To Hit or Not to Hit, That Is the Question - Genome-wide Structure-Based Druggability Predictions for Pseudomonas aeruginosa Proteins

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

Challenges in Drug Discovery for Intracellular Bacteria

Novel drugs are needed to treat a variety of persistent diseases caused by intracellular bacterial pathogens. Virulence pathways enable many functions required for the survival of these pathogens, including invasion, nutrient acquisition, and immune evasion. Inhibition of virulence pathways is an established route for drug discovery; however, many challenges remain. Here, we propose the biggest problems that must be solved to advance the field meaningfully. While it is established that we do not yet understand the nature of chemicals capable of permeating into the bacterial cell, this problem is compounded when targeting intracellular bacteria because we are limited to only those chemicals that can permeate through both human and bacterial outer envelopes. Unfortunately, many chemicals that permeate through the outer layers of mammalian cells fail to penetrate the bacterial cytoplasm. Another challenge is the lack of publicly available information on virulence factors. It is virtually impossible to know which virulence factors are clinically relevant and have broad cross-species and cross-strain distribution. In other words, we have yet to identify the best drug targets. Yes, standard genomics databases have much of the information necessary for short-term studies, but the connections with patient outcomes are yet to be established. Without comprehensive data on matters such as these, it is difficult to devise broad-spectrum, effective anti-virulence agents. Furthermore, anti-virulence drug discovery is hindered by the current state of technologies available for experimental investigation. Antimicrobial drug discovery was greatly advanced by the establishment and standardization of broth microdilution assays to measure the effectiveness of antimicrobials. However, the currently available models used for anti-virulence drug discovery are too broad, as they must address varied phenotypes, and too expensive to be generally adopted by many research groups. Therefore, we believe drug discovery against intracellular bacterial pathogens can be advanced significantly by overcoming the above hurdles.

An Experimental Toolbox for Structure-Based Hit Discovery for P. aeruginosa FabF, a Promising Target for Antibiotics

FabF (3-oxoacyl-[acyl-carrier-protein] synthase 2), which catalyses the rate limiting condensation reaction in the fatty acid synthesis II pathway, is an attractive target for new antibiotics. Here, we focus on FabF from P. aeruginosa (PaFabF) as antibiotics against this pathogen are urgently needed. To facilitate exploration of this target we have set up an experimental toolbox consisting of binding assays using bio-layer interferometry (BLI) as well as saturation transfer difference (STD) and WaterLOGSY NMR in addition to robust conditions for structure determination. The suitability of the toolbox to support structure-based design of FabF inhibitors was demonstrated through the validation of hits obtained from virtual screening. Screening a library of almost 5 million compounds resulted in 6 compounds for which binding into the malonyl-binding site of FabF was shown. For one of the hits, the crystal structure in complex with PaFabF was determined. Based on the obtained binding mode, analogues were designed and synthesised, but affinity could not be improved. This work has laid the foundation for structure-based exploration of PaFabF.

DrugPred_RNA-A Tool for Structure-Based Druggability Predictions for RNA Binding Sites

RNA is an emerging target for drug discovery. However, like for proteins, not all RNA binding sites are equally suited to be addressed with conventional drug-like ligands. To this end, we have developed the structure-based druggability predictor DrugPred_RNA to identify druggable RNA binding sites. Due to the paucity of annotated RNA binding sites, the predictor was trained on protein pockets, albeit using only descriptors that can be calculated for both RNA and protein binding sites. DrugPred_RNA performed well in discriminating druggable from less druggable binding sites for the protein set and delivered predictions for selected RNA binding sites that agreed with manual assignment. In addition, most drug-like ligands contained in an RNA test set were found in pockets predicted to be druggable, further adding confidence to the performance of DrugPred_RNA. The method is robust against conformational and sequence changes in the binding sites and can contribute to direct drug discovery efforts for RNA targets.

Perspective on Antibacterial Lead Identification Challenges and the Role of Hypothesis-Driven Strategies

For the past three decades, the pharmaceutical industry has undertaken many diverse approaches to discover novel antibiotics, with limited success. We have witnessed and personally experienced many mistakes, hurdles, and dead ends that have derailed projects and discouraged scientists and business leaders. Of the many factors that affect the outcomes of screening campaigns, a lack of understanding of the properties that drive efflux and permeability requirements across species has been a major barrier for advancing hits to leads. Hits that possess bacterial spectrum have seldom also possessed druglike properties required for developability and safety. Persistence in solving these two key barriers is necessary for the reinvestment into discovering antibacterial agents. This perspective narrates our experience in antibacterial discovery-our lessons learned about antibacterial challenges as well as best practices for screening strategies. One of the tenets that guides us is that drug discovery is a hypothesis-driven science. Application of this principle, at all steps in the antibacterial discovery process, should improve decision making and possibly the odds of what has become, in recent decades, an increasingly challenging endeavor with dwindling success rates.

Structure-based Druggability Assessment of Anti-virulence Targets from Pseudomonas aeruginosa

Antimicrobial Resistance (AMR) represents a serious threat to health and the global economy. However, interest in antibacterial drug development has decreased substantially in recent decades. Meanwhile, anti-virulence drug development has emerged as an attractive alternative to fight AMR. Although several macromolecular targets have been explored for this goal, their druggability is a vital piece of information that has been overlooked. This review explores this subject by showing how structure- based freely available in silico tools, such as PockDrug and FTMap, might be useful for designing novel inhibitors of the pyocyanin biosynthesis pathway and improving the potency/selectivity of compounds that target the Pseudomonas aeruginosa quorum sensing mechanism. The information provided by hotspot analysis, along with binding site features, reveals novel druggable targets (PhzA and PhzS) that remain largely unexplored. However, it also highlights that in silico druggability prediction tools have several limitations that might be overcome in the near future. Meanwhile, anti-virulence drug targets should be assessed by complementary methods, such as the combined use of FTMap/PockDrug, once the consensus druggability classification reduces the risk of wasting resources on undruggable proteins.

Enabling design of screening libraries for antibiotic discovery by modeling ChEMBL data

It is critical to identify novel antibiotics. Yet, the scientific community has struggled in this pursuit because we do not understand which molecules will penetrate the bacterial outer envelope. In this work, we have identified a large dataset of compounds known to reach their targets in bacterial cells (penetrators) and compared them with molecules that do not (non-penetrators). Our dataset, extracted from the ChEMBL database, is a useful tool to guide the selection of molecules for antibiotic screening. Simple random forest classification models are able to correctly identify penetrators from non-penetrators. The model demonstrated ~87% accuracy, with high precision (~88%) and recall (~97%) in identifying penetrators of Gram-positive bacteria. A paucity of data for non-penetrators was a major hurdle to model-building; we observed a ~86% negative predictive value, but only a ~57% specificity. Accumulation of data on non-penetrators is therefore necessary. Data for Gram-negative bacteria was also sparse, but a larger fraction of these data represented non-penetrators. Correspondingly, the resultant models performed well in predicting those molecules that would fail to enter Gram-negative cells, but were relatively weaker in correctly predicting penetrators. A comparison of physicochemical properties of penetrators and non-penetrators suggests only marginal differences exist. Therefore, it may be difficult to identify overarching rules for generation of screening libraries for antibiotic discovery, based purely on physicochemical properties alone. Instead, models such as ours should be of use. Our models are highly preliminary and based on phenotypic data, but a similar large dataset directly addressing accumulation of chemical matter in bacterial cells is currently unavailable. Hence, our models represent the cutting edge in design of screening libraries for antibiotic discovery until appropriate data can be compiled.