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Recent advances in photoenzymatic synthesis Yang MING, Bin CHEN, Xiaoqiang HUANG Synthetic Biology Journal    2023, 4 (4): 651-675.   DOI: 10.12211/2096-8280.2022-056 Abstract （2909）   HTML （269）    PDF（pc） （5785KB）（2315）       Knowledge map    Save Biocatalysis has the advantages in terms of sustainability, efficiency, selectivities and evolvability, thus it plays a more and more important role in green and sustainable synthesis, both in industrial production and academic research. However, compared with the well-known privileged chemocatalysts, enzymes suffer from the relatively limited types of reactions it can catalyze, which is unable to meet the future needs of green biomanufacturing. On the other hand, photocatalysis has emerged as one of the most effective strategies for the generation of reactive chemical intermediates under mild conditions, thereby providing a fertile testing ground for inventing new chemistry. However, the light-generated organic intermediates, including radicals, radical ions, ions, as well as excited states, are highly reactive resulting in the difficulties of controlling the chemo- and stereo-selectivities. The integration of biocatalysis and photocatalysis created a cross-disciplinary area, namely photoenzymatic catalysis, which can not only provide a new solution to stereochemical control of photochemical transformations with the exquisite and tunable active site of enzymes, but also open a new avenue to expand the reactivity of enzymes with visible-light-excitation. In addition, photoenzymatic catalysis inherits the inherent advantages of biocatalysis and photocatalysis, such as mild reaction conditions, representing green and sustainable synthetic methods. We have witnessed the booming development of photoenzymatic catalysis during the past several years. In this review paper, the recent advances in this field are highlighted. According to the cooperative modes of photocatalysis and enzymes, this paper is divided into following four parts: photoredox-enabled cofactor regeneration systems, cascade/cooperative reactions combining enzymes with photocatalysts, unnatural transformations with photoactivable oxidoreductase, and artificial photoenzymes. In this paper, we summarize the representative works and emphasize on the catalytic mechanisms of photoenzymatic transformations as well as the strategies for realizing abiological transformations. At the end of this review, by analyzing the challenges of photoenzymatic synthesis, the future directions are prospected. We hope that this review can inspire the discovery of more novel photoenzymatic systems and ultimately spur the applications of photoenzymes in industrial productions of high value-added enantiopure chiral products. {L-End} Table and Figures | Reference | Related Articles | Metrics

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Enzyme engineering in the age of artificial intelligence Liqi KANG, Pan TAN, Liang HONG Synthetic Biology Journal    2023, 4 (3): 524-534.   DOI: 10.12211/2096-8280.2023-009 Abstract （2702）   HTML （296）    PDF（pc） （1310KB）（2032）       Knowledge map    Save Enzymes have garnered significant attention in both research and industry due to their unparalleled specificity and functionality, and thus opportunities remain for enhancing their physichemical properties and fitness to improve catalytic performance. The primary objective of enzyme engineering is to optimize the fitness of targeted enzymes through various strategies for their modifications, even redesigning. This review provides a comprehensive overview for progress made in enzyme engineering, with a focus on artificial intelligence (AI)-guided design methodology. Several key strategies have been employed in enzyme engineering, including rational design, directed evolution, semi-rational design, and AI-guided design. Rational design relies on an extensive knowledge based on encompassing protein structures and catalytic mechanisms, allowing for purposeful manipulations of enzyme properties. Directed evolution, on the other hand, involves the generation of a library of random variants for subsequent high-throughput screening to identify beneficial mutations. Semi-rational design combines rational design and directed evolution, resulting in a smaller, yet more targeted, library of variants, which mitigates high cost associated with extensive screening of large libraries developed through directed evolution. In recent years, AI technologies, particularly deep neural networks, have emerged as a promising approach for enzyme engineering, and AI-guided methods leverage a vast amount of information regarding protein sequences, multiple sequence alignments, and protein structures to learn key features for correlations. These learned features can then be applied to various downstream tasks in enzyme engineering, such as predicting mutations with beneficial effect, optimizing protein stability, and enhancing catalytic activity. Herewith, we delves into advancements and successes in each of these strategies for enzyme engineering, highlighting the growing impact of AI-guided design on the process. By offering a detailed examination of the current state of enzyme engineering, we aim at providing valuable insight for researchers and engineers to further advance the development and optimization of enzymes for more applications. Table and Figures | Reference | Related Articles | Metrics

