A strategy combining denoising and cryo-EM single particle analysis.

In cryogenic electron microscopy (cryo-EM) single particle analysis (SPA), high-resolution three-dimensional structures of biological macromolecules are determined by iteratively aligning and averaging a large number of two-dimensional projections of molecules. Since the correlation measures are sensitive to the signal-to-noise ratio, various parameter estimation steps in SPA will be disturbed by the high-intensity noise in cryo-EM. However, denoising algorithms tend to damage high frequencies and suppress mid- and high-frequency contrast of micrographs, which exactly the precise parameter estimation relies on, therefore, limiting their application in SPA. In this study, we suggest combining a cryo-EM image processing pipeline with denoising and maximizing the signal's contribution in various parameter estimation steps. To solve the inherent flaws of denoising algorithms, we design an algorithm named MScale to correct the amplitude distortion caused by denoising and propose a new orientation determination strategy to compensate for the high-frequency loss. In the experiments on several real datasets, the denoised particles are successfully applied in the class assignment estimation and orientation determination tasks, ultimately enhancing the quality of biomacromolecule reconstruction. The case study on classification indicates that our strategy not only improves the resolution of difficult classes (up to 5 Å) but also resolves an additional class. In the case study on orientation determination, our strategy improves the resolution of the final reconstructed density map by 0.34 Å compared with conventional strategy. The code is available at https://github.com/zhanghui186/Mscale.

Cooperation of local features and global representations by a dual-branch network for transcription factor binding sites prediction.

Interactions between DNA and transcription factors (TFs) play an essential role in understanding transcriptional regulation mechanisms and gene expression. Due to the large accumulation of training data and low expense, deep learning methods have shown huge potential in determining the specificity of TFs-DNA interactions. Convolutional network-based and self-attention network-based methods have been proposed for transcription factor binding sites (TFBSs) prediction. Convolutional operations are efficient to extract local features but easy to ignore global information, while self-attention mechanisms are expert in capturing long-distance dependencies but difficult to pay attention to local feature details. To discover comprehensive features for a given sequence as far as possible, we propose a Dual-branch model combining Self-Attention and Convolution, dubbed as DSAC, which fuses local features and global representations in an interactive way. In terms of features, convolution and self-attention contribute to feature extraction collaboratively, enhancing the representation learning. In terms of structure, a lightweight but efficient architecture of network is designed for the prediction, in particular, the dual-branch structure makes the convolution and the self-attention mechanism can be fully utilized to improve the predictive ability of our model. The experiment results on 165 ChIP-seq datasets show that DSAC obviously outperforms other five deep learning based methods and demonstrate that our model can effectively predict TFBSs based on sequence feature alone. The source code of DSAC is available at https://github.com/YuBinLab-QUST/DSAC/.

MarkerDetector: A method for robust fiducial marker detection in electron micrographs using wavelet-based template.

Fiducial marker detection in electron micrographs becomes an important and challenging task with the development of large-field electron microscopy. The fiducial marker detection plays an important role in several steps during the process of electron micrographs, such as the alignment and parameter calibrations. However, limited by the conditions of low signal-to-noise ratio (SNR) in the electron micrographs, the performance of fiducial marker detection is severely affected. In this work, we propose the MarkerDetector, a novel algorithm for detecting fiducial markers in electron micrographs. The proposed MarkerDetector is built upon the following contributions: Firstly, a wavelet-based template generation algorithm is devised in MarkerDetector. By adopting a shape-based criterion, a high-quality template can be obtained. Secondly, a robust marker determination strategy is devised by utilizing statistic-based filtering, which can guarantee the correctness of the detected fiducial markers. The average running time of our algorithm is 1.67 seconds with promising accuracy, indicating its practical feasibility for applications in electron micrographs.

Identification of circRNA-disease associations via multi-model fusion and ensemble learning.

