In silico identification and characterization of antineoplastic asparaginase enzyme from endophytic bacteria

An immunoinformatic approach employing molecular docking and molecular dynamics simulation for evaluation of l-asparaginase produced by Bacillus velezensis

l-Asparaginase is one of the most important treatments for acute lymphoblastic leukemia. In this study, l-asparaginase-producing bacteria were isolated from the effluent and soil of the Isfahan slaughterhouse using M9 specific medium. Isolates were identified by 16SrRNA phylogenetic analysis. The immune characteristics were predicted. Molecular docking was performed between l-asparaginase and l-asparagine substrate using AutoDock tools 4.2 and AutoDock Vina. Molecular dynamics simulation studies were fulfilled using GROMACS. Five l-asparaginase-producing bacteria isolated that belonging to Stenotrophomonas maltophilia, Chryseobacterium sp. Chryseobacterium indologenes, Bacillus velezensis and Bacillus safensis. Predictions showed B. velezensis has better immune characteristics than B. safensis. The binding energies of the docked complex were calculated to be -4.34 and -4.9 kcal/mol. Molecular docking confirmed the interaction of l-asparaginase with its substrate. It was observed that the residues Thr36, Tyr50, Ala47, Thr116, Asp117, Met142, Thr193 and Thr192 were fundamental in protein-ligand complexation. Also, RMSD, RMSF, Rg, DSSP, SASA and MM-PBSA analysis showed that when l-asparaginase is bound to l-asparagine, it did not lose stability, secondary structure and compactness. Slaughterhouse soils and effluents are a potential source of l-asparaginase-producing bacteria that probably can probably produce l-asparaginase with more favorable immune properties than commercial enzymes.Communicated by Ramaswamy H. Sarma.

CD8TCEI-EukPath: A Novel Predictor to Rapidly Identify CD8+ T-Cell Epitopes of Eukaryotic Pathogens Using a Hybrid Feature Selection Approach

Computational prediction to screen potential vaccine candidates has been proven to be a reliable way to provide guarantees for vaccine discovery in infectious diseases. As an important class of organisms causing infectious diseases, pathogenic eukaryotes (such as parasitic protozoans) have evolved the ability to colonize a wide range of hosts, including humans and animals; meanwhile, protective vaccines are urgently needed. Inspired by the immunological idea that pathogen-derived epitopes are able to mediate the CD8⁺ T-cell-related host adaptive immune response and with the available positive and negative CD8⁺ T-cell epitopes (TCEs), we proposed a novel predictor called CD8TCEI-EukPath to detect CD8⁺ TCEs of eukaryotic pathogens. Our method integrated multiple amino acid sequence-based hybrid features, employed a well-established feature selection technique, and eventually built an efficient machine learning classifier to differentiate CD8⁺ TCEs from non-CD8⁺ TCEs. Based on the feature selection results, 520 optimal hybrid features were used for modeling by utilizing the LightGBM algorithm. CD8TCEI-EukPath achieved impressive performance, with an accuracy of 79.255% in ten-fold cross-validation and an accuracy of 78.169% in the independent test. Collectively, CD8TCEI-EukPath will contribute to rapidly screening epitope-based vaccine candidates, particularly from large peptide-coding datasets. To conduct the prediction of CD8⁺ TCEs conveniently, an online web server is freely accessible (http://lab.malab.cn/∼hrs/CD8TCEI-EukPath/).

Expanding the bio-catalysis scope and applied perspectives of nanocarrier immobilized asparaginases

l-asparaginase is an essential enzyme in medicine and a well-known chemotherapeutic agent. This enzyme's importance is not limited to its use as an anti-cancer agent; it also has a wide variety of medicinal applications. Antimicrobial properties, prevention of infectious disorders, autoimmune diseases, and canine and feline cancer are among the applications. Apart from the healthcare industry, its importance has been identified in the food industry as a food manufacturing agent to lower acrylamide levels. When isolated from their natural habitats, they are especially susceptible to different denaturing conditions due to their protein composition. The use of an immobilization technique is one of the most common approaches suggested to address these limitations. Immobilization is a technique that involves fixing enzymes to or inside stable supports, resulting in a heterogeneous immobilized enzyme framework. Strong support structures usually stabilize the enzymes' configuration, and their functions are maintained as a result. In recent years, there has been a lot of curiosity and focus on the ability of immobilized enzymes. The nanomaterials with ideal properties can be used to immobilize enzymes to regulate key factors that determine the efficacy of bio-catalysis. With applications in biotechnology, immunosensing, biomedicine, and nanotechnology sectors have opened a realm of opportunities for enzyme immobilization.

Prebiotic, Probiotic, Antimicrobial, and Functional Food Applications of Bacillus amyloliquefaciens

Bacillus amyloliquefaciens belongs to the genus Bacillus and family Baciliaceae. It is ubiquitously found in food, plants, animals, soil, and in different environments. In this review, the application of B. amyloliquefaciens in probiotic and prebiotic microbes in fermentation, synthesis, and hydrolysis of food compounds is discussed as well as further insights into its potential application and gaps. B. amyloliquefaciens is also a potential microbe in the synthesis of bioactive compounds including peptides and exopolysaccharides. In addition, it can synthesize antimicrobial compounds (e.g., Fengycin, and Bacillomycin Lb), which makes its novelty in the food sector greater. Moreover, it imparts and improves the functional, sensory, and shelf life of the end products. The hydrolysis of complex compounds including insoluble proteins, carbohydrates, fibers, hemicellulose, and lignans also shows that B. amyloliquefaciens is a multifunctional and potential microbe which can be applied in the food industry and in functional food processing.

Amino Acids Sequence-based Analysis of Arginine Deiminase from Different Prokaryotic Organisms: An In Silico Approach

BACKGROUND

Arginine deiminase is a bacterial enzyme, which degrades L-arginine. Some human cancers such as hepatocellular carcinoma (HCC) and melanoma are auxotrophic for arginine. Therefore, PEGylated arginine deiminase (ADI-PEG20) is a good anticancer candidate with antitumor effects. It causes local depletion of L-arginine and growth inhibition in arginineauxotrophic tumor cells. The FDA and EMA have granted orphan status to this drug. Some recently published patents have dealt with this enzyme or its PEGylated form.

OBJECTIVE

Due to increasing attention to it, we aimed to evaluate and compare 30 arginine deiminase proteins from different bacterial species through in silico analysis.

METHODS

The exploited analyses included the investigation of physicochemical properties, multiple sequence alignment (MSA), motif, superfamily, phylogenetic and 3D comparative analyses of arginine deiminase proteins thorough various bioinformatics tools.

RESULTS

The most abundant amino acid in the arginine deiminase proteins is leucine (10.13%) while the least amino acid ratio is cysteine (0.98%). Multiple sequence alignment showed 47 conserved patterns between 30 arginine deiminase amino acid sequences. The results of sequence homology among 30 different groups of arginine deiminase enzymes revealed that all the studied sequences located in amidinotransferase superfamily. Based on the phylogenetic analysis, two major clusters were identified. Considering the results of various in silico studies; we selected the five best candidates for further investigations. The 3D structures of the best five arginine deiminase proteins were generated by the I-TASSER server and PyMOL. The RAMPAGE analysis revealed that 81.4%-91.4%, of the selected sequences, were located in the favored region of arginine deiminase proteins.

CONCLUSION

The results of this study shed light on the basic physicochemical properties of thirty major arginine deiminase sequences. The obtained data could be employed for further in vivo and clinical studies and also for developing the related therapeutic enzymes.