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Microbiome-based biosynthetic gene cluster data mining techniques and application potentials Qilong LAI, Shuai YAO, Yuguo ZHA, Hong BAI, Kang NING Synthetic Biology Journal    2023, 4 (3): 611-627.   DOI: 10.12211/2096-8280.2022-075 Abstract （2528）   HTML （267）    PDF（pc） （3056KB）（1468）       Knowledge map    Save Biosynthetic gene cluster (BGC) is an important type of gene set, which is commonly found in the genomes of various organisms, and plays important metabolic and regulatory roles. In terms of linear gene structure, the set of genes in a BGC is usually located in close proximity to each other in the genome, but for functions, genes in a BGC usually work synergistically and are responsible for a class of pathways that generate specific small molecules. Therefore, BGCs are vital in synthetic biology research as a highly promising source for elements. However, current BGC databases and analytical platforms are limited by the number and types of experimentally validated BGCs, as well as by the preliminary BGC data mining techniques. The establishment of data-driven systematic discovery of BGCs and their validation, as well as translational studies, are of great value in both fundamental research and practical applications. This article focuses on mining BGCs from big data with microbiome for synthetic biology research. We start with discussing the definition and significance of BGC mining, and summarize current data resources and methods for BGC mining: including MIBiG, antiSMASH and IMG-ABC for artificial intelligence (AI) enabled web services to accelerate BGC mining. Then, we compile a walk-through on how a typical BGC data mining could be conducted, with the history of BGC mining methods highlighted, which underlines the route build-up from traditional machine learning to deep learning. We also diagnose bottlenecks in BGC mining, and propose possible solutions. Furthermore, according to several BGC mining and validation experiments, we demonstrate the profound diversity and breadth of application scenarios with BGC discovery, as well as the importance of combining dry and wet lab experiments for validating newly discovered BGCs. Finally, we envision that the combination of advanced BGC mining methods and synthetic biology could broaden and deepen current synthetic biology research. Table and Figures | Reference | Related Articles | Metrics

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Design of synthetic biology components based on artificial intelligence and computational biology Sheng WANG, Zechen WANG, Weihua CHEN, Ke CHEN, Xiangda PENG, Fafen OU, Liangzhen ZHENG, Jinyuan SUN, Tao SHEN, Guoping ZHAO Synthetic Biology Journal    2023, 4 (3): 422-443.   DOI: 10.12211/2096-8280.2023-004 Abstract （2507）   HTML （351）    PDF（pc） （1930KB）（1938）       Knowledge map    Save The primary objective of synthetic biology is to conceptualize, engineer, and construct novel biological components, devices, and systems based on established principles and extant information or to reconfigure existing natural biological systems. The core concept of synthetic biology encompasses the design, modification, reconstruction, or fabrication of biological components, reaction systems, metabolic pathways and processes, and even the creation of cells and organisms with functions or living characteristics. This burgeoning field offers innovative technologies to address challenges with sustainable development in environment, resource, energy, and so on. Undeniably, synthetic biology has yielded significant progress in numerous fields, ranging from DNA recombination to gene circuit design, yet its full potential remains insufficiently explored, but the emergence and application of artificial intelligence (AI) definitely can facilitate the development of synthetic biology for more applications. From a synthetic biology perspective, essence for life is rooted in digitalization and designability. This article reviews current advances in computational biology, particularly AI for synthetic biology to be more efficient and effective, focusing on the development of biocatalysts, regulators, and sensors. De novo enzyme design has been successfully implemented by using Rosetta software, as AI exhibiting significant potential for generating innovative structures and protein sequences with diverse functions. Also, the reprogramming of natural enzymes for specific purposes is crucial for synthetic biology applications. By employing various force fields and sampling techniques, promiscuity and thermal stability can be modified to accommodate specific requirements rather than those with natural hosts. AI can be integrated into the life-cycle of synthetic biology through an active learning paradigm, which enables alterations in enzyme specificity, and demonstrates potential for accurately and rapidly predicting mutation effects, surpassing force-field-based methods. The rapidly decreasing cost of sequencing has facilitated the characterization of cis-regulators, primarily DNA and RNA, with high-throughput. Concurrently, more trans-regulators have been identified in sequenced genomes. The expanding wealth in big data serves as a driving force for AI. AI models have successfully predicted the strength of promoters, ribosome binding sites (RBSs), and enhancers, and generated artificial protomers and RBSs. Recent progress in RNA structure prediction is expected to aid the design of RNA elements. Sensors, vital for genetic circuits and other applications such as toxin detection, typically involve interactions among various molecules, including nucleic acids, proteins, small organic molecules, and metal ions. Consequently, sensor design necessitates the integration of diverse computational biology tools to balance accuracy and computational cost. As the pool of data keeps growing, we anticipate that AI will be increasingly applied to the design of more bio-parts. Table and Figures | Reference | Related Articles | Metrics