Circular RNA (circRNA) is a common non-coding RNA and plays an important role in the diagnosis and therapy of human diseases, circRNA-disease associations prediction based on computational methods can provide a new way for better clinical diagnosis. In this article, we proposed a novel method for circRNA-disease associations prediction based on ensemble learning, named ELCDA. First, the association heterogeneous network was constructed via collecting multiple information of circRNAs and diseases, and multiple similarity measures are adopted here, then, we use metapath, matrix factorization and GraphSAGE-based models to extract features of nodes from different views, the final comprehensive features of circRNAs and diseases via ensemble learning, finally, a soft voting ensemble strategy is used to integrate the predicted results of all classifier. The performance of ELCDA is evaluated by fivefold cross-validation and compare with other state-of-the-art methods, the experimental results show that ELCDA is outperformance than others. Furthermore, three common diseases are used as case studies, which also demonstrate that ELCDA is an effective method for predicting circRNA-disease associations.

Chemoenzymatic Synthesis of Fluorinated Mycocyclosin Enabled by the Engineered Cytochrome P450-Catalyzed Biaryl Coupling Reaction.

Installing fluorine atoms onto natural products holds great promise for the generation of fluorinated molecules with improved or novel pharmacological properties. The enzymatic oxidative carbon-carbon coupling reaction represents a straightforward strategy for synthesizing biaryl architectures, but the exploration of this method for producing fluorine-substituted derivatives of natural products remains elusive. Here, in this study, we report the protein engineering of cytochrome P450 from Mycobacterium tuberculosis (MtCYP121) for the construction of a series of new-to-nature fluorine-substituted Mycocyclosin derivatives. This protocol takes advantage of a "hybrid" chemoenzymatic procedure consisting of tyrosine phenol lyase-catalyzed fluorotyrosine preparation from commercially available fluorophenols, intermolecular chemical condensation to give cyclodityrosines, and an engineered MtCYP121-catalyzed intramolecular biphenol coupling reaction to complete the strained macrocyclic structure. Computational mechanistic studies reveal that MtCYP121 employs Cpd I to abstract a hydrogen atom from the proximal phenolic hydroxyl group of the substrate to trigger the reaction. Then, conformational change makes the two phenolic hydroxyl groups close enough to undergo intramolecular hydrogen atom transfer with the assistance of a pocket water molecule. The final diradical coupling process completes the intramolecular C-C bond formation. The efficiency of the biaryl coupling reaction was found to be influenced by various fluorine substitutions, primarily due to the presence of distinct binding conformations.

Machine Learning for Single-Molecule Localization Microscopy: From Data Analysis to Quantification.

Single-molecule localization microscopy (SMLM) is a versatile tool for realizing nanoscale imaging with visible light and providing unprecedented opportunities to observe bioprocesses. The integration of machine learning with SMLM enhances data analysis by improving efficiency and accuracy. This tutorial aims to provide a comprehensive overview of the data analysis process and theoretical aspects of SMLM, while also highlighting the typical applications of machine learning in this field. By leveraging advanced analytical techniques, SMLM is becoming a powerful quantitative analysis tool for biological research.

A Biodegradable, Stretchable, Healable, and Self-Powered Optoelectronic Synapse Based on Ionic Gelatins for Neuromorphic Vision System.

Optoelectronic synapses have gained increasing attentions as a fundamental building block in the development of neuromorphic visual systems. However, it remains a challenge to integrate multiple functions into a single optoelectronic synapse that can be widely applied in wearable artificial intelligence and implantable neuromorphic vision systems. In this study, a stretchable optoelectronic synapse based on biodegradable ionic gelatin heterojunction is successfully developed. This device exhibits self-powered synaptic plasticity behavior with broad spectral response and excellent elastic properties, yet it degrades rapidly upon disposal. After complete cleavage, the device can be fully repaired within 1 min, which is mainly attributed to the non-covalent interactions between different molecular chains. Moreover, the recovery and reprocessing of the ionic gelatins result in optoelectronic properties that are virtually indistinguishable from their original state, showcasing the resilience and durability of ionic gelatins. The combination of biodegradability, stretchability, self-healing, zero-power consumption, ease of large-scale preparation, and low cost makes the work a major step forward in the development of biodegradable and stretchable optoelectronic synapses.

Improving the enzymatic activity and stability of N-carbamoyl hydrolase using deep learning approach.

