The study, led by Ryosaku Ota and Naoki Honda of Nagoya University Graduate School of Medicine together with colleagues at Kyoto University, Fujita Health University, and the University of Osaka, introduces a framework called Foldinsight. Its central premise is deceptively simple: protein function does not emerge from a one-dimensional string of amino acids, but from the physical and chemical interactions those amino acids form in three-dimensional space. If a computational model could see the protein the way physics sees it, the authors reasoned, it might both predict variant properties more meaningfully and explain where in the structure those properties come from. Existing machine learning approaches to protein engineering often rely primarily on sequence-derived features, which treat each position in the protein as an independent variable and can miss the spatial context that determines how a mutation actually behaves.

Foldinsight works by converting amino acid sequences into three-dimensional structures using AlphaFold2, the deep learning system that has reshaped structural biology by predicting protein conformations from sequence alone with remarkable accuracy. Once each variant’s structure is predicted, the framework aligns all of the structures in a common coordinate system so that equivalent regions of different variants occupy the same position in space. This alignment step is critical, because it allows the researchers to compare variants not as isolated molecules but as a family of related shapes that can be overlaid and interrogated systematically. The team also uses the pLDDT score, AlphaFold2’s per-residue confidence metric, to assess how reliable each predicted region is, and root-mean-square deviation measures to quantify how much the predicted structures differ from one another.

With the structures aligned, Foldinsight calculates molecular fields on a shared three-dimensional grid spanning the entire protein family. Two kinds of fields are computed: van der Waals fields, which capture the steric shape and volume of the protein at each grid point, and electrostatic fields, which describe the distribution of charge. The result is a fixed-length numerical descriptor for every variant, regardless of how its sequence differs from its relatives. This is the same conceptual machinery that medicinal chemists have long used to compare small drug molecules, where molecular field descriptors underpin classic quantitative structure-activity relationship models. Applying it to a large, flexible membrane protein like a channelrhodopsin is a considerably more ambitious undertaking, and the fixed-length nature of the descriptors is what makes them suitable for regression modeling.

To turn those descriptors into predictions, the researchers fitted regression models to a previously published dataset of channelrhodopsin variants with measured photocurrent properties. The study employed partial least squares regression, a technique well suited to situations where the number of descriptor variables vastly exceeds the number of measured samples, alongside Gaussian process regression, a flexible non-linear method that also provides uncertainty estimates. Model performance was assessed with cross-validation, using mean absolute error and root-mean-square error to gauge how well the models predicted properties they had not been trained on. According to the paper, the molecular-field descriptors captured predictive signals across the measured photocurrent properties, demonstrating that structure-derived features carry information relevant to channelrhodopsin function even when the structures themselves are predictions rather than experimentally determined crystals.

What sets the approach apart from many black-box protein models is its interpretability. Because each descriptor variable corresponds to a specific point in the shared three-dimensional grid, the fitted regression coefficients can be mapped back onto that grid, producing a spatial map of which regions of the protein are associated with which properties. When the authors performed this mapping for the channelrhodopsin dataset, the highlighted regions could be examined in relation to established functional and structural features of the protein, such as the ion-conducting pore and the light-absorbing retinal-binding pocket. This turns the regression model from a mere prediction engine into a hypothesis generator: instead of simply ranking variants, it points experimenters toward specific spatial neighborhoods where mutations are likely to matter.

The practical implications for protein engineering could be substantial. Channelrhodopsin variants are central tools in optogenetics, and variants with tailored properties, such as faster kinetics, red-shifted activation, or altered ion selectivity, are in constant demand across neuroscience and biotechnology. A workflow that prioritizes which variants to synthesize and test, and that explains its reasoning in spatial terms a structural biologist can evaluate, could shorten design cycles and reduce the burden of exhaustive screening. The authors are careful to frame Foldinsight as a tool for generating hypotheses for future mutational experiments rather than replacing them, a stance that reflects the inherent limits of models trained on datasets of finite size.

