Protein purification got you frustrated? What you're missing isn't experience, it's an AI.

At 2 a.m in the morning, you’re sitting in front of the AKTA system, staring at that stubborn impurity peak that just won’t go away, quietly doing the math in your head: two days of cell culture, a whole day of lysis and sample loading, hundreds of bucks on chromatography media and consumables… all wasted. What’s worse, you have no idea where the problem is—is the buffer pH off? Did you miscalculate the imidazole concentration? Or is the upstream sequence design itself flawed?

Protein purification is often said among insiders as a mystical task that’s '30% skill, 70% luck.' But let me be honest with you: it’s 2026, and it’s really time to leave these days of pure ‘‘gambling’’ behind.

Protein purification is a key bridge linking genetic sequence information with protein function research. Essentially, it’s about separating high-purity, naturally active target proteins from complex mixtures like cell lysates or tissue extracts through chromatography, centrifugation, filtration, and so on.

Frontline researchers know all too well how tough protein purification is: Ni columns getting clogged, proteins not binding, too many contaminant bands in elution, proteins precipitating during purification, and final samples losing activity—these issues are all too common. From cell lysis and crude separation to affinity chromatography, ion exchange, and gel filtration, a full experimental round can take anywhere from a few days to several weeks. Proteins are generally sensitive to temperature, pH, and ionic strength, and prone to denaturation, so even a minor deviation in handling can affect results. This has long led the industry to jokingly call it a semi-mystical job, relying on 'feel and experience.'

Even more crucially, purification isn’t an isolated step: upstream protein sequence design and choice of expression system directly affect purification difficulty, while downstream functional assays and activity verification depend on the quality of the purified sample. The entire chain is interconnected; a problem in any link can drastically increase research costs or even render investment wasted. Reducing the heavy reliance of protein purification on personal experience and gradually achieving predictability, standardization, and automation has been a long-term pursuit in the field of protein research.

I. The Three Major "Nightmares" of Protein Purification

Before discussing solutions, let's first lay it down and see where traditional protein purification is really getting stuck.

1. Process development heavily relies on experience and has a low degree of standardization

There is no universal "standard answer" for protein purification. Even for His-tag affinity chromatography, the optimal binding buffer pH, imidazole elution concentration, loading flow rate, and elution gradient may differ significantly among different proteins.

A R&D personnel capable of independently completing purification process development typically require 3~5 years of systematic experimental experience to develop mature process optimization and abnormality handling experience. For newcomers to the field, purification experiment failures and sample scraps are common; Even with the same scheme, experimental results between different operators and batches often fluctuate significantly, with weak process replicability and difficulty forming highly standardized development workflows.

2. Manual monitoring throughout the process, with clear resource constraints

The complete protein purification process covers multiple steps including sample preparation, lysis and centrifugation, multi-step chromatography, concentration and liquid exchange, and purity quality inspection. Most steps require manual operation, resulting in high labor and time costs.

At the same time, core equipment such as AKTA (Series of Fully Automated Chromatography Systems) is expensive, and the accompanying chromatography media and consumables are consumables, making it difficult for most small and medium-sized laboratories and startup teams to provide a complete purification platform. Daily equipment maintenance, consumable selection and procurement, and quality control further encroach on R&D personnel's core research efforts, making capacity and cost common industry bottlenecks.

3. Fragmented upstream and downstream links, low iteration efficiency

This is a particularly prominent structural pain point in traditional R&D models: upstream protein design often does not fully consider puriability, resulting in sequences with poor solubility, easy aggregation/formation of inclusions, insufficient label exposure, and other issues. It is only during the purification stage that it becomes difficult to obtain qualified samples; Moreover, failure data from the purification stage is difficult to accurately and quickly feed back to the design side for targeted optimization.

Information transmission between design and experiment teams relies on manual coordination, which easily leads to information bias and the single-round iteration cycle of "design-expression-purification-validation" often lasts weeks or even months. Multiple rounds of optimization often take several months, leading to inefficient consumption of R&D resources.

II. Breakthrough in Intelligence: The Technological Evolution Path of Protein Purification

The technological iteration of protein purification has always revolved around "reducing experience dependence, improving efficiency, and ensuring stability": from the earliest manual gravity column passing, to equipment automation for chromatography steps like AKTA, and now to the deep integration of AI technology with automated experimental platforms, the industry is entering a new stage of "intelligent decision-making, automated execution, and data closed-loop," effectively alleviating the core bottlenecks of traditional models.

