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Artificial intelligence-assisted protein engineering Jiahao BIAN, Guangyu YANG Synthetic Biology Journal    2022, 3 (3): 429-444.   DOI: 10.12211/2096-8280.2021-032 Abstract （3709）   HTML （396）    PDF（pc） （2456KB）（3124）       Knowledge map    Save Protein engineering is one of the important research fields of synthetic biology. However, de novo design of protein functions based on rational design is still challenging, because of the limited understanding on biological fundamentals such as protein folding and the natural evolution mechanism of enzymes. Directed evolution is capable of optimizing protein functions effectively by mimicking the principle of natural evolution in the laboratory without relying on structure and mechanism information. However, directed evolution is highly dependent on high-throughput screening methods, which also limits its applications on proteins which lack high-throughput screening methods. In recent years, artificial intelligence has been developed very rapidly for integrating into multidisciplinary fields. In synthetic biology, artificial intelligence-assisted protein engineering has become an efficient strategy for protein engineering besides rational design and directed evolution, which has shown unique advantages in predicting the structure, function, solubility of proteins and enzymes. Artificial intelligence models can learn the internal properties and relationships from given sequence-function data sets to make predictions on properties for virtual sequences. In this article, we review the application of artificial intelligence-assisted protein engineering. With the basic and process of the strategy introduced, three key points that affect the performance of the predictive model are analyzed: data, molecular descriptors and artificial intelligence algorithms. In order to provide useful tools for researchers who want to take advantage of this strategy, we summarize the main public database, diverse toolkits and web servers of the common molecular descriptors and artificial intelligence algorithms. We also comment on the functions, applications and websites of several artificial intelligence-assisted protein engineering platforms, through which a complete prediction task including protein sequences representation, feature analysis, model construction and output can be completed easily. Finally, we analyze some challenges that need to be solved in the artificial intelligence-assisted protein engineering, such as the lack of high-quality data, deviation in data sets and lacking of the universal models. However, with the development of automated gene annotations, ultra-high-throughput screening technologies and artificial intelligence algorithms, sufficient high-quality data and appropriate algorithms will be developed, which can enhance the performance of artificial intelligence-assisted protein engineering and thus facilitate the development of synthetic biology techniques. Table and Figures | Reference | Related Articles | Metrics

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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 （2938）   HTML （271）    PDF（pc） （5785KB）（2367）       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 （2771）   HTML （303）    PDF（pc） （1310KB）（2082）       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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Design, optimization and application of whole-cell microbial biosensors with engineered genetic circuits Lu YANG, Nan WU, Rongrong BAI, Weiliang DONG, Jie ZHOU, Min JIANG Synthetic Biology Journal    2022, 3 (6): 1061-1080.   DOI: 10.12211/2096-8280.2021-021 Abstract （2662）   HTML （301）    PDF（pc） （2989KB）（1825）       Knowledge map    Save Whole-cell microbial biosensors with engineered genetic circuits constructed based on the concept of synthetic biology is an important branch of the biosensor. Whole-cell microbial biosensor is mainly composed of the sensing module, the computing module and the output actuating module. It can sense the concentration of specific substances in the environment and then transfer it to specific signal outputs in time according to certain rules, which shows great potential in bioengineering process control, environmental monitoring, food safety, environmental quality monitoring and disease diagnosis and control, etc. With the improvement of various technologies in synthetic biology and the enrichment of genetic elements, more and more whole-cell microbial sensors based on different response mechanisms, different logic gates and logic circuits have been developed. However, the design and construction of genetically engineered whole-cell microbial biosensor still mainly rely on the empirical method of trial-and error-learning. Therefore, how to design and construct high performance genetically engineered microbial whole-cell biosensors and how to tune its response curves by optimizing genetic elements or circuits to meet the detection requirements of different practical application scenarios is the new and important challenge. Here we reviewed the principle, classification and