The brain extracellular matrix (ECM) is a highly glycosylated environment and plays important roles in many processes including cell communication, growth factor binding, and scaffolding. ![]() dentata proteome during different harvest periods, improves the information database construction and provides a framework for future research based on a comprehensive understanding. Our research integrated the global protein database, the first time bioinformatic analysis of the P. Potential bioactive peptides, protein structure, and potential ligand conformations were predicted, and the results suggest that bioactive peptides may be utilized as high-quality active fermentation substances and potential targets for drug production. Bioinformatics was used to study protein characteristics, functional expression, and interaction of two important functional annotations, amino acid, and carbohydrate metabolism. A total of 13,046 different peptides were identified and 419 co-expression target proteins were characterized. In this study, label-free shotgun proteomics was first applied to identify and characterize different harvest proteins in P. dentata is rich in bioactive substances and is a potential natural resource. ![]() It is widely cultivated and consumed in East Asia and has vast economic benefits. Porphyra dentata is an edible red seaweed with high nutritional value. More recently, with ever increasing amounts of data from, for example, advanced mutli-omics experiments, machine learning approaches have begun to make important contributions in synthetic biology and optimization of metabolic pathways for production of biofuels and chemicals. Physics-based modeling approaches such as density functional theory calculations and molecular dynamics simulations have been most impactful in studies aimed at exploring the molecular level details of solvent-biomass interactions, reaction mechanisms occurring in biomass-solvent systems, and the catalytic mechanisms and engineering of enzymes involved in biomass degradation. This manuscript surveys the latest developments in lignocellulosic biomass valorization with special attention given to highlighting computational approaches used in process optimization for lignocellulose pretreatment enzyme engineering for enhanced saccharification and delignification and prediction of the genome modification necessary for desired pathway fine-tuning to upgrade products from biomass deconstruction into value-added products. Herein, we advocate that advanced in silico approaches provide a theoretical framework for developing efficient processes for lignocellulosic biomass valorization and maximizing yields of sugars and lignin fragments during deconstruction and fuel and chemical titers during conversion. Over the past couple of decades, a significant amount of work has been done to develop innovative biomass deconstruction and conversion processes that efficiently solubilize biomass, separate lignin from the biomass, maximize yields of bioavailable sugars and lignin fragments and convert the majority of these carbon sources into fuels, commodity chemicals, and materials. ![]() Deconstruction is followed by a conversion step in which engineered host organisms assimilate the released sugar monomers and lignin fragments, and produce value-added fuels and chemicals. Biomass is first pretreated and deconstructed using chemical catalysts and/or enzymes to liberate sugar monomers and lignin fragments. Biorefinery processes for converting lignocellulosic biomass to fuels and chemicals proceed via an integrated series of steps.
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