The study also illustrates a broader trend in computational biology: the repurposing of AlphaFold2 predictions as inputs for downstream machine learning. Predicted structures are approximations, and their accuracy varies across a protein, which is why the framework’s use of confidence scores and structural alignment matters. For channelrhodopsins, a family of seven-transmembrane proteins whose structures have historically been difficult to capture experimentally, the ability to generate consistent, comparable structural models for every variant in a dataset is itself a meaningful advance. It converts a heterogeneous collection of sequences into a coherent structural ensemble on which quantitative analysis becomes possible.

Limitations remain, as the authors acknowledge. The framework was validated on a published dataset rather than on newly generated experiments, and the quality of any prediction ultimately depends on both the accuracy of the predicted structures and the size and diversity of the training data. Cross-validated performance on retrospective data does not guarantee that the spatial maps identify causal mechanisms, only candidate regions worth investigating. Still, the work, which was partly supported by the Japan Agency for Medical Research and Development, JSPS KAKENHI, and the Moonshot R&D MILLENNIA Program, and computed in part on the ROIS National Institute of Genetics supercomputer, offers an open-access, interpretable template that other protein engineers can adapt. As predicted structures grow more accurate and variant datasets grow larger, structure-derived molecular fields may become a standard lens through which the properties of engineered proteins, channelrhodopsins included, are understood and designed.

Subject of Research: Interpretable machine learning modeling of channelrhodopsin variant properties using AlphaFold2-predicted structures and molecular field descriptors

Article Title: Interpretable modeling of channelrhodopsin variant properties using AlphaFold-derived molecular fields

Article References: Ota, R., Sakamoto, M., Aoki, W., & Honda, N. (2026). Interpretable modeling of channelrhodopsin variant properties using AlphaFold-derived molecular fields. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06625-7

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06625-7

Keywords: channelrhodopsin, AlphaFold2, molecular fields, optogenetics, protein engineering, interpretable machine learning, partial least squares, Gaussian process regression, BMC Bioinformatics, structural bioinformatics, photocurrent properties, variant modeling

The core problem with conventional peripheral nerve stimulation lies in its lack of selectivity. When a cuff electrode wrapped around a nerve delivers current, it activates many types of fibers at once: the large, heavily myelinated Aα and Aβ fibers that carry touch and proprioceptive signals toward the spinal cord, the thinner Aδ fibers associated with sharp pain, and the efferent motor fibers that drive muscles. For pain relief, clinicians generally want to activate only the large sensory Aα/Aβ fibers, which can suppress pain transmission through gating mechanisms in the spinal cord. But because electrical stimulation cannot reliably distinguish afferent sensory fibers from efferent motor fibers, the current must be capped at levels that avoid unwanted muscle contractions. That ceiling can leave the therapeutic target population under-activated, blunting the treatment’s analgesic effect.

Optogenetics has long been proposed as an elegant solution. By introducing light-sensitive proteins such as channelrhodopsin 2 (ChR2) into specific populations of neurons, researchers can activate precisely those cells with pulses of light while leaving neighboring fibers untouched. In the new study, the team used transgenic mice expressing ChR2 and recorded compound action potentials (CAPs), the summed electrical signature of many axons firing together, from the sciatic nerve of anaesthetized animals. The results confirmed the selectivity promise: optogenetic stimulation activated Aα/Aβ fibers with impressive precision and produced no motor response at all, something electrical stimulation cannot achieve. For a field struggling to silence pain without triggering twitching limbs or tingling side effects, that selectivity is a major prize.

But there was a catch, and it is a serious one. When the researchers delivered tonic optogenetic stimulation, continuous trains of light pulses lasting up to 60 seconds at clinically relevant frequencies ranging from 4 to 100 Hz, the nerve responses faded dramatically. The optically evoked compound action potentials, or oCAPs, declined in an exponential and strongly frequency-dependent manner, with the statistical analysis showing a highly significant frequency effect (p < 0.001). At stimulation rates above 20 Hz, the response often vanished entirely, leaving no detectable signal even while the light continued to flash. In other words, the very thing that makes optogenetics selective also makes it fragile: the light-activated channels and the fibers themselves fatigue quickly, and the technique as it currently stands is simply untenable for the sustained stimulation that real-world pain therapy demands, even at low frequencies.

