Computational resources in the management of antibiotic resistance: Speeding up drug discovery

Antibiotic resistance: a global crisis, problems and solutions

Healthy state is priority in today's world which can be achieved using effective medicines. But due to overuse and misuse of antibiotics, a menace of resistance has increased in pathogenic microbes. World Health Organization (WHO) has announced ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) as the top priority pathogens as these have developed resistance against certain antibiotics. To combat such a global issue, it is utmost important to identify novel therapeutic strategies/agents as an alternate to such antibiotics. To name certain antibiotic adjuvants including: inhibitors of beta-lactamase, efflux pumps and permeabilizers for outer membrane can potentially solve the antibiotic resistance problems. In this regard, inhibitors of lytic domain of lytic transglycosylases provide a novel way to not only act as an alternate to antibiotics but also capable of restoring the efficiency of previously resistant antibiotics. Further, use of bacteriophages is another promising strategy to deal with antibiotic resistant pathogens. Taking in consideration the alternatives of antibiotics, a green synthesis nanoparticle-based therapy exemplifies a good option to combat microbial resistance. As horizontal gene transfer (HGT) in bacteria facilitates the evolution of new resistance strains, therefore identifying the mechanism of resistance and development of inhibitors against it can be a novel approach to combat such problems. In our perspective, host-directed therapy (HDT) represents another promising strategy in combating antimicrobial resistance (AMR). This approach involves targeting specific factors within host cells that pathogens rely on for their survival, either through replication or persistence. As many new drugs are under clinical trials it is advisable that more clinical data and antimicrobial stewardship programs should be conducted to fully assess the clinical efficacy and safety of new therapeutic agents.

Extensive screening reveals previously undiscovered aminoglycoside resistance genes in human pathogens

Antibiotic resistance is a growing threat to human health, caused in part by pathogens accumulating antibiotic resistance genes (ARGs) through horizontal gene transfer. New ARGs are typically not recognized until they have become widely disseminated, which limits our ability to reduce their spread. In this study, we use large-scale computational screening of bacterial genomes to identify previously undiscovered mobile ARGs in pathogens. From ~1 million genomes, we predict 1,071,815 genes encoding 34,053 unique aminoglycoside-modifying enzymes (AMEs). These cluster into 7,612 families (<70% amino acid identity) of which 88 are previously described. Fifty new AME families are associated with mobile genetic elements and pathogenic hosts. From these, 24 of 28 experimentally tested AMEs confer resistance to aminoglycoside(s) in Escherichia coli, with 17 providing resistance above clinical breakpoints. This study greatly expands the range of clinically relevant aminoglycoside resistance determinants and demonstrates that computational methods enable early discovery of potentially emerging ARGs.

Multiple machine learning methods aided virtual screening of NaV 1.5 inhibitors

Na_v 1.5 sodium channels contribute to the generation of the rapid upstroke of the myocardial action potential and thereby play a central role in the excitability of myocardial cells. At present, the patch clamp method is the gold standard for ion channel inhibitor screening. However, this method has disadvantages such as high technical difficulty, high cost and low speed. In this study, novel machine learning models to screen chemical blockers were developed to overcome the above shortage. The data from the ChEMBL Database were employed to establish the machine learning models. Firstly, six molecular fingerprints together with five machine learning algorithms were used to develop 30 classification models to predict effective inhibitors. A validation and a test set were used to evaluate the performance of the models. Subsequently, the privileged substructures tightly associated with the inhibition of the Na_v 1.5 ion channel were extracted using the bioalerts Python package. In the validation set, the RF-Graph model performed best. Similarly, RF-Graph produced the best result in the test set in which the Prediction Accuracy (Q) was 0.9309 and Matthew's correlation coefficient was 0.8627, further indicating the model had high classification ability. The results of the privileged substructures indicated Sulfa structures and fragments with large Steric hindrance tend to block Na_v 1.5. In the unsupervised learning task of identifying sulfa drugs, MACCS and Graph fingerprints had good results. In summary, effective machine learning models have been constructed which help to screen potential inhibitors of the Na_v 1.5 ion channel and key privileged substructures with high affinity were also extracted.

