When artificial proteins become a national strategy, who will speed up the R&D?

In the current era where synthetic biology has become a national strategic emerging industry, artificial proteins have leaped from cutting-edge lab research concepts to the core key track supporting the upgrade of China's bioeconomy and the transformation of green industries. With intense multi-level policy support and continually booming market demand, industrialization is still long limited by industry pain points such as low protein R&D efficiency, high trial-and-error costs, and difficulty in practical implementation.

In recent years, from top-level policy design to the implementation of local industries, it has become clear that artificial proteins are no longer a niche research field—China's National Development and Reform Commission's "14th Five-Year Plan for Bioeconomy Development" explicitly proposes exploring the R&D of new food materials, and multiple departments continue to promote the industrialization of synthetic biology, accelerating the commercial layout of future food resources like microbial proteins. Artificial proteins are becoming a core strategic issue concerning national economy, people's livelihood, and the future of industry.

1. Deep dive: What exactly are artificial proteins?

Artificial proteins (synthetic/artificial proteins) are primarily defined as functional protein molecules that are purposefully modified, newly designed, or synthetically created using artificial technologies. Unlike the common perception that 'alternative proteins = artificial proteins,' artificial proteins represent a complete, layered, precise biotech manufacturing system—the core lies in 'molecular-level artificial design,' rather than 'substituting animal sources.'

Based on design difficulty, technical routes, and innovation levels, artificial proteins can be clearly divided into three technical tiers, corresponding precisely to the full spectrum of industry applications from mature use to frontier research:

Tier 1: Directed Evolution Proteins (mainstream in industry)

Using naturally existing proteins as a core skeleton, protein performance is optimized through techniques like random mutations and high-throughput directed screening. This approach does not disrupt the core structure of natural proteins and falls under 'optimization and improvement.' It is currently the most mature technology, lowest-cost to implement, and the most widely applied in industrial scenarios, suitable for general applications such as food ingredients, industrial enzymes, and biomedicine.

Tier 2: Rationally Designed Proteins (refined advancement)

This relies on the relationship between protein structure and function, precisely targeting amino acid residues or functional regions for site-specific modifications and enhancements, giving natural proteins new functions and improved performance. For example, optimizing enzyme active sites for new substrates or enhancing antibody binding affinity and specificity. It addresses industry pain points like limited natural protein functionality or insufficient activity and is commonly used in biomedicine and high-end industrial manufacturing.

Tier 3: De Novo Designed Proteins (ultimate tier)

As the highest barrier in artificial protein technology, this path does not rely on any natural protein templates. It designs completely new amino acid sequences and 3D structures from scratch based purely on industrial functional needs, creating molecules that do not exist in nature and possess performance far beyond natural proteins.

The 2026 Nature review clearly points out that de novo design of artificial proteins has made breakthroughs in binding proteins, stabilizing the skeleton, and some enzyme activities, but core challenges such as catalyzing new reactions, dynamic allosterism, and in vivo function prediction remain cutting-edge challenges; The industry is gradually moving from "whether it can be designed" to a stage of "precisely designing for industrial needs."

2. Policy Support Blue Ocean Market: Artificial Protein Enters a Golden Window Period

The rapid rise of the artificial protein industry is the inevitable result of both policy dividends and market demand driving both direction. Among the three major technological levels of artificial protein, alternative proteins, as the main application direction in the food sector, have experienced a market explosion first—the market size and growth data described in this section are all based on the alternative protein track, reflecting the broad prospects of the overall artificial protein industry.

On the policy front, industrial support continues to be upgraded. From the National Development and Reform Commission's "14th Five-Year Plan for Bioeconomy Development" explicitly proposing to explore the development of new foods such as "artificial protein," to the 15th Five-Year Plan outline proposing to include biomanufacturing as a key cultivation for future industries and actively develop synthetic biology technologies to expand new protein sources, to Beijing's Pinggu District issuing the "Action Plan to Accelerate Innovation and Development of the Alternative Protein Industry (2025–2027)," aiming to cultivate 2 to 3 globally leading disruptive alternative protein technologies by 2027, China has established a complete policy support system with national-level top-level layout and precise local implementation.

At the market level, the global and domestic sectors have huge growth potential:

Global Market—According to Fortune Business Insights, the global alternative protein market is expected to reach $11.88 billion by 2025, is expected to reach $12.77 billion in 2026, and is expected to surpass $23.25 billion by 2034, with a compound annual growth rate of 7.78%; Industry data shows that in recent years, the compound growth rate has exceeded 9.9%. Additionally, according to Boston Consulting Group (BCG), the global alternative protein market is expected to reach $290 billion by 2035.

China Market—According to the IndexBox report, the total size of China's protein ingredient market will reach $75 to $90 billion by 2035, with the share of alternative proteins growing from less than 3% in 2026 to 8% to 12%, with several-fold growth potential over the next decade. Domestic industries can effectively reduce arable land occupation and industrial carbon emissions, leverage 15 to 25 billion yuan in GDP increment, create 500,000 to 900,000 jobs, and highlight both economic and social value.

