Altered protein homeostasis through proteasomal degradation of ubiquitinated proteins is a hallmark of many cancers. Ubiquitination, coordinated by E1, E2, and E3 enzymes, involves up to 40 E2-conjugating enzymes in humans to specify substrates and ubiquitin linkages. In a screen for E2 dependencies in acute myeloid leukemia (AML), ubiquitin conjugating enzyme E2 N (UBE2N) emerged as the top candidate. To investigate UBE2N's role in AML, we characterized an enzymatically defective mouse model of UBE2N, revealing UBE2N's requirement in AML without an impact on normal hematopoiesis. Unlike other E2s, which mediate lysine-48 (K48) polyubiquitination and degradation of proteins, UBE2N primarily synthesizes K63-linked chains, stabilizing or altering protein function. Proteomic analyses and a whole-genome CRISPR-activation screen in pharmacologically and genetically UBE2N-inhibited AML cells unveiled a network of UBE2N-regulated proteins, many of which are implicated in cancer. UBE2N inhibition reduced their protein levels, leading to increased K48-linked ubiquitination and degradation through the immunoproteasome and revealing UBE2N activity is enriched in immunoproteasome-positive AML. Furthermore, an interactome screen identified tripartite motif-containing protein 21 (TRIM21) as the E3 ligase partnering with activated UBE2N in AML to modulate UBE2N-dependent proteostasis. In conclusion, UBE2N maintains proteostasis in AML by stabilizing target proteins through K63-linked ubiquitination and prevention of K48 ubiquitin-mediated degradation by the immunoproteasome. Thus, inhibition of UBE2N catalytic function suppresses leukemic cells through selective degradation of critical proteins in immunoproteasome-positive AML.

Ancylostoma duodenale, a parasitic nematode worm, is found to be involved in various infections, including intestinal blood loss, protein malnutrition, and anemia. Antimicrobial resistance to the available therapeutics has prompted the search for new drug and vaccine targets against A. duodenale. Despite significant advances in vaccine development against A. duodenale, no commercial and FDA-approved vaccine exists to safeguard humans from infections caused by this pathogen. In this investigation, a stringent bioinformatics analysis identified 36 unique essential and host-interacting proteins. Based on their subcellular localization, 6 proteins located in the extracellular space and outer membrane were categorized as vaccine targets, while the remaining proteins were predicted to act as potential drug candidates. These vaccine candidates were further assessed for antigenicity, allergenicity, and physicochemical analysis to determine their suitability for the designing of a multi-epitope vaccine. Two candidate proteins were chosen as optimal targets in the development of vaccine design. The identified T- and B-cell epitopes from these proteins were then combined with appropriate linkers and adjuvants to design chimeric vaccine constructs aimed at inducing both cellular and humoral immune responses. Molecular docking, molecular dynamic simulations, PCA analysis, DCCM analysis, and binding free energy calculations proved stable interactions of the designed vaccine with human immune cell receptors. Within a bacterial cloning system, the vaccine constructs demonstrated the ability to be cloned and expressed. The immunological stimulation elicited significant immunological responses to the proposed vaccine. Our investigation identified new therapeutic targets and developed a peptide-based multi-epitope vaccine against A. duodenale infection. Additional experimental verification will open up new therapeutic alternatives for this emerging resistant pathogen.

Clinical mastitis (CM) remains a critical challenge in dairy production, exacerbated by the global rise of antibiotic-resistant pathogens, which threatens herd health and productivity. This study pioneers a dual genomic-computational strategy to develop bacteriocin-based therapeutics-a promising alternative to conventional antibiotics-by targeting conserved virulence mechanisms in CM-causing pathogens. We aimed to (i) identify essential core proteins in CM-causing pathogens of dairy cows using the genomic approach; and (ii) assess the efficacy of bacteriocin peptides (BPs) as novel therapeutic agents targeting the selected core proteins for sustainable management of mastitis. Through pan-genomic analysis of 16 clinically relevant pathogens, including Staphylococcus aureus, S. warneri, Streptococcus agalactiae, S. uberis, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, P. putida, and P. asiatica, we identified 65 evolutionarily conserved core proteins. Prioritization based on essentiality, virulence, and resistance potential revealed Rho (transcription termination factor) and HupB (nucleoid-associated protein) as high-value therapeutic targets due to their critical roles in bacterial survival and pathogenicity. A computational screen of 70 BPs identified 14 candidates with high binding affinity for both Rho and HupB proteins. Molecular dynamics simulations demonstrated that BP8, a novel dual-action bacteriocin, competitively inhibits Rho-mediated transcription termination and disrupts HupB-DNA interactions, effectively crippling bacterial replication and virulence. BP8 exhibited superior structural stability and binding efficacy compared to other candidates, positioning it as a potent broad-spectrum agent against diverse CM pathogens, including multidrug-resistant strains. Our study underscores the untapped potential of bacteriocins in veterinary medicine, offering a sustainable solution to mitigate antibiotic overuse and resistance. The computational validation of BP8 provides a foundational framework for developing targeted therapies, with implications for reducing dairy industry losses and improving animal welfare. Further in vitro and in vivo studies are warranted to translate these insights into practical therapeutics.

Brucella canis is a Gram-negative bacterium that causes canine brucellosis, a zoonotic disease with serious implications for public health and the global economy. Currently, there is no effective preventive vaccine for B. canis. Control measures include diagnostic testing, isolation, and euthanasia of infected animals. However, these measures face significant limitations, such as diagnostic challenges, ethical concerns, and limited success in preventing transmission. Epidemiologically, canine brucellosis exhibits seroprevalence rates ranging from less than 1% to over 15%, with higher rates reported in stray dogs and regions of low socioeconomic development. This study employed a reverse vaccinology approach to design and characterize a multiepitope vaccine candidate against B. canis, aiming to prevent infection caused by this pathogen. A comprehensive in silico analysis of the complete B. canis proteome was conducted to identify proteins with potential as vaccine targets. Predicted epitopes for B and T cells were analyzed, and those with the highest capacity to elicit a robust immune response were selected. These proteins were classified as plasma membrane proteins, outer membrane proteins (OMPs), or proteins with similarity to virulence factors. Selection criteria emphasized their essential roles in bacterial function, lack of homology with proteins from dogs or mice, and presence of fewer than two transmembrane domains. From this process, four candidate proteins were identified. Epitopes for B and T cells within these proteins were predicted and analyzed, selecting the most immunogenic sequences. The overlap between B- and T-cell epitopes narrowed the selection to six final epitopes. These selected epitopes were then assembled into a multiepitope vaccine construct using flexible linkers to ensure structural integrity and molecular adjuvants to enhance immunogenicity. The physicochemical properties, antigenicity, and toxicity of the designed vaccine were evaluated. Additionally, the secondary and tertiary structure of the vaccine was predicted and refined, followed by a molecular interaction analysis with the Toll-like receptor 4 (TLR4) receptor. The designed vaccine proved to be highly antigenic, nonallergenic, and nontoxic. Validation of its secondary and tertiary structures, along with molecular docking analysis, revealed a high binding affinity to the TLR4 receptor. Molecular dynamics simulations and normal mode analysis further confirmed the vaccine's structural stability and binding capacity. A multiepitope vaccine candidate against B. canis was successfully designed and characterized using a reverse vaccinology approach. This vaccine construct is expected to induce robust humoral and cellular immune responses, potentially conferring protective immunity against B. canis. The results of this study are promising; however, in vitro and in vivo tests are necessary to validate the vaccine's protective efficacy. Furthermore, the described method could serve as a framework for developing vaccines against other pathogens.

