In the previous post, we explored why proteins are the next frontier in biology. While genomics revealed the blueprint of life, proteins are the molecules that carry out its instructions. The challenge is that reading proteins is far harder than reading DNA.
For decades, scientists have relied on a technique called mass spectrometry to study proteins. Modern systems can identify thousands of proteins in a sample and track how they change across different tissues and diseases. But the picture they provide is still incomplete. Most mass spectrometry approaches don’t read a protein from beginning to end. Instead, they break proteins into fragments, measure the weight of those pieces, and match them against reference databases — a bit like trying to identify a book by reading random sentences rather than the whole text. This “protein fingerprinting” approach works well for proteins we already know, but it struggles to catch subtle variations, rare protein forms, or chemical modifications. Crucially, these measurements also represent averages across millions of molecules, which can hide important differences between individual proteins¹.
In diseases like cancer or neurodegeneration, those differences can matter enormously. Two cells that look genetically identical may produce slightly different versions of the same protein and that variation can influence how a disease develops or how a patient responds to treatment. This is known as the proteoform problem: a single gene can give rise to many distinct protein variants with different functions². It’s the central bottleneck in proteomics: we know the information is there, but our tools can’t yet read it clearly.
Researchers are now aiming to do for proteins what DNA sequencing did for genetics: build a technology that can read individual protein molecules directly, capturing their full sequence and any modifications. Several approaches are racing to make this possible³.
A New Generation of Protein Sequencing Technologies
Researchers and startups are exploring a range of methods to decode proteins one molecule at a time. They differ in approach but share the same goal: making protein sequencing faster, cheaper, and more scalable.
One approach builds on a technique from the 1950s called Edman degradation, which strips amino acids, the building blocks of proteins, off one at a time. Modern versions, such as fluorosequencing, combine this chemistry with fluorescent tags. By labelling specific amino acids and watching how the fluorescence signal changes as they are removed, scientists can study individual protein molecules rather than averaged populations⁴. Another approach uses molecules designed to recognise and bind to specific proteins, enabling detection. These methods work well for known targets and are already widely used in diagnostics, but they can’t easily discover unknown proteins or reveal full sequences⁵. A third strategy converts protein information into a DNA-like format. Some companies attach unique DNA tags to amino acids as they are removed from a protein; these tags can then be read using existing DNA sequencing machines, making use of decades of investment in genomics technology⁶.
All of these approaches are trying to tackle the same core problem: proteins are far more chemically and structurally complex than DNA.
Among the emerging technologies, one stands out for its potential: nanopore-based protein sequencing. The idea is simple and ambitious — what if we could sequence proteins one molecule at a time, as easily as we now sequence DNA?
Nanopore sequencing was originally developed for DNA. It works by feeding individual molecules through an extremely tiny hole — the “nanopore” — while an electric current flows through it. As different parts of the molecule pass through, they partially block the current in characteristic ways, producing a signal that can be decoded into sequence information⁷.
For DNA, nanopore sequencing has already been transformative. Compact, portable devices can read long DNA strands in real time, and machine-learning software decodes the signals with high accuracy. But applying the same idea to proteins is far more difficult. DNA has four building blocks; proteins have twenty. In nanopore sequencing, the signal at any moment reflects several neighbouring units at once which is what researchers call “k-mers.” With four DNA letters, the number of possible combinations is manageable. With twenty amino acids, the combinations multiply enormously, making the signals far harder to decode³.
Proteins also behave differently physically. DNA carries a uniform negative charge, which allows it to be pulled smoothly through a nanopore by an electric field. Proteins have irregular shapes and uneven charge distributions, and many need to be unfolded into straight chains before they can pass through⁸.
Researchers are developing several strategies to address these challenges. Some use molecular motors which are specialised proteins that can grab and pull other proteins through the nanopore at a controlled pace, similar to the proteins that help move DNA during sequencing. Others use chemicals that unfold proteins into straight chains, or create tiny liquid flows using electric fields to help move them through the pore. A different strategy, sometimes called “chop-and-drop”, uses enzymes positioned near the nanopore that snip off amino acids one by one; each amino acid then passes through the pore individually, allowing the sequence to be pieced together step by step⁹. Meanwhile, advances in machine learning are helping researchers make sense of the noisy electrical signals these measurements produce. The field is young, but progress has been rapid.
A Scientific Race
Today, academic labs and startups are competing to build the first scalable protein sequencing platform. Some are focused on fluorescence-based systems, others on DNA barcoding or hybrid sensing approaches. Several groups are pursuing nanopore-based methods, engineering both biological and synthetic nanopores to improve sensitivity and control³.
Each approach has its own strengths and weaknesses while some offer higher throughput, others better sensitivity, and some can detect protein modifications directly. The challenge is not just scientific but also an engineering one: building systems that can reliably and affordably measure millions of molecules at once.
In many ways, the field looks a lot like DNA sequencing did in the early 2000s — full of competing technologies and genuine uncertainty about which will ultimately win out.
But as DNA sequencing showed, the potential rewards are enormous. If scalable protein sequencing becomes a reality, it could transform biology and medicine. Rather than inferring what proteins are doing based on DNA, scientists could measure them directly, capturing their sequences, modifications, and moment-to-moment changes in real time. And that would bring us far closer to understanding how life actually works.
References
Aebersold R., Mann M. (2016). Mass-spectrometric exploration of proteome structure and function. Nature.
Smith L. M., Kelleher N. L. (2018). Proteoforms as the next proteomics currency. Science.
Swaminathan J. et al. (2020). A review of emerging technologies for single-molecule protein sequencing. Nature Biotechnology.
Swaminathan J. et al. (2018). Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures. Nature Biotechnology.
Gold L. et al. (2010). Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS ONE.
Chen Z. et al. (2019). DNA-encoded proteomics for multiplexed protein analysis. Nature Biotechnology.
Jain M. et al. (2016). The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community. Genome Biology.
Restrepo-Pérez L., Joo C., Dekker C. (2018). Paving the way to single-molecule protein sequencing. Nature Nanotechnology.
Brinkerhoff H. et al. (2021). Multiple rereads of single proteins at single-amino-acid resolution using nanopores. Science.