Contribution

AIS-China 2025 made integrated contributions to the synthetic biology community by developing both biological components and methodological frameworks that expand the design space of enzyme engineering, modeling, and sustainability evaluation.

Through our HullGuard project, we not only constructed new parts for zosteric acid (ZA) biosynthesis but also established a full modeling workflow, a public Modeling Whitebook, a commercial feasibility framework, and a cross-SDG analytical model.

AIS-China 2025 constructed and submitted over twenty standardized genetic parts in the HullGuard project, encompassing mutated enzymes, fusion proteins, linker modules, and composite constructs. Together, these parts form a modular system centered on Zosteric Acid (ZA) biosynthesis, enabling multi-enzyme cascade reactions and metabolic pathway optimization in broader synthetic biology research.

Existing pathway modules

To establish a complete ZA biosynthetic pathway, we integrated several standard BioBrick modules. SULT1A1, TAL, and cysDNCQ served as the core enzymatic components connecting precursor generation, intermediate conversion, and PAPS cofactor regeneration. Specifically, SULT1A1 catalyzes the sulfation of p-hydroxycinnamic acid, TAL converts L-tyrosine into p-coumaric acid, and the cysDNCQ operon supports intracellular PAPS synthesis. These existing parts together established the fundamental metabolic framework for ZA biosynthesis, upon which our new parts and design strategies were developed.

New basic parts

We performed rational mutagenesis on SULT1A1 (M1–M12) and identified the optimal variant M12 (Y42F, Y236W, P250T, T256C), achieving approximately 2.5 times higher ZA conversion than the wild type. To further improve pathway efficiency, we constructed two upstream enzymes—KIATPSL and PcAPSK—to enhance PAPS regeneration, and developed three types of linker modules: flexible (GGGGS)₂, rigid (EAAAK)₂, and modular SpyTag/SpyCatcher systems to support multi-enzyme cooperation under different spatial constraints. Among these, the flexible (GGGGS)₂ linker achieved the best catalytic throughput, improving ZA yield by approximately 3.6times. In addition, SULT1A1-2GS-TAL (BBa_25SPDVOA) was registered as a new basic part, demonstrating enhanced expression stability and catalytic efficiency.

Composite parts (representative examples)

• KIATPSL + PcAPSK (BBa_25FRDAI1) — enhances upstream cofactor regeneration.
• SULT1A1-M12 + TAL (BBa_25LD9YEH) — improves catalytic conversion via co-expression of optimized enzymes.
• TAL-SpyTag / SpyCatcher-SULT1A1 — enables non-covalent proximity-based channeling between enzymes, demonstrating the potential of modular assembly though with limited efficiency under certain spatial constraints.

Collectively, these parts establish a closed-loop workflow—from mutation screening to fusion design, modular assembly, and flux optimization—providing standardized, validated, and reusable tools for future studies in multi-enzyme pathway engineering, metabolic optimization, and eco-friendly antifouling systems. All parts have been uploaded to the iGEM Registry with complete sequence data, functional descriptions, and experimental validation, ensuring transparency, traceability, and reusability.

Module 1: Targeted enzyme engineering

By quantitatively analyzing ZA production data, we identified SULT1A1 as the key rate-limiting enzyme within the biosynthetic pathway. Using AutoDock Vina, the binding pockets for both PAPS and pHCA were mapped, enabling precise localization of catalytic residues and substrate interaction domains. This preliminary modeling step defined the structural foundation for subsequent mutagenesis, ensuring that modifications were directed toward regions with the highest potential to relieve kinetic bottlenecks.

Subsequently, large-scale ConSurf analysis covering more than one thousand homologous sequences was conducted to investigate residue conservation patterns. Variable regions overlapping with catalytic centers were identified, and four residues—Y42, Y236, P250, and T256—were prioritized as key mutation targets. These positions were hypothesized to influence substrate positioning, cofactor binding, and overall enzyme flexibility, providing a rational basis for systematic mutagenesis.

To further evaluate candidate variants, both FoldX and RosettaDDG were employed to calculate the free-energy changes (ΔΔG) associated with single and combined mutations. From these predictions, twelve SULT1A1 variants (M1–M12) were designed and experimentally verified. Among them, the M12 mutant showed approximately 2.5-fold higher ZA conversion efficiency compared to the wild type, validating the predictive accuracy of our computational modeling pipeline and confirming the success of this enzyme optimization strategy.

Module 2: Fusion Protein Design

We designed multi-domain fusion constructs by leveraging the structural complementarity between TAL and SULT1A1, and employed AlphaFold predictions to evaluate the conformational influence of different linker types, including flexible (2GS), rigid (2EA), and modular SpyTag/SpyCatcher systems. Experimental validation revealed that the flexible (GGGGS)₂ linker (SULT1A1-2GS-TAL) achieved the highest catalytic throughput, increasing ZA production by 3.6times higher compared to the control. The rigid (EAAAK)₂ linker exhibited moderate improvement only when TAL was positioned at the C-terminus, while the SpyTag/SpyCatcher module successfully enabled enzyme co-localization but showed limited efficiency due to spatial mismatch between catalytic domains.