BACKGROUND: Optically active D-amino acids are widely used as intermediates in the synthesis of antibiotics, insecticides, and peptide hormones. Currently, the two-enzyme cascade reaction is the most efficient way to produce D-amino acids using enzymes DHdt and DCase, but DCase is susceptible to heat inactivation. Here, to enhance the enzymatic activity and thermal stability of DCase, a rational design software "Feitian" was developed based on kcat prediction using the deep learning approach. RESULTS: According to empirical design and prediction of "Feitian" software, six single-point mutants with high kcat value were selected and successfully constructed by site-directed mutagenesis. Out of six, three mutants (Q4C, T212S, and A302C) showed higher enzymatic activity than the wild-type. Furthermore, the combined triple-point mutant DCase-M3 (Q4C/T212S/A302C) exhibited a 4.25-fold increase in activity (29.77 ± 4.52 U) and a 2.25-fold increase in thermal stability as compared to the wild-type, respectively. Through the whole-cell reaction, the high titer of D-HPG (2.57 ± 0.43 mM) was produced by the mutant Q4C/T212S/A302C, which was about 2.04-fold of the wild-type. Molecular dynamics simulation results showed that DCase-M3 significantly enhances the rigidity of the catalytic site and thus increases the activity of DCase-M3. CONCLUSIONS: In this study, an efficient rational design software "Feitian" was successfully developed with a prediction accuracy of about 50% in enzymatic activity. A triple-point mutant DCase-M3 (Q4C/T212S/A302C) with enhanced enzymatic activity and thermostability was successfully obtained, which could be applied to the development of a fully enzymatic process for the industrial production of D-HPG.

Hexagonal image segmentation on spatially resolved transcriptomics.

Spatial transcriptomics is a rapidly evolving field that enables researchers to capture comprehensive molecular profiles while preserving information about the physical locations. One major challenge in this research area involves the identification of spatial domains, which are distinct regions characterized by unique gene expression patterns. However, current unsupervised methods have struggled to perform well in this regard due to the presence of high levels of noise and dropout events in spatial transcriptomic profiles. In this paper, we propose a novel hexagonal Convolutional Neural Network (hexCNN) for hexagonal image segmentation on spatially resolved transcriptomics. To address the problem of noise and dropout occurrences within spatial transcriptomics data, we first extend an unsupervised algorithm to a supervised learning method that can identify useful features and reduce noise hindrance. Then, inspired by the classical convolution in convolutional neural networks (CNNs), we designed a regular hexagonal convolution to compensate for the missing gene expression patterns from adjacent spots. We evaluated the performance of hexCNN by applying it to the DLPFC dataset. The results show that hexCNN achieves a classification accuracy of 86.8% and an average Rand index (ARI) of 77.1% (1.4% and 2.5% higher than those of GNNs). The results also demonstrate that hexCNN is capable of removing the noise caused by batch effect while preserving the biological signal differences.

Computational insights into the binding modes, keto-enol tautomerization and stereo-electronically controlled decarboxylation of oxaloacetate in the active site of macrophomate synthase.

Oxaloacetic acid (OAA) is a ß-ketocarboxylic acid, which plays an important role as an intermediate in some metabolic pathways, including the tricarboxylic acid cycle, gluconeogenesis and fatty acid biosynthesis. Animal studies have indicated that supplementing oxaloacetic acid shows an increase of lifespan and other substantial health benefits including mitochondrial DNA protection, and protection of retinal, neural and pancreatic tissues. Most of the chemical transformations of OAA in the metabolic pathways have been extensively studied; however, the understanding of decarboxylation of OAA at the atomic level is relatively lacking. Here, we carried out MD simulations and combined quantum mechanical/molecular mechanical (QM/MM) calculations as an example to systematically elucidate the binding modes, keto-enol tautomerization and decarboxylation of OAA in the active site of macrophomate synthase (MPS), which is a Mg(II)-dependent bifunctional enzyme that catalyzes both the decarboxylation of OAA and [4+2] cycloaddition of 2-pyrone with the decarboxylated intermediate of OAA (pyruvate enolate). On the basis of our calculations, it was found that the Mg2+-coordinated oxaloacetate may exist in enol forms and keto forms. The four keto forms can be transformed into each other by simply rotating the C2-C3 single bond, nevertheless, the keto-enol tautomerization strictly requires the assistance of pocket water molecules. In addition, the decarboxylation is stereo-electronically controlled, i.e., it is the relative orientation of the terminal carboxyl anion that determines the rate of decarboxylation. As such, the chemistry of oxaloacetate in the active site of MPS is complex. On one hand, the most stable binding mode (K-I) may undergo enol-keto tautomerization to isomerize to the enol form, which may further react with the second substrate; on the other hand, K-I may isomerize to another binding mode K-II to proceed decarboxylation to generate pyruvate enolate and CO2. Starting from K-I, the enol-keto tautomerization corresponds to a barrier of 16.2 kcal mol-1, whereas the decarboxylation is associated with an overall barrier of 19.7 kcal mol-1. These findings may provide useful information for understanding the chemistry of OAA and the catalysis of related enzymes, and they are basically in agreement with the available experimental kinetic data.

