生物设计研究(英文)最新文献

Proteins are the fundamental building blocks of nature, constructing complex molecular machines and dynamic materials. They form the protein complexes that drive life cycles and the cellular skeletal components and muscle fibers responsible for movements. Due to their extensive molecular diversity, many biomedical and industrial challenges associated with natural proteins remain unsolved. This review presents a comprehensive analysis of the structure and function of fibrous proteins, elastin, and mucins, emphasizing their roles as protein materials. It also explores their diverse applications across food, environmental, and biomedical sectors. Additionally, we focus on strategies for optimizing protein structure, including chemical modifications and molecular design, by comparing current design methods and software to summarize recent technological developments. Finally, we explore the challenges and prospects of applying artificial intelligence to complex protein structure design. This application has the potential to advance the development and application of intricate protein materials, address functional defects or instability in biomedicine, and enhance our understanding of natural protein mechanisms. These interdisciplinary collaborations will pave the way for designing new multifunctional proteins.

Fermentation optimization is important for industrialization of biological manufacturing, and has been widely applied to diverse sectors including medicine, food, cosmetics and bioenergy, which is related to substantial economic benefits. Strain development is considered to be the core part of fermentation technology, as it directly influences the product yield and overall success of the fermentation process. However, fermentation design and process optimization also play a crucial role in fully exploring the genetic potential of engineered strains for efficient bioproduction. Due to the fact that fermentation process is influenced by complex factors, so far, machine learning has been widely used in this area with its strong capabilities of simulation and prediction. This review provides a brief introduction to the process of fermentation design and process optimization based on machine learning. In the workflow, experimental design strategy is fundamental to explore and characterize the performance of fermentation system. Then, machine learning modelling is employed to simulate the operation of fermentation system and the appropriate fermentation conditions, such as medium composition and process parameters, will be determined. Moreover, in recent years, some extension ideas of fermentation design based on machine learning have also been proposed, including automated fermentation process control, data mining for exploring strain characteristics, transfer learning, hybrid model building, and soft sensor construction. These strategies have created more application scenarios for machine learning, enhancing its adaptability in designing and optimizing the complex fermentation system for efficient bioproduction.

As an essential industrial microorganism, Corynebacterium glutamicum has been employed in amino acids production with a long history. Recent progress in its metabolic engineering has accelerated the establishment and optimization of this species as cell factories, making it another unique chassis. In this comprehensive review, we first highlight the progress made in the metabolic engineering of C. glutamicum to broaden its substrate spectrum, including sugars in lignocellulosic hydrolysate, cheap glycerol, and one-carbon compounds. Furthermore, we discuss the development of C. glutamicum as cell factories to produce various amino acids, with a focus on bulk, branched-chain and aromatic amino acids, organic acids such as succinic acid, lactic acid, and shikimic acid, and terpenoids. Finally, potential challenges faced by engineering C. glutamicum cell factories when using non-model feedstocks for biochemical production are discussed, focusing on stress tolerance, non-model substrate utilization, and the design of multifunctional cell factories, along with envisioned directions for future research.

Photosynthesis is the main process by which biomass of living matter is created from inorganic carbon. However, the efficiency of photosynthesis is low. The main reason for the low efficiency of photosynthesis is the properties of the enzyme that fixes carbon dioxide - ribulose-1,5-bisphosphate carboxylase/oxidase (RuBisCO). There are reasons to believe that it is impossible to improve the properties of RuBisCO. It is apparently possible to significantly increase the efficiency of photosynthesis only by replacing RuBisCO with another enzyme. Among the known enzymes capable of fixing CO₂, there is only one enzyme whose kinetic properties exceed those of RuBisCO, this is phosphoenolpyruvate carboxylase (PEPC). However, plants using PEPC are not able to use it as the only carbon dioxide fixer and to do without RubisCO. The cause for this is the absence of a metabolic cycle reproducing the substrate PEPC from its product. Advances in techniques of genetic, metabolic, protein and enzymes engineering make it possible to accomplish tasks that were previously considered impossible. Arren Bar-Even et al. suggested a hypothetical MOG cycle (from malonyl-CoA-oxaloacetate-glyoxylate) for the reproduction of PEPC substrate from its product, which should make it possible to abandon the use of RuBisCO and replace it with PEPC, which is expected to increase the efficiency of plant photosynthesis. This review analyzes the properties of the MOG cycle, and also suggests options for its modifications by adding other metabolic pathways. The possibility of further increasing the efficiency of photosynthesis through CO₂ fixation at night is also being discussed.

