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Sec1/Munc18-family (SM) proteins are required for SNARE-mediated membrane fusion, but their mechanism(s) of action remain controversial. Using single-molecule force spectroscopy, we found that the SM protein Munc18-1 catalyzes step-wise zippering of three synaptic SNAREs (syntaxin, VAMP2, and SNAP-25) into a four-helix bundle. Catalysis requires formation of an intermediate template complex in which Munc18-1 juxtaposes the N-terminal regions of the SNARE motifs of syntaxin and VAMP2, while keeping their C-terminal regions separated. SNAP-25 binds the templated SNAREs to induce full SNARE zippering. Munc18-1 mutations modulate the stability of the template complex in a manner consistent with their effects on membrane fusion, indicating that chaperoned SNARE assembly is essential for exocytosis. Two other SM proteins, Munc18-3 and Vps33, similarly chaperone SNARE assembly via a template complex, suggesting that SM protein mechanism is conserved.

Many biological processes rely on protein–membrane interactions in the presence of mechanical forces, yet high resolution methods to quantify such interactions are lacking. Here, we describe a single-molecule force spectroscopy approach to quantify membrane binding of C2 domains in Synaptotagmin-1 (Syt1) and Extended Synaptotagmin-2 (E-Syt2). Syts and E-Syts bind the plasma membrane via multiple C2 domains, bridging the plasma membrane with synaptic vesicles or endoplasmic reticulum to regulate membrane fusion or lipid exchange, respectively. In our approach, single proteins attached to membranes supported on silica beads are pulled by optical tweezers, allowing membrane binding and unbinding transitions to be measured with unprecedented spatiotemporal resolution. C2 domains from either protein resisted unbinding forces of 2–7 pN and had binding energies of 4–14 kBT per C2 domain. Regulation by bilayer composition or Ca2+ recapitulated known properties of both proteins. The method can be widely applied to study protein–membrane interactions.

Single-molecule switching nanoscopy overcomes the diffraction limit of light by stochastically switching single fluorescent molecules on and off, and then localizing their positions individually. Recent advances in this technique have greatly accelerated the data acquisition speed and improved the temporal resolution of super-resolution imaging. However, it has not been quantified whether this speed increase comes at the cost of compromised image quality. The spatial and temporal resolution depends on many factors, among which laser intensity and camera speed are the two most critical parameters. Here we quantitatively compare the image quality achieved when imaging Alexa Fluor 647-immunolabeled microtubules over an extended range of laser intensities and camera speeds using three criteria - localization precision, density of localized molecules, and resolution of reconstructed images based on Fourier Ring Correlation. We found that, with optimized parameters, single-molecule switching nanoscopy at high speeds can achieve the same image quality as imaging at conventional speeds in a 5-25 times shorter time period. Furthermore, we measured the photoswitching kinetics of Alexa Fluor 647 from single-molecule experiments, and, based on this kinetic data, we developed algorithms to simulate single-molecule switching nanoscopy images. We used this software tool to demonstrate how laser intensity and camera speed affect the density of active fluorophores and influence the achievable resolution. Our study provides guidelines for choosing appropriate laser intensities for imaging Alexa Fluor 647 at different speeds and a quantification protocol for future evaluations of other probes and imaging parameters.

Significance Enveloped viruses infect cells via fusion of viral and host cell membranes mediated by highly conserved fusion proteins. HIV-1 glycoprotein 41 (gp41) represents a family of fusion proteins with similar structures and fusion mechanisms. They couple their energetic folding to draw two membranes close for fusion, forming trimers of helical hairpins. Yet, the energy release, force generation, and kinetics associated with folding of these proteins are poorly quantified. We found that gp41 hairpins fold sequentially but in a kinetically coupled manner and that an anti-HIV drug blocked gp41 folding by a new mechanism. As major proteins on viral surfaces, fusion proteins are primary targets for vaccine development and fusion inhibitors to intervene in major infectious diseases such as AIDS, Ebola, and influenza. HIV-1 glycoprotein 41 (gp41) mediates viral entry into host cells by coupling its folding energy to membrane fusion. Gp41 folding is blocked by fusion inhibitors, including the commercial drug T20, to treat HIV/AIDS. However, gp41 folding intermediates, energy, and kinetics are poorly understood. Here, we identified the folding intermediates of a single gp41 trimer-of-hairpins and measured their associated energy and kinetics using high-resolution optical tweezers. We found that folding of gp41 hairpins was energetically independent but kinetically coupled: Each hairpin contributed a folding energy of ∼−23 k BT, but folding of one hairpin successively accelerated the folding rate of the next one by ∼20-fold. Membrane-mimicking micelles slowed down gp41 folding and reduced the stability of the six-helix bundle. However, the stability was restored by cooperative folding of the membrane-proximal external region. Surprisingly, T20 strongly inhibited gp41 folding by actively displacing the C-terminal hairpin strand in a force-dependent manner. The inhibition was abolished by a T20-resistant gp41 mutation. The energetics and kinetics of gp41 folding established by us provides a basis to understand viral membrane fusion, infection, and therapeutic intervention.

Resistive pressure sensors are appealing due to having several advantages, such as simple reading mechanisms, simple construction, and quick dynamic response. Achieving a constantly changeable microstructure of sensing materials is critical for the flexible pressure sensor and remains a difficulty. Herein, a flexible, tunable resistive pressure sensors is developed via simple, low-cost microsphere self-assembly and graphene/carbon nanotubes (CNTs) solution drop coating. The sensor uses polystyrene (PS) microspheres to construct an interlocked dome microstructure with graphene/CNTs as a conductive filler. The results indicate that the interlocked microdome-type pressure sensor has better sensitivity than the single microdome-type and single planar-type without surface microstructure. The pressure sensor’s sensitivity can be adjusted by varying the diameter of PS microspheres. In addition, the resistance of the sensor is also tunable by adjusting the number of graphene/CNT conductive coating layers. The developed flexible pressure sensor effectively detected human finger bending, demonstrating tremendous potential in human motion monitoring.