Mediterraneibacter gnavus, also known as Ruminococcus gnavus, is a Gram-positive anaerobic bacterium that resides in the human gut microbiota. Notably, this bacterium plays dual roles in health and disease. On one side it supports nutrient metabolism essential for bodily functions and on the other it contributes to the development of Inflammatory Bowel Disease (IBD) and other gastrointestinal disorders. R. gnavus strain RJX1120 is an encapsulated strain and has been linked to develop IBD. Despite the advances made on its role in gut homeostasis, limited information is available on strain-specific virulence factors, metabolic pathways, and regulatory mechanisms. The study of such aspects is crucial to make microbiota-targeted therapy and understand its implications in host health. A multi-epitope vaccine against R. gnavus strain RJX1120 was designed using reverse vaccinology-based subtractive proteomics approach. Among the 3,219 proteins identified in the R. gnavus strain RJX1120, two critical virulent and antigenic proteins, a Single-stranded DNA-binding protein SSB (A0A2N5PT08) and Cell division ATP-binding protein FtsE (A0A2N5NK05) were screened and identified as potential targets. The predicted B-cell and T-cell epitopes from these proteins were screened for essential immunological properties such as antigenicity, allergenicity, solubility, MHC binding affinity, and toxicity. Epitopes chosen were cross-linked using suitable spacers and an adjuvant to develop a multi-epitope vaccine. Structural refinement of the construct revealed that 95.7% of the amino acid residues were located in favored regions, indicating a high-quality structural model. Molecular docking analysis demonstrated a robust interaction between the vaccine construct and the human Toll-like receptor 4 (TLR4), with a binding energy of -1277.0 kcal/mol. The results of molecular dynamics simulations further confirmed the stability of the vaccine-receptor complex under physiological conditions. In silico cloning of the vaccine construct yielded a GC content of 48% and a Codon Adaptation Index (CAI) value of 1.0, indicating optimal expression in the host system. These results indicate the possibility of the designed vaccine construct as a candidate for the prevention of R. gnavus-associated diseases. However, experimental validation is required to confirm its immunogenicity and protective efficacy.

Deoxyribonucleic acid (DNA) serves as fundamental genetic blueprint that governs development, functioning, growth, and reproduction of all living organisms. DNA can be altered through germline and somatic mutations. Germline mutations underlie hereditary conditions, while somatic mutations can be induced by various factors including environmental influences, chemicals, lifestyle choices, and errors in DNA replication and repair mechanisms which can lead to cancer. DNA sequence analysis plays a pivotal role in uncovering the intricate information embedded within an organism's genetic blueprint and understanding the factors that can modify it. This analysis helps in early detection of genetic diseases and the design of targeted therapies. Traditional wet-lab experimental DNA sequence analysis through traditional wet-lab experimental methods is costly, time-consuming, and prone to errors. To accelerate large-scale DNA sequence analysis, researchers are developing AI applications that complement wet-lab experimental methods. These AI approaches can help generate hypotheses, prioritize experiments, and interpret results by identifying patterns in large genomic datasets. Effective integration of AI methods with experimental validation requires scientists to understand both fields. Considering the need of a comprehensive literature that bridges the gap between both fields, contributions of this paper are manifold: It presents diverse range of DNA sequence analysis tasks and AI methodologies. It equips AI researchers with essential biological knowledge of 44 distinct DNA sequence analysis tasks and aligns these tasks with 3 distinct AI-paradigms, namely, classification, regression, and clustering. It streamlines the integration of AI into DNA sequence analysis tasks by consolidating information of 36 diverse biological databases that can be used to develop benchmark datasets for 44 different DNA sequence analysis tasks. To ensure performance comparisons between new and existing AI predictors, it provides insights into 140 benchmark datasets related to 44 distinct DNA sequence analysis tasks. It presents word embeddings and language models applications across 44 distinct DNA sequence analysis tasks. It streamlines the development of new predictors by providing a comprehensive survey of 39 word embeddings and 67 language models based predictive pipeline performance values as well as top performing traditional sequence encoding-based predictors and their performances across 44 DNA sequence analysis tasks.

Francisella tularensis is a category A potential thread agent, making the development of vaccines and countermeasures a high priority. Therefore, identifying new vaccine candidates and novel drug targets is essential for addressing this significant public health concern.

This study presents an in silico analysis of two strategies against F. tularensis infection: the development of a multi-epitope vaccine (MEV) and the identification of novel drug targets. Outer membrane proteins (OMPs) were predicted using subcellular localization tools and immunogenicity was evaluated using a reverse vaccinology pipeline. Epitopes from these OMPs were combined to create candidate MEV for prophylactic protection. Concurrently, cytoplasmic proteins were subjected to rigorous analysis to identify potential novel drug targets.

Of 1,921 proteins, we identified 12 promising protein vaccine candidates from F. tularensis OMPs and proposed a multi-epitope vaccine (MEV) designed using seven immunodominant epitopes derived from four of these OMPs, including two hypothetical proteins (WP_003026145.1 and WP_003029346.1), an OmpA family protein (WP_003020808.1), and PD40 (WP_003021546.1). In addition, we proposed 10 novel drug targets for F. tularensis: Asp-tRNA (Asn)/Glu-tRNA (Gln) amidotransferase subunit GatC (WP_003017413.1), NAD(P)-binding protein (WP_042522581.1), 30S ribosomal protein S16 (WP_003023081.1), Class I SAM-dependent methyltransferase (WP_003022345.1), haloacid dehalogenase (WP_003014157.1), uroporphyrinogen-III synthase (WP_003022220.1), and four hypothetical proteins (WP_003017784.1, WP_003020080.1, WP_003020066.1, and WP_003022350.1).

This study designed an MEV and proposed novel drug targets to address tularemia, offering broad protection against various F. tularensis strains. MEV, with favorable physicochemical properties, showed strong potential through molecular docking and dynamic simulations. Immune simulations suggest that it may elicit robust responses against pathogens. The identification of novel drug targets can lead to the discovery of new antimicrobial agents. However, further in vitro and in vivo studies are required to validate their efficacy and capability.

Kingella kingae is a Gram-negative bacterium that causes invasive infections in children and older or immunocompromised individuals, making it a significant public health concern. In this study, a pan-proteomic mediated vaccine target mining was attempted to identify potential vaccine targets in K. kingae. Currently, there is no vaccine available against this pathobiont. Therefore, we designed and validated an in silico vaccine construct by targeting the lactoferrin/transferrin-binding TonB-dependent receptor. Antigenic regions of the TonB receptor were mapped, and the predicted epitopes were anticipated to be effective in a broad range of the world population. Using their combinations with linkers and various adjuvants, 12 vaccine constructs were prepared. The best construct (C7) with no allergenicity and high antigenicity was subjected to molecular modeling, docking with important immune receptors of humans, and then molecular dynamics (MD) simulation. After binding validation and stability assessment, it was cloned into a pet-28a + plasmid vector. Immune response was also simulated, and the vaccine was observed to invoke B- and T-cell induction. These findings can help accelerate the development of a new vaccine against K. kingae or other pathogens targeting the homolog of TonB. Nevertheless, we propose additional testing of C7 construct for efficacy and safety in vitro and in vivo.

