(1)Development of Knowledgebases and Models for Industrial Organisms: We created comprehensive knowledgebases for model organisms, including E. coli and C. glutamicum, by aggregating data from various databases and literature sources and integrating it with analytical tools. Through standardized network reconstruction and quality control processes, we reconstructed genome-scale metabolic models for multiple organisms. Additionally, we developed a framework for building multi-constraint models (MCMs) by incorporating enzyme and thermodynamic constraints, successfully applying this approach to construct high-quality MCMs for several industrial organisms.   

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)Design of metabolic pathways and metabolic engineering strategies: We developed web tools, including CAVE and QHEPath, to calculate and visualize optimal metabolic pathways and heterologous pathways. We designed and constructed several nonnatural C1 utilization pathways through a novel CombFBA algorithm. New tools for high throughput editing sequence design and optimization of gene operation schedules are developed, facilitating the seamless integration of computation design and experimental construction of strains in the Biofoundry platform.

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)Enzyme Function Prediction, Mining, and Design via AI and Structural Analysis: We developed an AI-based approach to accurately predict the EC number and subunit composition of proteins. By combining multiple methods for assessing reaction similarity and quantitatively predicting enzyme parameters, we created a web tool named REME, which enables the efficient mining and screening of enzyme candidates for non-natural reactions. Additionally, we established several workflows for optimizing protein stability, activity, and selectivity through detailed structural analysis, successfully applying these methods in the design of numerous enzymes and biosensors.

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.