development process of genetically engineered whole-cell biosensor. We also focused on the design and construction of genetic circuit based on transcription factors and riboswitches, discussed optimization strategies for improving biosensor detection performance including dynamic range, specificity and working range, and then summarized its application progress in different detection fields. The optimization strategies are mainly involved in changing the expression level of genetic elements, adjusting the binding affinity between metabolites and genetic elements, restructuring the position of functional domains, etc. Finally, some challenges, such as biological safety, cumbersome design and construction, and inconvenience to enter the sensor market were discussed. It is expected that emerging technologies such as artificial intelligence, synthetic biology, and droplet microfluidics, will accelerate the development of genetic regulatory elements and the design and construction of novel biosensors. Table and Figures | Reference | Related Articles | Metrics

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Cell-free protein synthesis: from basic research to engineering applications Jiaqi HOU, Nan JIANG, Lianju MA, Yuan LU Synthetic Biology Journal    2022, 3 (3): 465-486.   DOI: 10.12211/2096-8280.2021-064 Abstract （2656）   HTML （248）    PDF（pc） （2584KB）（2289）       Knowledge map    Save Cell-free protein synthesis (CFPS), also known as in vitro gene expression, is a multifunctional technique used to complement cell-based protein expression, which is at the core of cell-free synthetic biology. Since the CFPS system does not require a living cell, it can simulate the entire cellular transcription and translation process in vitro in a controlled environment, and allows for an in-depth study of individual components and biological networks. Therefore, as a platform technology, it is expected to overcome the loopholes caused by the limitations of cell membranes in the current in vivo manufacturing systems, which has a broad research prospect in fundamental and applied scientific research. The cell-free operation is simple and easy to control, and its advantages over in vivo protein expression include its nature with open systems, eliminating the dependence on living cells and using all system energy for the production of the target proteins. This article reviews the composition of CFPS systems and their development based on different component types, including different biological extracts or purified transcription and translation components. Furthermore, different CFPS reaction patterns are introduced, including batch and continuous exchange modes, and the research progress of CFPS systems in genetic circuits, protein engineering, and the construction of artificial life is described. Among them, the genetic circuit research progress mainly summarizes the latest applications and contributions of cell-free technology in the prototype design, biosensors, and in vitro metabolic engineering. The protein engineering research progress lists the advantages and advances of the CFPS systems for producing membrane proteins, virus-like particles, post-translational modifications, unnatural amino acid incorporation and protein evolution. In the construction of artificial "living systems", the synthesis of bacteriophages and the construction of artificial cells have opened up a novel frontier field. Finally, the opportunities and challenges of the CFPS platforms for future scientific research and industrial applications are highlighted. Table and Figures | Reference | Related Articles | Metrics

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Computational design and directed evolution strategies for optimizing protein stability Qingyun RUAN, Xin HUANG, Zijun MENG, Shu QUAN Synthetic Biology Journal    2023, 4 (1): 5-29.   DOI: 10.12211/2096-8280.2022-038 Abstract （2636）   HTML （343）    PDF（pc） （2169KB）（3547）       Knowledge map    Save Most natural proteins tend to be marginally stable, which allows them to gain flexibility for biological functions. However, marginal stability is often associated with protein misfolding and aggregation under stress conditions, presenting a challenge for protein research and applications such as proteins as biocatalysts and therapeutic agents. In addition, protein instability has been increasingly recognized as one of the major factors causing human diseases. For example, the formation of toxic protein aggregates is the hallmark of many neurodegenerative diseases, including Alzheimer's and Parkinson's diseases. Therefore, optimizing protein folding and maintaining protein homeostasis in cells are long-standing goals for the scientific community. Confronting these challenges, various methods have been developed to stabilize proteins. In this review, we classify and summarize various techniques for engineering protein stability, with a focus on strategies for optimizing protein sequences or cellular folding environments. We first outline the principles of protein folding, and describe factors that affect protein stability. Then, we describe two main approaches for