Starch-Binding Domain Modulates the Specificity of Maltopentaose Production at Moderate Temperatures

Carbohydrate binding modules: Compact yet potent accessories in the specific substrate binding and performance evolution of carbohydrate-active enzymes

Carbohydrate binding modules (CBMs) are independent non-catalytic domains widely found in carbohydrate-active enzymes (CAZymes), and they play an essential role in the substrate binding process of CAZymes by guiding the appended catalytic modules to the target substrates. Owing to their precise recognition and selective affinity for different substrates, CBMs have received increasing research attention over the past few decades. To date, CBMs from different origins have formed a large number of families that show a variety of substrate types, structural features, and ligand recognition mechanisms. Moreover, through the modification of specific sites of CBMs and the fusion of heterologous CBMs with catalytic domains, improved enzymatic properties and catalytic patterns of numerous CAZymes have been achieved. Based on cutting-edge technologies in computational biology, gene editing, and protein engineering, CBMs as auxiliary components have become portable and efficient tools for the evolution and application of CAZymes. With the aim to provide a theoretical reference for the functional research, rational design, and targeted utilization of novel CBMs in the future, we systematically reviewed the function-related characteristics and potentials of CAZyme-derived CBMs in this review, including substrate recognition and binding mechanisms, non-catalytic contributions to enzyme performances, module modifications, and innovative applications in various fields.

Multidomain chimeric enzymes as a promising alternative for biocatalysts improvement: a minireview

Searching for new and better biocatalysts is an area of study in constant development. In nature, mechanisms generally occurring in evolution, such as genetic duplication, recombination, and natural selection processes, produce various enzymes with different architectures and properties. The recombination of genes that code proteins produces multidomain chimeric enzymes that contain two or more domains that sometimes enhance their catalytic properties. Protein engineering has mimicked this process to enhance catalytic activity and the global stability of enzymes, searching for new and better biocatalysts. Here, we present and discuss examples from both natural and synthetic multidomain chimeric enzymes and how additional domains heighten their stability and catalytic activity. Moreover, we also describe progress in developing new biocatalysts using synthetic fusion enzymes and revise some methodological strategies to improve their biological fitness.

Starch-Derived Superabsorbent Polymer in Remediation of Solid Waste Sludge Based on Water–Polymer Interaction

The purpose of this study is to assess water–polymer interaction in synthesized starch-derived superabsorbent polymer (S-SAP) for the treatment of solid waste sludge. While S-SAP for solid waste sludge treatment is still rare, it offers a lower cost for the safe disposal of sludge into the environment and recycling of treated solid as crop fertilizer. For that to be possible, the water–polymer interaction on S-SAP must first be fully comprehended. In this study, the S-SAP was prepared through graft polymerization of poly (methacrylic acid-co-sodium methacrylate) on the starch backbone. By analyzing the amylose unit, it was possible to avoid the complexity of polymer networks when considering S-SAP using molecular dynamics (MD) simulations and density functional theory (DFT). Through the simulations, formation of hydrogen bonding between starch and water on the H06 of amylose was assessed for its flexibility and less steric hindrance. Meanwhile, water penetration into S-SAP was recorded by the specific radial distribution function (RDF) of atom–molecule interaction in the amylose. The experimental evaluation of S-SAP correlated with high water capacity by measuring up to 500% of distilled water within 80 min and more than 195% of the water from solid waste sludge for 7 days. In addition, the S-SAP swelling showed a notable performance of a 77 g/g swelling ratio within 160 min, while a water retention test showed that S-SAP was capable of retaining more than 50% of the absorbed water within 5 h of heating at 60 °C. The water retention of S-SAP adheres to pseudo-second-order kinetics for chemisorption reactions. Therefore, the prepared S-SAP might have potential applications as a natural superabsorbent, especially for the development of sludge water removal technology.

Applications of machine learning in computer-aided drug discovery

Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.