Proteins play essential roles in virtually all cellular functions, and accurate profiling of the cellular proteome is critical for understanding biological processes and diagnosing diseases. However, current protein identification methods often lack the sensitivity required to reliably detect low-abundance proteins such as signaling molecules or early-stage biomarkers. Over the past decade, highly sensitive single-molecule protein identification methods, referred to as single-molecule protein sequencing, have been proposed, mainly those based on nanopore and fluorescence techniques. Yet, a fully developed method capable of identifying full-length proteins has not been realized. This Progress Report highlights recent developments in single-molecule protein identification methods using nanomechanical approaches that leverage 2D materials for label-free mass detection. We discuss strategies to enhance nanoelectromechanical resonators for precise mass measurements of single protein molecules and outline the prospects and remaining challenges of protein identification using 2D material-based nanodevices.

Biological function is fundamentally determined by nucleic acid and protein sequence. Beyond encoding genetic information, nucleic acids also display complex physicochemical parameters that shape structure, dynamics, and interactions. Understanding how sequence variation sculpts the energetic landscapes underlying these properties requires methods that capture both molecular diversity and dynamic behavior. Single-molecule techniques are ideally suited to this task, but conventional formats remain time and cost intensive. Recent breakthroughs have enabled highly multiplexed approaches for observing molecular dynamics across millions of individual molecules representing thousands of sequences or barcoded entities. Though still in development, these methods have begun to bridge sequence, structure, dynamics, and function at scale, opening new opportunities in drug discovery, molecular diagnostics, and functional genomics.

Synthetic macrocycles offer exceptional potential as therapeutics. However, most high-throughput discovery platforms rely on genetically encoded libraries of large peptide macrocycles, which typically are not optimized for drug like properties. Fully synthetic libraries offer greater flexibility in accessing broader chemical space. Leveraging recent advances in mass spectrometry based library techniques, here we report CycloSEL (Cyclic Self-Encoded Libraries), an end-to-end workflow, that screens synthetic macrocycle libraries enriched in drug-like ‘beyond rule of five’ features. The workflow relies on affinity selections and hit identification by tandem mass spectrometry, eliminating the need for genetic barcodes. We construct a 16 million-member library and validate the approach against the oncology target carbonic anhydrase IX, achieving robust enrichment and accurate identification of true binders. Applying CycloSEL to the acute myeloid leukemia target WD repeat-containing protein 5 (WDR5) yields a macrocycle with subnamolar affinity, and potent inhibition of the WDR5–Mixed-Lineage Leukemia 1 (MLL1) interaction. Subsequent modifications produce a chameleonic macrocycle with passive membrane permeability, serum stability, and anti-proliferative activity in leukemia cells. Together, these results demonstrate that CycloSEL enables discovery of drug-like macrocycles from fully synthetic libraries for intracellular targets.

Recent discoveries have shown the presence of ribonucleic acid (RNA) on the cell surface, defying the view that RNA only functions intracellularly. However, how RNA is presented on the cell surface and what its biological relevance is are poorly understood. We established Toll-like receptor 7 (TLR7) as a cell-surface RNA (csRNA) probe. Employing it in a genome-wide knockout screening, we identified heparan sulfate (HS) as a crucial factor for csRNA presentation. Cell-surface proximity labeling revealed that HS-associated csRNAs (hepRNAs) are in the vicinity of RNA-binding proteins (RBPs). These observations led us to a model wherein cell-surface HS, RNA, and RBP form ternary complexes, validated by our spatio-selective RNA-protein crosslinking technology in a TLR7-orthogonal manner. We further revealed the identities of hepRNA and found that they can recruit the immune receptor killer cell immunoglobulin-like receptor 2DL5 (KIR2DL5), potentially enhancing receptor-ligand interactions. Employing human cell lines, our findings lay the groundwork for investigating how cell-surface ribonucleoproteins contribute to immune modulation.

A recent ground-breaking study suggested that small RNA from mammalian cells can undergo N-glycan modifications (termed glycoRNA)¹. The discovery relied upon a metabolic glycan labeling strategy in combination with commonly used phase-separation-based RNA isolation. Following the reported procedure, here we likewise identify an N-glycosylated species in the RNA fraction. However, our results suggest that the reported RNase sensitivity of the glycosylated species depends on the specific RNA purification method. This suggests the possibility of copurifying unexpected RNase-insensitive N-glycoconjugates during glycoRNA isolation. The co-existence of two independent, yet highly similar molecular entities, complicates biochemical assays on glycoRNA and calls for more specific approaches for glycoRNA analysis. To address this, we propose a control experiment that can help distinguish genuine glycoRNA species from copurified glycoconjugates.

Continuous biosensing provides real-time information about biochemical processes and holds great potential for health monitoring. Aptamers have emerged as promising alternatives over traditional biorecognition elements. However, the underlying aptamer-target binding interactions are often poorly understood. Here, we present a technique that can decode aptamer-protein binding interactions at the single-molecule level. We demonstrate that our single-molecule assay is able to decode the underlying binding kinetics of aptamers despite their similar binding affinity. Guided by computational simulations and validated with quartz crystal microbalance experiments, we show that the quantitative insights generated by this single-molecule technique enabled the rational understanding of biosensor performance (i.e., the sensitivity and limit of detection). This capability was demonstrated with thrombin as the analyte and the structurally similar aptamers HD1, RE31, and NU172 as the biorecognition elements. This work decodes aptamer-protein interactions with high temporal resolution, paving the way for the rational design of aptamer-based biosensors.