MatwingsVenus™

MatwingsVenus™ (Xiaowu™) protein research AI introduced by Matwings Technology is a representative practice in this technological direction. It’s not just a single purification tool, but seamlessly integrates protein purification into the entire research process, upgrading the purification stage from three aspects:

First, AI-assisted prediction of purification characteristics to output standardized reference plans. Based on protein sequence and structural information, combined with algorithms trained on massive experimental data, it can help predict key protein attributes like solubility, tag binding efficiency, and stability. It can specifically recommend chromatography media, buffer formulas, elution gradients, and flow rate parameters, greatly reducing trial-and-error attempts and lowering reliance on personal experience. Even less experienced researchers can quickly get standardized purification plans that serve as valuable references.

Second, connect with automated experimental platforms to let purification processes run unmanned. The design plan is automatically synced to the automated lab system via standardized interfaces. The mechanical platform can then complete the entire procedure according to preset plans, including sample preprocessing, chromatography purification, concentration and buffer exchange, and purity/activity testing, reducing human error and improving batch-to-batch process stability. With cloud-based scheduling, small and medium teams can also access standardized purification capabilities without investing in expensive equipment.

Third, experimental data is automatically fed back, creating a dry-wet iterative closed loop. The dry-wet loop refers to a research model where dry experiments (AI design, computational simulation) and wet experiments (lab operations) are interconnected for continuous optimization. Experimental data from the purification stage, such as purity, yield, and activity, is automatically fed back to the AI model. This continuously iterates and optimizes the purification parameter library and informs upstream protein design, improving protein purifiability and stability at the sequence level. It enables rapid iteration in the 'design-purification-feedback-redesign' cycle, effectively shortening the overall R&D timeline.

III. Practical application: Industrial value of intelligent purification

1. R&D of immune regulatory target-binding proteins

In a de novo design project for an immune regulatory receptor-binding protein, the AI incorporated purifiability constraints during the design stage and simultaneously provided a purification process reference plan. According to public reports, Matwings Technology has successfully obtained dozens of new binding molecules with in vitro cell-blocking activity using this platform, completing the full validation process for de novo designed binding molecules. Samples prepared via the automated experimental platform performed excellently in in vitro cell activity tests, with dozens of molecules showing both functional inhibition and high-affinity potential.

De novo antibody design targeting specific epitopes of the antigen target

2. Stability Modification of Sweet Protein Monellin

Monellin is a heterodimeric sweet protein composed of A and B peptide chains. In its natural state, the non-covalent interface between the two chains tends to dissociate when the pH deviates from neutral or when heated, leading to irreversible aggregation and inactivation—this is also the structural reason why it easily precipitates during purification. The project uses a multi-round iterative strategy of “intelligent design—automated purification—data feedback,” optimizing the protein's folding stability and stress resistance step by step. After modification, the protein’s precipitation rate during purification is significantly reduced, and activity recovery is noticeably improved. According to experimental data, multiple final mutants have sweetness more than 10 times that of the wild type and much better heat resistance, with a Tm value increased to around 75°C (compared to 50–55°C for the wild type).

3. Alkali-Resistance Modification of Single-Domain Antibodies

In biopharmaceutical production, affinity chromatography is a common protein purification method. However, its core material—the affinity resin—has a limited lifespan under strong alkaline cleaning conditions, resulting in high purification costs. Tainwu Technology, in collaboration with Genor Biopharma, used a protein engineering general model for design, combined with small-scale wet lab iterative validation, and in less than a year improved the alkali resistance of a non-alkali-resistant single-domain antibody used in affinity resins by four times. This allowed it to withstand high-concentration alkali cleaning and significantly extended the resin's lifespan. The product was successfully applied in 5000-liter scale production, becoming the world’s first protein product designed with a large model to achieve 5000-liter industrial production, marking the maturity of AI large-model customized development for high alkali-resistant affinity resin technology.

Industry Outlook: Protein purification will become a standardized R&D infrastructure

Standardized Protein Purification

The fundamental challenge in protein purification is figuring out how to balance the 'complex individual differences of proteins' with the 'stable and efficient R&D needs.' From manual operations to equipment automation, and now to full-process intelligent decision-making loops, the industry is gradually reducing its reliance on personal experience, pushing protein purification to evolve from a highly customized 'craft' into a R&D infrastructure that can be standardized and scaled.

In the future, cloud-based intelligent purification platforms will further lower the R&D barrier—small research teams and startups won't need to build a full purification lab to access the process development and experimental capabilities that used to be available only to big pharma and top institutions. Researchers will also be freed from repetitive trial-and-error experiments and tedious operational details, allowing them to focus more on core explorations like target mechanism studies and functional innovation. This is exactly how tech tools fundamentally empower research efficiency.