A systematic strategy for the investigation of vaccines and drugs targeting bacteria

Identification of core therapeutic targets for Monkeypox virus and repurposing potential of drugs against them: An in silico approach

Monkeypox virus (mpox virus) outbreak has rapidly spread to 82 non-endemic countries. Although it primarily causes skin lesions, secondary complications and high mortality (1-10%) in vulnerable populations have made it an emerging threat. Since there is no specific vaccine/antiviral, it is desirable to repurpose existing drugs against mpox virus. With little knowledge about the lifecycle of mpox virus, identifying potential inhibitors is a challenge. Nevertheless, the available genomes of mpox virus in public databases represent a goldmine of untapped possibilities to identify druggable targets for the structure-based identification of inhibitors. Leveraging this resource, we combined genomics and subtractive proteomics to identify highly druggable core proteins of mpox virus. This was followed by virtual screening to identify inhibitors with affinities for multiple targets. 125 publicly available genomes of mpox virus were mined to identify 69 highly conserved proteins. These proteins were then curated manually. These curated proteins were funnelled through a subtractive proteomics pipeline to identify 4 highly druggable, non-host homologous targets namely; A20R, I7L, Top1B and VETFS. High-throughput virtual screening of 5893 highly curated approved/investigational drugs led to the identification of common as well as unique potential inhibitors with high binding affinities. The common inhibitors, i.e., batefenterol, burixafor and eluxadoline were further validated by molecular dynamics simulation to identify their best potential binding modes. The affinity of these inhibitors suggests their repurposing potential. This work can encourage further experimental validation for possible therapeutic management of mpox.

Genome-Wide Subtraction Analysis and Reverse Vaccinology to Detect Novel Drug Targets and Potential Vaccine Candidates Against Ehrlichia chaffeensis

Human monocytotropic ehrlichiosis is an emerging tick-borne infection caused by the obligate intracellular pathogen, Ehrlichia chaffeensis. The non-specific symptoms can range from a self-limiting fever to a fatal septic-like syndrome and may be misdiagnosed. The limited treatment choices including doxycycline are effective only in the initiation phase of the infection. It seems that novel therapeutic targets and new vaccine strategies could be effective to control this pathogen. This study is comprised of two major phases. First, the common proteins retrieved through subtractive analysis and potential drug targets were evaluated by subcellular localization, homology prediction, metabolic pathways, druggability, essentiality, protein-protein interaction networks, and protein data bank availability. In the second phase, surface-exposed proteins were assessed based on antigenicity, allergenicity, physiochemical properties, B cell and T cell epitopes, conserved domains, and protein-protein interaction networks. A multi-epitope vaccine was designed and characterized using molecular dockings and immune simulation analysis. Six proteins including WP_011452818.1, WP_011452723.1, WP_006010413.1, WP_006010278.1, WP_011452938.1, and WP_006010644.1 were detected. They belong to unique metabolic pathways of E. chaffeensis that are considered as new essential drug targets. Based on the reverse vaccinology, WP_011452702.1, WP_044193405.1, WP_044170604.1, and WP_006010191.1 proteins were potential vaccine candidates. Finally, four B cell epitopes, including SINNQDRNC, FESVSSYNI, SGKKEISVQSN, and QSSAKRKST, were used to generate the multi-epitope vaccine based on LCL platform. The vaccine showed strong interactions with toll-like receptors and acceptable immune-reactivity by immune simulation analysis. The findings of this study may represent a turning point in developing an effective drug and vaccine against E. chaffeensis. However, further experimental analyses have remained.

Decoding the Identification Mechanism of an SAM-III Riboswitch on Ligands through Multiple Independent Gaussian-Accelerated Molecular Dynamics Simulations

S-Adenosyl-l-methionine (SAM)-responsive riboswitches play a central role in the regulation of bacterial gene expression at the level of transcription attenuation or translation inhibition. In this study, multiple independent Gaussian-accelerated molecular dynamics simulations were performed to decipher the identification mechanisms of SAM-III (S_MK) on ligands SAM, SAH, and EEM. The results reveal that ligand binding highly affects the structural flexibility, internal dynamics, and conformational changes of SAM-III. The dynamic analysis shows that helices P3 and P4 as well as two junctions J23 and J24 of SAM-III are highly susceptible to ligand binding. Analyses of free energy landscapes suggest that ligand binding induces different free energy profiles of SAM-III, which leads to the difference in identification sites of SAM-III on ligands. The information on ligand-nucleotide interactions not only uncovers that the π-π, cation-π, and hydrogen bonding interactions drive identification of SAM-III on the three ligands but also reveals that different electrostatic properties of SAM, SAH, and EEM alter the active sites of SAM-III. Meanwhile, the results also verify that the adenine group of SAM, SAH, and EEM is well recognized by conserved nucleotides G7, A29, U37, A38, and G48. We expect that this study can provide useful information for understanding the applications of SAM-III in chemical, synthetic RNA biology, and biomedical fields.

