Precision Fermentation Meets AI: Transforming Personal Care Ingredient Sourcing and Innovation

Precision Fermentation for Sustainable Ingredient Solutions

While fermentation has been used for the production of products like beer, yogurt, and bread for millennia, most consider the start of precision fermentation (PF) to be in the late 1970s with its transformation of medicine. Genentech’s recombinant human insulin was one of the first major PF products. In 1978, Genentech genetically engineered bacteria to produce human insulin, replacing the old method of extracting insulin from pig and cow pancreases. This biosynthetic insulin (marketed as “Humulin”) proved to be as effective as insulin from the human pancreas while eliminating the allergic reactions caused by animal-derived insulin (1). The new process also solved supply issues—previously it took >50,000 animal pancreases to make one kilogram of insulin (2)—by enabling production at scale via fermentation tanks.

Beyond pharmaceuticals, precision fermentation has rapidly expanded into other markets like the beauty and personal care sector (3). Biotechnology now offers a way to “brew” high-value ingredients in microbes providing an alternative to sourcing them from animals or petrochemicals. Fermentation derived cosmetic actives are growing in demand as brands seek natural, sustainable, and high-performance ingredients. Perhaps the most notable example of this change is Hyaluronic acid (HA), valued for its hydration and viscoelastic properties in skincare. HA was originally obtained from animal tissues, notably rooster combs (4). For decades, companies extracted HA by processing tons of rooster combs, which contain the highest levels of this molecule found in animals. However, this method had downsides: it was costly, yielded limited quantities, and raised ethical and safety concerns (animal parts can introduce impurities or pathogens). HA from animal sources can also vary in quality and carries a risk of immune reactions if not purified thoroughly. Since the early 1990s, most HA has been produced via precision fermentation using microbes, eliminating the need for animal harvest (4, 5). Typically, strains of Streptococcus bacteria are engineered to secrete hyaluronic acid during fermentation. This shift to microbial production was driven by the need for a more controllable, scalable process—it’s easier to brew HA in tanks than to depend on slaughterhouse byproducts.

As with HA, precision fermentation has recently been used to produce proteins of interest such as collagen for personal care applications. Animal collagen production is resource-intensive, and can be linked to environmental degradation and deforestation, particularly in regions such as the Amazon, where livestock farming drives significant ecological damage. Investigations have revealed direct links between collagen sourcing and deforestation practices associated with major industry players, spotlighting an urgent need for sustainable alternatives (6, 7).

On top of the sustainability challenges, commercial collagen for food and cosmetics typically consists of only two fibrillar collagens types: type I and a small fraction of type III (8). For example, type I collagen constitutes around 85-90% of the organic content of skin, followed by type III collagen at less than 10%. These two types (I and III) are valued for their skin-plumping and structural benefits, but they represent just the “structural scaffold” collagens. This is because traditional methods of boiling skins and acid/enzymatic extraction cannot easily isolate other collagen types as those exist in much lower quantities or in hard-to-extract tissues (9, 10). In practice, this means nearly all collagen ingredients available until recently have been the same few types (mostly I, occasionally II or III), but most of the 28 collagens (which account for less than 3% of the total collagen pool) are not accessible in useful quantities from conventional animal sources.

Type 21 collagen is one of the more recently discovered collagen types. It was identified through the Human Genome Project in 2001 when researchers mining genomic data found a gene (COL21A1) encoding a collagen protein (11). Expression studies showed COL21A1 (type 21) is present in many tissues–including heart, skeletal muscle, stomach, skin, kidney, and placenta—and it is described as helping to maintain the integrity of the extracellular matrix (12, 13). A proteomic study of human dermis across ages found COL21A1 is highly expressed in toddler and young adult skin but drastically drops off with age. This reduction could contribute to the reduced dermal elasticity and impaired wound healing seen during aging (14). While Type 21 collagen is present at extremely low concentrations and nearly impossible to isolate from tissues, precision fermentation makes it possible to produce it in the quantities and purity needed for beauty and personal care applications regardless of its natural scarcity.

In conclusion, precision fermentation has fundamentally transformed ingredient sourcing for personal care by providing alternatives to traditional sourcing that can have limitations such as ethical concerns, environmental impacts, and ingredient scarcity.