Identification of unique transcriptomic signatures through integrated multispecies comparative analysis and WGCNA in bovine oocyte development.

BACKGROUND: Cattle (Bos taurus) are a major large livestock, however, compared with other species, the transcriptional specificity of bovine oocyte development has not been emphasised. RESULTS: To reveal the unique transcriptional signatures of bovine oocyte development, we used integrated multispecies comparative analysis and weighted gene co-expression network analysis (WGCNA) to perform bioinformatic analysis of the germinal follicle (GV) and second meiosis (MII) gene expression profile from cattle, sheep, pigs and mice. We found that the expression levels of most genes were down-regulated from GV to MII in all species. Next, the multispecies comparative analysis showed more genes involved in the regulation of cAMP signalling during bovine oocyte development. Moreover, the green module identified by WGCNA was closely related to bovine oocyte development. Finally, integrated multispecies comparative analysis and WGCNA picked up 61 bovine-specific signature genes that participate in metabolic regulation and steroid hormone biosynthesis. CONCLUSION: In a short, this study provides new insights into the regulation of cattle oocyte development from a cross-species comparison.

Correction of image distortion in large-field ssEM stitching by an unsupervised intermediate-space solving network.

MOTIVATION: Serial-section electron microscopy (ssEM) is a powerful technique for cellular visualization, especially for large-scale specimens. Limited by the field of view, a megapixel image of whole-specimen is regularly captured by stitching several overlapping images. However, suffering from distortion by manual operations, lens distortion or electron impact, simple rigid transformations are not adequate for perfect mosaic generation. Non-linear deformation usually causes 'ghosting' phenomenon, especially with high magnification. To date, existing microscope image processing tools provide mature rigid stitching methods but have no idea with local distortion correction. RESULTS: In this article, following the development of unsupervised deep learning, we present a multi-scale network to predict the dense deformation fields of image pairs in ssEM and blend these images into a clear and seamless montage. The model is composed of two pyramidal backbones, sharing parameters and interacting with a set of registration modules, in which the pyramidal architecture could effectively capture large deformation according to multi-scale decomposition. A novel 'intermediate-space solving' paradigm is adopted in our model to treat inputted images equally and ensure nearly perfect stitching of the overlapping regions. Combining with the existing rigid transformation method, our model further improves the accuracy of sequential image stitching. Extensive experimental results well demonstrate the superiority of our method over the other traditional methods. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/HeracleBT/ssEM_stitching. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer.

MOTIVATION: Cryo-electron microscopy (cryo-EM) is a widely used technology for ultrastructure determination, which constructs the 3D structures of protein and macromolecular complex from a set of 2D micrographs. However, limited by the electron beam dose, the micrographs in cryo-EM generally suffer from the extremely low signal-to-noise ratio (SNR), which hampers the efficiency and effectiveness of downstream analysis. Especially, the noise in cryo-EM is not simple additive or multiplicative noise whose statistical characteristics are quite different from the ones in natural image, extremely shackling the performance of conventional denoising methods. RESULTS: Here, we introduce the Noise-Transfer2Clean (NT2C), a denoising deep neural network (DNN) for cryo-EM to enhance image contrast and restore specimen signal, whose main idea is to improve the denoising performance by correctly learning the noise distribution of cryo-EM images and transferring the statistical nature of noise into the denoiser. Especially, to cope with the complex noise model in cryo-EM, we design a contrast-guided noise and signal re-weighted algorithm to achieve clean-noisy data synthesis and data augmentation, making our method authentically achieve signal restoration based on noise's true properties. Our work verifies the feasibility of denoising based on mining the complex cryo-EM noise patterns directly from the noise patches. Comprehensive experimental results on simulated datasets and real datasets show that NT2C achieved a notable improvement in image denoising, especially in background noise removal, compared with the commonly used methods. Moreover, a case study on the real dataset demonstrates that NT2C can greatly alleviate the obstacles caused by the SNR to particle picking and simplify the identifying of particles. AVAILABILITYAND IMPLEMENTATION: The code is available at https://github.com/Lihongjia-ict/NoiseTransfer2Clean/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Weakly Supervised Framework for Cancer Region Detection of Hepatocellular Carcinoma in Whole-Slide Pathologic Images Based on Multiscale Attention Convolutional Neural Network.