protein stability engineering, namely, computational design and directed evolution. Computational design can be further classified into structure-based, phylogeny-based, folding energy calculation-based and artificial intelligence-assisted methods. We present the principles of several methods under each category, and also introduce easily accessible web-based tools. For directed evolution approaches, we focus on library-based, high-throughput screening or selection techniques, including cellular or cell-free display and stability biosensors, which link protein stability to easily detectable phenotypes. We not only introduce the applications of these techniques in protein sequence optimization, but also highlight their roles in identifying novel folding factors, including molecular chaperones, chemical chaperones, and inhibitors of protein aggregation. Moreover, we demonstrate the applications of protein stability engineering in biomedicine and pharmacotherapeutics, including identifying small molecules to stabilize disease-related, aggregation-prone proteins, obtaining conformation-fixed and stable antigens for vaccine development, and targeting protein stability as a means to control protein homeostasis. Finally, we look forward to the trends and prospects of protein stabilization technologies, and believe that protein stability engineering will lead to a better understanding of protein folding processes to facilitate the development of precision medicine. {L-End} Table and Figures | Reference | Related Articles | Metrics

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Recent development of directed evolution in protein engineering Yanping QI, Jin ZHU, Kai ZHANG, Tong LIU, Yajie WANG Synthetic Biology Journal    2022, 3 (6): 1081-1108.   DOI: 10.12211/2096-8280.2022-025 Abstract （2634）   HTML （288）    PDF（pc） （3627KB）（3353）       Knowledge map    Save Directed evolution aims to accelerate the natural evolution process in vitro or in vivo through iterative cycles of genetic diversification and screening or selection. It has been one of the most solid and widely used tools in protein engineering. This review outlines the representative methods developed in the past 10 years that increase the throughput of directed evolution, including in vitro and in vivo gene diversification methods, high-throughput selection and screening methods, continuous evolution strategies, automation-assisted evolution strategies, and AI-assisted protein engineering. To illustrate the significant applications of directed evolution in protein engineering, this review subsequently discusses some remarkable cases to show how directed evolution was used to improve various properties of enzymes, such as the tolerance to elevated temperature or organic solvent, the activities on non-native substrates, and chemo-, regio-, stereo-, and enantio-selectivities. In addition, directed evolution has also been widely used to expand the biocatalytic repertories by engineering enzymes with abiotic activities. In addition to the native enzymes, directed evolution has also been used to engineer de novo designed enzymes and artificial metalloenzymes with activities comparable to or exceeding the ones of the native enzymes. Finally, this review has pointed out that further improving the efficiency and effectiveness of directed evolution remains challenging. Some advanced continuous evolution and high throughput screening strategies have been succesfully demonstrated in improving the throughput of directed evolutions extensively, but they have been limited to engineering certain protein targets. To resolve those issues, continuously improved computational modeling tools and machine learning strategies can assist us to create a smaller but more accurate library to enhance the probabilities of discovering variants with improved properties. Additionally, laboratorial automation platforms coupled with advanced screening and selection techniques also have great potential to extensively explore the protein fitness landscape by evolving multiple targets continuously in a high throughput manner. 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 （2584）   HTML （268）    PDF（pc） （3056KB）（1524）       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 （2539）   HTML （356）    PDF（pc） （1930KB）（1977）       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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Application of imine reductase in the synthesis of chiral amines Lu YANG, Xudong QU Synthetic Biology Journal    2022, 3 (3): 516-529.   DOI: 10.12211/2096-8280.2021-054 Abstract （2289）   HTML （139）    PDF（pc） （2499KB）（1845）       Knowledge map    Save Chiral amines with bioactivities are important chiral auxiliaries, and also key intermediates for the synthesis of many natural products and chiral drugs. Among the top 200 drugs for market revenues in 2019, more than 30% contain chiral amine structures. Therefore, the development of efficient and effective methods to synthesize chiral amine compounds is of interest for research. Due to its high efficiency, environmental friendliness and economic competitiveness, more attention has been paid on the enzyme-catalyzed production of chiral amines by academia and industry. Imine reductases (IREDs) reviewed in this article are a class of NAD(P) H-dependent oxidoreductases that catalyze asymmetric reduction of imines to chiral amines. The reduction of C\underset{\text{??????