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Research progress of artificial intelligence in desiging protein structures Zhihang CHEN, Menglin JI, Yifei QI Synthetic Biology Journal    2023, 4 (3): 464-487.   DOI: 10.12211/2096-8280.2023-008 Abstract （1849）   HTML （222）    PDF（pc） （3481KB）（1892）       Knowledge map    Save Proteins are essential to life as they carry out a great variety of biological functions. Protein sequences determine their three-dimensional structures, and therefore physiological functions. Proteins with specific functions have important applications in many fields such as biomedicine, where they are utilized in drug design and delivery. In the past, protein engineering and directed evolution are commonly used to improve the activity and stability of proteins. These methods, however, are both complex and expensive, as they require a large number of biological experiments for validation. Computational protein design (CPD) allows the design of amino acid sequences based on desired protein functions and structures, and more intriguingly, generation of proteins even not found in nature. Conventional CPD uses energy function and optimization algorithm to design protein sequences. In recent years, with the rapid development of artificial intelligence (AI) technique, the accumulation of big data and the development of high speed computing, AI has made great progresses in learning, and been successfully applied in CPD. In this review, based on the input constraints and sampling space size, we present a systematic overview of recent applications of AI in protein design from three aspects: fixed-backbone design, flexible-backbone design, and sequence structure generation. We focus on algorithms and protein feature encoding, present the effect of dataset size and architectural improvements on model performance in prediction, and showcase several enzymes, antibodies, and binding proteins that were successfully designed using these models. The advantages of AI compared with traditional CPD methods are also discussed. Finally, we highlight challenges in AI-aided protein design, and propose some strategies for solutions. Table and Figures | Reference | Related Articles | Metrics

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Research progress on recombinant collagen expression system Jiahao PAN, Weisong PAN, Jian QIU, Donling XIE, Qi ZOU, Chuan WU Synthetic Biology Journal    2023, 4 (4): 808-823.   DOI: 10.12211/2096-8280.2023-020 Abstract （1685）   HTML （156）    PDF（pc） （1550KB）（1111）       Knowledge map    Save Collagen is the most abundant protein in mammals, and its production has been widely used in biomedicine, cosmetics, leather, biotechnology, etc. At present, collagen is generally divided into animal collagen and recombinant collagen. Although animal collagen is the main source of collagen, most of it comes from animal carcasses, and its collagen has been cross-linked and embedded in native tissues, which is more demanding on extraction and purification technology. In addition, pathogen contamination and allergy risks have become unavoidable problems for animal collagen. Recombinant collagen is a protein obtained by using human collagen cDNA fragments as the backbone gene, cloning the gene to the selected expression vector and converting it into an expression cell, and finally achieved by purification technology. Due to its single molecule, clear structure and easy control, recombinant collagen is the best alternative to replace animal collagen in biomedicine and tissue engineering. In this paper, the structure, category, biosynthesis mechanism and market scale of collagen are briefly described. Emphasis is placed on the construction strategies, advantages and limitations of different expression systems of recombinant collagen, including prokaryotic, yeast, plant, baculovirus and mammalian or human cell expression systems. Prokaryotes and yeast have a short cycle of producing recombinant collagen, but do not have a triple helix structure. The plant expression system produces recombinant collagen with a moderate cycle and a certain triple helix structure. The baculovirus-insect expression system and the mammalian expression system have a long cycle of recombinant collagen production and a complete triple helix structure. The practical application of recombinant collagen in ophthalmology, cartilage engineering, skin treatment and other biological medicine is described. Currently, the most commercially valuable use of collagen is subcutaneous injection of soluble protein to repair damaged skin. At the same time, collagen, as the main component of animal skin, can cross-link collagen in raw hides through chemical processes such as tanning, so that collagen becomes harder, more durable, and corrosion-resistant leather. By designing collagen scaffolds that are familiar with the natural cytoplasmic matrix environment, it can effectively reveal the pathogenesis of cell behavior and disease etiology. It is expected to provide suggestions on the research of recombinant collagen and future industrial development. {L-End} Table and Figures | Reference | Related Articles | Metrics