Synaptic soluble N-ethylmaleimide–sensitive factor attachment protein receptors (SNAREs) couple their stepwise folding to fusion of synaptic vesicles with plasma membranes. In this process, three SNAREs assemble into a stable four-helix bundle. Arguably, the first and rate-limiting step of SNARE assembly is the formation of an activated binary target (t)-SNARE complex on the target plasma membrane, which then zippers with the vesicle (v)-SNARE on the vesicle to drive membrane fusion. However, the t-SNARE complex readily misfolds, and its structure, stability, and dynamics are elusive. Using single-molecule force spectroscopy, we modeled the synaptic t-SNARE complex as a parallel three-helix bundle with a small frayed C terminus. The helical bundle sequentially folded in an N-terminal domain (NTD) and a C-terminal domain (CTD) separated by a central ionic layer, with total unfolding energy of ∼17 kBT, where kB is the Boltzmann constant and T is 300 K. Peptide binding to the CTD activated the t-SNARE complex to initiate NTD zippering with the v-SNARE, a mechanism likely shared by the mammalian uncoordinated-18-1 protein (Munc18-1). The NTD zippering then dramatically stabilized the CTD, facilitating further SNARE zippering. The subtle bidirectional t-SNARE conformational switch was mediated by the ionic layer. Thus, the t-SNARE complex acted as a switch to enable fast and controlled SNARE zippering required for synaptic vesicle fusion and neurotransmission.

Functions of proteins rely on their dynamic structures and intramolecular interactions. My thesis research projects focused on protein folding studies using single-molecule force spectroscopy based on high-resolution optical tweezers. Specifically, my first project is to study the function and working mechanism of Secl/Muncl8-family (SM) proteins. SM proteins are required for SNARE-mediated membrane fusion, but their mechanism(s) of action are poorly understood. We found that the SM protein Muncl 8-1 catalyzes step-wise assembly of three synaptic SNAREs (syntaxin, VAMP2, and SNAP-25) into a four-helix bundle. Muncl 8-1 aligns the SNARE motifs of syntaxin and VAMP2 on its surface. Next, SNAP-25 quickly binds the templated SNAREs, completing the assembly. Muncl8-1 enhances the rate and accuracy of SNARE assembly, both of which are crucial for exocytosis. A second SM protein, Vps33, chaperones vacuolar SNARE assembly in an analogous manner, suggesting that SM protein mechanism is conserved. The second project is to develop a novel single-molecule method to study membrane protein folding and stability. Functions of membrane proteins critically depend on their folding and stability . It is difficult to measure folding energy and kinetics of transmembrane proteins. I described my efforts to develop a novel approach to study membrane protein folding and stability combining single-molecule force spectrometry and lipid bilayers supported on the surfaces of silica beads.

This paper contributes to addressing the item cold start problem in large-scale recommender systems, focusing on how to efficiently gain initial visibility for newly ingested content. We propose an exploration system designed to efficiently allocate impressions to these fresh items. Our approach leverages a learned probabilistic model to predict an item's discoverability, which then informs a scalable and adaptive traffic allocation strategy. This system intelligently distributes exploration budgets, optimizing for the long-term benefit of the recommendation platform. The impact is a demonstrably more efficient cold-start process, leading to a significant increase in the discoverability of new content and ultimately enriching the item corpus available for exploitation, as evidenced by its successful deployment in a large-scale production environment.

Recommender systems face a critical challenge in the item cold-start problem, which limits content diversity and exacerbates popularity bias by struggling to recommend new items. While existing solutions often rely on auxiliary data, but this paper illuminates a distinct, yet equally pressing, issue stemming from the inherent user-centricity of many recommender systems. We argue that in environments with large and rapidly expanding item inventories, the traditional focus on finding the \"best item for a user\" can inadvertently obscure the ideal audience for nascent content. To counter this, we introduce the concept of item-centric recommendations, shifting the paradigm to identify the optimal users for new items. Our initial realization of this vision involves an item-centric control integrated into an exploration system. This control employs a Bayesian model with Beta distributions to assess candidate items based on a predicted balance between user satisfaction and the item's inherent quality. Empirical online evaluations reveal that this straightforward control markedly improves cold-start targeting efficacy, enhances user satisfaction with newly explored content, and significantly increases overall exploration efficiency.

We introduce Gemini Embedding 2, a native multimodal embedding model that allows embedding video, audio, image, and text modalities in a unified representation space. We leverage the multimodal capabilities of Gemini to produce embeddings for arbitrary combinations of interleaved inputs across all these modalities that generalize well across a wide variety of tasks. Applying large-scale contrastive learning in a multi-task multi-stage training setup, we achieve state-of-the-art performance on key embedding benchmarks including unimodal, cross-modal, and multimodal retrieval spanning a diverse set of tasks. We show that our embedding model demonstrates strong performance (with a score of 62.9 R@1 on MSCOCO, 68.8 NDCG@10 on Vatex, 69.9 on MTEB multilingual and 84.0 on MTEB Code) across a variety of tasks surpassing the performance of specialized models. These unified capabilities make Gemini Embedding 2 a promising candidate for downstream use cases such as RAG, recommendation and search. Furthermore, its robust zero-shot performance across distinct fields - from astronomy and bioscience to fine arts and the culinary arts - establishes it as a highly reliable, out-of-the-box representation even for specialized domains.