Module 3: Structure–Function Coupling

This module links the outcomes of enzyme optimization and fusion design to structural insights, demonstrating the practical value of our modeling workflow. By comparing the WT and M12 binding models, we observed an increase in the substrate entrance angle—from 112.4° to 130.4°—which corresponds to an expanded catalytic pocket and improved domain alignment. This structural change supports the experimentally observed enhancement in catalytic efficiency, confirming that our computational predictions accurately reflected functional improvements.

Through this structure–function coupling analysis, we verified that the workflow not only guides rational enzyme design but also provides a mechanistic framework connecting modeling results to measurable biochemical outcomes.

To make computational modeling accessible to more iGEM teams, AIS-China 2025 authored and released the Modeling Whitebook — a standardized and open-source guide for protein modeling and enzyme design. This document consolidates our team’s entire modeling workflow into a reproducible framework, lowering technical barriers and enabling future teams to perform rational enzyme design with greater clarity and confidence. By transforming our internal workflow into a structured guide, we aimed to turn computational modeling from a niche skill into a practical, teachable, and collaborative tool for all iGEM participants.

The Whitebook provides step-by-step guidance through every stage of the modeling process. It includes detailed instructions for installing and configuring AutoDock, PyMOL, FoldX, Rosetta, and ConSurf, ensuring smooth environment setup and troubleshooting. It explains how to clean and prepare PDB structures, minimize ligands, and convert between computational formats to ensure consistent inputs. The document also compares FoldX (for rapid ΔΔG screening) and Rosetta (for precise energy validation), as illustrated through the optimization of the M12 SULT1A1 mutant. Additional sections introduce best practices for molecular visualization in PyMOL and provide cross-platform compatibility guidance for Windows, Linux, and macOS.

Ultimately, the Modeling Whitebook redefines the role of modeling in iGEM — transforming it from an isolated analytical step into a standardized, teachable, and reproducible engineering process. It empowers even high-school-level teams to conduct structure-based design independently, promotes methodological transparency, and fosters a community culture of open and collaborative modeling within the field of synthetic biology.

AIS-China 2025 expanded the purpose of modeling beyond scientific validation by establishing a commercial modeling framework that evaluates both economic feasibility and environmental sustainability. This framework integrates Techno-Economic Analysis (TEA) and Life-Cycle Assessment (LCA) to connect the biological design of HullGuard with its real-world industrial and ecological context.

The TEA model provides a transferable method for quantifying production cost, scalability, and market viability, allowing future teams to evaluate whether a biosynthetic pathway can be economically sustained at an industrial scale. The LCA model offers a standardized approach to assessing the environmental footprint of biomanufacturing routes, enabling comparison with conventional chemical or material alternatives.

Together, these models redefine the role of computational modeling in iGEM — transforming it from a purely theoretical exercise into a decision-making framework that bridges science, business, and sustainability. This approach can be adopted by any team seeking to explore the real-world impact and viability of synthetic-biology innovations, effectively bridging the gap between laboratory design and societal implementation.

We established a systematic sustainability-analysis framework spanning from the Event Layer to the Mindset Layer, with the Iceberg Model and SDG Interaction Map at its core.

Iceberg Model

This framework dissects the multi-dimensional impacts of sustainable development through a three-layer structure:

• Event Layer — The ZA bio-coating technology mitigates hull pollution and heavy-metal exposure, directly contributing to SDG 3 (Good Health and Well-Being) and SDG 14 (Life Below Water).
• Patterns & Systems Layer — A collaborative network of Small and Medium-Sized Enterprises (SMEs) and low-cost production restructuring drives coordinated progress toward SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production).
• Mindsets Layer — Open education and public science communication foster sustainability awareness, promoting long-term synergy between SDG 4 (Quality Education) and SDG 12 (Responsible Consumption and Production).

Figure. Iceberg Model: illustrating the connection between SDG 14 (Life Below Water) and SDG 3 (Good Health and Well-Being).

This iceberg model-based analytical framework serves as a universal reference for other SDG researchers. It enables direct reuse of its Event–Structural–Mindset layered logic to avoid fragmented analysis, helps identify SDG correlations and positive feedback mechanisms between goals, and provides insights for balancing short-term feasibility with long-term sustainability.

Interaction Model

The Interaction Model visualizes both positive synergies and negative trade-offs among SDGs, capturing the complex interconnections between global social, economic, and environmental systems. Each goal influences others through shared resources, overlapping stakeholder interests, and systemic interdependencies—revealing the inherent complexity of sustainable development.

Analyzing these interactions is essential: it identifies positive linkages to amplify benefits, forecasts negative spillovers for early mitigation, and shifts sustainability actions from isolated initiatives to integrated strategies. This framework transforms SDGs from symbolic goals into quantifiable, interactive analytical tools, showcasing how synthetic biology can structurally contribute to global sustainable governance.

Through our parts, modeling systems, and sustainability frameworks, AIS-China 2025 has established a replicable platform for rational enzyme design, industrial translation, and global sustainability assessment.

Our contributions extend iGEM’s collaborative ethos — transforming experimental innovation into open knowledge frameworks that future teams can directly use, validate, and expand upon.