Aspartate α-decarboxylase a new therapeutic target in the fight against Helicobacter pylori infection

Effective eradication therapy for Helicobacter pylori is a worldwide demand. Aspartate α-decarboxylase (ADC) was reported as a drug target in H. pylori, in an in silico study, with malonic acid (MA) as its inhibitor. We evaluated eradicating H. pylori infection through ADC inhibition and the possibility of resistance development. MA binding to ADC was modeled via molecular docking. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of MA were determined against H. pylori ATCC 43504, and a clinical H. pylori isolate. To confirm selective ADC inhibition, we redetermined the MIC in the presence of products of the inhibited enzymatic pathway: β-alanine and pantothenate. HPLC was used to assay the enzymatic activity of H. pylori 6x-his tagged ADC in the presence of different MA concentrations. H. pylori strains were serially exposed to MA for 14 passages, and the MICs were determined. Cytotoxicity in different cell lines was tested. The efficiency of ADC inhibition in treating H. pylori infections was evaluated using a Sprague–Dawley (SD) rat infection model. MA spectrum of activity was determined in different pathogens. MA binds to H. pylori ADC active site with a good docking score. The MIC of MA against H. pylori ranged from 0.5 to 0.75 mg/mL with MBC of 1.5 mg/mL. Increasing β-alanine and pantothenate concentrations proportionally increased MA MIC. The 6x-his tagged ADC activity decreased by increasing MA concentration. No resistance to ADC inhibition was recorded after 14 passages; MA lacked cytotoxicity in all tested cell lines. ADC inhibition effectively eradicated H. pylori infection in SD rats. MA had MIC between 0.625 to 1.25 mg/mL against the tested bacterial pathogens. In conclusion, ADC is a promising target for effectively eradicating H. pylori infection that is not affected by resistance development, besides being of broad-spectrum presence in different pathogens. MA provides a lead molecule for the development of an anti-helicobacter ADC inhibitor. This provides hope for saving the lives of those at high risk of infection with the carcinogenic H. pylori.

Elucidation of the conformational dynamics and assembly of Argonaute−RNA complexes by distinct yet coordinated actions of the supplementary microRNA

Argonaute (AGO) proteins, the core of RNA-induced silencing complex, are guided by microRNAs (miRNAs) to recognize target RNA for repression. The miRNA–target RNA recognition forms initially through pairing at the seed region while the additional supplementary pairing can enhance target recognition and compensate for seed mismatch. The extension of miRNA lengths can strengthen the target affinity when pairing both in the seed and supplementary regions. However, the mechanism underlying the effect of the supplementary pairing on the conformational dynamics and the assembly of AGO–RNA complex remains poorly understood. To address this, we performed large-scale molecular dynamics simulations of AGO–RNA complexes with different pairing patterns and miRNA lengths. The results reveal that the additional supplementary pairing can not only strengthen the interaction between miRNA and target RNA, but also induce the increased plasticity of the PAZ domain and enhance the domain connectivity among the PAZ, PIWI, N domains of the AGO protein. The strong community network between these domains tightens the mouth of the supplementary chamber of AGO protein, which prevents the escape of target RNA from the complex and shields it from solvent water attack. Importantly, the inner stronger matching pairs between the miRNA and target RNA can compensate for weaker mismatches at the edge of supplementary region. These findings provide guidance for the design of miRNA mimics and anti-miRNAs for both clinical and experimental use and open the way for further engineering of AGO proteins as a new tool in the field of gene regulation.

Binding of Inhibitors to BACE1 Affected by pH-Dependent Protonation: An Exploration from Multiple Replica Gaussian Accelerated Molecular Dynamics and MM-GBSA Calculations

To date, inhibiting the activity of β-amyloid cleaving enzyme 1 (BACE1) has been considered an efficient approach for treating Alzheimer's disease (AD). In the current work, multiple replica Gaussian accelerated molecular dynamics (MR-GaMD) simulations and the molecular mechanics general Born surface area (MM-GBSA) method were combined to investigate the effect of pH-dependent protonation on the binding of the inhibitors CS9, C6U, and 6WE to BACE1. Dynamic analyses based on the MR-GaMD trajectory show that pH-dependent protonation strongly affects the structural flexibility, correlated motions, and dynamic behavior of inhibitor-bound BACE1. According to the constructed free energy profiles, in the protonated state at low pH, inhibitor-bound BACE1 tends to populate at more conformations than in high pH. The binding free energies calculated by MM-GBSA suggest that inhibitors possess stronger binding abilities under the protonation conditions at high pH than under the protonation conditions at low pH. Moreover, pH-dependent protonation exerts a significant effect on the hydrogen bonding interactions of CS9, C6U, and 6WE to BACE1, which correspondingly alters the binding abilities of the three inhibitors to BACE1. Furthermore, in different protonated environments, three inhibitors share common interaction clusters and similar binding sites in BACE1, which are reliably used as efficient targets for the design of potent inhibitors of BACE1.