Streptococcus pneumoniae is a Gram-positive bacterium responsible for severe infections such as meningitis and pneumonia. The increasing prevalence of antibiotic resistance necessitates the identification of new therapeutic targets. This study aimed to discover potential drug targets against S. pneumoniae using an in silico subtractive genomics approach.

The S. pneumoniae genome was compared to the human genome to identify non-homologous sequences using CD-HIT and BLASTp. Essential genes were identified using the Database of Essential Genes (DEG), with consideration for human gut microflora. Protein-protein interaction analyses were conducted to identify key hub genes, and gene ontology (GO) studies were performed to explore associated pathways. Due to the lack of crystal structure data, a potential target was modeled in silico and subjected to structure-based virtual screening.

Approximately 2,000 of the 2,027 proteins from the S. pneumoniae genome were identified as non-homologous to humans. The DEG identified 48 essential genes, which was reduced to 21 after considering human gut microflora. Key hub genes included gpi, fba, rpoD, and trpS, associated with 20 pathways. Virtual screening of 2,509 FDA-approved compounds identified Bromfenac as a leading candidate, exhibiting a binding energy of -26.335 ± 29.105 kJ/mol.

Bromfenac, particularly when conjugated with AuAgCu2O nanoparticles, has demonstrated antibacterial and anti-inflammatory properties against Staphylococcus aureus. This suggests that Bromfenac could be repurposed as a potential therapeutic agent against S. pneumoniae, pending further experimental validation. The approach highlights the potential for drug repurposing by targeting proteins essential in pathogens but absent in the host.

Histomonas meleagridis, a protozoan parasite responsible for histomonosis (syn. Blackhead disease, histomoniasis), presents an increasing challenge for poultry health, particularly with the ban of licensed prophylactic and treatment options. Recent studies have explored H. meleagridis proteome, exoproteome, and surfaceome, linking molecular data to virulence and in vitro attenuation. Nevertheless, proteins involved in interactions with hosts remain largely unknown. In this study, we conducted immunoproteome analyses to identify key antigens involved in the humoral immune response of the parasite's main hosts, turkeys and chickens. Immunogenic proteins were isolated via immunoprecipitation using sera from chickens and turkeys that were vaccinated with a single attenuated strain and challenged with virulent strains of the protozoan, respectively. Mass spectrometry identified 155 putative H. meleagridis immunogenic proteins, of which 43 were recognized by sera from both hosts. In silico antigenicity screening (VaxElan) identified 33 pan-reactive antigens, with VaxiDL further highlighting 10 as potential vaccine candidates. Comparative analysis revealed host-specific immune responses, with 16 differential immunogenic proteins in chickens (6 specific to virulent and 10 to attenuated preparations) and 19 unique proteins in turkeys, all associated with virulent strains. These results enhance our understanding of H. meleagridis immunogenic protein dynamics and host-pathogen specificities, supporting the development of improved diagnostic tools and potential protective measures against the infection.

Adenoid cystic carcinoma (ACC) is a rare glandular malignancy, commonly originating in salivary glands of the head and neck. Given its protracted growth, ACC is usually diagnosed in advanced stage. Treatment of ACC is limited to surgery and/or adjuvant radiotherapy, which often fails to prevent disease recurrence, and no FDA-approved targeted therapies are currently available. As such, identification of new therapeutic targets specific to ACC is crucial for improved patients' outcomes.

After thoroughly evaluating the gene expression and signaling patterns characterizing ACC, we applied PandaOmics (an AI-driven software platform for novel therapeutic target discovery) on the unique transcriptomic dataset of 87 primary ACCs. Identifying protein arginine methyl transferase 5 (PRMT5) as a putative candidate with the top-scored druggability, we next determined the applicability of PRMT5 inhibitors (PRT543 and PRT811) using ACC cell lines, organoids, and patient derived xenograft (PDX) models. Molecular changes associated with response to PRMT5 inhibition and anti-proliferative effect of the combination therapy with lenvatinib was then analyzed.

Using a comprehensive AI-powered engine for target identification, PRMT5 was predicted among potential therapeutic target candidates for ACC. Here we show that monotherapy with selective PRMT5 inhibitors induced a potent anti-tumor activity across several cellular and animal models of ACC, which was paralleled by downregulation of genes associated with ACC tumorigenesis, including MYB and MYC (the recognized drivers of ACC progression). Furthermore, as a subset of genes targeted by lenvatinib is upregulated in ACC, we demonstrate that addition of lenvatinib enhanced the growth inhibitory effect of PRMT5 blockade in vitro, suggesting a potential clinical benefit for patients expressing lenvatinib favorable molecular profile.

Taken together, our study underscores the role of PRMT5 in ACC oncogenesis and provides a strong rationale for the clinical development of PRMT5 inhibitors as a targeted monotherapy or combination therapy for treatment of patients with this rare disease, based on the analysis of their underlying molecular profile.

Essential genes are necessary to sustain the life of a species under adequate nutritional conditions. These genes have attracted significant attention for their potential as drug targets, especially in developing broad-spectrum antibacterial drugs. However, studying essential genes remains challenging due to their variability in specific environmental conditions. In this study, the authors aim to develop a powerful prediction model for identifying essential genes in humans. The authors first obtained the essential gene data from human cancer cell lines and characterised gene sequences using 7 feature encoding methods such as Kmer, the Composition of K-spaced Nucleic Acid Pairs, and Z-curve. Subsequently, feature fusion and feature optimisation strategies were employed to select the impactful features. Finally, machine learning algorithms were applied to construct the prediction models and evaluate their performance. The single-feature-based model achieved the highest area under the Receiver Operating Characteristic curve (AUC) of 0.830. After fusing and filtering these features, the classical machine learning models achieved the highest AUC at 0.823 while the deep learning model reached 0.860. Results obtained by the authors show that compared to using individual features, feature fusion and feature optimisation strategies significantly improved model performance. Moreover, the study provided an advantageous method for essential gene identification compared to other methods.

The genome is a sequence that encodes the DNA, RNA, and proteins that orchestrate an organism's function. We present Evo, a long-context genomic foundation model with a frontier architecture trained on millions of prokaryotic and phage genomes, and report scaling laws on DNA to complement observations in language and vision. Evo generalizes across DNA, RNA, and proteins, enabling zero-shot function prediction competitive with domain-specific language models and the generation of functional CRISPR-Cas and transposon systems, representing the first examples of protein-RNA and protein-DNA codesign with a language model. Evo also learns how small mutations affect whole-organism fitness and generates megabase-scale sequences with plausible genomic architecture. These prediction and generation capabilities span molecular to genomic scales of complexity, advancing our understanding and control of biology.