}}{?}N bonds constitutes a physiological reaction present in a number of biosynthetic pathways, leading to a variety of metabolites. The imine reductases have excellent characteristics such as high catalytic efficiency, strong regioselectivity and stereoselectivity, etc., which stand out among many other methods for the synthesis of chiral amines, and attract attention and enthusiasm of many researchers. In the past decade, with the rapid development of bioinformatics, structural biology, high-throughput screening approaches and the continuous expansion of the gene databases, many imine reductases with different functions have been identified. Significant achievements have been made in the discovery of IREDs, protein engineering and multi-enzyme cascade applications, among which some successful modification cases have industrial application potentials. This review summarizes the structural characteristics, catalytic mechanisms and applications of IREDs, with emphasis on their protein engineering and applications in multi-enzyme cascade reactions, as well as the bottleneck, breakthrough and progress in asymmetric catalytic chiral amine biosynthesis. In addition, the challenges and potentials of the enzymatic synthesis of chiral amine compounds for industrial production, and the importance of novel artificial biosynthesis pathway design to overcome these challenges are highlighted. Table and Figures | Reference | Related Articles | Metrics

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Biosynthesis of elastin-like polypeptides and their applications in drug delivery Zhaoying YANG, Fan ZHANG, Jianwen GUO, Weiping GAO Synthetic Biology Journal    2022, 3 (4): 728-747.   DOI: 10.12211/2096-8280.2021-094 Abstract （2284）   HTML （138）    PDF（pc） （4210KB）（1809）       Knowledge map    Save Elastin-like polypeptides (ELPs) are artificially synthetic peptide polymers inspired by human elastin. ELPs are composed of repeat units of a Val-Pro-Gly-X-Gly, where X can be any amino acid except proline, and they can exhibit different biological functions along with X residue changes. ELPs are thermally responsive and demonstrate lower critical solution temperature phase behavior. They are soluble at temperatures below a characteristic transition temperature (Tt) and reversibly phase separate into an insoluble, coacervate phase above the Tt. Moreover, the phase behavior is retained when the ELP is either genetically fused to peptides or covalently conjugated to small molecules, and this phase behavior can be adjusted through changing X residue and chain length of ELPs. As ELPs are typically produced from synthetic genes, the structure and function of ELPs can be accurately regulated through genetic engineering. The amino acids or peptides with reactive side chains can be incorporated into ELPs through recombination synthesis as well. This precision control over ELP is unmatched by synthetic polymers. Based on these properties, ELPs can be engineered to assemble into unique architecture and used as soluble macromolecular carriers, therapeutic drug depots, hyperthermia-targeted drug carriers and self-assembled micelles. Lastly, as ELPs are derived from natural protein sequences, they show desirable biological properties including excellent biocompatibility, low immunogenicity, and non-toxic effects. Due to these attributes, ELPs have been widely used in biomedical fields including protein expression and purification, in vitro diagnosis, drug delivery and tissue engineering. By focusing primarily on applications of ELPs in drug delivery, this review introduces the design principles, physicochemical properties, biosynthetic methods of ELPs and ELP conjugates, as well as exemplifies representative applications of ELPs in drug delivery, as extending the half-life of drugs, tumor targeted delivery, local delivery, hyperthermia-targeted delivery and sustained released of the drugs in vivo. Challenges and problems faced in this emerging field are discussed at the end of this review. Table and Figures | Reference | Related Articles | Metrics

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Recent advances in heterologous production of natural products using Aspergillus oryzae Jiayu DONG, Min LI, Zonghua XIAO, Ming HU, Yudai MATSUDA, Weiguang WANG Synthetic Biology Journal    2022, 3 (6): 1126-1149.   