 was identified as the most promising compound based on comprehensive computational assessments. Collectively, this approach significantly improved the discovery efficiency of promising inhibitors against pyrazinamide-resistant Mycobacterium tuberculosis.