Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones

A metabolomics perspective on clorobiocin biosynthesis: discovery of bromobiocin and novel derivatives through LC-MSE-based molecular networking

Clorobiocin is a well-known, highly effective inhibitor of DNA gyrase belonging to the aminocoumarin antibiotics. To identify potentially novel derivatives of this natural product, we conducted an untargeted investigation of clorobiocin biosynthesis in the known producer Streptomyces roseochromogenes DS 12.976 using LC-MSE, molecular networking, and analysis of fragmentation spectra. Previously undescribed clorobiocin derivatives uncovered in this study include bromobiocin, a variant halogenated with bromine instead of chlorine, hydroxylated clorobiocin, carrying an additional hydroxyl group on its 5-methyl-pyrrole 2-carboxyl moiety, and two other derivatives with modifications on their 3-dimethylallyl 4-hydroxybenzoate moieties. Furthermore, we identified several compounds not previously considered clorobiocin pathway products, which provide new insights into the clorobiocin biosynthetic pathway. By supplementing the medium with different concentrations of potassium bromide, we confirmed that the clorobiocin halogenase can utilize bromine instead of chlorine. The reaction, however, is impeded such that non-halogenated clorobiocin derivatives accumulate. Preliminary assays indicate that the antibacterial activity of bromobioin against Bacillus subtilis and efflux-impaired Escherichia coli matches that of clorobiocin. Our findings emphasize that yet unexplored compounds can be discovered from established strains and biosynthetic gene clusters by means of metabolomics analysis and highlight the utility of LC-MSE-based methods to contribute to unraveling natural product biosynthetic pathways.

IMPORTANCE

The aminocoumarin clorobiocin is a well-known gyrase inhibitor produced by the gram-positive bacterium Streptomyces roseochromogenes DS 12.976. To gain a deeper understanding of the biosynthetic pathway of this complex composite of three chemically distinct entities and the product spectrum, we chose a metabolite-centric approach. Employing high-resolution LC-MSE analysis, we investigated the pathway products in extracted culture supernatants of the natural producer. Novel pathway products were identified that expand our understanding of three aspects of the biosynthetic pathway, namely the modification of the noviose, transfer and methylation of the pyrrole 2-carboxyl moiety, and halogenation. For the first time, brominated products were detected. Their levels and the levels of non-halogenated products increased in medium supplemented with KBr. Based on the presented data, we propose that the enzyme promiscuity contributes to a broad product spectrum.

The pharmacokinetic-pharmacodynamic modelling framework as a tool to predict drug resistance evolution

Pharmacokinetic-pharmacodynamic (PKPD) models, which describe how drug concentrations change over time and how that affects pathogen growth, have proven highly valuable in designing optimal drug treatments aimed at bacterial eradication. However, the fast rise of antimicrobial resistance calls for increased focus on an additional treatment optimization criterion: avoidance of resistance evolution. We demonstrate here how coupling PKPD and population genetics models can be used to determine treatment regimens that minimize the potential for antimicrobial resistance evolution. Importantly, the resulting modelling framework enables the assessment of resistance evolution in response to dynamic selection pressures, including changes in antimicrobial concentration and the emergence of adaptive phenotypes. Using antibiotics and antimicrobial peptides as an example, we discuss the empirical evidence and intuition behind individual model parameters. We further suggest several extensions of this framework that allow a more comprehensive and realistic prediction of bacterial escape from antimicrobials through various phenotypic and genetic mechanisms.

Predicting drug-target binding affinity through molecule representation block based on multi-head attention and skip connection

Exiting computational models for drug-target binding affinity prediction have much room for improvement in prediction accuracy, robustness and generalization ability. Most deep learning models lack interpretability analysis and few studies provide application examples. Based on these observations, we presented a novel model named Molecule Representation Block-based Drug-Target binding Affinity prediction (MRBDTA). MRBDTA is composed of embedding and positional encoding, molecule representation block and interaction learning module. The advantages of MRBDTA are reflected in three aspects: (i) developing Trans block to extract molecule features through improving the encoder of transformer, (ii) introducing skip connection at encoder level in Trans block and (iii) enhancing the ability to capture interaction sites between proteins and drugs. The test results on two benchmark datasets manifest that MRBDTA achieves the best performance compared with 11 state-of-the-art models. Besides, through replacing Trans block with single Trans encoder and removing skip connection in Trans block, we verified that Trans block and skip connection could effectively improve the prediction accuracy and reliability of MRBDTA. Then, relying on multi-head attention mechanism, we performed interpretability analysis to illustrate that MRBDTA can correctly capture part of interaction sites between proteins and drugs. In case studies, we firstly employed MRBDTA to predict binding affinities between Food and Drug Administration-approved drugs and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication-related proteins. Secondly, we compared true binding affinities between 3C-like proteinase and 185 drugs with those predicted by MRBDTA. The final results of case studies reveal reliable performance of MRBDTA in drug design for SARS-CoV-2.

