Watching Designed Proteins Assemble with High-Speed AFM

How the surface beneath a protein can influence the structures it forms

What determines whether nanoscale building blocks assemble into an ordered structure or remain disordered?

In a study published in Nature Communications, Sakshi Yadav Schmid and colleagues investigated this question using designed protein nanorods at liquid–solid interfaces. Combining high-speed atomic force microscopy (HS-AFM), machine-learning analysis and computational modelling, they explored how the surface beneath the proteins influences their movement and organization.

The researchers observed a striking contrast: the same protein nanorods formed different arrangements on two types of mica, even though both surfaces have the same potassium-ion sublattice to which the proteins were designed to bind. Their findings reveal why understanding nanoscale assembly requires looking beyond the interactions deliberately built into the protein design.

Following protein assembly in real time

Self-assembly occurs when individual building blocks organize into larger structures through interactions with one another and their surroundings. To understand how these structures form, it helps to observe the process as it unfolds—not just examine the final arrangement.

High-speed AFM allowed the researchers to observe the protein nanorods in liquid as the assembly process unfolded. Using NanoWorld ARROW-UHFAuD and USC-F1.2-k0.15 cantilevers in their high-speed AFM experiments, they could follow how the nanorods moved and how their arrangements changed over time, rather than relying solely on images of the final structures.

The experiments compared two substrates: muscovite mica (m-mica) and fluorophlogopite mica (f-mica). Under the high-salt conditions investigated, the nanorods behaved very differently on the two surfaces.

On muscovite mica, the nanorods were highly mobile. Small groups of aligned rods appeared, grew and rearranged until a more stable, layered structure emerged. This arrangement is known as smectic order: the elongated building blocks point predominantly in the same direction and are organized into layers.

Time-lapse high-speed AFM images and corresponding nanorod identification images showing protein assembly on muscovite mica. Graphs below show the number of rods, nematic alignment and development of smectic order over time.
Figure 3. Development of smectic order on muscovite mica. Time-lapse high-speed AFM images (a) show designed protein nanorods assembling on muscovite mica. The corresponding machine-learning-based identification of the rods (b) supports quantitative measurements of the number of rods (c), nematic order (d) and smectic order (e). Together, the measurements reveal how alignment and layered organization develop over time.
Source: Yadav Schmid et al., Nature Communications 17, 2446 (2026), https://doi.org/10.1038/s41467-026-69170-0. © Battelle Memorial Institute and The Authors 2026. Licensed under CC BY 4.0. Reproduced without modification.

On fluorophlogopite mica, the rods showed translational and rotational movement but were less mobile overall. Their orientations followed three directions associated with the substrate’s potassium-ion sublattice. As more rods accumulated, they formed a high-density disordered assembly, rather than the layered structure seen on muscovite mica.

Time-lapse high-speed AFM images and corresponding nanorod identification images showing protein assembly on fluorophlogopite mica. Graphs below show rod counts and comparatively low nematic and smectic order parameters over time.
Figure 4. High-density disordered assembly on fluorophlogopite mica. Time-lapse high-speed AFM images (a) and corresponding machine-learning-based identification of the rods (b) document protein nanorods on fluorophlogopite mica. Measurements of the number of rods (c), nematic order (d) and smectic order (e) show that the assembly remains comparatively disordered.
Source: Yadav Schmid et al., Nature Communications 17, 2446 (2026), https://doi.org/10.1038/s41467-026-69170-0. © Battelle Memorial Institute and The Authors 2026. Licensed under CC BY 4.0. Reproduced without modification.

The difference was particularly intriguing because the potassium-ion patterns that guide protein binding are identical on the two substrates. Something beyond that shared pattern was influencing the outcome.

Measuring how order develops

Watching nanorods move provides a view of self-assembly in action. But how can researchers measure the transition from individual movements to collective organization?

The team used machine-learning methods to identify nanorods in the high-speed AFM images and determine their positions and orientations. From these measurements, they could track surface coverage and calculate how the degree of order changed over time.