Taphrina deformans is a plant-pathogenic fungus and a responsible agent for causing peach leaf curl disease. Taphrina deformans affects peach fruit production and contributes to global economic losses. Commercial fungicides may provide temporary relief; however, their overuse resulted in adverse environmental consequences as well as led to drug-resistant strains of T. deformans. Therefore, the discovery of novel drug targets for the future synthesis of antifungal drugs against Taphrina deformans is needed. Here we studied Taphrina deformans by computational proteomics approaches. The whole genome and proteome of T. deformans were subjected to subtractive proteomics, high-throughput virtual screening, and molecular dynamic simulations. We employed subtractive proteomics analysis of 4,659 proteins extracted from UniProtKB database; after filtering out homologous and non-essential proteins, we identified 189 essential ones, including nine that participated in the crucial metabolic pathways of the pathogen. These proteins were categorized as nuclear (n = 116), cytoplasmic (n = 37), and membrane (n = 36). Of those essential proteins, glutamate-cysteine ligase (GCL) emerged as one promising target due to its essential function for glutathione biosynthesis process which facilitates T. deformans survival and pathogenicity. To validate GCL as an antifungal target, virtual screening and molecular docking studies with various commercial fungicides were carried out to better characterize GCL as a drug target. The data showed strong binding affinities for polyoxin D, fluoxastrobin, trifloxystrobin, and azoxystrobin within the active site of GCL. Polyoxin D showed a strong affinity when the measured docking score was at -7.34 kcal/mol, while molecular dynamics simulations confirmed stable interactions (three hydrogen bonds, two hydrophobic bonds, and one salt bridge interaction), supporting our findings that GCL represents an excellent target for antifungal drug development efforts. The results showed that GCL, as an innovative target for future fungicide designs to combat T. deformans infections, provides an avenue toward creating more effective peach leaf curl disease treatments while mitigating environmental harm caused by its current use.

Bacterial vaccines play a crucial role in combating bacterial infectious diseases. Apart from the prevention of disease, bacterial vaccines also help to reduce the mortality rates in infected populations. Advancements in vaccine development technologies have addressed the constraints of traditional vaccine design, providing novel approaches for the development of next-generation vaccines. Advancements in reverse vaccinology, bioinformatics, and comparative proteomics have opened horizons in vaccine development. Specifically, the use of protein structural data in crafting multi-epitope vaccines (MEVs) to target pathogens has become an important research focus in vaccinology. In this review, we focused on describing the methodologies and tools for epitope vaccine development, along with recent progress in this field. Moreover, this article also discusses the challenges in epitope vaccine development, providing insights for the future development of bacterial multi-epitope genetically engineered vaccines.

Whipple disease caused by Tropheryma whipplei a gram-positive bacterium is a systemic disorder that impacts not only the gastrointestinal tract but also the vascular system, joints, central nervous system, and cardiovascular system. Due to the lack of an approved vaccine, this study aimed to utilize immunoinformatic approaches to design multiepitope -based vaccine by utilizing the proteomes of five representative T. whipplei strains. The genomes initially comprised a total of 4,844 proteins ranging from 956 to 1012 proteins per strain. We collected 829 nonredundant lists of core proteins, that were shared among all the strains. Following subtractive proteomics, one extracellular protein, WP_033800108.1, a WhiB family transcriptional regulator, was selected for the chimeric-based multiepitope vaccine. Five immunodominant epitopes were retrieved from the WhiB family transcriptional regulator protein, indicating MHC-I and MHC-II with a global population coverage of 70.61%. The strong binding affinity, high solubility, nontoxicity, nonallergenic properties and high antigenicity scores make the selected epitopes more appropriate. Integration of the epitopes into a chimeric vaccine was carried out by applying appropriate adjuvant molecules and linkers, leading to the vaccine construct having enhanced immunogenicity and successfully eliciting both innate and adaptive immune responses. Moreover, the abilityof the vaccine to bind TLR4, a core innate immune receptor, was confirmed. Molecular dynamics simulations have also revealed the promising potential stability of the designed vaccine at 400 ns. In summary, we have designed a potential vaccine construct that has the ability not only to induce targeted immunogenicity for one strain but also for global T. whipplei strains. This study proposes a potential universal vaccine, reducing Whipple's disease risk and laying the groundwork for future research on multi-strain pathogens.

Helicobacter pylori, a bacterium associated with severe gastrointestinal diseases and malignancies, poses a significant challenge because of its increasing antibiotic resistance rates. This study aimed to identify potential drug targets and inhibitors against H. pylori using a structure-based virtual screening (SBVS) approach.

Core-proteome analysis of 132 H. pylori genomes was performed using the EDGAR database. Essential genes were identified and human and gut microbiota homolog proteins were excluded. The DAH7PS protein involved in the shikimate pathway was selected for the structure-based virtual screening (SBVS) approach. The tertiary structure of the protein was predicted through homology modeling (based on PDB ID: 5UXM). Molecular docking was performed to identify potential inhibitors of DAH7PS among StreptomeDB compounds using the AutoDock Vina tool. Molecular dynamics (MD) simulations assessed the stability of DAH7PS-ligand complexes. The complexes were further evaluated in terms of their binding affinity, Lipinski's Rule of Five, and ADMET properties.

A total of 54 novel drug targets with desirable properties were identified. DAH7PS was selected for further investigation, and virtual screening of StreptomeDB compounds yielded 36 high-affinity binding of the ligands. Two small molecules, 6,8-Dihydroxyisocoumarin-3-carboxylic acid and Epicatechin, also showed favorable RO5 and ADMET properties. MD simulations confirmed the stability and reliability of DAH7PS-ligand complexes, indicating their potential as inhibitors.

This study identified 54 novel drug targets against H. pylori. The DAH7PS protein as a promising drug target was evaluated using a computer-aided drug design. 6,8-Dihydroxyisocoumarin-3-carboxylic acid and Epicatechin demonstrated desirable properties and stable interactions, highlighting their potential to inhibit DAH7PS as an essential protein. Undoubtedly, more experimental validations are needed to advance these findings into practical therapies for treating drug-resistant H. pylori.

Isoprenoids are highly valued targets for microbial chemical production, allowing the creation of fragrances, biofuels, and pharmaceuticals from renewable carbon feedstocks. To increase isoprenoid production, previous efforts have manipulated pyruvate dehydrogenase (PDH) bypass pathway flux to increase cytosolic acetyl-coA; however, this results in mevalonate secretion and does not necessarily translate into higher isoprenoid production. Identification and disruption of the transporter mediating mevalonate secretion would allow us to determine whether increasing PDH bypass activity in the absence of secretion improves conversion of mevalonate into downstream isoprenoids. Attempted identification of the mevalonate transporter was accomplished using a pooled CRISPR library targeting all nonessential transporters and two different screening methods. Using a high throughput screen, based on growth of a mevalonate auxotrophic Escherichia coli strain, it was found that ZRT3 disruption largely abolished accumulation of extracellular mevalonate. However, disruption of ZRT3 was found to lower overall mevalonate pathway activity, rather than prevent secretion, indicating a previously unreported interaction between zinc availability and the mevalonate pathway. In a second screen, significant differences in PDR5/15 and QDR1/2 library representation were found between wild-type and mevalonate secreting Saccharomyces cerevisiae strains. However, no single deletion (or selected pair of double deletions) abolishes mevalonate secretion, indicating that this process appears to be mediated through multiple redundant transporters.