Vaccine Candidates for the Control and Prevention of the Sexually Transmitted Disease Gonorrhea

The World Health Organization (WHO) has placed N. gonorrhoeae on the global priority list of antimicrobial resistant pathogens and is urgently seeking the development of new intervention strategies. N. gonorrhoeae causes 86.9 million cases globally per annum. The effects of gonococcal disease are seen predominantly in women and children and especially in the Australian Indigenous community. While economic modelling suggests that this infection alone may directly cost the USA health care system USD 11.0–20.6 billion, indirect costs associated with adverse disease and pregnancy outcomes, disease prevention, and productivity loss, mean that the overall effect of the disease is far greater still. In this review, we summate the current progress towards the development of a gonorrhea vaccine and describe the clinical trials being undertaken in Australia to assess the efficacy of the current formulation of Bexsero^® in controlling disease.

Position preference of essential genes in prokaryotic operons

Essential genes, which form the basis of life activities, are crucial for the survival of organisms. Essential genes tend to be located in operons, but how they are distributed in operons is still unclear for most prokaryotes. In order to clarify the general rule of position preference of essential genes in operons, an index of the average position of genes in an operon was proposed, and the distributions of essential and non-essential genes in operons in 51 bacterial genomes and two archaeal genomes were analyzed based on this new index. Consequently, essential genes were found to preferentially occupy the front positions of the operons, which tend to be expressed at higher levels.

Comparison of the binding characteristics of SARS-CoV and SARS-CoV-2 RBDs to ACE2 at different temperatures by MD simulations

Temperature plays a significant role in the survival and transmission of SARS-CoV (severe acute respiratory syndrome coronavirus) and SARS-CoV-2. To reveal the binding differences of SARS-CoV and SARS-CoV-2 receptor-binding domains (RBDs) to angiotensin-converting enzyme 2 (ACE2) at different temperatures at atomic level, 20 molecular dynamics simulations were carried out for SARS-CoV and SARS-CoV-2 RBD-ACE2 complexes at five selected temperatures, i.e. 200, 250, 273, 300 and 350 K. The analyses on structural flexibility and conformational distribution indicated that the structure of the SARS-CoV-2 RBD was more stable than that of the SARS-CoV RBD at all investigated temperatures. Then, molecular mechanics Poisson-Boltzmann surface area and solvated interaction energy approaches were combined to estimate the differences in binding affinity of SARS-CoV and SARS-CoV-2 RBDs to ACE2; it is found that the binding ability of ACE2 to the SARS-CoV-2 RBD was stronger than that to the SARS-CoV RBD at five temperatures, and the main reason for promoting such binding differences is electrostatic and polar interactions between RBDs and ACE2. Finally, the hotspot residues facilitating the binding of SARS-CoV and SARS-CoV-2 RBDs to ACE2, the key differential residues contributing to the difference in binding and the interaction mechanism of differential residues that exist at all investigated temperatures were analyzed and compared in depth. The current work would provide a molecular basis for better understanding of the high infectiousness of SARS-CoV-2 and offer better theoretical guidance for the design of inhibitors targeting infectious diseases caused by SARS-CoV-2.

Network-Based Target Prioritization and Drug Candidate Identification for Multiple Sclerosis: From Analyzing "Omics Data" to Druggability Simulations

Multiple sclerosis (MS) is the most common chronic inflammatory demyelinating disease of the central nervous system. While the drugs currently available for MS provide symptomatic benefit, there is no curative treatment. The emergence of large-scale multiomics data and network theory provide new opportunities for drug discovery in MS, as these are promising strategies for developing novel drugs. In this study, we proposed a computational framework that combined biomolecular network modeling and structural dynamics analysis to facilitate the discovery of new drugs with potential activity in MS. First, we developed a new shortest path-based algorithm that prioritized differentially expressed genes using a newly topological and functional exploration of protein-protein interaction network. Then, pathway enrichment analysis and an assessment of target druggability suggested that TNF-α-induced protein 3 (TNFAIP3), which is involved in NF-κ B signaling, could be a potential therapeutic target for MS. Finally, druggability simulations and mutation enrichment analysis of the TNFAIP3 dimer presented two druggable sites. Follow-up pharmacophore model-based virtual screening of the two sites yielded 30 hit compounds with low energy scores. In summary, this novel method based on analyzing "omics data" and performing druggability simulations, is a systematic approach that unravels disease mechanisms and links them to the chemical space to develop treatments and can be applied to other complex diseases.