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Application of deep learning in protein function prediction Yidong SONG, Qianmu YUAN, Yuedong YANG Synthetic Biology Journal    2023, 4 (3): 488-506.   DOI: 10.12211/2096-8280.2022-078 Abstract （1586）   HTML （164）    PDF（pc） （1457KB）（1855）       Knowledge map    Save Protein function prediction is essential for bioinformatics analysis, which benefits a wide range of biological studies such as understanding the functions of metagenomes, uncovering mechanism underlying diseases, and finding new drug targets. With the rapid development of high-throughput sequencing technology, protein sequence data have been increased quickly, but functions of most proteins have not yet been identified. Since traditional biochemical experiments to determine protein functions are usually expensive, time-consuming, and less efficient, developing more efficient and effective computational methods for protein function prediction is of great significance. Deep learning technology has made breakthroughs in many fields, including image recognition, natural language processing, genomic analysis and drug discovery. In this review, we address applications of deep learning in protein function prediction, which can be divided into residue-level binding site prediction and protein-level gene ontology (GO) prediction. Protein binding sites are regions that bind to specific ligands, which play an important role in signal transduction, metabolism, revealing molecular mechanisms underlying diseases, and designing new drugs. Gene ontology is a standard function classification system for genes, which provides a set of annotations to comprehensively describe the properties of genes and gene products. Firstly, we introduce commonly used large-scale protein structure and function databases. Secondly, discriminative protein sequence and structure features are described. Thirdly, we summarize the latest protein function prediction methods: in terms of the prediction of binding sites, we introduce the latest methods based on the ligand type, including protein, peptide, nucleic acid and small molecule as well as ion ligand, and in the aspect of GO prediction, we highlight the latest sequence-based, structure-based, and protein interaction network-based methods developed with protein information. Finally, we comment the advantages and disadvantages of the current protein function prediction methods, and discuss the future development in this field. Table and Figures | Reference | Related Articles | Metrics

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Prediction of protein complex structure: methods and progress He HUANG, Tong WU, Wenda WANG, Jiashan LI, Daiwen SUN, Qiwei YE, Xinqi GONG Synthetic Biology Journal    2023, 4 (3): 507-523.   DOI: 10.12211/2096-8280.2022-079 Abstract （1502）   HTML （124）    PDF（pc） （1732KB）（1425）       Knowledge map    Save Protein complexes carry out a variety of biological functions, and obtaining the three-dimensional structure of protein complexes is critical for understanding their functions. In many cases, not only can two proteins interact to form a protein dimer, but also multiple proteins interact to form a protein multimer. It is difficult and time-consuming to resolve the structure of protein complexes by experiments. Recently, there have been some attempts and methods to predict the structure of multimers based on the structure prediction for the monomers. Several groups in the CASP14 competition submitted the prediction of protein complex targets, which mainly included template -based methods or protein docking. Later, on the basis of AlphaFold2, researchers developed some end-to-end structure prediction methods for complexes, which accelerates the study of protein complex structure prediction. However, compared with the prediction of monomeric protein structure, the accuracy of prediction for protein complex structure is still lower. This review surveys updated methods and advances in protein complex prediction, including inter-chain residue contact prediction, protein docking, and end-to-end protein complex structure prediction. Firstly, AI algorithms for protein structure prediction are briefly introduced, including coevolutionary analysis and protein contact prediction, deep learning method and protein structure prediction, pretraining model, and protein representation learning. Secondly, basic methods for predicting interactions between protein complexes are systematically summarized, from the construction of multiple sequence alignments of the complexes to the prediction of the inter-residue contact between chains of homologous or heterologous complexes. Finally, basic methods and ideas for protein complex structure prediction are explored from the viewpoint of interaction sites guiding complex structure prediction, protein molecular docking algorithm, end-to-end complex structure prediction methods, etc. In order to better predict the structure of protein complexes, we need to devote our effort to following aspects: 1) constructing protein complexes datasets for training and evaluation of prediction methods for the structure of multimers, 2) developing efficient algorithms to improve the prediction accuracy such as MSA paring algorithm and building templates for multi-chain protein complex, and 3) enlarging databases for protein sequences and structures for better modeling protein complex with pretraining and self-supervised learning methods. In all, predicting protein complex structure still remains a challenge, and new methods to improve accuracy will be helpful for analyzing protein functions, designing proteins and drug discovery. Table and Figures | Reference | Related Articles | Metrics

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Research advances in biosynthesis of natural product drugs in the past decade Jin FENG, Haixue PAN, Gongli TANG Synthetic Biology Journal    DOI: 10.12211/2096-8280.2023-092 Accepted: 04 February 2024