Identification of bacterial protein-protein interactions and predicting the structures of these complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here we developed RoseTTAFold2-Lite, a rapid deep learning model that leverages residue-residue coevolution and protein structure prediction to systematically identify and structurally characterize protein-protein interactions at the proteome-wide scale. Using this pipeline, we searched through 78 million pairs of proteins across 19 human bacterial pathogens and identified 1,923 confidently predicted complexes involving essential genes and 256 involving virulence factors. Many of these complexes were not previously known; we experimentally tested 12 such predictions, and half of them were validated. The predicted interactions span core metabolic and virulence pathways ranging from post-transcriptional modification to acid neutralization to outer-membrane machinery and should contribute to our understanding of the biology of these important pathogens and the design of drugs to combat them.

Inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis, is significantly influenced by intestinal flora. Understanding the genetic and microbiotic interplay is crucial for IBD prediction and treatment.

We used Mendelian randomization (MR), transcriptomic analysis, and machine learning techniques, integrating data from the MiBioGen Consortium and various GWAS datasets. SNPs associated with intestinal flora were mapped to genes, with LASSO regression refining gene selection. Differentially expressed genes (DEGs) and immune infiltration patterns were identified through transcriptomic analysis. Six machine learning models were used for predictive modeling.

MR analysis identified 25 gut microbiota classifications causally related to IBD. SNP mapping and gene expression analysis highlighted 24 significant genes. Drug target MR and colocalization validated these genes' causal relationships with IBD. Key pathways identified included the PI3K-Akt signaling pathway and epithelial-mesenchymal transition. Immune infiltration analysis revealed distinct patterns between high and low LASSO score groups. Machine learning models demonstrated high predictive value, with soft voting enhancing reliability.

By integrating MR, transcriptomic analysis, and sophisticated machine learning approaches, this study elucidates the causal relationships between intestinal flora and IBD. The application of machine learning not only enhanced predictive modeling but also offered new insights into IBD pathogenesis, highlighted potential therapeutic targets, and established a robust framework for predicting IBD onset.

Antibiotic resistance is a critical global health concern, causing millions of prolonged bacterial infections every year and straining our healthcare systems. Novel antibiotic strategies are essential to combating this health crisis and bacterial non-coding RNAs are promising targets for new antibiotics. In particular, a class of bacterial non-coding RNAs called riboswitches has attracted significant interest as antibiotic targets. Riboswitches reside in the 5'-untranslated region of an mRNA transcript and tune gene expression levels in cis by binding to a small-molecule ligand. Riboswitches often control expression of essential genes for bacterial survival, making riboswitch inhibitors an exciting prospect for new antibacterials. Synthetic ligand mimics have predominated the search for new riboswitch inhibitors, which are designed based on static structures of a riboswitch's ligand-sensing aptamer domain or identified by screening a small-molecule library. However, many small-molecule inhibitors that bind an isolated riboswitch aptamer domain with high affinity in vitro lack potency in vivo. Importantly, riboswitches fold and respond to the ligand during active transcription in vivo. This co-transcriptional folding is often not considered during inhibitor design, and may explain the discrepancy between a low Kd in vitro and poor inhibition in vivo. In this review, we cover advances in riboswitch co-transcriptional folding and illustrate how intermediate structures can be targeted by antisense oligonucleotides-an exciting new strategy for riboswitch inhibitor design.

Neisseria meningitidis, commonly known as the meningococcus, leads to substantial illness and death among children and young adults globally, revealing as either epidemic or sporadic meningitis and/or septicemia. In this study, we have designed a novel peptide-based chimeric vaccine candidate against the N. meningitidis strain 331,401 serogroup X. Through rigorous analysis of subtractive genomics, two essential cytoplasmic proteins, namely UPI000012E8E0(UDP-3-O-acyl-GlcNAc deacetylase) and UPI0000ECF4A9(UDP-N-acetylglucosamine acyltransferase) emerged as potential drug targets. Additionally, using reverse vaccinology, the outer membrane protein UPI0001F4D537 (Membrane fusion protein MtrC) identified by subcellular localization and recognized for its known indispensable role in bacterial survival was identified as a novel chimeric vaccine target. Following a careful comparison of MHC-I, MHC-II, T-cell, and B-cell epitopes, three epitopes derived from UPI0001F4D537 were linked with three types of linkers-GGGS, EAAAK, and the essential PADRE-for vaccine construction. This resulted in eight distinct vaccine models (V1-V8). Among them V1 model was selected as the final vaccine construct. It exhibits exceptional immunogenicity, safety, and enhanced antigenicity, with 97.7 % of its residues in the Ramachandran plot's most favored region. Subsequently, the vaccine structure was docked with the TLR4/MD2 complex and six different HLA allele receptors using the HADDOCK server. The docking resulted in the lowest HADDOCK score of 39.3 ± 9.0 for TLR/MD2. Immune stimulation showed a strong immune response, including antibodies creation and the activation of B-cells, T Cytotoxic cells, T Helper cells, Natural Killer cells, and interleukins. Furthermore, the vaccine construct was successfully expressed in the Escherichia coli system by reverse transcription, optimization, and ligation in the pET-28a (+) vector for the expression study. The current study proposes V1 construct has the potential to elicit both cellular and humoral responses, crucial for the developing an epitope-based vaccine against N. meningitidis strain 331,401 serogroup X.

Morganella morganii is a Gram-negative bacterial pathogen that causes bacteremia, urinary tract infections, intra-abdominal infections, chorioamnionitis, neonatal sepsis, and newborn meningitis. To control this bacterial pathogen a total of 3565 putative proteins targets in Morganella morganii were screened using comparative subtractive analysis of biochemical pathways annotated by the KEGG that did not share any similarities with human proteins. One of the targets, D-alanyl-D-alanine carboxypeptidase DacB [Morganella] was observed to be implicated in the majority of cell wall synthesis pathways, leading to its selection as a novel pharmacological target. The drug that interacted optimally with the identified target was observed to be Cefoperazone (DB01329) with the estimated free energy of binding -8.9 Kcal/mol. During molecular dynamics simulations; it was observed that DB01328-2exb and DB01329-2exb complexes showed similar values as the control FMX-2exb complex near 0.2 nm with better stability. Furthermore, MMPBSA total free energy calculation showed better binding energy than the control complex for DB01329-2exb interaction i.e. -31.50 (±0.93) kcal/mol. Our presented research suggested that D-alanyl-D-alanine carboxypeptidase DacB could be a therapeutic target and cefoperazone could be a promising ligand to inhibit the D-alanyl-D-alanine carboxypeptidase DacB protein of Morganella morganii. To identify prospective therapeutic and vaccine targets in Morganella morganii, this is the first computational and subtractive genomics investigation of various metabolic pathways exploring other therapeutic targets of Morganella morganii. In vitro/in vivo experimental validation of the identified target D-alanyl-D-alanine carboxypeptidase and the design of its inhibitors is suggested to figure out the best dose, the drug's effectiveness, and its toxicity.Communicated by Ramaswamy H. Sarma.

