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Background:The evolution of antibiotic-resistant bacteria (ARB) and antibiotic-resistance genes (ARGs) has been accelerated recently by the indiscriminate application of antibiotics. Antibiotic resistance has challenged the success of medical interventions and therefore is considered a hazardous threat to human health.Objectives:The present study aimed to describe the use of zinc finger nuclease (ZFN) technology to target and disrupt a plasmid-encoded β-lactamase, which prevents horizontal gene transfer-mediated evolution of ARBs.Materials and Methods:An engineered ZFN was designed to target a specific sequence in the ampicillin resistance gene (ampR) of the pTZ57R plasmid. The Escherichia coli bacteria already contained the pZFN kanamycin-resistant (kanaR) plasmid as the case or the pP15A, kanaR empty vector as the control, were transformed with the pTZ57R; the ability of the designed ZFN to disrupt the β-lactamase gene was evaluated with the subsequent disturbed ability of the bacteria to grow on ampicillin (amp) and ampicillin-kanamycin (amp-kana)-containing media. The effect of mild hypothermia on the ZFN gene targeting efficiency was also evaluated.Results:The growth of bacteria in the case group on the amp and amp-kana-containing media was significantly lower compared with the control group at 37°C (P < 0.001). Despite being more efficient in hypothermic conditions at 30°C (P < 0.001), there were no significant associations between the incubation temperature and the ZFN gene targeting efficiency.Conclusions:Our findings revealed that the ZFN technology could be employed to overcome ampicillin resistance by the targeted disruption of the ampicillin resistance gene, which leads to inactivation of β-lactam synthesis. Therefore, ZFN technology could be engaged to decrease the antibiotic resistance issue with the construction of a ZFN archive against different ARGs. To tackle the resistance issue at the environmental level, recombinant phages expressing ZFNs against different ARGs could be constructed and released into both hospital and urban wastewater systems.

<b><i>Background:</i></b> A large number of allergens are derived from plant and animal proteins. A major challenge for researchers is to study the possible allergenic properties of proteins. The aim of this study was in silico analysis and comparison of several physiochemical and structural features of plant- and animal-derived allergen proteins, as well as classifying these proteins based on Chou’s pseudo-amino acid composition (PseAAC) concept combined with bioinformatics algorithms. <b><i>Methods:</i></b> The physiochemical properties and secondary structure of plant and animal allergens were studied. The classification of the sequences was done using the PseAAC concept incorporated with the deep learning algorithm. Conserved motifs of plant and animal proteins were discovered using the MEME tool. B-cell and T-cell epitopes of the proteins were predicted in conserved motifs. Allergenicity and amino acid composition of epitopes were also analyzed via bioinformatics servers. <b><i>Results:</i></b> In comparison of physiochemical features of animal and plant allergens, extinction coefficient was different significantly. Secondary structure prediction showed more random coiled structure in plant allergen proteins compared with animal proteins. Classification of proteins based on PseAAC achieved 88.24% accuracy. The amino acid composition study of predicted B- and T-cell epitopes revealed more aliphatic index in plant-derived epitopes. <b><i>Conclusions:</i></b> The results indicated that bioinformatics-based studies could be useful in comparing plant and animal allergens.

Post translational modification (PTM) is one of the critical levels in regulation of gene expression that determines the fate of proteins after translation in eukaryotic cells. Since the detection of PTM sites in proteins is useful for diagnosis and prevention of diseases, numerous web-tool predictors are introduced to predict various types of PTMs. In this paper, an attempt is made to summarize a number of server predictors for the prediction of PTM sites.