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Data-driven prediction and design for enzymatic reactions Tao ZENG, Ruibo WU Synthetic Biology Journal    2023, 4 (3): 535-550.   DOI: 10.12211/2096-8280.2022-066 Abstract （1365）   HTML （170）    PDF（pc） （1714KB）（1297）       Knowledge map    Save Enzymes are efficient catalysts with substrate specificity and stereo- and regioselectivity, which are widely used in producing chemicals, drugs and materials. Enzymes are cores for biocatalysis, and thus prediction on their functions and design of enzymatic reactions are driving forces for intelligent biomanufacturing through biocatalysis. So far limited understanding on enzymatic catalysis hinders the exploration of enzymatic reactions for industrial applications. For example, it is difficult to predict enzymatic activities on unreported substrates, to elucidate synthetic routes for newly found structures of enzymes, and to redesign enzymes for specific scenarios. In the era of big data, data-driven approaches have exhibited powerful capabilities for exploring enzymatic reactions, by filling gap between the large corpora of enzymatic data and limited understanding on functions of the enzymes. Recently, computational tools and platforms have greatly accelerated experimental research, and improved the design-build-test-learn cycle. Herein we review progress in computational tools for enzymatic reaction prediction and design, focusing on the application of deep learning methods in this field. Referring to key elements (substrate, product and enzyme) for enzymatic reactions, related databases are summarized. Then, the data-driven approaches for forward and backward prediction of enzymatic reaction routes and functions of enzymes, their design and theoretical calculation for enzymatic catalysis are addressed. Finally, the status and prospective of data-driven approaches for enzymatic catalysis prediction and design, including the data, model, algorithm and platform, are discussed. Table and Figures | Reference | Related Articles | Metrics

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Biological degradation and utilization of lignin Kuanqing LIU, Yiheng ZHANG Synthetic Biology Journal    DOI: 10.12211/2096-8280.2023-062 Accepted: 02 November 2023

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Rewiring and application of Yarrowia lipolytica chassis cell Meili SUN, Kaifeng WANG, Ran LU, Xiaojun JI Synthetic Biology Journal    2023, 4 (4): 779-807.   DOI: 10.12211/2096-8280.2022-060 Abstract （1250）   HTML （175）    PDF（pc） （2749KB）（2163）       Knowledge map    Save Engineering microbial chassis cells to efficiently synthesize high value-added products has received increasing attention. This biomanufacturing mode based on excellent performance microbial chassis cells has become the research frontier in the field of synthetic biology. Yarrowia lipolytica, an unconventional oleaginous yeast, is emerging as one of the popular microbial chassis cells in the field of advanced and green biomanufacturing. This is due to its unique physiological and biochemical characteristics, such as the inherent mevalonate pathway, adequate acetyl-CoA supply, broad substrate spectrum, and high tolerance to multiple extreme environments. These characteristics make Y. lipolytica a superior chassis candidate for the advanced and green biomanufacturing. In recent years, the researches and applications on the rewiring of Y. lipolytica chassis cell for biomanufacturing have gradually increased, which promoted the further upgrading of Y. lipolytica chassis cells. This review firstly describes the development of the genetic elements for rewiring Y. lipolytica chassis cell, including promoters, terminators, and selecting markers. Then, this review summarizes the expression modes and integration methods for endogenous and heterogenous genes, including gene expression based on episomal plasmid, genomic integration based on homologous recombination (HR) and non-homologous end joining (NHEJ). This review further summarizes the research progress of various synthetic biology tools developed for Y. lipolytica, including various gene overexpression methods, biosensor-based dynamic regulation strategies, CRISPR/Cas-based gene expression regulation methods, and the emerging strategies such as genome-scale metabolic modelling, genome-wide mutational screening, etc. This review also introduces the achievements of rewiring Y. lipolytica chassis cell for the synthesis of different high value-added products, including proteins, organic acids, terpenes, functional sugars and sugar alcohols, fatty acids and their derivatives, flavonoids and polyketides, and amino acid derivatives. In addition, the prospects of Y. lipolytica chassis cell-based biomanufacturing are discussed in light of the current progresses, challenges, and trends in this field. Finally, guidelines for building next-generation Y. lipolytica chassis cell for production of the aforementioned products are also emphasized. {L-End} Table and Figures | Reference | Related Articles | Metrics

