Modares Journal of Biotechnology

Abstract  As a starch hydrolyzing enzyme, α-amylase is important in the food industry and ethanol and biofuel production. This research aims to investigate the production of α-amylase enzyme in microorganisms from different regions of Iran and microbial collection centers of Iran. The results showed that 183 out of 331 isolates screened had α-amylase activity. Of these, two strains isolated from agricultural soil, named AGR228 and AGR91, had the maximum halo-to-colony ratio. In addition, all the strains belonging to the microbial collection centers showed amylase activity, of which PTCC-1732 and PTCC-1156 showed the highest activity. The amount of amylase produced in PTCC-1732 and AGR228 was estimated to be 20.5 and 11.18 units/ml, respectively. The maximum activity of the amylase enzyme from PTCC-1732 is at 80°C, and  pH 10. In the case of AGR228, the maximum activity was measured at 60°C, and  pH 8.
 

Abstract Epidermal growth factor receptor (EGFR) is one of the most important tyrosine kinase receptors that plays a key role in regulating cellular processes and the progression of many cancers, including lung cancer. In this study, the effects of second-generation EGFR inhibitors, including Afatinib, Dacomitinib, and Neratinib, as well as the candidate drugs Canertinib and Poziotinib, on wild-type EGFR were investigated using molecular dynamics (MD) simulations. For this purpose, structural data were collected and analyzed from reliable databases. Molecular docking studies led to the identification of drug binding sites, and molecular dynamics (MD) simulations under physiological conditions investigated stability and ligand-protein interactions. The parameters such as RMSD, radius of gyration (Rg), SASA, and hydrogen bonds were calculated to evaluate the stability of the protein-ligand complex. The results of the MMPBSA analysis showed that Neratinib, with the lowest free energy of binding (ΔG), has a higher binding affinity to EGFR and demonstrated greater stability during the simulation. Also, the principal component analysis (PCA) showed that the EGFR-Neratinib complex has less dynamics and occupies less phase space, which indicates more stability of this complex.
These results show that of all the compounds studied, Neratinib may be the most potent and promising candidate in advancing combination therapies against EGFR.

Abstract Anaplastic lymphoma kinase (ALK) is an important target in the treatment of cancer, especially non-small cell lung cancer (NSCLC) with gene rearrangements. In this study, the effects of three tyrosine kinase inhibitors (TKIs) including Crizotinib, Ceritinib, and Alectinib on the ALK tyrosine kinase domain were investigated and compared using molecular dynamics (MD) simulations. Various analyses such as RMSD, radius of gyration (RG), solvent accessible surface area (SASA), hydrogen bond number (Hbond), principal component analysis (PCA), and MMPBSA were used in this study.
Molecular dynamics simulations for 100 ns showed that Alectinib resulted in structural stability, reduced solvent accessible surface area (SASA) compared to Crizotinib and Ceritinib, and greater protein compaction. MMPBSA results also indicated a lower binding free energy and higher binding affinity of Alectinib to ALK, which enhances its efficacy. The use of Alectinib, due to its high ability to cross the blood-brain barrier and its effect on brain metastases, can improve treatment efficacy and reduce drug resistance. These results and findings indicate that Alectinib can be an option for first-line treatment.

Abstract  L-glutaminases (L-glutamine amidohydrolase, 3.5.1.2) belong to the superfamily of serine-dependent β-lactamases and penicillin-binding proteins. L-glutaminases have received much attention in the last few decades due to their catalytic ability to deaminate glutamine to glutamic acid and ammonia; a property that has made them valuable in different industrial applications, especially in medicine. Research on glutaminase has progressed during the last four decades, though slowly in comparison to other industrially important enzymes. The relatively high cost of glutaminase is one of the major drawbacks hindering its industrial applications. The current production levels of glutaminase are also insufficient for the required clinical trials to facilitate its medical uses and for other applications.The purpose of this study is the solubilized recombinant expression and functional assay of L-glutaminase from Alteribacillus bidgolensis (P4BT) in an E. coli BL21 (DE3) expression system. In this study, the L-glutaminase gene was successfully cloned with the pET30a expression vector in the E. coli BL21 (DE3) expression system. Solubilized expression was achieved with the aid of the pG-KJE8 vector, which contains molecular chaperones. Ultimately, the specific activity of the purified and dialyzed enzyme was assessed at 40°C and pH 8, yielding an enzymatic activity of 0.53 ± 0.01 U/mg at a substrate concentration of 8 mM. The Km value for L-glutaminase was calculated at 3.10 mM, with a Vmax of 0.62 U/mg.
 