Mechanisms of antibiotic action shape the fitness landscapes of resistance mutations

Antibiotic-resistant pathogens are a major public health threat. A deeper understanding of how an antibiotic's mechanism of action influences the emergence of resistance would aid in the design of new drugs and help to preserve the effectiveness of existing ones. To this end, we developed a model that links bacterial population dynamics with antibiotic-target binding kinetics. Our approach allows us to derive mechanistic insights on drug activity from population-scale experimental data and to quantify the interplay between drug mechanism and resistance selection. We find that both bacteriostatic and bactericidal agents can be equally effective at suppressing the selection of resistant mutants, but that key determinants of resistance selection are the relationships between the number of drug-inactivated targets within a cell and the rates of cellular growth and death. We also show that heterogeneous drug-target binding within a population enables resistant bacteria to evolve fitness-improving secondary mutations even when drug doses remain above the resistant strain's minimum inhibitory concentration. Our work suggests that antibiotic doses beyond this "secondary mutation selection window" could safeguard against the emergence of high-fitness resistant strains during treatment.

vCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding

Background

As antibiotic resistance creates a significant global health threat, we need not only to accelerate the development of novel antibiotics but also to develop better treatment strategies using existing drugs to improve their efficacy and prevent the selection of further resistance. We require new tools to rationally design dosing regimens from data collected in early phases of antibiotic and dosing development. Mathematical models such as mechanistic pharmacodynamic drug-target binding explain mechanistic details of how the given drug concentration affects its targeted bacteria. However, there are no available tools in the literature that allow non-quantitative scientists to develop computational models to simulate antibiotic-target binding and its effects on bacteria.

Results

In this work, we have devised an extension of a mechanistic binding-kinetic model to incorporate clinical drug concentration data. Based on the extended model, we develop a novel and interactive web-based tool that allows non-quantitative scientists to create and visualize their own computational models of bacterial antibiotic target-binding based on their considered drugs and bacteria. We also demonstrate how Rifampicin affects bacterial populations of Tuberculosis bacteria using our vCOMBAT tool.

Conclusions

The vCOMBAT online tool is publicly available at https://combat-bacteria.org/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04536-3.

Current Approaches of Building Mechanistic Pharmacodynamic Drug-Target Binding Models

Mechanistic pharmacodynamic models that incorporate the binding kinetics of drug-target interactions have several advantages in understanding target engagement and the efficacy of a drug dose. However, guidelines on how to build and interpret mechanistic pharmacodynamic drug-target binding models considering both biological and computational factors are still missing in the literature. In this chapter, current approaches of building mechanistic PD models and their advantages are discussed. We also present a methodology on how to select a suitable model considering both biological and computational perspectives, as well as summarize the challenges of current mechanistic PD models.

Proximate and ultimate causes of the bactericidal action of antibiotics

During the past 85 years of antibiotic use, we have learned a great deal about how these ‘miracle’ drugs work. We know the molecular structures and interactions of these drugs and their targets and the effects on the structure, physiology and replication of bacteria. Collectively, we know a great deal about these proximate mechanisms of action for virtually all antibiotics in current use. What we do not know is the ultimate mechanism of action; that is, how these drugs irreversibly terminate the ‘individuality’ of bacterial cells by removing barriers to the external world (cell envelopes) or by destroying their genetic identity (DNA). Antibiotics have many different ‘mechanisms of action’ that converge to irreversible lethal effects. In this Perspective, we consider what our knowledge of the proximate mechanisms of action of antibiotics and the pharmacodynamics of their interaction with bacteria tell us about the ultimate mechanisms by which these antibiotics kill bacteria.

We know a lot about antibiotics and their targets; however, how antibiotics actually kill bacteria is not entirely clear and is up for debate. In this Perspective, Baquero and Levin reflect on this ultimate action of antibiotics and consider different mechanisms and modulating factors.