Electrical stimulation did not escape unscathed either. The electrically evoked compound action potentials, or eCAPs, also declined over the course of the 60-second stimulation trains, and they did so in a frequency-dependent, dual-phase exponential pattern that the team captured statistically (p < 0.001). This biphasic decay suggests two overlapping processes: a rapid initial drop, likely reflecting activity-dependent changes in axonal excitability such as hyperpolarization or potassium accumulation, followed by a slower secondary decline. Clinicians and device engineers have long observed that nerve responses wane during continuous stimulation, and this study quantifies that phenomenon precisely in the large sensory fiber population that pain therapy seeks to recruit, across the exact frequency range used in clinical peripheral nerve stimulation devices.

The most striking findings emerged when the researchers combined the two modalities. Delivering light and electrical stimulation together produced what the team describes as a facilitated response: the combined electrically and optically evoked CAP, or cCAP, was larger than what either stimulus could achieve alone. Across the entire stimulation period, more than 85 percent of the recorded responses showed this facilitation, and critically, the effect was maintained regardless of stimulation frequency (p = 0.33). The rapid initial reduction in response amplitude for combined stimulation was actually greater than that seen with electrical stimulation alone (p < 0.05), but after that fast phase, the response settled into a similar secondary decline as the electrical-only condition (p = 0.10). The net result, however, was a response that stayed larger and more sustained than either modality could deliver on its own.

Why does combining light and electricity boost the response? The mechanistic picture that emerges from this and earlier work is that the two stimuli act on the fibers through partially independent pathways. Electrical stimulation directly depolarizes the axonal membrane at the electrode, while optogenetic stimulation opens light-gated channels distributed along the ChR2-expressing sensory neurons. When both are applied, the depolarizations summate, preferentially boosting activation in the genetically targeted population of large sensory fibers. Because the electrical component does not fatigue in the same way as the optical one, it appears to prop up the response during the periods when the optically driven component would otherwise collapse. The consequence is a stimulus that retains the selectivity of the optogenetic approach, targeting only the Aα/Aβ fibers, while achieving the sustained responsiveness that pure light-based stimulation lacks.

The clinical implications are considerable. Peripheral nerve stimulation devices are already implanted in patients for chronic pain, but their efficacy is limited by the current ceilings imposed by motor fiber activation and by the inherent non-selectivity of electrical currents. If a hybrid device could deliver a small, safe electrical pulse alongside targeted optical activation of ChR2-expressing sensory fibers, it might achieve stronger and more sustained recruitment of the therapeutic fiber population without crossing into motor territory. The Melbourne team’s demonstration that facilitated responses persist across the full 4 to 100 Hz clinical frequency range, and throughout stimulation trains lasting a full minute, provides exactly the kind of preclinical evidence needed to justify pushing this approach toward translational development. This study adds to growing evidence that combined stimulation could improve the analgesic effect of existing peripheral nerve stimulation methods.

Significant hurdles remain before any such device reaches patients. Optogenetics in humans requires gene delivery, typically via adeno-associated viruses, to introduce light-sensitive proteins into the target neurons, and translating the transgenic mouse approach used here into a safe and durable human therapy is a formidable challenge involving dosing, immune responses and long-term expression. Light delivery to deep peripheral nerves also requires implanted LEDs or optical fibers, adding engineering complexity. The study itself was conducted in isoflurane-anaesthetized mice over relatively short stimulation windows, so questions about responses over hours, days or weeks of continuous use remain open. Ethical oversight was rigorous: all procedures were approved by the St Vincent’s Hospital Animal Ethics Committee in Melbourne and conducted under Australian animal welfare guidelines.

Nevertheless, the study represents a methodical and important step forward. By systematically comparing electrical, optogenetic and combined stimulation across clinically relevant frequencies and durations, and by quantifying the decay dynamics of each with rigorous statistical modeling, Ardren, Wrobel, Matarazzo, Thompson, Fallon, Richardson and their colleagues have given the neuromodulation field a clear-eyed assessment of what each approach can and cannot do. Optogenetics alone, however selective, cannot yet sustain the signals needed for therapy. Electrical stimulation alone, however durable, cannot select its targets. Together, the data suggest, they cover each other’s weaknesses. For the millions of chronic pain patients whose conditions resist every existing treatment, that combination, light and current working in concert, may be the most promising avenue yet for making nerve stimulation devices finally live up to their promise. The research was funded by the National Health and Medical Research Council and the Bionics Institute Incubation Fund, with support from the Victorian Government, and is published open access so that researchers worldwide can build on its findings immediately.