DOI: 10.12211/2096-8280.2022-007 Abstract （2189）   HTML （155）    PDF（pc） （3318KB）（1868）       Knowledge map    Save Organic molecules produced by living organisms, generally termed as natural products, are rich sources of pharmaceutical drugs and biopesticides, and fungi are one of the most prolific producers of medicinally important natural products, as represented by penicillins, lovastatin, and cyclosporins. Heterologous expression is a commonly used approach to study the function of biosynthetic genes of natural products, and a number of heterologous hosts have been developed and utilized over the last decades. The filamentous fungus Aspergillus oryzae has long been utilized for the production of fermented food and drinks in East Asia, and intensive genetic and molecular biological studies on the fungus have allowed for its genetic engineering in an efficient manner. Importantly, A. oryzae is known to possess a relatively clean metabolic background with a low level of secondary metabolite production, providing an attractive feature as a heterologous host. Furthermore, unlike prokaryotic and yeast hosts, most coding sequences of fungal biosynthetic proteins can be directly introduced into A. oryzae in their intact form without removing introns, which simplifies the transformation procedures. Collectively, A. oryzae is a robust platform for heterologous production of natural products, which not only facilitates the elucidation of the biosynthetic pathway of a given natural product but also allows the activation of silent or cryptic biosynthetic gene clusters. Thus, the A. oryzae host has been widely utilized for biosynthetic studies, genome mining, and synthetic biology of fungal natural products. It should be noted that more than ten biosynthetic genes can readily be introduced into the fungus, indicating that the majority of fungal biosynthetic gene clusters can be easily transferred to the A. oryzae host. This review first provides the general transformation procedure of A. oryzae and the molecular biological tools available for the fungus. Next, recent successful applications of this fungal host for the heterologous production of natural products are summarized. With the recent rapid advance in molecular biology, such as the development of genome editing tools, we believe that the heterologous expression of biosynthetic genes in A. oryzae will be performed in a much faster and more versatile manner in the near future, which would ultimately lead to the discovery of useful natural products for drug leads and other applications. Table and Figures | Reference | Related Articles | Metrics

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Advances and applications of droplet-based microfluidics in evolution and screening of engineered microbial strains Ran TU, Shixin LI, Haoni LI, Meng WANG Synthetic Biology Journal    2023, 4 (1): 165-184.   DOI: 10.12211/2096-8280.2021-105 Abstract （2180）   HTML （188）    PDF（pc） （2702KB）（1774）       Knowledge map    Save Microbial strains are perquisites for biomanufacting through microbial culture and fermentation. However, most strains usually need to be engineered to improve their performances for industrial applications. Therefore how to efficiently screen and isolate robust strains is a critical step of strain engineering. As an advanced high-throughput screening technology, droplet-based microfluidics developed with micro-chips can generate highly independent and uniform micro- or nano-liter droplets, in which single cells can be encapsulated, inoculated, detected, and analyzed for strain engineering. It is especially useful in the evolution of microbial strains for producing extracellular products. In this review, we first introduce the basic components of the droplet-based microfluidic system and the main steps involved in the strain screening. We then summarize key factors for the application of the droplet-based microfluidic technology in strain engineering, such as the signal sources of droplet detection, the difficulties of handling droplet screening, and the scopes of droplet sorting instruments. Based on the instruments used for the droplet sorting, we group the application cases into two types either via fluorescence-activated droplet sorting (FADS) using microfluidic equipment or via fluorescence-activated cell sorting (FACS) using flow cytometry instrument. While FADS using single-layer water-in-oil droplet can be further classified into cellular signature, fluorescent reporter protein, and substrate-based reaction according to the signal sources, FACS can be divided into double-layers water-in-oil-in-water (W/O/W) droplet or microgel droplet according to the droplet property. Finally, we outline challenges and prospects for the droplet microfluidic technology, and provide some guidelines for its applications in synthetic biology. Compared with traditional screening methods such as shaking flask or microplate with a throughput of hundreds to thousands of samples per day in milli- or micro-liter volume, the droplet-based microfluidic technology can achieve millions of samples per day in pico- or nano-liter volume, resulting in an increase of thousand-folds in screening speed and cost-saving for million-folds. By integrating with an automated station, the droplet-based microfluidic technology can be further improved for its screening efficiencies and application potentials in microbial synthetic biology. {L-End} Table and Figures | Reference | Related Articles | Metrics

