Infections caused by antibiotic-resistant bacteria are a rising public health threat and make the identification of new antibiotics a priority. From a cell-based screen for bactericidal compounds against Mycobacterium tuberculosis under nutrient-deprivation conditions we identified auranofin, an orally bioavailable FDA-approved antirheumatic drug, as having potent bactericidal activities against both replicating and nonreplicating M. tuberculosis. We also found that auranofin is active against other Gram-positive bacteria, including Bacillus subtilis and Enterococcus faecalis, and drug-sensitive and drug-resistant strains of Enterococcus faecium and Staphylococcus aureus. Our biochemical studies showed that auranofin inhibits the bacterial thioredoxin reductase, a protein essential in many Gram-positive bacteria for maintaining the thiol-redox balance and protecting against reactive oxidative species. Auranofin decreases the reducing capacity of target bacteria, thereby sensitizing them to oxidative stress. Finally, auranofin was efficacious in a murine model of methicillin-resistant S. aureus infection. These results suggest that the thioredoxin-mediated redox cascade of Gram-positive pathogens is a valid target for the development of antibacterial drugs, and that the existing clinical agent auranofin may be repurposed to aid in the treatment of several important antibiotic-resistant pathogens. Significance The identification of new antibiotics with novel mechanisms of action has become a pressing need considering the growing threat of drug-resistant infections. We have identified auranofin, an FDA-approved drug, as having potent bactericidal activity against Gram-positive pathogenic bacteria. Auranofin inhibits an enzyme, thioredoxin reductase, not targeted by other antibiotics, and thus retains efficacy against many clinically relevant drug-resistant strains, including in a mouse model of infection. Because auranofin is an approved drug, its route to the clinic may be expedited with reduced cost. Our work suggests that auranofin is a candidate for drug repurposing in antibacterial therapy.

Prediction of enzyme kinetic parameters is essential for designing and optimizing enzymes for various biotechnological and industrial applications, but the limited performance of current prediction tools on diverse tasks hinders their practical applications. Here, we introduce UniKP, a unified framework based on pretrained language models for the prediction of enzyme kinetic parameters, including enzyme turnover number (k_cat), Michaelis constant (K_m), and catalytic efficiency (k_cat / K_m), from protein sequences and substrate structures. A two-layer framework derived from UniKP (EF-UniKP) has also been proposed to allow robust k_cat prediction in considering environmental factors, including pH and temperature. In addition, four representative re-weighting methods are systematically explored to successfully reduce the prediction error in high-value prediction tasks. We have demonstrated the application of UniKP and EF-UniKP in several enzyme discovery and directed evolution tasks, leading to the identification of new enzymes and enzyme mutants with higher activity. UniKP is a valuable tool for deciphering the mechanisms of enzyme kinetics and enables novel insights into enzyme engineering and their industrial applications.

Significance: This work describes a facile system for incorporating noncanonical amino acids containing long side-chain thiols using an expanded genetic code. These amino acids begin to overcome the distance and geometric constraints of the cysteine disulfide and can pair with cysteines to cross-link more remote sites in proteins. To demonstrate this notion, we constructed a library of random β-lactamase mutants containing these noncanonical amino acids and grew them at nonpermissive temperatures. We identified a mutant enzyme that is cross-linked by one such extended disulfide bond that has significantly enhanced thermal stability. This study suggests that an expanded set of amino acid building blocks can provide novel solutions to evolutionary challenges.

Accurate prediction of enzyme kinetic parameters is crucial for enzyme exploration and modification. Existing models face the problem of either low accuracy or poor generalization ability due to overfitting. In this work, we first developed unbiased datasets to evaluate the actual performance of these methods and proposed a deep learning model, CataPro, based on pre-trained models and molecular fingerprints to predict turnover number (k _{c a t} ), Michaelis constant (K _m ), and catalytic efficiency (k _{c a t} /K _m ). Compared with previous baseline models, CataPro demonstrates clearly enhanced accuracy and generalization ability on the unbiased datasets. In a representational enzyme mining project, by combining CataPro with traditional methods, we identified an enzyme (SsCSO) with 19.53 times increased activity compared to the initial enzyme (CSO2) and then successfully engineered it to improve its activity by 3.34 times. This reveals the high potential of CataPro as an effective tool for future enzyme discovery and modification.

A propeptide strategy increases uptake of phosphotyrosine and a nonhydrolyzable analog to facilitate their incorporation into proteins by recombinant methods, aided by a specific aminoacyl-tRNA synthetase with a reconfigured active site. Tyrosine phosphorylation is a common protein post-translational modification that plays a critical role in signal transduction and the regulation of many cellular processes. Using a propeptide strategy to increase cellular uptake of O-phosphotyrosine (pTyr) and its nonhydrolyzable analog 4-phosphomethyl-L-phenylalanine (Pmp), we identified an orthogonal aminoacyl-tRNA synthetase–tRNA pair that allows site-specific incorporation of both pTyr and Pmp into recombinant proteins in response to the amber stop codon in Escherichia coli in good yields. The X-ray structure of the synthetase reveals a reconfigured substrate-binding site, formed by nonconservative mutations and substantial local structural perturbations. We demonstrate the utility of this method by introducing Pmp into a putative phosphorylation site and determining the affinities of the individual variants for the substrate 3BP2. In summary, this work provides a useful recombinant tool to dissect the biological functions of tyrosine phosphorylation at specific sites in the proteome.