Current treatment of clostridial infections includes broad-spectrum antibiotics and antitoxins, yet antitoxins are ineffective against all Clostridiumspecies. Moreover, rising antimicrobial resistance (AMR) threatens treatment effectiveness and public health. This study therefore aimed to discover a common drug target for four pathogenic clostridial species, Clostridium botulinum, C. difficile, C. tetani, and C. perfringens through an in-silico core genomic approach. Using four reference genomes of C. botulinum, C. difficile, C. tetani, and C. perfringens, we identified 1484 core genomic proteins (371/genome) and screened them for potential drug targets. Through a subtractive approach, four core proteins were finally identified as drug targets, represented by type III pantothenate kinase (CoaX) and, selected for further analyses. Interestingly, the CoaX is involved in the phosphorylation of pantothenate (vitamin B5), which is a critical precursor for coenzyme A (CoA) biosynthesis. Investigation of druggability analysis on the identified drug target reinforces CoaX as a promising novel drug target for the selected Clostridium species. During the molecular screening of 1201 compounds, a known agonist drug compound (Vibegron) showed strong inhibitory activity against targeted clostridial CoaX. Additionally, we identified tazobactam, a beta-lactamase inhibitor, as effective against the newly proposed target, CoaX. Therefore, identifying CoaX as a single drug target effective against all four clostridial pathogens presents a valuable opportunity to develop a cost-effective treatment for multispecies clostridial infections.

Bacterial vaginosis (BV), primarily attributed to Gardnerella vaginalis, poses significant challenges due to antibiotic resistance and suboptimal treatment outcomes. This study presents an integrated approach to identify potential drug targets and screen compounds against this bacterium by leveraging a computational methodology. Subtractive proteomics of the reference strain ASM286196v1/UMB0386 (assembly accession: GCA_002861965.1) facilitated the prioritization of proteins with essential bacterial functions and pathways as potential drug targets. We selected 3-deoxy-7-phosphoheptulonate synthase (aroG gene product; also known as DAHP synthase) for downstream analysis. Molecular docking was employed in PyRx (AutoDock Vina) to predict binding affinities between aroG inhibitors from the ZINC database and 3-deoxy-7-phosphoheptulonate synthase. Molecular dynamics simulations of 100 ns, using GROMACS, validated the stability of drug-target interactions. Additionally, ADMET profiling aided in the selection of compounds with favorable pharmacokinetic properties and safety profile for human hosts. PBPK profiling showed that ZINC98088375 had the highest bioavailability and efficient systemic circulation. Conversely, ZINC5113880 demonstrated the lowest absorption rate (39.661%). Moreover, cirrhosis, steatosis, and renal impairment appeared to influence blood concentration of the drug, impacting bioavailability. The integrative -omics approach utilized in this study underscores the potential of computer-aided drug design and offers a rational strategy for targeted inhibitor discovery against G. vaginalis. The strategy is an attempt to address the limitations of current BV treatments, including antibiotic resistance, and pave way for the development of safer and more effective therapeutics.

Codon usage bias (CUB), the uneven usage of synonymous codons encoding the same amino acid, differs among genes within and across bacteria genomes. CUB is known to be influenced by gene expression and accordingly, CUB differs between the high-expression and low-expression genes in several bacteria. In this article, we have extended codon usage study considering gene essentiality as a feature. Using machine learning (ML) based approaches, we have analysed Relative Synonymous Codon Usage (RSCU) values between essential and non-essential genes in Escherichia coli and thirty-four other bacterial genomes whose gene essentiality features were available in public databases. We observed significant differences in codon usage patterns between essential and non-essential genes for majority of the bacterial genomes and accordingly, ML based classifiers achieved high area under curve (AUC) scores, with a minimum score of 70.0 across twenty-eight organisms. Further, importance of the codons towards classifying genes found to differ among the codons in each genome. Arg codon CGT and Gly codon GGT were observed to be the most preferred codons among essential genes in Escherichia coli. Interestingly, some of the codons like CGT, ATA, GGT and GGG observed to be contributing consistently towards classifying essential genes across thirty-five bacteria genomes studied. In other hand, codons TGY and CAY encoding amino acids Cys and His respectively were among the least contributing codons towards classification among all these bacteria. This study demonstrates the gene essentiality based differences in synonymous codon usage in bacteria genomes and presents a common codon usage pattern across bacteria.

Pseudomonas aeruginosa (P. aeruginosa) poses a significant threat as a nosocomial pathogen due to its robust resistance mechanisms and virulence factors. This study integrates subtractive proteomics and ensemble docking to identify and characterize essential proteins in P. aeruginosa, aiming to discover therapeutic targets and repurpose commercial existing drugs. Using subtractive proteomics, we refined the dataset to discard redundant proteins and minimize potential cross-interactions with human proteins and the microbiome proteins. We identified 12 key proteins, including a histidine kinase and members of the RND efflux pump family, known for their roles in antibiotic resistance, virulence, and antigenicity. Predictive modeling of the three-dimensional structures of these RND proteins and subsequent molecular ensemble-docking simulations led to the identification of MK-3207, R-428, and Suramin as promising inhibitor candidates. These compounds demonstrated high binding affinities and effective inhibition across multiple metrics. Further refinement using non-covalent interaction index methods provided deeper insights into the electronic effects in protein-ligand interactions, with Suramin exhibiting superior binding energies, suggesting its broad-spectrum inhibitory potential. Our findings confirm the critical role of RND efflux pumps in antibiotic resistance and suggest that MK-3207, R-428, and Suramin could be effectively repurposed to target these proteins. This approach highlights the potential of drug repurposing as a viable strategy to combat P. aeruginosa infections.

The unique epibiotic-parasitic relationship between Nanosynbacter lyticus type strain TM7x, a member of the newly identified Candidate Phyla Radiation, now referred to as Patescibacteria, and its basibiont, Schaalia odontolytica strain XH001 (formerly Actinomyces odontolyticus), require more powerful genetic tools for deeper understanding of the genetic underpinnings that mediate their obligate relationship. Previous studies have mainly characterized the genomic landscape of XH001 during or post TM7x infection through comparative genomic or transcriptomic analyses followed by phenotypic analysis. Comprehensive genetic dissection of the pair is currently cumbersome due to the lack of robust genetic tools in TM7x. However, basic genetic tools are available for XH001 and this study expands the current genetic toolset by developing high-throughput transposon insertion sequencing (Tn-seq). Tn-seq was employed to screen for essential genes in XH001 under laboratory conditions. A highly saturated Tn-seq library was generated with nearly 660,000 unique insertion mutations, averaging one insertion every 2-3 nucleotides. 203 genes, 10.5% of the XH001 genome, were identified as putatively essential.

Salmonella infections pose a significant global public health concern due to the substantial expenses associated with monitoring, preventing, and treating the infection. In this study, we explored the core proteome of Salmonella to design a multi-epitope vaccine through Subtractive Proteomics and immunoinformatics approaches. A total of 2395 core proteins were curated from 30 different isolates of Salmonella (strain NZ CP014051 was taken as reference). Utilizing the subtractive proteomics approach on the Salmonella core proteome, Curlin major subunit A (CsgA) was selected as the vaccine candidate. csgA is a conserved gene that is related to biofilm formation. Immunodominant B and T cell epitopes from CsgA were predicted using numerous immunoinformatics tools. T lymphocyte epitopes had adequate population coverage and their corresponding MHC alleles showed significant binding scores after peptide-protein based molecular docking. Afterward, a multi-epitope vaccine was constructed with peptide linkers and Human Beta Defensin-2 (as an adjuvant). The vaccine could be highly antigenic, non-toxic, non-allergic, and have suitable physicochemical properties. Additionally, Molecular Dynamics Simulation and Immune Simulation demonstrated that the vaccine can bind with Toll Like Receptor 4 and elicit a robust immune response. Using in vitro, in vivo, and clinical trials, our findings could yield a Pan-Salmonella vaccine that might provide protection against various Salmonella species.