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Advances and applications of evolutionary analysis and big-data guided bioinformatics in natural product research Fanzhong ZHANG, Changjun XIANG, Lihan ZHANG Synthetic Biology Journal    2023, 4 (4): 629-650.   DOI: 10.12211/2096-8280.2022-073 Abstract （1189）   HTML （178）    PDF（pc） （3724KB）（1117）       Knowledge map    Save Nature has invented a myriad of natural products through billions of years of evolution. Natural products own unique structural features selected by evolutionary pressure and serve as a treasure trove for drug discovery. The rapid growth of microbial genomic data now provides new opportunities for evolutionary and big data analysis of biosynthetic gene clusters, which not only gives us a clearer picture about the global landscape of natural products, but also enables us to reveal the evolutionary trajectory of natural products. Such holistic understanding of natural products can facilitate the phylogeny-guided genome mining, allow better functional prediction of biosynthetic enzymes, and even open the door to biosynthetic redesign to create non-natural molecules by evolution-guided engineering. The core essence of evolutionary and large-scale bioinformatics lies in that it visualizes the entire sequence space and their distribution of a particular analyte family. Therefore, big data-driven bioinformatics has the potential to answer some challenging questions such as "How many natural products remain to be discovered?", and "How long can natural products discovery be sustainable?" This review summarizes recent advances in the application of evolution and big data-guided bioinformatics for natural products research from several perspectives including: ① natural product discovery; ② functional and structural prediction of biosynthetic enzymes and their products; ③ bioengineering, with the emphasis on the assembly line enzymes such as polyketide synthases and non-ribosomal peptide synthetases. Due to the modular domain architecture they have, the assembly line enzymes have been the main targets for genome mining. The phylogenetic analysis of their domains has shown to be a powerful and effective way to predict their enzymatic function and substrate specificity. Recently, the evolutionary mechanism of the assembly line enzymes has been investigated, and several evolution-guided engineering strategies were shown to have much higher efficiency for the assembly line reprogramming, providing a potential breakthrough for the bioproduction of complex polyketides and peptides. Non-modular enzymes are also discussed with selected representative examples. Finally, we present current challenges and future prospects of big data-driven natural products research. {L-End} Table and Figures | Reference | Related Articles | Metrics

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Research progress in synthesis of astaxanthin by microbial fermentation Qiang ZHOU, Dawei ZHOU, Jingxiang SUN, Jingnan WANG, Wankui JIANG, Wenming ZHANG, Yujia JIANG, Fengxue XIN, Min JIANG Synthetic Biology Journal    2024, 5 (1): 126-143.   DOI: 10.12211/2096-8280.2023-065 Abstract （1131）   HTML （165）    PDF（pc） （2271KB）（896）       Knowledge map    Save Astaxanthin is a value-added terpene with strong antioxidant activity as well as other physiological functions, such as anti-cancer, enhancing immunity, eye protection, and cardio-cerebrovascular protection. Natural astaxanthin mainly comes from algae and aquatic crustaceans such as lobster shell. Astaxanthin presents with stereoisomerism and geometric isomerism, which have different biological activities and applications. Currently, astaxanthin in the market is obtained primarily through natural extraction from Haematococcus pluvialis or Xanthophyllomyces dendrorhous and chemical synthesis as well. While H. pluvialis has a long growth cycle and high light demand, leading to low biomass productivity and extraction rate for high production cost of astaxanthin, X. dendrorhous has a low astaxanthin yield and is easy to degenerate, making them challenging for the large-scale commercial production. The chemical synthesis of astaxanthin involves multiple reactions with complicated processes, producing mixed isomers and various byproducts, which consequently compromises its antioxidant capacity. Moreover, the assimilation and utilization of chemically synthesized astaxanthin in vivo is poor compared to its natural product, making it not suitable for being used by human being. With the continuous development of synthetic biology, microbial fermentation has been developed as an effective way for the commercial production of astaxanthin to better meet consumer demand. At present, astaxanthin-producing microorganisms include bacteria, fungi, and algae. This review introduces astaxanthin's structure, properties, production methods, and processes for its extraction and purification, with an emphasis on natural and engineered biosynthetic pathways. The latest progress in the production of astaxanthin by different microorganisms such as H. pluvialis, Yarrowia lipolytica and Escherichia coli is summarized, along with strategies for increasing astaxanthin production through genetic engineering and fermentation process optimization. Future metabolic engineering strategies are proposed, such as over-expression of astaxanthin synthesis genes, promoters with higher substitution intensity, subcellular localization, metabolic pathway optimization, etc, to increase astaxanthin yield for wide usage in food, medical, cosmetic and feed industries. Table and Figures | Reference | Related Articles | Metrics

