Integration of immunoinformatics and cheminformatics to design and evaluate a multitope vaccine against Klebsiella pneumoniae and Pseudomonas aeruginosa coinfection.

Introduction: Klebsiella pneumoniae (K. pneumoniae) and Pseudomonas aeruginosa (P. aeruginosa) are the most common Gram-negative bacteria associated with pneumonia and coinfecting the same patient. Despite their high virulence, there is no effective vaccine against them. Methods: In the current study, the screening of several proteins from both pathogens highlighted FepA and OmpK35 for K. pneumonia in addition to HasR and OprF from P. aeruginosa as promising candidates for epitope mapping. Those four proteins were linked to form a multitope vaccine, that was formulated with a suitable adjuvant, and PADRE peptides to finalize the multitope vaccine construct. The final vaccine's physicochemical features, antigenicity, toxicity, allergenicity, and solubility were evaluated for use in humans. Results: The output of the computational analysis revealed that the designed multitope construct has passed these assessments with satisfactory scores where, as the last stage, we performed a molecular docking study between the potential vaccine construct and K. pneumonia associated immune receptors, TLR4 and TLR2, showing affinitive to both targets with preferentiality for the TLR4 receptor protein. Validation of the docking studies has proceeded through molecular dynamics simulation, which estimated a strong binding and supported the nomination of the designed vaccine as a putative solution for K. pneumoniae and P. aeruginosa coinfection. Here, we describe the approach for the design and assessment of our potential vaccine.

Computational Analysis of Deleterious SNPs in NRAS to Assess Their Potential Correlation With Carcinogenesis.

The NRAS gene is a well-known oncogene that acts as a major player in carcinogenesis. Mutations in the NRAS gene have been linked to multiple types of human tumors. Therefore, the identification of the most deleterious single nucleotide polymorphisms (SNPs) in the NRAS gene is necessary to understand the key factors of tumor pathogenesis and therapy. We aimed to retrieve NRAS missense SNPs and analyze them comprehensively using sequence and structure approaches to determine the most deleterious SNPs that could increase the risk of carcinogenesis. We also adopted structural biology methods and docking tools to investigate the behavior of the filtered SNPs. After retrieving missense SNPs and analyzing them using six in silico tools, 17 mutations were found to be the most deleterious mutations in NRAS. All SNPs except S145L were found to decrease NRAS stability, and all SNPs were found on highly conserved residues and important functional domains, except R164C. In addition, all mutations except G60E and S145L showed a higher binding affinity to GTP, implicating an increase in malignancy tendency. As a consequence, all other 14 mutations were expected to increase the risk of carcinogenesis, with 5 mutations (G13R, G13C, G13V, P34R, and V152F) expected to have the highest risk. Thermodynamic stability was ensured for these SNP models through molecular dynamics simulation based on trajectory analysis. Free binding affinity toward the natural substrate, GTP, was higher for these models as compared to the native NRAS protein. The Gly13 SNP proteins depict a differential conformational state that could favor nucleotide exchange and catalytic potentiality. A further application of experimental methods with all these 14 mutations could reveal new insights into the pathogenesis and management of different types of tumors.

In Silico Designing of a Multitope Vaccine against Rhizopus microsporus with Potential Activity against Other Mucormycosis Causing Fungi.

During the current era of the COVID-19 pandemic, the dissemination of Mucorales has been reported globally, with elevated rates of infection in India, and because of the high rate of mortality and morbidity, designing an effective vaccine against mucormycosis is a major health priority, especially for immunocompromised patients. In the current study, we studied shared Mucorales proteins, which have been reported as virulence factors, and after analysis of several virulent proteins for their antigenicity and subcellular localization, we selected spore coat (CotH) and serine protease (SP) proteins as the targets of epitope mapping. The current study proposes a vaccine constructed based on top-ranking cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B cell lymphocyte (BCL) epitopes from filtered proteins. In addition to the selected epitopes, ß-defensins adjuvant and PADRE peptide were included in the constructed vaccine to improve the stimulated immune response. Computational tools were used to estimate the physicochemical and immunological features of the proposed vaccine and validate its binding with TLR-2, where the output data of these assessments potentiate the probability of the constructed vaccine to stimulate a specific immune response against mucormycosis. Here, we demonstrate the approach of potential vaccine construction and assessment through computational tools, and to the best of our knowledge, this is the first study of a proposed vaccine against mucormycosis based on the immunoinformatics approach.