[This corrects the article DOI: 10.34133/bdr.0026.].

Multiomics research is a transformative approach in the biological sciences that integrates data from genomics, transcriptomics, proteomics, metabolomics, and other omics technologies to provide a comprehensive understanding of biological systems. This review elucidates the fundamental principles of multiomics, emphasizing the necessity of data integration to uncover the complex interactions and regulatory mechanisms underlying various biological processes. We explore the latest advances in computational methodologies, including deep learning, graph neural networks (GNNs), and generative adversarial networks (GANs), which facilitate the effective synthesis and interpretation of multiomics data. Additionally, this review addresses the critical challenges in this field, such as data heterogeneity, scalability, and the need for robust, interpretable models. We highlight the potential of large language models to enhance multiomics analysis through automated feature extraction, natural language generation, and knowledge integration. Despite the important promise of multiomics, the review acknowledges the substantial computational resources required and the complexity of model tuning, underscoring the need for ongoing innovation and collaboration in the field. This comprehensive analysis aims to guide researchers in navigating the principles and challenges of multiomics research to foster advances in integrative biological analysis.

Terpenes are natural secondary metabolites with isoprene as the basic structural unit; they are widely found in nature and have potential applications as advanced fuels, pharmaceutical ingredients, and agricultural chemicals. However, traditional methods are inefficient for obtaining terpenes because of complex processes, low yields, and environmental unfriendliness. The unconventional oleaginous yeast Yarrowia lipolytica, with a clear genetic background and complete gene editing tools, has attracted increasing attention for terpenoid synthesis. Here, we review the synthetic biology tools for Y. lipolytica, including promoters, terminators, selection markers, and autonomously replicating sequences. The progress and emerging trends in the metabolic engineering of Y. lipolytica for terpenoid synthesis are further summarized. Finally, potential future research directions are envisioned.

Dihydroxy acid dehydratase (DHAD) is the third enzyme in the plant branched-chain amino acid biosynthetic pathway and the target for commercial herbicide development. We have previously reported the discovery of fungal natural product aspterric acid (AA) as a submicromolar inhibitor of DHAD through self-resistance gene directed genome mining. Here, we reveal the mechanism of AA inhibition on DHAD and the self-resistance mechanism of AstD, which is encoded by the self-resistance gene astD. As a competitive inhibitor, the hydroxycarboxylic acid group of AA mimics the binding of the natural substrate of DHAD, while the hydrophobic moiety of AA occupies the substrate entrance cavity. Compared to DHAD, AstD has a relatively narrow substrate channel to prevent AA from binding. Several mutants of DHAD were generated and assayed to validate the self-resistance mechanism and to confer Arabidopsis thaliana DHAD with AA resistance. These results will lead to the engineering of new type of herbicides targeting DHAD and provide direction for the ecological construction of herbicide-resistant crops.

Microbial cell factories (MCFs) are extensively used to produce a wide array of bioproducts, such as bioenergy, biochemical, food, nutrients, and pharmaceuticals, and have been regarded as the "chips" of biomanufacturing that will fuel the emerging bioeconomy era. Biotechnology advances have led to the screening, investigation, and engineering of an increasing number of microorganisms as diverse MCFs, which are the workhorses of biomanufacturing and help develop the bioeconomy. This review briefly summarizes the progress and strategies in the development of robust and efficient MCFs for sustainable and economic biomanufacturing. First, a comprehensive understanding of microbial chassis cells, including accurate genome sequences and corresponding annotations; metabolic and regulatory networks governing substances, energy, physiology, and information; and their similarity and uniqueness compared with those of other microorganisms, is needed. Moreover, the development and application of effective and efficient tools is crucial for engineering both model and nonmodel microbial chassis cells into efficient MCFs, including the identification and characterization of biological parts, as well as the design, synthesis, assembly, editing, and regulation of genes, circuits, and pathways. This review also highlights the necessity of integrating automation and artificial intelligence (AI) with biotechnology to facilitate the development of future customized artificial synthetic MCFs to expedite the industrialization process of biomanufacturing and the bioeconomy.