Subject of Research: How optogenetic, electrical and combined stimulation recruit large sensory nerve fibers in the mouse sciatic nerve during sustained neuromodulation for chronic pain treatment.

Article Title: Recruitment of Aα/Aβ fibers during tonic electrical, optogenetic and combined stimulation in the mouse sciatic nerve

Article References: Ardren, M. G., Wrobel, B., Matarazzo, J. V., Thompson, A. C., Fallon, J. B., & Richardson, R. T. (2026). Recruitment of Aα/Aβ fibers during tonic electrical, optogenetic and combined stimulation in the mouse sciatic nerve. BMC Neuroscience. https://doi.org/10.1186/s12868-026-01049-8

Image Credits: AI Generated

DOI: 10.1186/s12868-026-01049-8

Keywords: optogenetics, peripheral nerve stimulation, chronic pain, neuromodulation, sciatic nerve, A-alpha/A-beta fibers, compound action potential, channelrhodopsin, electrical stimulation, neuroengineering, BMC Neuroscience, pain therapy

The central challenge in cardiac electrophysiology is that heart cells communicate through rapidly propagating electrical waves. In a healthy heart, a precisely timed wave of depolarization sweeps across the muscle, triggering coordinated contraction. In diseased tissue, these waves can fragment, circle back on themselves, or originate from ectopic sites, producing arrhythmias that range from benign to lethal. Conventional interventions, from antiarrhythmic drugs to implanted pacemakers and ablation catheters, act on slow timescales or with coarse spatial resolution. What has been missing is a tool that can both observe and modulate cardiac electrical activity at the scale of individual cells, on millisecond timescales, within a continuous feedback loop.

The new work addresses this gap by exploiting optogenetics, a technique in which light-sensitive proteins borrowed from microbes are expressed in target cells. When blue light strikes channelrhodopsin, a light-gated ion channel embedded in the cell membrane, the channel opens and positive ions flow inward, depolarizing the cell and triggering an action potential. By genetically engineering human induced pluripotent stem cell-derived cardiomyocytes to express such opsins, researchers gain a remote, genetically specified actuator: any region of the cellular network can be electrically stimulated simply by illuminating it, with no physical contact required.

Stimulation alone, however, is only half of the control problem. The other half is sensing. The system pairs optogenetic actuation with optical voltage imaging, using fluorescent indicators whose emission changes with membrane potential. High-speed cameras capture the fluorescence of the cardiomyocyte network frame by frame, allowing the researchers to reconstruct the electrical state of the tissue in real time: which cells are resting, which are firing, and how excitation waves are propagating across the culture. This optical readout replaces the electrode arrays traditionally used to map cardiac activity, eliminating the invasiveness, wiring complexity, and spatial limitations of contact-based sensing.

The truly novel element is the holographic light engine that ties sensing and actuation together. Rather than illuminating the culture with a uniform beam or scanning a single laser spot, the researchers use a spatial light modulator to shape light into arbitrary two-dimensional patterns, projected onto the cell layer through holographic principles. A computer-generated hologram determines, pixel by pixel, where light intensity is delivered. This means the system can stimulate a single cell, a stripe of tissue, a curved wavefront mimicking the sinus node, or multiple disconnected regions simultaneously, all with subcellular spatial resolution and microsecond-scale temporal precision. The hologram can be updated faster than the dynamics of a cardiac action potential, which is essential for genuine real-time control.

