In silico approaches to investigate enzyme immobilization: a comprehensive systematic review.

Enzymes are popular catalysts with many applications, especially in industry. Biocatalyst usage on a large scale is facing some limitations, such as low operational stability, low recyclability, and high enzyme cost. Enzyme immobilization is a beneficial strategy to solve these problems. Bioinformatics tools can often correctly predict immobilization outcomes, resulting in a cost-effective experimental phase with the least time consumed. This study provides an overview of in silico methods predicting immobilization processes via a comprehensive systematic review of published articles till 11 December 2022. It also mentions the strengths and weaknesses of the processes and explains the computational analyses in each method that are required for immobilization assessment. In this regard, Web of Science and Scopus databases were screened to gain relevant publications. After screening the gathered documents (n = 3873), 60 articles were selected for the review. The selected papers have applied in silico procedures including only molecular dynamics (MD) simulations (n = 20), parallel tempering Monte Carlo (PTMC) and MD simulations (n = 3), MD and docking (n = 1), density functional theory (DFT) and MD (n = 1), only docking (n = 11), metal ion binding site prediction (MIB) server and docking (n = 2), docking and DFT (n = 1), docking and analysis of enzyme surfaces (n = 1), only DFT (n = 1), only MIB server (n = 2), analysis of an enzyme structure and surface (n = 12), rational design of immobilized derivatives (RDID) software (n = 3), and dissipative particle dynamics (DPD; n = 2). In most included studies (n = 51), enzyme immobilization was investigated experimentally in addition to in silico evaluation.

Apple pectin-based Zataria multiflora essential oil (ZEO) nanoemulsion: An approach to enhance ZEO DNA damage induction in breast cancer cells as in vitro and in silico studies reveal.

Zataria multiflora essential oil (ZEO) is a natural complex of compounds with a high apoptotic potential against breast cancer cells and minor toxicity toward normal cells; however, similar to many essential oils, ZEO utilization in pharmaceutical industries has limitations due to its labile and sensitive ingredients. Nanoemulsification based on natural polymers is one approach to overcome this issue. In this study, an apple pectin-ZEO nanoemulsion (AP-ZEONE) was prepared and its morphology, FTIR spectra, and physical properties were characterized. Furthermore, it was shown that AP-ZEONE substantially suppresses the viability of MDA-MB-231, T47D, and MCF-7 breast cancer cells. AP-ZEONE significantly induced apoptotic morphological alterations and DNA fragmentation as confirmed by fluorescent staining and TUNEL assay. Moreover, AP-ZEONE induced apoptosis in MDA-MB-231 cells by loss of mitochondrial membrane potential (ΔΨm) associated with the accumulation of reactive oxygen species (ROS), G2/M cell cycle arrest, and DNA strand breakage as flow cytometry, DNA oxidation, and comet assay analysis revealed, respectively. Spectroscopic and computational studies also confirmed that AP-ZEONE interacts with genomic DNA in a minor groove/partial intercalation binding mode. This study demonstrated the successful inhibitory effect of AP-ZEONE on metastatic breast cancer cells, which may be beneficial in the therapy process.

Spectrum of deep learning algorithms in drug discovery.

Deep learning (DL) algorithms are a subset of machine learning algorithms with the aim of modeling complex mapping between a set of elements and their classes. In parallel to the advance in revealing the molecular bases of diseases, a notable innovation has been undertaken to apply DL in data/libraries management, reaction optimizations, differentiating uncertainties, molecule constructions, creating metrics from qualitative results, and prediction of structures or interactions. From source identification to lead discovery and medicinal chemistry of the drug candidate, drug delivery, and modification, the challenges can be subjected to artificial intelligence algorithms to aid in the generation and interpretation of data. Discovery and design approach, both demand automation, large data management and data fusion by the advance in high-throughput mode. The application of DL can accelerate the exploration of drug mechanisms, finding novel indications for existing drugs (drug repositioning), drug development, and preclinical and clinical studies. The impact of DL in the workflow of drug discovery, design, and their complementary tools are highlighted in this review. Additionally, the type of DL algorithms used for this purpose, and their pros and cons along with the dominant directions of future research are presented.

Emerging biosensors in detection of natural products.

Natural products (NPs) are a valuable source in the food, pharmaceutical, agricultural, environmental, and many other industrial sectors. Their beneficial properties along with their potential toxicities make the detection, determination or quantification of NPs essential for their application. The advanced instrumental methods require time-consuming sample preparation and analysis. In contrast, biosensors allow rapid detection of NPs, especially in complex media, and are the preferred choice of detection when speed and high throughput are intended. Here, we review diverse biosensors reported for the detection of NPs. The emerging approaches for improving the efficiency of biosensors, such as microfluidics, nanotechnology, and magnetic beads, are also discussed. The simultaneous use of two detection techniques is suggested as a robust strategy for precise detection of a specific NP with structural complexity in complicated matrices. The parallel detection of a variety of NPs structures or biological activities in a mixture of extract in a single detection phase is among the anticipated future advancements in this field which can be achieved using multisystem biosensors applying multiple flow cells, sensing elements, and detection mechanisms on miniaturized folded chips.

Biochemical and computational study of an alginate lyase produced by Pseudomonas aeruginosa strain S21.

OBJECTIVES: Alginates play a key role in mucoid Pseudomonas aeruginosa colonization, biofilm formation, and driving out of cationic antibiotics. P. aeruginosa alginate lyase (AlgL) is a periplasmic enzyme that is necessary for alginate synthesis and secretion. It also has a role in depolymerization of alginates. Using AlgLs in cystic fibrosis patients along with antibiotics enhances bacterial killing and host healing. In this study, we investigated the different biochemical properties of a newly isolated AlgL from P. aeruginosa S21 to complete the databank of AlgLs. MATERIALS AND METHODS: The enzyme was extracted from the periplasmic space of the bacteria by the heat shock method. Using the TBA method, the enzyme activity and biochemical properties were assessed. The mutability of P. aeruginosa S21 AlgL to increase its thermal stability was investigated. The most favorable mutations were studied computationally. The molecular dynamics simulation (MDS) package GROMACS was used for determining the effect of S34R mutation on enzyme's thermal stability. RESULTS: Data showed that this enzyme has the best activity at 37 °C and pH 7.5 and it can degrade mannuronate blocks, guluronate blocks, and sodium alginate. After 7 hr at 80 °C, 45% of the enzyme activity was retained. This enzyme needed 15 min to completely degrade accessible sodium alginate. Tris buffer, pH 8.5 and Britton-Robinson buffer, pH 7.0 were the preferable buffers for the enzyme activity. MDS of native and mutated enzymes showed desirable results. CONCLUSION: P. aeruginosa S21 AlgL can be used in medical and industrial applications to degrade alginates.