It is worth noting that alternative proteins (such as microbial proteins and cell-cultured meat) are just one subcategory of artificial protein applications in the food sector. The industrial landscape of artificial proteins is far broader than "alternative animal and plant sources"—from antibody drugs for cancer treatments to artificial enzymes that break down plastics, and biodegradable protein materials, artificial proteins are permeating every corner of the industry.

3. Core Bottleneck in Industrialization: Why Is Mature Technology Difficult to Implement?

 Traditional Research and Development and AI-powered precision screening

Although the path for artificial protein technology is clear and the market prospects are broad, the industry still faces fundamental bottlenecks that are hard to overcome when moving from lab research results to industrial-scale production. The main pain points focus on three areas: R&D efficiency, cost, and success rate.

Protein molecules have extremely complex structures. The traditional R&D model heavily relies on the experience of senior experts and massive trial-and-error experiments, making the process long, costly, and with a very low success rate. Take a protein with 361 amino acids as an example: in theory, there are nearly 7,000 possibilities for a single amino acid change, over 23 million possibilities for double amino acid changes, and a staggering 53.3 billion possibilities for triple amino acid changes. Although not all combinations are tested in actual research, the vast search space still makes traditional trial-and-error R&D extremely difficult.

In terms of industry R&D cycles, it takes about five years to train a qualified protein engineer using the traditional model, and completing one successful protein modification iteration can take another 2-5 years. The overall R&D efficiency simply can't keep up with the fast pace of industrial development. At the same time, traditional trial-and-error R&D requires huge numbers of samples, expensive equipment, and consumables, and multimillion-dollar investments may not even yield effective results. Overall, the industry's success rate in R&D has remained low for a long time.

In short, the core issues that have long held back the large-scale development of the artificial protein industry are: "hard to design, slow to iterate, expensive, and difficult to implement."

4. AI Breakthrough: How Conversational R&D is Reshaping the Artificial Protein Industry Paradigm

 End-to-end AI-driven protein design for artificial intelligence agents

Facing the combinatorial explosion dilemma of traditional R&D, the core value of AI lies in shifting from 'trial and error one by one' to 'precise navigation.' Deep neural networks learn from massive protein sequence-structure-function data, mastering the mapping of 'what kind of sequence is likely to have what function,' enabling the precise identification of high-probability candidates directly from an astronomically large sequence space, compressing the screening scope from billions to hundreds.

In 2024, AlphaFold2 won the Nobel Prize in Chemistry for its breakthrough in protein structure prediction, marking the official mainstreaming of AI-driven protein design in the industry. Extending AI capabilities from structure prediction to functional design and linking experimental verification into a closed loop is the focus of continuous exploration by domestic teams on the industrial front.

MatwingsVenus™ (Xiaowu™) by Shanghai Matwings Technology represents this direction. Released in April 2026, this conversational protein R&D agent builds a one-stop R&D closed loop centered around the agent: users input functional requirements in natural language, the system automatically decomposes tasks, schedules tools, completes sequence design, and connects with automated labs through a dry-wet closed-loop model—the AI-designed candidate sequences can directly enter experimental preparation and functional testing, with real experimental data fed back to drive the next iteration, forming a continuous 'design → validate → redesign' optimization cycle.

In practical projects, the platform has delivered and validated 40 artificial protein design projects, covering various fields such as innovative drugs, in vitro diagnostics, industrial enzymes, and nutritional health. In a de novo design project for a certain immunomodulatory receptor target, the platform successfully generated dozens of entirely new binding molecules with in vitro cell blocking activity from scratch, fully validating the feasibility of the entire chain from artificial design to functional realization.

5. Outlook: The 'iPhone moment' for artificial proteins

The popularization of artificial protein technology is overturning the old model of relying on animals and plants for protein feeding and live extraction, ushering in a new era of bio-manufacturing characterized by 'artificial creation, precise customization, and green mass production.' Backed by the national dual-carbon strategy, food security strategy, and bio-economy strategy, artificial protein has already become a highly promising emerging field.

With the iteration and spread of AI intelligent research platforms, the professional barriers to artificial protein development are continuously breaking down—what used to be cutting-edge technology mastered by a few experts is gradually becoming standardized capabilities that the industry can access.

Technological innovation is accelerating the shift of artificial protein from the lab to large-scale industrialization. From breakthroughs in underlying AI technologies to full-chain empowerment through intelligent research platforms, and onto diverse applications downstream, the foundation for the artificial protein industry is being fully strengthened. In the future, driven by AI and implemented via intelligent research platforms, the artificial protein industry will continue to fuel innovation in biomedicine, green industrial transformation, and the upgrade of future food, opening a brand-new vision for high-quality development of China’s bio-economy.