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Mining, engineering and functional expansion of CRISPR/Cas systems Ke LIU, Guihong LIN, Kun LIU, Wei ZHOU, Fengqing WANG, Dongzhi WEI Synthetic Biology Journal    2023, 4 (1): 47-66.   DOI: 10.12211/2096-8280.2021-022 Abstract （2177）   HTML （209）    PDF（pc） （2351KB）（1736）       Knowledge map    Save The clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas) were derived from an acquired immune system in microbes. Since their functions on gene editing have been reported, they have been rapidly used to enhance our ability to edit, regulate, annotate, detect, and image DNA and RNA fragments of various organisms, which consequently have faciliated fundamental research in life science, medicine, bioengineering and so on, and drived the development of synthetic biology and other disciplines. However, CRISPR/Cas systems also have some inherent drawbacks, such as off-target effect, constraint of protospacer-adjacent motif (PAM) on the target, and controllability of the gene editing activity, which substantially compromise their applications in highly precise and controllable gene editing. In order to overcome these challenges, two important strategies have been employed to develop enhanced CRISPR/Cas systems and expand the CRISPR toolbox, including modifying the Cas proteins by protein engineering and mining novel CRISPR/Cas systems with bioinformatics. In the review, focusing on the most widely used the type II CRISPR/Cas systems, we mainly introduce the basic structures and functions of three representative systems, including CRISPR/Cas9, CRISPR/Cas12a and CRISPR/Cas13a, as well as recent progress in their structural modifications and functional expansion. Thereinto, engineering strategies for CRISPR/Cas systems have been systematically commented, which include modification methods for Cas proteins and the way to expand the function of CRISPR/Cas systems by coupling specfic proteins with Cas. In addition, we also review some novel CRISPR/Cas systems with important characteristics and potential applications that have been discovered in recently years, such as CRISPR/CasФ and CRISPR/Cas12k. These engineering modifications and mining work have greatly addressed the inherent problems of the CRISPR/Cas systems, and effectively expanded their functions and applicability, which will further promote the applications of the CRISPR/Cas systems in many fields. {L-End} Table and Figures | Reference | Related Articles | Metrics

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Biosynthesis of antimicrobial peptides and its medical application Daixu WEI, Hailun GONG, Xuwei ZHANG Synthetic Biology Journal    2022, 3 (4): 709-727.   DOI: 10.12211/2096-8280.2022-001 Abstract （2171）   HTML （202）    PDF（pc） （1946KB）（1782）       Knowledge map    Save Due to their broad-spectrum antibacterial activity and low incidence of drug resistance, natural antimicrobial peptides have become a potential alternative to antibiotics. In addition to being able to control pathogenic bacteria and fungi, antimicrobial peptides also have many other biological effects, such as anticancer, antiviral, antiparasitic and immunomodulatory activity, exhibiting broad biomedical application prospects. This review introduces the distribution and mechanisms of antimicrobial peptides, and summarizes the biosynthesis methods of antimicrobial peptides. We further compare and analyze the advantages and disadvantages of various antimicrobial peptide biosynthesis approaches relying on microbial expression systems and introduce new interdisciplinary peptide-design strategies based on synthetic biology. In addition, we also briefly summarize the applications of antimicrobial peptides. The application prospects of antimicrobial peptides can be classified into seven medical fields, including antiinflammatory drugs, antiviral drugs, antiparasitic drugs, anticancer drugs, medical tissue engineering, drug delivery systems, skin care and cosmetology. Furthermore, we also identify potential problems such as low expression yield, difficulty in extraction, high process cost, poor stability and insufficient biosafety of existing antimicrobial peptides. To solve these issues, computational prediction and directed gene editing technology can be used to create new antimicrobial peptides with improved antibacterial properties and reduced toxicity. It is also important to improve the industrial infrastructure of antibacterial peptide biosynthesis and develop strategies for rapid recovery of high-purity antibacterial peptides. Antimicrobial peptides can also be combined with existing antibiotics to prevent bacterial resistance to traditional antibiotics. Finally, antimicrobial peptides can be combined with new biomaterials to reduce their toxicity to tissues and organs in vivo. Table and Figures | Reference | Related Articles | Metrics