Metagenomic exploration of antibiotic resistance genes and their hosts in aquaculture waters of the semi-closed Dongshan Bay (China)

In marine environments, increasing occurrence and numbers of microbial Antibiotic Resistance Gene (ARG) subtypes, especially of new beta-lactamases, have received lots of attention in recent years. Updated databases with novel developed tools provide new opportunities to obtain more comprehensive ARG profiles as well as ARG-carrying hosts. Yet, ARGs in human-associated marine aquaculture environments, e.g. in China, remains largely unknown. Using metagenomic data, we revealed high numbers of Multi-drug Resistance, beta-lactamase and aminoglycoside genes throughout the year. Thereby, Alpha- and Gamma-proteobacteria were assigned to the majority of beta-lactamase-carrying hosts. From Metagenome-assembled genomes, three blaF-like beta-lactamases (91.7-94.7% identity with beta-lactamase from Mycobacterium fortuitum (blaF)) were exclusively observed in an unclassified Mycobacterium genus. Notably, other new beta-lactamases, VMB-1-like (n = 3) (58.5-67.4% identity to Vibrio metallo-beta-lactamase 1 (VMB-1)), were found in Gammaproteobacteria. Additionally, 175 Multi-drug Resistant Organisms possessed at least 3 ARG subtypes, and seven of the potentially pathogenic genera (n = 17) were assigned to Gammaproteobacteria. These results, together with high-risk ARGs (e.g. tetM, dfrA14 and dfrA17), provide hosts and new beta-lactamases of ARGs in Chinese coastal aquaculture.

Strengths and caveats of identifying resistance genes from whole genome sequencing data

INTRODUCTION

Antimicrobial resistance (AMR) continues to present major challenges to modern healthcare. Recent advances in whole-genome sequencing (WGS) have made the rapid molecular characterization of AMR a realistic possibility for diagnostic laboratories; yet major barriers to clinical implementation exist.

AREAS COVERED

We describe and compare short- and long-read sequencing platforms, typical components of bioinformatics pipelines, tools for AMR gene detection and the relative merits of read- or assembly-based approaches. The challenges of characterizing mobile genetic elements from genomic data are outlined, as well as the complexities inherent to the prediction of phenotypic resistance from WGS. Practical obstacles to implementation in diagnostic laboratories, the critical role of quality control and external quality assurance, as well as standardized reporting standards are also discussed. Future directions, such as the application of machine-learning and artificial intelligence algorithms, linked to clinically meaningful outcomes, may offer a new paradigm for the clinical application of AMR prediction.

EXPERT OPINION

AMR prediction from WGS data presents an exciting opportunity to advance our capacity to comprehensively characterize infectious pathogens in a rapid manner, ultimately aiming to improve patient outcomes. Collaborative efforts between clinicians, scientists, regulatory bodies and healthcare administrators will be critical to achieve the full promise of this approach.

In-Silico Tool for Predicting, Scanning, and Designing Defensins

Defensins are host defense peptides present in nearly all living species, which play a crucial role in innate immunity. These peptides provide protection to the host, either by killing microbes directly or indirectly by activating the immune system. In the era of antibiotic resistance, there is a need to develop a fast and accurate method for predicting defensins. In this study, a systematic attempt has been made to develop models for predicting defensins from available information on defensins. We created a dataset of defensins and non-defensins called the main dataset that contains 1,036 defensins and 1,035 AMPs (antimicrobial peptides, or non-defensins) to understand the difference between defensins and AMPs. Our analysis indicates that certain residues like Cys, Arg, and Tyr are more abundant in defensins in comparison to AMPs. We developed machine learning technique-based models on the main dataset using a wide range of peptide features. Our SVM (support vector machine)-based model discriminates defensins and AMPs with MCC of 0.88 and AUC of 0.98 on the validation set of the main dataset. In addition, we created an alternate dataset that consists of 1,036 defensins and 1,054 non-defensins obtained from Swiss-Prot. Models were also developed on the alternate dataset to predict defensins. Our SVM-based model achieved maximum MCC of 0.96 with AUC of 0.99 on the validation set of the alternate dataset. All models were trained, tested, and validated using standard protocols. Finally, we developed a web-based service "DefPred" to predict defensins, scan defensins in proteins, and design the best defensins from their analogs. The stand-alone software and web server of DefPred are available at https://webs.iiitd.edu.in/raghava/defpred.