Abstract A critical aspect of laboratory techniques in plant in vitro culture involves the disinfection of explants and the maintenance of sterile environments. Ideally, disinfectants should demonstrate efficacy against a broad spectrum of microorganisms at minimal concentrations. However, the rising application of antibiotics has led to the development of microbial resistance to these agents. Currently, compounds such as essential oils and nanoparticles are being explored in microbiological research. This study aimed to evaluate the effectiveness of three antibiotic groups (cefazolin, penicillin, and chloramphenicol) alongside cumin essential oil and silver nanoparticles in managing in vitro contamination in potato plants (Solanum tuberosum L.). The explants underwent 1 min disinfection with 70% ethanol, followed by a 10 min treatment with a commercial bleach solution. Additional disinfection treatments, including cefazolin, chloramphenicol, penicillin, silver nanoparticles, and cumin essential oil, were applied at concentrations of 10, 20, 40, and 80 mg l-1 for nanoparticles, and 100 and 200 mg l-1 for the other agents in the cultivation medium. The findings indicated that cumin essential oil exhibited the highest level of microbial inhibition, while cefazolin showed the least. Notably, the application of nanosilver at 40 mg L-1 resulted in a 73% reduction in bacterial contamination, whereas penicillin at 100 mg L-1 achieved a 67% inhibition rate. Ultimately, essential oils and nanoparticles present viable alternatives to conventional chemical treatments for the elimination and management of in vitro contamination in plant explants under laboratory conditions.

Abstract Three-dimensional culture has many advantages, which can be briefly mentioned in cancer research, toxicity analysis, investigation and development of products such as vaccines, or production of monoclonal antibodies and recombinant proteins. One of the applied methods in monoclonal antibody production is the cultivation of hybridoma cells, which has undergone significant progress in recent years. Monoclonal antibodies have a variety of applications due to their high specificity, two of which can be mentioned as use in disease diagnosis and cancer treatment kits.
Bioreactors can help improve the efficiency of monoclonal antibody production by providing controlled conditions for three-dimensional cultivation of hybridoma cells, such as dissolved gases, pH, and nutrients. Also, studies show that the use of biological scaffolds and bioreactors can lead to the creation of more efficient models for pharmaceutical and medical research.
Regardless of other determining factors in choosing a monoclonal antibody production approach, "time" seems to be the most important factor at the beginning.
 

Abstract Non-biodegradable metals exhibit toxicity to living organisms at high concentrations. They enter aquatic systems through various pathways, including industrial activities, often in an uncontrolled manner. Given the role of bacteria as effective biosorbents, they can be employed for the removal of heavy metals from industrial wastewater. The aim of this study was to investigate the biosorption of lead and tellurite by Shinella zoogloeoides-DSM287 and assess its potential for nanoparticle biosynthesis.
In this study, the biosorption of lead and tellurite was evaluated under various conditions, including different pH levels, temperatures, initial metal concentrations, and incubation times. Subsequently, the potential formation of nanoparticles during the biosorption process was investigated, and the presence of nanoparticles was confirmed using various analytical techniques. According to the results, Shinella zoogloeoides-DSM287 exhibited resistance to lead and tellurite, with MICs of 2400 µg/mL and 100 µg/mL, respectively. Furthermore, the bacterium acted as an efficient biosorbent for lead and tellurite ions under alkaline conditions, at temperatures between 28–32 °C, and across different incubation periods. Additionally, the bacterium was able to convert tellurite ions into tellurium nanoparticles during the biosorption process. DLS and zeta potential analyses revealed that the biosynthesized nanoparticles had an average size of approximately 150 nm and a surface charge of −34 mV. Transmission Electron Microscopy (TEM) showed the nanoparticles to be irregularly spherical in shape. In summary, the studied bacterium demonstrated a potential for the biosorption of lead and tellurite and could be considered a promising candidate for the removal of these contaminants from aquatic environments.
 

Abstract Phosphorylation is one of the most important types of post-translational modification (PTM) that plays an important role in protein function studies and experimental design. Considering the importance of phosphorylation in proteins and the increasing number of protein sequences in the database, the need to improve computational methods for predicting phosphorylation sites becomes more important day by day in terms of speed and accuracy. Although many predictive tools have been introduced to predict phosphorylation sites using different machine learning methods, there is still a long way to go to a very efficient tool and efforts to achieve such a tool continue. Recent studies have shown that deep learning-based methods are the best approach for predicting phosphorylation sites. This is because deep learning, as an advanced machine learning method, can automatically recognize complex representations of phosphorylation patterns from raw sequences, therefore providing a powerful tool for improved phosphorylation site prediction.
In this study, a hybrid structure based on the convolutional deep learning method named ConvoPhos has been introduced for predicting phosphorylation sites. In such a way that the CkSAApair feature vector obtained from sequences is used as the input for part of the classifier and the conversion of sequences to images, as the input for another part of the convolutional networks.The results of 10-fold cross-validation show an accuracy value of 94% for phosphosite data and an AUC of 90%, which is the highest performance compared to the other methods.