Vandammella animalimorsus is a Gram-negative and non-motile bacterium typically transmitted to humans through direct contact with the saliva of infected animals, primarily through biting, scratches, or licks on fractured skin. The absence of a confirmed post-exposure treatment of V. animalimorsus bacterium highlights the imperative for developing an effective vaccine. We intended to determine potential vaccine candidates and paradigm a chimeric vaccine against V. animalimorsus by accessible public data analysis of the strain by utilizing reverse vaccinology. By subtractive genomics, five outer membranes were prioritized as potential vaccine candidates out of 2590 proteins. Based on the instability index and transmembrane helices, a multidrug transporter protein with locus ID A0A2A2AHJ4 was designated as a potential candidate for vaccine construct. Sixteen immunodominant epitopes were retrieved by utilizing the Immune Epitope Database. The epitope encodes the strong binding affinity, nonallergenic properties, non-toxicity, high antigenicity scores, and high solubility revealing the more appropriate vaccine construct. By utilizing appropriate linkers and adjuvants alongside a suitable adjuvant molecule, the epitopes were integrated into a chimeric vaccine to enhance immunogenicity, successfully eliciting both adaptive and innate immune responses. Moreover, the promising physicochemical features, the binding confirmation of the vaccine to the major innate immune receptor TLR-4, and molecular dynamics simulations of the designed vaccine have revealed the promising potential of the selected candidate. The integration of computational methods and omics data has demonstrated significant advantages in discovering novel vaccine targets and mitigating vaccine failure rates during clinical trials in recent years.

Splice-switching oligonucleotides (SSOs) are antisense compounds that act directly on pre-mRNA to modulate alternative splicing (AS). This study demonstrates the value that artificial intelligence/machine learning (AI/ML) provides for the identification of functional, verifiable, and therapeutic SSOs. We trained XGboost tree models using splicing factor (SF) pre-mRNA binding profiles and spliceosome assembly information to identify modulatory SSO binding sites on pre-mRNA. Using Shapley and out-of-bag analyses we also predicted the identity of specific SFs whose binding to pre-mRNA is blocked by SSOs. This step adds considerable transparency to AI/ML-driven drug discovery and informs biological insights useful in further validation steps. We applied this approach to previously established functional SSOs to retrospectively identify the SFs likely to regulate those events. We then took a prospective validation approach using a novel target in triple negative breast cancer (TNBC), NEDD4L exon 13 (NEDD4Le13). Targeting NEDD4Le13 with an AI/ML-designed SSO decreased the proliferative and migratory behavior of TNBC cells via downregulation of the TGFβ pathway. Overall, this study illustrates the ability of AI/ML to extract actionable insights from RNA-seq data.

American foulbrood (AFB), caused by the highly virulent, spore-forming bacterium Paenibacillus larvae, poses a significant threat to honey bee brood. The widespread use of antibiotics not only fails to effectively combat the disease but also raises concerns regarding honey safety. The current computational study was attempted to identify a novel therapeutic drug target against P. larvae, a causative agent of American foulbrood disease in honey bee.

We investigated effective novel drug targets through a comprehensive in silico pan-proteome and hierarchal subtractive sequence analysis. In total, 14 strains of P. larvae genomes were used to identify core genes. Subsequently, the core proteome was systematically narrowed down to a single protein predicted as the potential drug target. Alphafold software was then employed to predict the 3D structure of the potential drug target. Structural docking was carried out between a library of phytochemicals derived from traditional Chinese flora (n > 36,000) and the potential receptor using Autodock tool 1.5.6. Finally, molecular dynamics (MD) simulation study was conducted using GROMACS to assess the stability of the best-docked ligand.

Proteome mining led to the identification of Ketoacyl-ACP synthase III as a highly promising therapeutic target, making it a prime candidate for inhibitor screening. The subsequent virtual screening and MD simulation analyses further affirmed the selection of ZINC95910054 as a potent inhibitor, with the lowest binding energy. This finding presents significant promise in the battle against P. larvae.

Computer aided drug design provides a novel approach for managing American foulbrood in honey bee populations, potentially mitigating its detrimental effects on both bee colonies and the honey industry.

Cronobacter sakazakii is an opportunistic pathogen that has been associated with severe infection in neonates such as necrotizing enterocolitis (NEC), neonatal meningitis, and bacteremia. This pathogen can survive in a relatively dry environment, especially in powdered infant formula (PIF). Unfortunately, conventional drugs that were once effective against C. sakazakii are gradually losing their efficacy due to rising antibiotic resistance. In this study, a subtractive genomic approach was followed in order to identify potential therapeutic targets in the pathogen. The whole proteome of the pathogen was filtered through a step-by-step process, which involved removing paralogous proteins, human homologs, sequences that are less essential for survival, proteins with shared metabolic pathways, and proteins that are located in cells other than the cytoplasmic membrane. As a result, nine novel drug targets were identified. Further, the analysis also unveiled that the FDA-approved drug Terbinafine can be repurposed against the Glutathione/l-cysteine transport system ATP-binding/permease protein CydC of C. sakazakii. Moreover, molecular docking and dynamics studies of Terbinafine and CydC suggested that this drug can be used to treat C. sakazakii infection in neonates. However, for clinical purposes further in vitro and in vivo studies are necessary.

Tuberculosis (TB) stands as the second most fatal infectious disease globally, causing 1.3 million deaths in 2022. The resurgence of TB and the alarming rise of antibiotic resistance demand urgent call to develop novel antituberculosis drugs. Despite concerted efforts to control TB, the disease persists and spreads rapidly on a global scale. Targeting stress response pathways in Mycobacterium tuberculosis (Mtb) has become imperative to achieve complete eradication. This study employs subtractive genomics to identify and prioritize potential drug targets among the hypothetical proteins of Mtb, focusing on indispensable pathways. Amongst 177 essential hypothetical proteins, 152 were nonhomologous to human. These proteins participated in 34 pathways, and a 20-fold enrichment of SUF pathway genes led to its selection as a target pathway. Fe-S clusters are fundamental, widely distributed protein cofactors involved in vital cellular processes. The survival of Mtb in a hypoxic environment relies on the iron-sulfur (Fe-S) cluster biogenesis pathway for the repair of damaged Fe-S clusters. It also protects pathogen against drugs, ensuring controlled iron utilization and contributing to drug resistance. In Mtb, six proteins of Fe-S cluster assembly pathway are encoded by the suf operon. The present study was focused on SufD because of its role in iron acquisition and prevention of Fenton reaction. The research further delves into the in silico characterization of SufD, utilizing bioinformatics tools for sequence and structure based analysis. The protein's structural features, including the identification of conserved regions, motifs, and 3D structure prediction enhanced functional annotation. Target based virtual screening of compounds from the ChEMBL database resulted in 12 inhibitors with best binding affinities. Drug likeness and ADMET profiling of potential inhibitors identified promising compounds with favorable drug-like properties. The study also involved cloning in SUMO-pRSF-Duet1 expression vector, overexpression, and purification of recombinant SufD from E. coli BL21 (DE3) cells. Optimization of expression conditions resulted in soluble production, and subsequent purification highlighting the efficacy of the SUMO fusion system for challenging Mtb proteins in E. coli. These findings provide valuable insights into pharmacological targets for future experimental studies, holding promise for the development of targeted therapy against Mtb.