From Rational Design to Nobel Prize-Winning AI

Early protein engineering was very laborious. It was heavily dependent on sourcing naturally occurring sequences, followed by extensive chemical mutagenesis and the screening of thousands of variants to identify beneficial changes. The first protein created entirely from scratch (de novo-designed protein) without relying on natural sequences was a four-helix bundle protein created by rational design in 1987. It was a landmark development that highlighted the initial potential of computational methods even though it was limited by computational constraints at the time (15, 16). In the 1990s, the landscape changed significantly with directed evolution, pioneered by Frances Arnold, who introduced iterative mutation and selection methods to evolve enzymes with enhanced or novel functionalities. Arnold’s pioneering work was awarded the Nobel Prize in Chemistry in 2018, recognizing directed evolution as a powerful tool for enzyme and protein development (17).

Traditional computational approaches to protein engineering remained limited by predictive inaccuracies. Initial computational attempts at rational protein design typically had low success rates (often below 1%), requiring significant experimental validation (18). Over the subsequent decades, substantial improvements in computational accuracy were achieved, particularly with the development of sophisticated software like Rosetta, which facilitated early successes in protein structure modeling and design (19). Despite these advances, it was the introduction of artificial intelligence, especially deep-learning techniques, that dramatically changed the trajectory of protein engineering, significantly increasing the speed and accuracy of computational protein design.

Recent breakthroughs in AI-driven protein design, notably recognized by the 2024 Nobel Prize in Chemistry, have profoundly reshaped the biotechnology landscape. AlphaFold, developed by DeepMind, has provided scientists with a transformative tool capable of accurately predicting three-dimensional protein structures from amino acid sequences. Nobel Prize laureates Demis Hassabis and John Jumper revolutionized structural biology by developing AlphaFold, which provides researchers with computationally accurate structural predictions rapidly, and at an unprecedented scale (20).

Complementary advances such as RoseTTAFold, developed by David Baker’s laboratory at the University of Washington, similarly harness deep-learning approaches to facilitate accurate predictions and de novo protein design (21). Baker’s work, also recognized with a Nobel Prize, allowed the design of novel proteins with specific functions and folds never previously observed in nature (20). These tools have dramatically improved success rates in protein engineering, from initial success rates of around 1% to contemporary design success rates approaching 10%–100%, depending on the specific protein library and design goals (22).

Moreover, innovative AI platforms such as BindCraft have further refined the capability of computational design by accurately predicting protein-ligand interactions and significantly enhancing specificity and binding affinity. BindCraft leverages the predictive power of AlphaFold2, enabling the rapid design of highly specific and potent protein binders without extensive high-throughput screening (22). Collectively, these developments have transitioned protein engineering from a predominantly empirical discipline to a highly precise computational science, unlocking previously unimaginable innovations in functional protein design.

The Evolution of Protein Engineering in Personal Care

Consumers and brands are increasingly seeking high-performance bioactive ingredients. Brands are eager to introduce novel protein peptides and polypeptides with unique benefits that can improve skin firmness, reduce wrinkles, or protect hair to differentiate their products. These bioactives are becoming a focal point for innovation, as they align with consumer preferences for clean, ethical beauty and promise enhanced efficacy compared to legacy ingredients.

Biotechnology companies are leveraging AI to design and discover these novel bioactive protein peptide and polypeptide ingredients at an unprecedented pace and many are using precision fermentation to sustainably produce them. Leaders in the space include companies like Geltor, Nuritas, and Shiru, that have developed internal AI models in their designs and actively work with other companies through partnerships (23, 24, 25).

The recent explosion of AI models for protein folding and design, both open-source and proprietary, is a key factor enabling new innovations. Importantly, many AI models and databases have been made publicly available, lowering barriers for innovation. DeepMind open-sourced the AlphaFold protein structure predictions for hundreds of millions of proteins, and academic groups have released open tools like ProteinMPNN (for sequence design given a structure) and diffusion models for protein generation. This means even smaller companies or research teams can leverage state-of-the-art AI in their ingredient discovery.