Two quantities were especially useful. Nematic order describes how strongly elongated objects align in a common direction. Smectic order additionally captures their organization into layers.

On muscovite mica, the nanorods aligned relatively quickly, while their layered organization developed more gradually as surface coverage increased. On fluorophlogopite mica, the rods accumulated without developing comparable long-range alignment or smectic order.

This quantitative analysis showed not only that the final structures differed, but also how the two assembly processes followed different paths.

Why the surface matters

What could explain such different behavior on substrates with identical potassium-ion sublattices?

An important clue lies in the water next to the solid surface. The atomic structures of muscovite and fluorophlogopite mica differ in ways that influence the arrangement of nearby water molecules. These interfacial hydration structures can, in turn, affect the interactions experienced by the protein nanorods.

To investigate this explanation, the researchers used Monte Carlo simulations that considered the mobility of rod-shaped building blocks and the influence of preferred orientations imposed by the substrate. When the model introduced a directional preference representing the broken symmetry associated with muscovite mica, smectic ordering could emerge. Without that preference, the model reproduced behavior consistent with disordered assembly.

The distinction between the methods is important: high-speed AFM revealed how the nanorods moved and assembled, machine-learning analysis quantified the developing order, and the simulations helped explain how surface-related effects could produce the observed differences.

The findings highlight a factor that is easy to overlook when designing protein assemblies: the surrounding liquid and the structure of the underlying surface can influence the outcome, even when the intended protein–surface binding pattern is the same.

By revealing these effects as assembly happens, high-speed AFM provides an experimental window into processes that may be difficult to understand from finished structures alone. Combined with quantitative analysis and modelling, such observations can help researchers develop a more complete understanding of how designed proteins organize at interfaces.

Full citation

Yadav Schmid, S.; Helfrecht, B.; Stegmann, A.; Legg, B. A.; Pyles, H.; Chen, J.; Edison, J. R.; Ziatdinov, M.; Preisler, Z.; Dollar, O.; Whitelam, S.; Kalinin, S.; Baker, D.; Mundy, C. J.; Zhang, S.; De Yoreo, J. J.
Impact of solvent forces and broken symmetry on the assembly of designed proteins at a liquid-solid interface.
Nature Communications 2026, 17, 2446.
https://doi.org/10.1038/s41467-026-69170-0

Creative Commons Attribution 4.0 International (CC BY 4.0)

https://creativecommons.org/licenses/by/4.0/

Controllable Gliders in a Nanomagnetic Metamaterial: MFM Reveals Magnetic “Snakes”

Artificial Spin Ice (ASI) has emerged as a fascinating platform for studying collective behaviour in interacting nanomagnets, with potential applications ranging from magnetic devices to neuromorphic computing. In this article, Arthur Penty, Johannes H. Jensen, Ida Breivik, Anders Strømberg, Erik Folven, and Gunnar Tufte demonstrate how a mobile magnetic structure — a “snake” glider — can be created and precisely controlled within a nanomagnetic metamaterial.

Inspired by the concept of gliders in Cellular Automata, the researchers use an evolutionary algorithm to discover a simple moving structure within a pinwheel Artificial Spin Ice system. The resulting snake can move through the nanomagnetic array under a global magnetic-field protocol, allowing controlled manipulation of magnetic textures on a scale of approximately 100 nm.

Artificial Spin Ice as a Platform for Information Processing

Artificial Spin Ice consists of interacting nanomagnets arranged in a two-dimensional lattice. These nanoscale magnetic elements can exhibit collective states and emergent behaviour, making ASI attractive for exploring unconventional approaches to information processing.

A key challenge is integrating information transformation, transmission, and storage into the same physical system. The snake glider introduced in this article addresses this challenge by providing a controllable magnetic structure that can move through the ASI lattice while retaining its functionality.

The researchers demonstrate that the snake can move either left or right depending on its orientation. Its motion is controlled through a sequence of in-plane magnetic fields, while simulations and experiments are used to investigate the mechanism responsible for the glider’s movement and its robustness against disorder.