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Plant synthetic biology for carbon peak and carbon neutrality Jianzhao YANG, Xinguang ZHU Synthetic Biology Journal    2022, 3 (5): 847-869.   DOI: 10.12211/2096-8280.2022-034 Abstract （2145）   HTML （185）    PDF（pc） （2323KB）（1314）       Knowledge map    Save Synthetic biology is an interdisciplinary research field, for which complete quantitative research systems have been established in bacteria, yeast, and mammalian cells. However, synthetic biology in plants is still at its infancy. Plant synthetic biology can play important roles in synthesizing plant natural products, developing molecular farming, improving photosynthesis to increase light energy utilization efficiency, designing carbon farming plants, and building plant factories. In the current efforts in creating a carbon neutral society, plant synthetic biology can help to address challenges of food shortage, energy crisis, and environmental pollution. Specifically, innovative methods can be developed to reduce the emission of CO2 and pollutants through plant production of high value products, whose industrial production is mostly associated with high CO2 emission. Moreover, plant synthetic biology can be used to optimize plant production through minimizing carbon emissions and reducing the use of chemical fertilizers and pesticides. Furthermore, plants specialized in carbon capturing, such as high photosynthetic efficiency, large root systems, and high resistance to degradation, should be developed as well. Various options for increased photosynthetic efficiency, such as optimizing the antenna size of photosystem, converting C3 to C4 photosynthesis, introducing CO2 concentrating mechanisms, and establishing the photorespiration bypasses into C3 crops, holds the potential to dramatically increase the carbon capturing capacity for improved productivity. In the future, in addition to crops, trees and algae can also be engineered to become efficient carbon sinks. Photosynthetic algae are expected to become a source of clean energy and industrial production system with zero or negative carbon emissions. In the long term, a complete plant factory system, which has optimal control of light, temperature, CO2, water, and nutrient, will be developed to achieve optimal plant growth and production while maintaining maximal carbon capturing capacity. Finally, artificial photosynthesis also promises to be an ideal solution as an energy production system. These aspects will be facilitated by the rapid development of plant synthetic biology tools, including biological part standardization, genetic circuits design, and directed evolution. This paper summarizes the major progresses of plant synthetic biology and prospects the major roles of plant synthetic biology in the future efforts in carbon emission peak and carbon neutrality. Table and Figures | Reference | Related Articles | Metrics

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Biological carbon fixation: from natural to synthetic Lu XIAO, Yin LI Synthetic Biology Journal    2022, 3 (5): 833-846.   DOI: 10.12211/2096-8280.2022-042 Abstract （2105）   HTML （241）    PDF（pc） （1528KB）（1654）       Knowledge map    Save In recent years, the increase in the atmospheric concentration of CO2 has caused serious environmental problems such as climate change, and carbon neutrality is presently a topic of global interest. Achieving carbon neutrality means to convert the atmospheric CO2 into carbon-based compounds. Converting CO2 into organics that can be used by humans is one of the effective ways to utilize CO2. Among them, biological carbon fixation has received great interest. In nature, plants and microbes can fix CO2 through carbon fixation pathways. Researchers have also designed several novel artificial carbon fixation pathways for CO2 fixation. Research on biological carbon fixation has mainly focused on the modification of natural carbon fixation pathways and the design and synthesis of artificial carbon fixation pathways. Since the carbon atom in the CO2 is in the highest oxidation state and the reduction of CO2 into organics requires energy input, the input of reducing power and energy is one of the key factors determining the efficiency of carbon biofixation. This review summarizes the advances achieved in recent years in the engineering of natural carbon fixation pathways and the design and construction of artificial carbon fixation pathways. The efficiencies of the artificial carbon fixation pathways, including the utilization of CO2-derived one carbon compounds, and the natural carbon fixation pathways are compared. Subsequently, we highlight the importance of reducing power and energy supply in the process of artificial biological carbon fixation, including chemical energy such as ATP and reducing power, light energy and electric energy. Finally, we analyze the challenges and trends of biological carbon fixation in terms of pathways and energy, and propose strategies for future research on biological carbon fixation. Table and Figures | Reference | Related Articles | Metrics