This growing availability of AI models is directly fueling innovation in the personal care market. Open tools let researchers identify new bioactive motifs or simulate protein-surface interactions quickly, inspiring novel ingredient concepts. Proprietary models, fine-tuned on specialized data (such as human skin proteomics, peptide datasets, or natural product libraries), enable companies to discover unique actives with specific benefits (like a polypeptide that triggers extracellular matrix production, or an enzyme that generates a new cosmetic compound). The result is a faster pipeline from idea to ingredient: AI can suggest completely new protein candidates in silico, which are then synthesized via fermentation and tested, shortening development cycles that once took years. We’re already seeing an uptick in AI-discovered personal care ingredients hitting the market and this is just the beginning (26).

Strain Engineering and Metabolic Pathway Optimization

Parallel to protein design advancements, artificial intelligence has also revolutionized strain engineering for microbial production hosts, significantly enhancing metabolic pathway optimization. A key factor in improving sustainability is improving strain performance in converting carbon sources like sugar into the target molecule being produced while limiting CO2 production. Traditional strain optimization relied on random mutagenesis and iterative trial-and-error, often consuming significant resources and time. Similarly to protein design, biotech companies have invested in developing internal proprietary models to aid in the strain improvement process but also utilize publicly available ones.

As an example, Evo 2 represents a major step forward in strain engineering due to its ease of use and extensive data foundation. Developed by the Arc Institute in collaboration with NVIDIA, Stanford University, UC Berkeley, and UC San Francisco, it was trained on an unprecedented dataset containing 9.3 trillion nucleotides from over 128,000 different organisms. Leveraging this massive dataset, Evo 2 can help more accurately analyze and design genetic modifications in order to optimize genomes, design novel genetic circuits, and redirect metabolic pathways (26).

This predictive capability has profound implications for sustainability and scalability. By quickly pinpointing precise genetic changes, models like Evo 2 can significantly reduce the traditional strain engineering timelines by decreasing the need for extensive laboratory trial-and-error approaches. These targeted genetic adjustments can enable production of ingredients with higher yields, improved consistency, and reduced environmental footprint.

Importantly, models like Evo 2 are often open-source which democratizes this powerful technology, enabling even small biotechnology companies and research teams to more rapidly enhance their production strains, accelerate innovation, and further improve their carbon footprint (27).

Challenges and Opportunities

Precision fermentation and AI offer enormous potential to more sustainably produce previously scarce, ethically complex, or completely novel ingredients. This innovation aligns with consumer demands for clean, high-performance ingredients, reshapes markets, and reduces environmental impacts. However, significant challenges remain. Regulatory frameworks must also balance rapid innovation with proactive oversight to manage biosecurity risks (28). Scaling PF processes from lab to commercial volumes frequently encounters hurdles like decreased yields, fermentation inefficiencies, and resource intensity.

Historical examples from pioneering companies like Zymergen illustrate how AI-driven success in the lab often faces significant barriers in commercial production (29). Ingredient manufactures looking to utilize precision fermentation should prioritize standardizing fermentation and downstream purification processes capable of producing diverse proteins using the same infrastructure and process. Companies like Geltor exemplify this approach, having developed standardized precision fermentation and downstream purification protocols that enable manufacturing diverse protein targets using the same scaled production process (23). Such uniformity significantly reduces development timelines from years to months, allowing facilities to rapidly switch between product targets and accelerate time to market.

As the integration and expectations around AI intensify, careful management of the substantial electrical and computational requirements is critical (30). Companies must ensure that the implementation of AI tools genuinely leads to tangible benefits, such as improved sustainability or efficiency, rather than inadvertently increasing carbon emissions or resource consumption. Ultimately, achieving the full promise of precision fermentation and AI-driven synthetic biology requires a balanced approach, combining innovation-friendly regulatory frameworks, sustainable scaling practices, and efficient technological integration.

Conclusion

In conclusion, precision fermentation, together with advancements in AI-driven protein design and strain engineering, is continuing to transform how we create and source ingredients for personal care. These technologies can provide scalable, sustainable solutions to traditional challenges, enabling the rapid development of novel, bioactive ingredients. While hurdles remain, the use and democratization of powerful AI models along with precision fermentation offer tremendous opportunities for innovation, efficiency, and sustainability, positioning the personal care industry for unprecedented growth and consumer satisfaction.