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Target structure based computational design of cyclic peptides Fanhao WANG, Luhua LAI, Changsheng ZHANG Synthetic Biology Journal    2023, 4 (3): 551-570.   DOI: 10.12211/2096-8280.2023-006 Abstract （1095）   HTML （102）    PDF（pc） （2810KB）（1052）       Knowledge map    Save Cyclic peptides (macrocycles) possess head-to-tail cyclic or partially cyclized substructures, which have received more and more attention in developing new drugs recently, since they have unique advantages in regulating protein-protein interactions (PPIs). Comparing to small-molecule compounds, it is easier to design cyclic peptide molecules that bind to target sites with high affinity and specificity, due to the broad and flat interfaces of PPIs and their large surfaces. Moreover, cyclic peptides are generally more rigid and difficult for digestion by proteases than their linear counterparts, making them more stable than linear peptides or proteins. Meanwhile, cyclic peptides are easier for modifications to increase transmembrane activity, targeting intracellular proteins through conformation adaptation or chemical modifications. 3D structure data and structure modeling technics are basis for designing structure based cyclic-peptide drugs. In this review, we assess the structures of cyclic peptides and target proteins available in protein structure database (PDB). Then, we review the algorithms of conformation generation or structure prediction for cyclic peptides, including homologous modeling, secondary structure prediction and optimization, backbone torsion sampling, and distance geometry method. We also summarize progress in target structure based computational design for cyclic peptides, including structure-based virtual screening, molecular dynamic simulation aided methods, de novo design algorithms, and the transmembrane cyclic peptide design. However, more generalized structure-based de novo design algorithms remains to be further explored, and methods to adopt unnatural amino acids or chemical modifications are also needs to be developed. It's worth noting that, with the increase of data for cyclic peptide 3D structures, the data-driven machine learning method may provide a more promising solution for improving the efficiency and effectiveness of structure based cyclic peptide de novo design and conformation generation to develop cyclic peptide drugs in the future. Table and Figures | Reference | Related Articles | Metrics

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Progress in the construction of microbial cell factories for efficient biofuel production Xiongying YAN, Zhen WANG, Jiyun LOU, Haoyu ZHANG, Xingyu HUANG, Xia WANG, Shihui YANG Synthetic Biology Journal    2023, 4 (6): 1082-1121.   DOI: 10.12211/2096-8280.2023-047 Abstract （1015）   HTML （133）    PDF（pc） （3042KB）（859）       Knowledge map    Save Biofuels are important supplements and alternatives to fossil fuels, which can alleviate the current global energy crisis and environmental pollution. Using microbes mined from nature or engineered in the lab to produce biofuels from renewable biomass of both economic and social benefits has become a major direction of sustainable biomanufacturing. It is necessary to develop robust microbial cell factories through synthetic biology for efficient and economic biofuel production, combining the strategy of systems biology to understand and design the synthetic pathways for biofuels and regulatory networks in microbes. This review discussed the major types of biofuels, the corresponding metabolic pathways, and current progress for producing these biofuels, including bioethanol, higher alcohols, biodiesel, fatty acid derivatives and isoprenoid derivatives. The strategies to understand, construct, and engineer synthetic microbial chassis as cell factories for diverse biofuel production were summarized, especially from substance metabolism, energy balance, physiological modification, and information regulation. In addition, current status and challenges for microbial biofuel production were analyzed. The insufficient understanding of natural biosynthetic pathways and the functions of biological components, lack of genetic manipulation tools for non-model biofuel chassis cells, low efficiency of gene editing, incompatibility between different heterologous pathways and chassis cells, toxicity of heterologous products and metabolic intermediates to cell factories, inhibition of many stress factors when using cheap renewable resources as raw materials, and engineering obstacles in industrial scale-up are the barriers and challenges to the industrial biofuel production. However, the rapid development of artificial intelligence and bioinformatics provides new solutions to these challenges. Finally, this review proposed future directions and key tasks based on the need for biofuel commercialization, emphasizing the combination of information technology and biotechnology as the trend in developing biofuel cell factories, which can provide tools and resources for strain engineering and accelerate the industrialization process of biofuels. Table and Figures | Reference | Related Articles | Metrics

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The Enlightenment of the Chinese Philosophy “Tao-Fa-Shu-Qi” to Industrial Biomanufacturing Yi-Heng P. Job Zhang Synthetic Biology Journal    DOI: 10.12211/2096-8280.2023-066 Accepted: 19 December 2023

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Base editing technology and its application in microbial synthetic biology Yannan WANG, Yuhui SUN Synthetic Biology Journal    2023, 4 (4): 720-737.   DOI: 10.12211/2096-8280.2022-053 Abstract （1006）   HTML （92）    PDF（pc） （1250KB）（981）       Knowledge map    Save The discovery and development of the CRISPR/Cas system have a revolutionary influence on life sciences. A series of tools derived from the CRISPR/Cas system have brought great convenience to research in the field of life sciences. The base editors developed based on the CRISPR/Cas system are gene editing tools that can achieve base conversions and transversion on target. The base editors are constructed by fusing cytosine or adenosine deaminase, and other functional elements to Cas proteins with abolished double strand DNA cleavage activity to convert cytidine or adenine into other bases at genome on-target sites guided by sgRNAs. Base editors have shown great potential in biology, medicine and related fields. Although they have already been continuously optimized, there are still problems affecting further application of base editors. In this review, we briefly describe the development of DNA base editors. Furthermore, we introduce in detail the problems of the limited editing range of base editors as well as the corresponding optimization strategies by increasing the target sites recognized by the locator moiety and expanding or narrowing the editing window of the effector moiety. At the same time, we introduce several off-target editing detection methods specially developed for base editing. Based on usual and developed detection methods, multiple and frequent off-target editing caused by base editors were found at both DNA and RNA levels. We also introduced various effective optimization strategies to improve the editing specificity of the base editors in every respect. Most of these strategies are based on protein modification, but also on optimization of sgRNA and spatio-temporal regulation of base editing systems. These measures greatly enrich the application scenarios of the base editors. Then, we discuss the progress on applying base editors to the field of microbial synthetic biology, including revealing the metabolic pathway and synthesis mechanism of natural products as well as improving the production of target compounds in multiple species. Finally, we envisage the promising development of base editing in synthetic biology in the future. {L-End} Table and Figures | Reference | Related Articles | Metrics

