In silico design and validation of a novel multi-epitope vaccine candidate against structural proteins of Chikungunya virus using comprehensive immunoinformatics analyses

Chikungunya virus (CHIKV) is an emerging viral infectious agent with the potential of causing pandemic. There is neither a protective vaccine nor an approved drug against the virus. The aim of this study was design of a novel multi-epitope vaccine (MEV) candidate against the CHIKV structural proteins using comprehensive immunoinformatics and immune simulation analyses. In this study, using comprehensive immunoinformatics approaches, we developed a novel MEV candidate using the CHIKV structural proteins (E1, E2, 6 K, and E3). The polyprotein sequence was obtained from the UniProt Knowledgebase and saved in FASTA format. The helper and cytotoxic T lymphocytes (HTLs and CTLs respectively) and B cell epitopes were predicted. The toll-like receptor 4 (TLR4) agonist RS09 and PADRE epitope were employed as promising immunostimulatory adjuvant proteins. All vaccine components were fused using proper linkers. The MEV construct was checked in terms of antigenicity, allergenicity, immunogenicity, and physicochemical features. The docking of the MEV construct and the TLR4 and molecular dynamics (MD) simulation were also performed to assess the binding stability. The designed construct was non-allergen and was immunogen which efficiently stimulated immune responses using the proper synthetic adjuvant. The MEV candidate exhibited acceptable physicochemical features. Immune provocation included prediction of HTL, B cell, and CTL epitopes. The docking and MD simulation confirmed the stability of the docked TLR4-MEV complex. The high-level protein expression in the Escherichia coli (E. coli) host was observed through in silico cloning. The in vitro, in vivo, and clinical trial investigations are required to verify the findings of the current study.

Structure and activity of a novel robust peroxidase from Alkanna frigida cell culture

Demands for peroxidases (POX)s with diverse physicochemical properties have steadily grown as more applications of POXs are demonstrated. Plants are among the best sources of versatile POXs, and plant biotechnology, as an agricultural hassle-free technology, promises to circumvent the limitations of natural resource exploitation and to address the demands. Following this trend, it was shown that POX production steadily increased during the 31-day subculture of Alkanna frigida (from Boraginaceae) callus on Murashige-Skoog medium containing 2,4-dichlorophenoxyacetic acid (10^-6 M) and kinetin (10^-5 M). The purified cationic enzyme (POX_alf) maintained its optimal activity over pH 4-7 for 2 years. It was resistant to H₂O₂ high concentrations (IC50 = 543.7 mM) and showed high specific activity in the reaction with phenol (4320.5 AU mg^-1 > 20-fold of HRP AU). Furthermore, the specificity constant ratio of guaiacol to phenol indicated a 100 times faster reaction of POX_alf with guaiacol. However, in contrast to HRP, it had little effect on diazo derivatives of aniline and meta-diaminobenzene. Based on the resulting primary structure from the tandem mass analysis, the POX_alf 3D structure was constructed via homology modelling. Despite the high topological similarity between the HRP and POX_alf structures, there were important differences between the active site pockets that could explain the observed differences in the corresponding substrate spectra and the specific activities. Considering the dynamics of POX_alf production, its inactivity towards IAA and its high affinity for guaiacol, POX_alf may have associated roles with A. frigida cell wall construction and monolignol metabolism.

Critical assessment of genome-scale metabolic models of Arabidopsis thaliana

Genome-scale metabolic models (GEMs) have enabled researchers to perform systems-level studies of living organisms. Flux balance analysis (FBA), as a constraint-based technique, enables computation of reaction fluxes and prediction of the metabolic phenotypes of a cell under a set of specified conditions. The quality of a GEM is important for obtaining accurate predictions. In this study, we evaluated the quality of five available GEMs for Arabidopsis thaliana from various points of views. To do this, we inspected some of their important features, including the number of reactions with well-defined gene-protein-reaction rules, number of blocked reactions, mass-unbalanced reactions, prediction accuracy in the simulation of key metabolic functions and existence of erroneous energy generating cycles (EGCs). All of the models were found to include some mass-unbalanced reactions. Moreover, four out of five models were found to include EGCs. However, Aracell includes the maximum number of blocked reactions, which suggests the presence of several incomplete pathways. These results clearly show that simulation by using these models may result in erroneous predictions and all of the publicly available GEMs for A. thaliana require extensive curations before being applied in practice.

In silico prediction of enzymatic reactions catalyzed by acid phosphatases

In present work, we describe a methodology for prediction of an enzymatic reaction for which no experimental data are available except for a gene sequence. As a challenging case, we have developed the method for identifying the putative substrates of monoester phosphatases, commonly known as acid phosphatase enzymes, which have no strong substrate specificity. Finding a preferable substrate for each one is an important task to unravel pathways involved in plant phosphate metabolism. Having used an Arabidopsis thaliana haloacid dehalogenase (HAD)-related acid phosphatases, HRP9, with an experimentally known structure and preferred substrate as an instance, we firstly predicted the 3 D-structure of HRP1 for subsequent analysis. Then, molecular docking was used to find the best protein interaction with a ligand existing in a set of possible substrates compiled from genome scale metabolic networks of A. thaliana based on binding energy, binding mode as well as the distance between phosphoric ester and cofactor, Mg²⁺, localized in the active site of HRP1. Molecular dynamics simulation ratified stable protein-ligand complex model. Our analysis predicted HRP1 preferably bind to pyridoxamine-5'-phosphate (PMP). Thus, it is deduced that the conversion of PMP to pyridoxamine must be catalyzed by HRP1. This procedure is expected to make a reliable pipeline to predict the enzymatic reactions catalyzed by acid phosphatases. Taken as a whole, it could be applicable for discovery of the interacting ligands, inhibitors as well as interacting proteins which limits lab works or used for gap filling in biosystems.Communicated by Ramaswamy H. Sarma.