Virtual screening of antibacterial compounds by similarity search of Enoyl-ACP reductase (FabI) inhibitors

Machine Learning-Based Virtual Screening of Antibacterial Agents against Methicillin-Susceptible and Resistant Staphylococcus aureus

The application of computer-aided drug discovery (CADD) approaches has enabled the discovery of new antimicrobial therapeutic agents in the past. The high prevalence of methicillin-resistantStaphylococcus aureus(MRSA) strains promoted this pathogen to a high-priority pathogen for drug development. In this sense, modern CADD techniques can be valuable tools for the search for new antimicrobial agents. We employed a combination of a series of machine learning (ML) techniques to select and evaluate potential compounds with antibacterial activity against methicillin-susceptible S. aureus (MSSA) and MRSA strains. In the present study, we describe the antibacterial activity of six compounds against MSSA and MRSA reference (American Type Culture Collection (ATCC)) strains as well as two clinical strains of MRSA. These compounds showed minimal inhibitory concentrations (MIC) in the range from 12.5 to 200 μM against the different bacterial strains evaluated. Our results constitute relevant proven ML-workflow models to distinctively screen for novel MRSA antibiotics.

Computational docking investigation of phytocompounds from bergamot essential oil against Serratia marcescens protease and FabI: Alternative pharmacological strategy

The rapid development of multi-drug resistant (MDR) pathogens adds urgency to search for novel and safe drugs having promising action on new and re-emerging infectious pathogens. Serratia marcescens is an MDR pathogen that causes several-healthcare associated infections. Curbing bacterial virulence, rather than inhibiting its growth, is a promising strategy to diminish the pathogenesis of infectious bacteria, reduce the development of antimicrobial resistance, and boost the host immune power to eradicate infections. Bergamot essential oil (BEO) is a remarkable source of promising therapeutics against pathogens. Therefore, the present investigation aimed to analyze the major phytocompounds from BEO against S. marcescens virulent proteins using in silico studies. The analysis of BEO phytocompounds was achieved by Gas chromatography-mass spectrometry (GC-MS) method. The molecular docking was carried out using the SP and XP docking protocol of the Glide program. The drug-likeness and pharmacokinetics properties (ADMET properties) were analyzed with SwissADME and pkCSM server. The results revealed that the major compounds present in BEO are Linalool (8.17%), D-Limonene (21.26%), and Linalyl acetate (26.91%). Molecular docking analysis revealed that these compounds docked strongly within the binding cavities of Serratia protease and FabI model which in turn curb the pathogenesis of this bacteria. Linalool interacted with the Serratia protease and FabI with a binding energy of - 3.130 kcal/mol and - 3.939 kcal/mol, respectively. Based on the pharmacokinetics findings all lead BEO phytocompounds appear to be promising drug candidates. Overall, these results represent a significant step in the development of plant-based compounds as a promising inhibitor of the virulent proteins of the MDR S. marcescens.

The Brazilian compound library (BraCoLi) database: a repository of chemical and biological information for drug design

The Brazilian Compound Library (BraCoLi) is a novel open access and manually curated electronic library of compounds developed by Brazilian research groups to support further computer-aided drug design works, available on https://www.farmacia.ufmg.br/qf/downloads/ . Herein, the first version of the database is described comprising 1176 compounds. Also, the chemical diversity and drug-like profiles of BraCoLi were defined to analyze its chemical space. A significant amount of the compounds fitted Lipinski and Veber's rules, alongside other drug-likeness properties. A comparison using principal component analysis showed that BraCoLi is similar to other databases (FDA-approved drugs and NuBBE_DB) regarding structural and physicochemical patterns. Furthermore, a scaffold analysis showed that BraCoLi presents several privileged chemical skeletons with great diversity. Despite the similar distribution in the structural and physicochemical spaces, Tanimoto coefficient values indicated that compounds present in the BraCoLi are generally different from the two other databases, where they showed different kernel distributions and low similarity. These facts show an interesting innovative aspect, which is a desirable feature for novel drug design purposes.

Recent Advances in the Prediction of Pharmacokinetics Properties in Drug Design Studies: A Review

This review presents the main aspects related to pharmacokinetic properties, which are essential for the efficacy and safety of drugs. This topic is very important because the analysis of pharmacokinetic aspects in the initial design stages of drug candidates can increase the chances of success for the entire process. In this scenario, experimental and in silico techniques have been widely used. Due to the difficulties encountered with the use of some experimental tests to determine pharmacokinetic properties, several in silico tools have been developed and have shown promising results. Therefore, in this review, we address the main free tools/servers that have been used in this area, as well as some cases of application. Finally, we present some studies that employ a multidisciplinary approach with synergy between in silico, in vitro, and in vivo techniques to assess ADME properties of bioactive substances, achieving successful results in drug discovery and design.

FabI (enoyl acyl carrier protein reductase) - A potential broad spectrum therapeutic target and its inhibitors

Development of new anti-bacterial agents acting upon underexploited targets and thus evading known mechanisms of resistance is the need of the hour. The highly conserved and distinct bacterial fatty acid biosynthesis pathway (FAS-II), presents a validated and yet relatively underexploited target for drug discovery. FabI and its isoforms (FabL, FabK, FabV and InhA) are essential enoyl-ACP reductases present in several microorganisms. In addition, the components of the FAS-II pathway are distinct from the multi-enzyme FAS-I complex found in mammals. Thus, inhibition of FabI and its isoforms is anticipated to result in broad-spectrum antibacterial activity. Several research groups from industry and academic laboratories have devoted significant efforts to develop effective FabI-targeting antibiotics, which are currently in various stages of clinical development for the treatment of multi-drug resistant bacterial infections. This review summarizes all the natural as well as synthetic inhibitors of gram-positive and gram-negative enoyl ACP reductases (FabI). The knowledge of the reported inhibitors can aid in the development of broad-spectrum antibacterials specifically targeting FabI enzymes from S. aureus, S. epidermidis, B. anthracis, B. cereus, E. coli, P. aeruginosa, P. falciparum and M. tuberculosis.

The application of machine learning techniques to innovative antibacterial discovery and development

INTRODUCTION

After the initial wave of antibiotic discovery, few novel classes of antibiotics have emerged, with the latest dating back to the 1980's. Furthermore, the pace of antibiotic drug discovery is unable to keep up with the increasing prevalence of antibiotic drug resistance. However, the increasing amount of available data promotes the use of machine learning techniques (MLT) in drug discovery projects (e.g. construction of regression/classification models and ranking/virtual screening of compounds).

AREAS COVERED

In this review, the authors cover some of the applications of MLT in medicinal chemistry, focusing on the development of new antibiotics, the prediction of resistance and its mechanisms. The aim of this review is to illustrate the main advantages and disadvantages and the major trends from studies over the past 5 years.

EXPERT OPINION

The application of MLT to antibacterial drug discovery can aid the selection of new and potent lead compounds, with desirable pharmacokinetic and toxic profiles for further optimization. The increasing volume of available data along with the constant improvement in computational power and algorithms has meant that we are experiencing a transition in the way we face modern issues such as drug resistance, where our decisions are data-driven and experiments can be focused by data-suggested hypotheses.