Identification of bacterial protein-protein interactions and predicting the structures of the complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here, we developed a deep learning-based pipeline that leverages residue-residue coevolution and protein structure prediction to systematically identify and structurally characterize protein-protein interactions at the proteome-wide scale. Using this pipeline, we searched through 78 million pairs of proteins across 19 human bacterial pathogens and identified 1923 confidently predicted complexes involving essential genes and 256 involving virulence factors. Many of these complexes were not previously known; we experimentally tested 12 such predictions, and half of them were validated. The predicted interactions span core metabolic and virulence pathways ranging from post-transcriptional modification to acid neutralization to outer membrane machinery and should contribute to our understanding of the biology of these important pathogens and the design of drugs to combat them.

Kingella negevensis belongs to the Neisseriaceae family. It is implied that it has significant virulence potential due to RTX toxin production, which can cause hemolysis. It usually colonizes the orophayrynx of pediatric population, along with Kingella kingae but has also been isolated from vagina. Todate no report on its drug targets is present, therefore putative therapeutic targets were identified from its genomic sequence data. Traditional Chinese (n > 36,000) and Indian medicinal compounds (n > 2000) were then screened against its pyridoxine 5'-phosphate synthase, a vital therapeutic target. Prioritized TCM compounds included ZINC02525131, ZINC33833737 and ZINC85486932, and Cadiyenol, 9,11,13-Octadecatrienoic acid and 6-Gingerol from Indian medicinal library. Molecular dynamics simulation of top compounds revealed ZINC02525131 as having best stability for 100 ns, compared to Cadiyenol. ADMET profiling was then done, along with physiologically based pharmacokinetic simulation of these compounds in a population of 200 individuals, for 12 h to see fate of the ingested compound. Additionally, the impact of these compounds in a population with cirrhosis and renal impairment was also simulated. We imply in light of all the studied parameters of safety and bioavailability, etc., that 6-Gingerol from Zingiber officinalis rhizome must be proceeded further for in vitro and in vivo testing for inhibition of K. negevensis.

Proteins are considered indispensable for facilitating an organism's viability, reproductive capabilities, and other fundamental physiological functions. Conventional biological assays are characterized by prolonged duration, extensive labor requirements, and financial expenses in order to identify essential proteins. Therefore, it is widely accepted that employing computational methods is the most expeditious and effective approach to successfully discerning essential proteins. Despite being a popular choice in machine learning (ML) applications, the deep learning (DL) method is not suggested for this specific research work based on sequence features due to the restricted availability of high-quality training sets of positive and negative samples. However, some DL works on limited availability of data are also executed at recent times which will be our future scope of work. Conventional ML techniques are thus utilized in this work due to their superior performance compared to DL methodologies. In consideration of the aforementioned, a technique called EPI-SF is proposed here, which employs ML to identify essential proteins within the protein-protein interaction network (PPIN). The protein sequence is the primary determinant of protein structure and function. So, initially, relevant protein sequence features are extracted from the proteins within the PPIN. These features are subsequently utilized as input for various machine learning models, including XGB Boost Classifier, AdaBoost Classifier, logistic regression (LR), support vector classification (SVM), Decision Tree model (DT), Random Forest model (RF), and Naïve Bayes model (NB). The objective is to detect the essential proteins within the PPIN. The primary investigation conducted on yeast examined the performance of various ML models for yeast PPIN. Among these models, the RF model technique had the highest level of effectiveness, as indicated by its precision, recall, F1-score, and AUC values of 0.703, 0.720, 0.711, and 0.745, respectively. It is also found to be better in performance when compared to the other state-of-arts based on traditional centrality like betweenness centrality (BC), closeness centrality (CC), etc. and deep learning methods as well like DeepEP, as emphasized in the result section. As a result of its favorable performance, EPI-SF is later employed for the prediction of novel essential proteins inside the human PPIN. Due to the tendency of viruses to selectively target essential proteins involved in the transmission of diseases within human PPIN, investigations are conducted to assess the probable involvement of these proteins in COVID-19 and other related severe diseases.

Xanthomonas oryzae pv. oryzae is a plant pathogen responsible for causing one of the most severe bacterial diseases in rice, known as bacterial leaf blight that poses a major threat to global rice production. Even though several experimental compounds and chemical agents have been tested against X. oryzae pv. oryzae, still no approved drug is available. In this study, a subtractive genomic approach was used to identify potential therapeutic targets and repurposible drug candidates that could control of bacterial leaf blight in rice plants.

The entire proteome of the pathogen underwent an extensive filtering process which involved removal of the paralogous proteins, rice homologs, non-essential proteins. Out of the 4382 proteins present in Xoo proteome, five hub proteins such as dnaA, dnaN, recJ, ruvA, and recR were identified for the druggability analysis. This analysis led to the identification of dnaN-encoded Beta sliding clamp protein as a potential therapeutic target and one experimental drug named [(5R)-5-(2,3-dibromo-5-ethoxy-4hydroxybenzyl)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl]acetic acid that can be repurposed against it. Molecular docking and 100 ns long molecular dynamics simulation suggested that the drug can form stable complexes with the target protein over time.

Findings from our study indicated that the proposed drug showed potential effectiveness against bacterial leaf blight in rice caused by X. oryzae pv. oryzae. It is essential to keep in consideration that the procedure for developing novel drugs can be challenging and complicated. Even the most promising results from in silico studies should be validated through further in vitro and in vivo investigation before approval.

Lyme disease caused by the Borrelia species is a growing health concern in many parts of the world. Current treatments for the disease may have side effects, and there is also a need for new therapies that can selectively target the bacteria. Pathogens responsible for Lyme disease include B. burgdorferi, B. afzelii, and B. garinii. In this study, we employed structural docking-based screening to identify potential lead-like inhibitors against the bacterium. We first identified the core essential genome fraction of the bacterium, using 37 strains. Later, we screened a library of lead-like marine microbial metabolites (n = 4730) against the arginine deiminase (ADI) protein of Borrelia garinii. This protein plays a crucial role in the survival of the bacteria, and inhibiting it can kill the bacterium. The prioritized lead compounds demonstrating favorable binding energies and interactions with the active site of ADI were then evaluated for their drug-like and pharmacokinetic parameters to assess their suitability for development as drugs. Results from molecular dynamics simulation (100 ns) and other scoring parameters suggest that the compound CMNPD18759 (common name: aureobasidin; IUPAC name: 2-[(4R,6R)-4,6-dihydroxydecanoyl]oxypropan-2-yl (3S,5R)-3,5-dihydroxydecanoate) holds promise as a potential drug candidate for the treatment of Lyme disease, caused by B. garinii. However, further experimental studies are needed to validate the efficacy and safety of this compound in vivo.