代表性成果

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（1）菌种知识库和高精度细胞模型构建：针对大肠杆菌和谷氨酸棒杆菌等工业模式菌种，汇集数据并整合相关数据分析方法构建一站式菌种知识库; 开发基因组规模代谢网络模型构建和质控流程，针对十余个物种构建高质量代谢网络模型；开发整合酶和热力学约束的多约束细胞模型构建框架，针对大肠杆菌、谷棒杆菌等多种模式生物构建了高精度多约束细胞模型。

1. Wang Y, Mao Z, Dong J, Zhang P, Gao Q, Liu D, et al. Construction of an enzyme-constrained metabolic network model for Myceliophthora thermophila using machine learning-based k(cat) data. Microb Cell Fact. 2024;23(1):138.

2. Mao Z, Niu J, Zhao J, Huang Y, Wu K, Yun L, et al. ECMpy 2.0: A Python package for automated construction and analysis of enzyme-constrained models. Synth Syst Biotechnol. 2024;9(3):494-502.

3. Yuan Q, Wei F, Deng X, Li A, Shi Z, Mao Z, et al. Reconstruction and metabolic profiling of the genome-scale metabolic network model of Pseudomonas stutzeri A1501. Synth Syst Biotechnol. 2023;8(4):688-96.

4. Yu J, Wang X, Yuan Q, Shi J, Cai J, Li Z, et al. Elucidating the impact of in vitro cultivation on Nicotiana tabacum metabolism through combined in silico modeling and multiomics analysis. Front Plant Sci. 2023;14:1281348.

5. Wu Y, Yuan Q, Yang Y, Liu D, Yang S, Ma H. Construction and application of high-quality genome-scale metabolic model of Zymomonas mobilis to guide rational design of microbial cell factories. Synth Syst Biotechnol. 2023;8(3):498-508.

6. Wu P, Yuan Q, Cheng T, Han Y, Zhao W, Liao X, et al. Genome sequencing and metabolic network reconstruction of a novel sulfur-oxidizing bacterium Acidithiobacillus Ameehan. Front Microbiol. 2023;14:1277847.

7. Wu K, Mao Z, Mao Y, Niu J, Cai J, Yuan Q, et al. ecBSU1: A Genome-Scale Enzyme-Constrained Model of Bacillus subtilis Based on the ECMpy Workflow. Microorganisms. 2023;11(1).

8. Niu J, Mao Z, Mao Y, Wu K, Shi Z, Yuan Q, et al. Construction and Analysis of an Enzyme-Constrained Metabolic Model of Corynebacterium glutamicum. Biomolecules. 2022;12(10).

9. Mao Z, Wang R, Li H, Huang Y, Zhang Q, Liao X, et al. ERMer: a serverless platform for navigating, analyzing, and visualizing Escherichia coli regulatory landscape through graph database. Nucleic Acids Res. 2022;50(W1):W298-304.

10. Luo J, Yuan Q, Mao Y, Wei F, Zhao J, Yu W, et al. Reconstruction of a Genome-Scale Metabolic Network for Shewanella oneidensis MR-1 and Analysis of its Metabolic Potential for Bioelectrochemical Systems. Front Bioeng Biotechnol. 2022;10:913077.

11. Yang X, Mao Z, Zhao X, Wang R, Zhang P, Cai J, et al. Integrating thermodynamic and enzymatic constraints into genome-scale metabolic models. Metab Eng. 2021;67:133-44.

（2）代谢途径和菌种代谢工程改造策略设计：开发了面向过表达靶点和外源基因插入靶点确定的最优途径分析设计工具CAVE和异源途径设计工具QHEPath，方便生物学家使用相关工具快速确定改造策略。开发了非天然途径设计组合算法并设计构建多条全新非天然一碳利用途径。开发了面向生物铸造厂高通量大批量菌种改造的编辑序列设计工具和基因操作排程设计方法，实现菌种设计和构建的无缝衔接。

1. Yang C, Yang Y, Chu G, Wang R, Li H, Mao Y, et al. AutoESDCas: A Web-Based Tool for the Whole-Workflow Editing Sequence Design for Microbial Genome Editing Based on the CRISPR/Cas System. ACS Synth Biol. 2024;13(6):1737-49.

2. Wei F, Cai J, Mao Y, Wang R, Li H, Mao Z, et al. Unveiling Metabolic Engineering Strategies by Quantitative Heterologous Pathway Design. Adv Sci (Weinh). 2024:e2404632.

3. Yang X, Mao Z, Huang J, Wang R, Dong H, Zhang Y, et al. Improving pathway prediction accuracy of constraints-based metabolic network models by treating enzymes as microcompartments. Synth Syst Biotechnol. 2023;8(4):597-605.