NanoWorld MFMR for Magnetic Force Microscopy

Experimental investigation of the nanomagnetic structures was performed using NanoWorld POINTPROBE MFMR, a magnetic force microscopy AFM probe designed for magnetic imaging.

The NanoWorld MFMR was used for both writing the initial magnetic state and imaging the resulting magnetic structures. To initialise the snake, the MFM probe was used to write an approximately 1 µm magnetic line in the pinwheel Artificial Spin Ice while a 10 mT bias field was applied. The writing process was performed with the MFM probe in contact with the sample at a scan speed of 55 µm/s.

Fig. 5: Analysis of neighbour influence in the snake.
Fig. 5: Analysis of neighbour influence in the snake. Neighbour influence during (a) shrinking and (b) growth. The five insets illustrate how the highlighted magnet is influenced by its neighbours through their dipolar fields. The influence of a neighbour acting on a magnet is given by the resulting change in the magnet’s proximity to the switching astroid when considering the neighbour’s dipolar field. Positive influence values (blue) indicate an increased distance (stabilising) and negative values (red) indicate a decreased distance (destabilising). The centre legend shows the parallel (∥) and perpendicular (⊥) nearest neighbours, as well as the second nearest neighbours (2).

Following magnetic-field-driven evolution of the structure, the researchers used MFM to image the magnetic state after each applied field. Imaging was performed at remanence using a lift height between 55 and 60 nm and scan speeds between 50 and 55 µm/s. All experiments were conducted at room temperature.

These measurements allowed the researchers to directly observe how the magnetic texture evolved as the snake moved through the nanomagnetic array.

Imaging a Controllable Magnetic Texture

The experimental MFM results provide a direct view of the magnetic states underlying the glider behaviour. By repeatedly applying the field protocol and imaging the resulting configurations, the researchers could track the movement of individual snakes and investigate how their structure responds to the magnetic environment.

The experiments also demonstrated the robustness of the concept. Multiple snakes could be initialised within the same Artificial Spin Ice array while maintaining sufficient separation to avoid unwanted interactions. The researchers further investigated how fabrication disorder affects the movement of the gliders, finding evidence of self-correction and graceful degradation before motion eventually breaks down.

The combination of experimental MFM imaging and micromagnetic simulations provides a detailed picture of the mechanism behind the snake’s movement. This makes the approach particularly interesting for studying how local magnetic interactions can be harnessed to produce controlled, collective behaviour.

From Magnetic Gliders to Neuromorphic Computing

The snake glider provides more than a visually compelling magnetic structure. Because its position and movement can be controlled, it can act as a physical carrier of information within the Artificial Spin Ice substrate.

The researchers demonstrate how the glider can contribute to information transmission, storage, and transformation, potentially allowing these functions to be integrated into a single magnetic material. Such concepts could contribute to the development of ultra-low-power computing architectures based on nanoscale magnetic states rather than conventional electronic switching.

For this type of research, high-quality MFM probes are

essential for resolving and manipulating nanoscale magnetic structures. The NanoWorld POINTPROBE MFMR provides a dedicated solution for magnetic force microscopy, enabling both magnetic writing and high-resolution imaging of nanomagnetic systems.

The work by Penty and co-authors demonstrates how MFM can do more than simply characterise magnetic materials: it can become an experimental tool for exploring programmable magnetic textures, emergent behaviour, and new approaches to information processing.

Full citation:
Penty, A.; Jensen, J. H.; Breivik, I.; Strømberg, A.; Folven, E.; Tufte, G.
Controllable gliders in a nanomagnetic metamaterial.
Nature Communications 2025, 16, 7500.
DOI: 10.1038/s41467-025-62515-1

License: CC BY 4.0

Plant-Growth Synchronized, Acid Phosphatase-Responsive Lignin-Based Controlled Release Phosphorus Nanofertilizers

Developing smarter fertilizer systems that deliver nutrients when and where plants need them could help improve nutrient efficiency while reducing unnecessary losses to the environment. In this article, Alice Boarino, Nicola Carrara, Joaquin Clua, Nick Zahnd, Yves Poirier, and Harm-Anton Klok present a lignin-based nanofertilizer platform designed to respond to a biological signal associated with phosphate deficiency in plants.