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Rewiring and application of Pichia pastoris chassis cell Qi LIU, Zhilan QIAN, Lili SONG, Chaoying YAO, Mingqiang XU, Yanna REN, Menghao CAI Synthetic Biology Journal    2022, 3 (6): 1150-1173.   DOI: 10.12211/2096-8280.2022-039 Abstract （2050）   HTML （254）    PDF（pc） （2981KB）（1828）       Knowledge map    Save Microbial chassis hosts are important platforms for green and sustainable biomanufacturing. Pichia pastoris has served as a preferred chassis for heterologous protein expression and fermentation production, which is attributed to its numerous advantages in expression capacity, post-translational modification, high cell density culture, and extracellular product purification. Moreover, as an industrial methylotrophic yeast, P. pastoris effectively utilizes cheap and widely sourced methanol as the sole carbon source, making it a potential biotransformation platform for C1 compounds. Recently, scientists have endowed this nonconventional yeast as an efficient microbial cell factory for biosynthesis of small molecule products beyond its traditional role of a protein expression workhorse. The growing of synthetic biology and biopharmaceutical technology has promoted the rapid development on the genetic rewiring of P. pastoris chassis host. A series of engineering strategies have been developed to break the restrictions and bottlenecks of P. pastoris in both academic and industrial applications. This allowed the updated chassis versions adapting to diversified application scenarios. In this review, we briefly introduce the advances and current status of P. pastoris. We describe the development and application of this chassis from the genetic manipulation technology, regulation of gene expression, and metabolic engineering. We summarize the establishment and characterization of synthetic biological techniques, regulatory parts and devices, novel expression platform, and bioconversion system in P. pastoris. We emphasize the CRISPR-mediated gene editing, transcription regulation, rewiring of natural transcription system, and the design of artificial biosystems. Then the production of glycoprotein and the synthesis of natural products based on alcohols are concisely summarized. Also, the advantages and limitations of this host in practical application are analyzed and discussed. Finally, we propose the research directions for further updating versions of P. pastoris and provide a perspective on their future application scenarios. Table and Figures | Reference | Related Articles | Metrics

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Review of research on unspecific peroxygenases (UPOs) Mingyuan LAI, Jian WEI, Jianhe XU, Huilei YU Synthetic Biology Journal    2022, 3 (6): 1235-1249.   DOI: 10.12211/2096-8280.2022-028 Abstract （2043）   HTML （109）    PDF（pc） （2023KB）（1052）       Knowledge map    Save The selective insertion of oxygen species through unactivated C—H bonds is one of the most challenging tasks in organic synthesis. Fungal unspecific peroxygenases (UPOs) are a class of highly glycosylated thioheme enzymes that catalyze reactions including hydroxylation of unactivated C—H bonds in n-alkanes, epoxidation of alkenes and aromatics, oxidation of heteroatom (N, S) compounds, ether cleavage, N-dealkylation, deacylation and one-electron oxidation of phenols. As one of the most promising oxidases in synthetic chemistry,UPOs use H2O2 as the oxygen donor and the electron acceptor, and do not require cofactors other than heme. This paper reviews the classification and development process of UPOs, and focuses on the heterologous expression, selectivity engineering and H2O2in situ regeneration of UPOs. Since the first discovery of UPOs from Agrocybe aegerita in 2004, UPOs have attracted much attention due to the advantages described above. However, the difficulties of heterologous expression and poor selectivity of UPOs still limit their development. The difficulty of heterologous expression makes it hard to mine new variants of UPOs, and native UPOs are difficult to be characterized and applied in biocatalysis due to the slow growth rate of their hosts. In the past two years, important breakthroughs have been made in the heterologous expression of UPOs through the modification or replacement of signal peptides, revealing the important role of signal peptides in this process. However, the specific role of signal peptides in the secretory expression and three-dimensional structure formation of UPOs remain elusive. With the in-depth research on the mechanism of signal peptides affecting the heterologous expression of UPOs and the development of artificial intelligence (AI) algorithms, the combination of genome mining and signal peptide prediction will be the key for discovering new UPOs. The poor selectivity of UPOs also hinders the development and application of UPOs. This paper reviews different types of reactions that UPOs catalyze, and reveals the problem that UPOs have broad substrate range but poor selectivity. In-depth research on the structure-function relationship of UPOs and the development of protein structure prediction algorithms will help the engineering of UPOs and lay a foundation for solving the problem of poor substrate selectivity. This paper also compares several methods for the in situ regeneration of H2O2, and concluded that the multi-enzyme cascade method is the most economical and practical method for the in situ regeneration of H2O2. 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 （1869）   HTML （225）    PDF（pc） （3481KB）（1931）       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