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Applications of foldability in intelligent enzyme engineering and design: take AlphaFold2 for example Qiaozhen MENG, Fei GUO Synthetic Biology Journal    2023, 4 (3): 571-589.   DOI: 10.12211/2096-8280.2023-011 Abstract （964）   HTML （103）    PDF（pc） （1986KB）（1447）       Knowledge map    Save Natural enzymes often have advantages of environmental friendliness, high catalytic efficiency and so on. However, due to inappropriate pH, temperature and other conditions in industrial environment, the application of natural enzymes in industrial production is unsatisfactory owing to challenges such as misfolding of proteins and limited functions. Compared with traditional methods, enzyme design and engineering with the help of artificial intelligence (AI) have advantages of high efficiency, high speed and low cost, but most work does not consider the 'foldability' in the process of enzyme engineering. A designed enzyme may fold to another state for minimum energy, so called misfolding. As we all know, protein design is regarded as an inverse folding process. Can we utilize protein folding tools to constrain the foldability of the designed enzyme? In recent years, protein structure prediction tools represented by AlphaFold2 have made breakthroughs with the help of AI for accuracy at atomic levels, which enriches existing enzyme structure data for subsequent studies to address the above question. Therefore, we discuss applying protein structural tools to fulfill the task of enzyme design and engineering, increase the proportion of reliable enzymes designed and reduce the cost of experiments. Firstly, we review the application of artificial intelligence technology in enzyme design and engineering from the perspective of sequence and structure. Then, we summarize existing protein structure prediction tools into four types and introduce their methods and prediction ability respectively. Furthermore, taking AlphaFold2 as an example, we group the applications which improve the rationality of enzyme modification and the "foldability" of design into three categories: 1) Structure 'Analyzer', 2) Mutation 'Filter' and 3) Folding 'Monitor'. Finally, we highlight drawbacks with existing algorithms for further improvements. With the rapid development of AI and understanding on protein function mechanism, the precision of enzyme modifications and designs will be increased. Table and Figures | Reference | Related Articles | Metrics

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Research progress in biosensors based on bacterial two-component systems Jingyu ZHAO, Jian ZHANG, Qingsheng QI, Qian WANG Synthetic Biology Journal    2024, 5 (1): 38-52.   DOI: 10.12211/2096-8280.2023-016 Abstract （964）   HTML （109）    PDF（pc） （2074KB）（703）       Knowledge map    Save Two-component systems (TCSs) in bacteria, are capable of sensing and making responses to physical, chemical, and biological stimuli within and outside the cells, and subsequently induce a wide range of cellular processes through the role played by the regulatory component and the response component in combination, which is a ubiquitous signal transduction pathway. At present, an growing number of synthetic biologists have devoted their effort to using the specific and irreplaceable properties of TCSs to design biosensors with the aim of applying in optogenetics, materials science, engineering of gut microbiome, biorefining and soil improvement, and the like. The purpose of this review is to focus on the most recent research advances in the development of biosensors based on TCSs and their potential applications. At the same time, topics of great importance are discussed on how to use novel engineering methods with synthetic biology to improve the reliability and robustness of the performance of the biosensors, such as genetic remodeling, DNA-binding domain swapping, tuning of the detection threshold and isolation of phosphorylation crosstalk as well as on how to customize the signal characteristics of TCSs to meet particular needs according to the requirements of specific applications. It would be possible in the future for scientists to combine these methods with gene synthesis on a large scale and high-throughput screening in order to speed up and give synthetic biologists a hand in the discovery of TCSs with numerous uncharacterized signal inputs and the development of genetically encoded novel biosensors that may be capable of responding to a broad range of stimuli. This allows for extending the applications of the biosensors in different fields. Table and Figures | Reference | Related Articles | Metrics