4. Mao Z, Yuan Q, Li H, Zhang Y, Huang Y, Yang C, et al. CAVE: a cloud-based platform for analysis and visualization of metabolic pathways. Nucleic Acids Res. 2023;51(W1):W70-w7.

5. Cai J, Liao X, Mao Y, Wang R, Li H, Ma H. Designing gene manipulation schedules for high throughput parallel construction of objective strains. Biotechnol J. 2023:e2200578.

6. Wang J, Chen Z, Deng X, Yuan Q, Ma H. Engineering Escherichia coli for Poly-β-hydroxybutyrate Production from Methanol. Bioengineering (Basel). 2023;10(4).

7. Yang Y, Mao Y, Wang R, Li H, Liu Y, Cheng H, et al. AutoESD: a web tool for automatic editing sequence design for genetic manipulation of microorganisms. Nucleic Acids Res. 2022;50(W1):W75-82.

8. Mao Y, Yuan Q, Yang X, Liu P, Cheng Y, Luo J, et al. Non-natural Aldol Reactions Enable the Design and Construction of Novel One-Carbon Assimilation Pathways in vitro. Front Microbiol. 2021;12:677596.

9. Cai T, Sun H, Qiao J, Zhu L, Zhang F, Zhang J, et al. Cell-free chemoenzymatic starch synthesis from carbon dioxide. Science. 2021;373(6562):1523-7.

10. Yang X, Yuan Q, Luo H, Li F, Mao Y, Zhao X, et al. Systematic design and in vitro validation of novel one-carbon assimilation pathways. Metab Eng. 2019;56:142-53.

11. Zheng Y, Yuan Q, Yang X, Ma H. Engineering Escherichia coli for poly-(3-hydroxybutyrate) production guided by genome-scale metabolic network analysis. Enzyme Microb Technol. 2017;106:60-6.

（3）基于结构机理和人工智能的酶功能预测和挖掘设计：开发了基于蛋白语言模型的蛋白催化功能预测和多聚体蛋白亚基个数预测方法。整合多种反应相似性计算和人工智能蛋白功能预测方法开发了针对非天然反应酶挖掘与虚拟筛选的工具平台REME。开发了基于蛋白结构机理的针对蛋白稳定性、活性和选择性进行设计的流程并应用于数十个酶和生物传感器的设计改造。

1. Shi Z, Wang D, Li Y, Deng R, Lin J, Liu C, et al. REME: an integrated platform for reaction enzyme mining and evaluation. Nucleic Acids Res. 2024.

2. Deng R, Wu K, Lin J, Wang D, Huang Y, Li Y, et al. DeepSub: Utilizing Deep Learning for Predicting the Number of Subunits in Homo-Oligomeric Protein Complexes. Int J Mol Sci. 2024;25(9).

3. Cao Y, Qiu B, Ning X, Fan L, Qin Y, Yu D, et al. Enhancing Machine-Learning Prediction of Enzyme Catalytic Temperature Optima through Amino Acid Conservation Analysis. Int J Mol Sci. 2024;25(11).

4. Shi Z, Deng R, Yuan Q, Mao Z, Wang R, Li H, et al. Enzyme Commission Number Prediction and Benchmarking with Hierarchical Dual-core Multitask Learning Framework. Research (Wash D C). 2023;6:0153.

5. Naz S, Liu P, Liu C, Cui M, Ma H. In silico prediction of mutation sites for anthranilate synthase from Serratia marcesens to deregulate tryptophan feedback inhibition. J Biomol Struct Dyn. 2023:1-11.

6. Naz S, Liu P, Farooq U, Ma H. Insight into de-regulation of amino acid feedback inhibition: a focus on structure analysis method. Microb Cell Fact. 2023;22(1):161.

7. Zhang T, Liu P, Wei H, Sun X, Zeng Y, Zhang X, et al. Protein Engineering of Glucosylglycerol Phosphorylase Facilitating Efficient and Highly Regio- and Stereoselective Glycosylation of Polyols in a Synthetic System. ACS Catalysis. 2022;12(24):15715-27.

8. Tian C, Yang J, Liu C, Chen P, Zhang T, Men Y, et al. Engineering substrate specificity of HAD phosphatases and multienzyme systems development for the thermodynamic-driven manufacturing sugars. Nat Commun. 2022;13(1):3582.

9. Lu X, Li J, Li C, Lou Q, Peng K, Cai B, et al. Enzymatic DNA Synthesis by Engineering Terminal Deoxynucleotidyl Transferase. ACS Catalysis. 2022;12(5):2988-97.

10. Li J, Wang S, Liu C, Li Y, Wei Y, Fu G, et al. Going Beyond the Local Catalytic Activity Space of Chitinase Using a Simulation-Based Iterative Saturation Mutagenesis Strategy. ACS Catalysis. 2022;12(16):10235-44.