Closing the loop requires software that can translate what the cameras see into what the light projector should do next. The control algorithm continuously monitors the optical voltage signals, compares the observed electrical behavior against a desired target state, and computes the illumination pattern needed to drive the network toward that state. If an excitation wave propagates too slowly, the system can deliver light pulses ahead of the wavefront to accelerate it. If an unwanted wave appears in the wrong location, the system can suppress it or redirect it. If the goal is a specific pacing frequency, the controller adjusts the timing and geometry of optical stimuli on every beat, compensating for the natural variability of biological tissue. This is the defining feature of closed-loop control: the intervention is not preprogrammed but continuously recalculated from live measurements.

The researchers demonstrated that this architecture can reliably entrain human cardiomyocyte networks to desired pacing patterns, guiding the rhythm of electrically active tissue that would otherwise beat at its own intrinsic rate. Beyond simple pacing, the holographic system’s spatial freedom enables more sophisticated interventions, such as shaping the direction and curvature of propagating waves or confining activity to defined regions of the network. Such capabilities are directly relevant to the study of arrhythmia mechanisms, where reentrant waves, spiral waves, and conduction blocks are the underlying culprits. A tool that can create, steer, and terminate such waves on demand in human-derived tissue provides an unprecedented experimental platform for arrhythmia research.

The significance for drug development and precision medicine is considerable. Human induced pluripotent stem cell-derived cardiomyocytes already allow pharmaceutical researchers to test compounds on human heart cells rather than animal tissue, but standard assays capture only bulk behavior, such as average beat rate or field potential duration. A closed-loop optical system adds an active dimension: it can probe how a tissue responds to perturbation, measure its vulnerability to arrhythmia induction, and quantify the effects of drugs on conduction velocity, refractory periods, and wave dynamics under precisely controlled stimulation conditions. In principle, patient-specific cell lines could be engineered with opsins and screened not just for passive responses but for behavior under stress, revealing proarrhythmic risks that conventional tests miss.

Looking further ahead, the all-optical nature of the approach suggests possibilities beyond the laboratory dish. Because neither sensing nor actuation requires physical contact, the conceptual framework is compatible with future cardiac therapies in which light delivered through optical fibers or implanted micro-LEDs could pace or resynchronize heart tissue in a feedback-controlled manner, guided by optical or electrical sensors. Such light-based pacemakers could adapt their stimulation pattern beat by beat, something conventional devices, which deliver fixed electrical pulses on fixed schedules, cannot do. Significant hurdles remain before any clinical translation, including delivering opsins safely to adult human myocardium, achieving sufficient light penetration in thick tissue, and ensuring long-term stability of both the genetic and optical components. The current study is confined to engineered cell networks in vitro, and the authors’ achievement should be understood as a foundational demonstration of control methodology rather than a therapy.

Even within that scope, the work marks a conceptual milestone. It shows that a living, electrically excitable human tissue can be observed, modeled, and steered in real time by a machine that touches nothing, intervening only through shaped light. The convergence of optogenetics, holographic projection, fast fluorescence imaging, and feedback control points toward a broader paradigm in synthetic biology and bioelectronic medicine: organs and organoids treated not as passive specimens but as dynamic systems that can be regulated the way engineers regulate any other process. For cardiac science, where rhythm is everything, the ability to write rhythm into human heart tissue with light, and to correct it when it goes wrong, may reshape how arrhythmias are studied, how drugs are validated, and, eventually, how failing electrical systems in the heart are repaired.

Subject of Research: All-optical closed-loop control of human cardiomyocyte networks using holographic optogenetics

Article Title: All-optical closed-loop control of human cardiomyocyte networks exploiting holographic optogenetics

Article References: Wendland, R., Schmieder, F., Sikandar, M. A., Knüppel, F. P., Zimmermann, W.-H., Bergmann, O., Büttner, L., & Czarske, J. W. (2026). All-optical closed-loop control of human cardiomyocyte networks exploiting holographic optogenetics. Communications Engineering, 5(1), Article 159. https://doi.org/10.1038/s44172-026-00779-1

Image Credits: AI Generated

DOI: 10.1038/s44172-026-00779-1

Keywords: holographic optogenetics, cardiomyocytes, closed-loop control, cardiac electrophysiology, optical voltage imaging, arrhythmia, induced pluripotent stem cells, channelrhodopsin, spatial light modulator, cardiac tissue engineering, bioelectronic medicine, optogenetic pacing