The researchers developed tripolyphosphate (TPP)-cross-linked lignin nanoparticles engineered to release phosphorus in response to acid phosphatase. This enzyme is upregulated by plants under phosphate-starvation conditions, providing a mechanism for synchronizing nutrient release with plant demand. The lignin/TPP nanoparticles therefore combine a biodegradable polymer-based carrier with an enzyme-responsive release mechanism rather than relying on a conventional uncontrolled release process.

The nanofertilizers were prepared from aminated lignin and tripolyphosphate through ionic gelation followed by covalent cross-linking. The resulting nanoparticles were investigated to determine their structural and functional properties and to understand how exposure to acid phosphatase affects their stability. The authors found that phosphorus release was triggered by acid phosphatase activity and occurred together with nanoparticle disintegration, demonstrating the responsiveness of the lignin-based delivery system to the targeted enzymatic stimulus.

The biological experiments further demonstrated the potential of the nanoparticles as a phosphorus source for Arabidopsis thaliana. Treatment with lignin-TPP nanoparticles suppressed the growth inhibition and molecular responses normally associated with phosphate deficiency, supporting their potential as controlled-release nanofertilizers for plant growth and development.

Atomic force microscopy (AFM) was used to characterize the morphology and size distribution of the lignin-based nanoparticles. AFM imaging was performed in tapping mode using a NanoWorld PointProbe® NCSTR-50 AFM probe, an aluminum-coated silicon cantilever with a spring constant of 7.4 N/m and a resonance frequency of approximately 160 kHz.

For AFM analysis, nanoparticle dispersions were deposited onto cleaned silicon wafers and dried overnight at room temperature. Samples prepared in Milli-Q water were used to characterize nanoparticle dimensions, while dispersions in MES buffer were used to investigate the effect of acid phosphatase on nanoparticle stability. The resulting AFM images enabled direct nanoscale characterization of the particles, with nanoparticle sizes determined from the measured particle heights.

The use of a NanoWorld AFM probe in tapping mode provided a suitable approach for imaging these nanoscale polymer-based structures while limiting the interaction between the AFM probe and the deposited nanoparticles. The NanoWorld PointProbe® NCSTR-50 AFM probe therefore played an important role in confirming the morphology and dimensions of the lignin-TPP nanofertilizers.

This work highlights how NanoWorld AFM probes can support nanoscale characterization of responsive polymer nanoparticles and advanced agricultural materials. By combining AFM-based morphological analysis with biochemical response testing and plant experiments, the authors demonstrate a promising approach toward nanofertilizers capable of delivering phosphorus in a plant-growth-synchronized manner.

The study also illustrates the broader potential of nanoscale characterization in the development of responsive agricultural materials, where nanoparticle size, morphology, stability, and stimulus-dependent behavior can all influence the performance of the final delivery system.

Figure 6.AFM images of cross-linked lignin/TPP nanoparticles after A) 0 h, B) 24 h, C) 48 h, and D) 72 h of incubation with acid phosphatase (10 mU/mL).
Figure 6.
AFM images of cross-linked lignin/TPP nanoparticles after A) 0 h, B) 24 h, C) 48 h, and D) 72 h of incubation with acid phosphatase (10 mU/mL).

 

Full citation:
Boarino, A.; Carrara, N.; Clua, J.; Zahnd, N.; Poirier, Y.; Klok, H.-A.
Plant-Growth Synchronized, Acid Phosphatase-Responsive Lignin-Based Controlled Release Phosphorus Nanofertilizers.
Biomacromolecules 2026, 27, 5, 3176–3187.
https://doi.org/10.1021/acs.biomac.5c02594

Creative Commons license: CC BY 4.0