Understanding the structure of biomolecules is vital for deciphering their roles in biological systems. Single-molecule techniques have emerged as alternatives to conventional ensemble structure analysis methods for uncovering new biology in molecular dynamics and interaction studies, yet only limited structural information could be obtained experimentally. Here, we address this challenge by introducing iMAX FRET, a one-pot method that allows ab initio 3D profiling of individual molecules using two-color FRET measurements. Through the stochastic exchange of fluorescent weak binders, iMAX FRET simultaneously assesses multiple distances on a biomolecule within a few minutes, which can then be used to reconstruct the coordinates of up to four points in each molecule, allowing structure-based inference. We demonstrate the 3D reconstruction of DNA nanostructures, protein quaternary structures, and conformational changes in proteins. With iMAX FRET, we provide a powerful approach to advance the understanding of biomolecular structure by expanding conventional FRET analysis to three dimensions.

Argonaute proteins are the central effectors of RNA-guided RNA silencing pathways in eukaryotes, playing crucial roles in gene repression and defense against viruses and transposons. Eukaryotic Argonautes are subdivided into two clades: AGOs generally facilitate miRNA- or siRNA-mediated silencing, while PIWIs generally facilitate piRNA-mediated silencing. It is currently unclear when and how Argonaute-based RNA silencing mechanisms arose and diverged during the emergence and early evolution of eukaryotes. Here, we show that in Asgard archaea, the closest prokaryotic relatives of eukaryotes, an evolutionary expansion of Argonaute proteins took place. In particular, a deep-branching PIWI protein (HrAgo1) encoded by the genome of the Lokiarchaeon ‘Candidatus Harpocratesius repetitus’ shares a common origin with eukaryotic PIWI proteins. Contrasting known prokaryotic Argonautes that use single-stranded DNA as guides and/or targets, HrAgo1 mediates RNA-guided RNA cleavage, and facilitates gene silencing when expressed in human cells and supplied with miRNA precursors. A cryo-EM structure of HrAgo1, combined with quantitative single-molecule experiments, reveals that the protein displays structural features and target-binding modes that are a mix of those of eukaryotic AGO and PIWI proteins. Thus, this deep-branching archaeal PIWI may have retained an ancestral molecular architecture that preceded the functional and mechanistic divergence of eukaryotic AGOs and PIWIs.

Fluorescence resonance energy transfer (FRET) is a photophysical phenomenon that has been repurposed as a biophysical tool to measure nanometer distances. With FRET by DNA eXchange, or FRET X, many points of interest (POIs) in a single object can be probed, overcoming a major limitation of conventional single-molecule FRET. In FRET X, short fluorescently labeled DNA imager strands specifically and transiently bind their complementary docking strands on a target molecule, such that at most a single FRET pair is formed at each point in time and multiple POIs on a single molecule can be readily probed. Here, we describe the sample preparation, image acquisition, and data analysis for structural analysis of DNA nanostructures with FRET X.

The inherent properties of 2D materials—light mass, high out-of-plane flexibility, and large surface area—promise great potential for precise and accurate nanomechanical mass sensing, but their application is often hampered by surface contamination. Here we demonstrate a tri-layer graphene nanomechanical resonant mass sensor with sub-attogram resolution at room temperature, fabricated by a bottom-up process. We found that Joule-heating is effective in cleaning the graphene membrane surface, which results in a large improvement in the stability of the resonance frequency. We characterized the sensor by depositing Cr metal using a stencil mask and found a mass-resolution that is sufficient to weigh very small particles, like large proteins and protein complexes, with potential applications in the fields of nanobiology and medicine.

Next-generation sequencing techniques have led to a new quantitative dimension in the biological sciences. In particular, integrating sequencing techniques with biophysical tools allows sequence-dependent mechanistic studies. Using the millions of DNA clusters that are generated during sequencing to perform high-throughput binding affinity and kinetics measurements enabled the construction of energy landscapes in sequence space, uncovering relationships between sequence, structure, and function. Here, we review the approaches to perform ensemble fluorescence experiments on next-generation sequencing chips for variations of DNA, RNA, and protein sequences. As the next step, we anticipate that these fluorescence experiments will be pushed to the single-molecule level, which can directly uncover kinetics and molecular heterogeneity in an unprecedented high-throughput fashion. Molecular biophysics in sequence space, both at the ensemble and single-molecule level, leads to new mechanistic insights. The wide spectrum of applications in biology and medicine ranges from the fundamental understanding of evolutionary pathways to the development of new therapeutics.

Nanomechanical resonator devices are widely used as ultrasensitive mass detectors for fundamental studies and practical applications. The resonance frequency of the resonators shifts when a mass is loaded, which is used to estimate the mass. However, the shift signal is often blurred by the thermal noise, which interferes with accurate mass detection. Here, we demonstrate the reduction of the noise interference in mass detection in suspended graphene-based nanomechanical resonators, by using applied machine learning. Featurization is divided into image and sequential datasets, and those datasets are trained and classified using 2D and 1D convolutional neural networks (CNNs). The 2D CNN learning-based classification shows a performance with f1-score over 99% when the resonance frequency shift is more than 2.5% of the amplitude of the thermal noise range.