Visual inspection of hepatocellular carcinoma cancer regions by experienced pathologists in whole-slide images (WSIs) is a challenging, labor-intensive, and time-consuming task because of the large scale and high resolution of WSIs. Therefore, a weakly supervised framework based on a multiscale attention convolutional neural network (MSAN-CNN) was introduced into this process. Herein, patch-based images with image-level normal/tumor annotation (rather than images with pixel-level annotation) were fed into a classification neural network. To further improve the performances of cancer region detection, multiscale attention was introduced into the classification neural network. A total of 100 cases were obtained from The Cancer Genome Atlas and divided into 70 training and 30 testing data sets that were fed into the MSAN-CNN framework. The experimental results showed that this framework significantly outperforms the single-scale detection method according to the area under the curve and accuracy, sensitivity, and specificity metrics. When compared with the diagnoses made by three pathologists, MSAN-CNN performed better than a junior- and an intermediate-level pathologist, and slightly worse than a senior pathologist. Furthermore, MSAN-CNN provided a very fast detection time compared with the pathologists. Therefore, a weakly supervised framework based on MSAN-CNN has great potential to assist pathologists in the fast and accurate detection of cancer regions of hepatocellular carcinoma on WSIs.

Bacterial communities associated with mushrooms in the Qinghai-Tibet Plateau are shaped by soil parameters.

Fungi capable of producing fruit bodies are essential food and medicine resources. Despite recent advances in the study of microbial communities in mycorrhizospheres, little is known about the bacterial communities contained in fruit bodies. Using high-throughput sequencing, we investigated the bacterial communities in four species of mushrooms located on the alpine meadow and saline-alkali soil of the Qinghai-Tibet Plateau (QTP). Proteobacteria (51.7% on average) and Actinobacteria (28.2% on average) were the dominant phyla in all of the sampled fairy ring fruit bodies, and Acidobacteria (27.5% on average) and Proteobacteria (25.7% on average) dominated their adjacent soils. For the Agria. Bitorquis, Actinobacteria was the dominant phylum in its fruit body (67.5% on average) and adjacent soils (65.9% on average). The alpha diversity (i.e., Chao1, Shannon, Richness, and Simpson indexes) of the bacterial communities in the fruit bodies were significantly lower than those in the soil samples. All of the fungi shared more than half of their bacterial phyla and 16.2% of their total operational taxonomic units (OTUs) with their adjacent soil. Moreover, NH4+ and pH were the key factors associated with bacterial communities in the fruit bodies and soils, respectively. These results indicate that the fungi tend to create a unique niche that selects for specific members of the bacterial community. Using culture-dependent methods, we also isolated 27 bacterial species belonging to three phyla and five classes from fruit bodies and soils. The strains isolated will be useful for future research on interactions between mushroom-forming fungi and their bacterial endosymbionts.

Regioselective [3 + 2] cycloaddition of di/trifluoromethylated hydrazonoyl chlorides with fluorinated nitroalkenes: a facile access to 3-di/trifluoroalkyl-5-fluoropyrazoles.

Herein we describe the base-mediated [3 + 2] cycloaddition reaction of di/trifluoromethylated hydrazonoyl chlorides with fluorinated nitroalkenes. The reaction protocol provides a direct and facile strategy for the dual incorporation of a fluorine atom and fluoroalkyl group into pyrazole cores, thus allowing rapid access to a wide variety of densely functionalized 3-di/trifluoroalkyl-5-fluoropyrazoles in generally high yields with excellent regioselectivities. Furthermore, several drug-like 3-di/trifluoroalkyl-5-fluoropyrazoles have been synthesized, demonstrating potent inhibitory activities against cyclooxygenase 2 (COX-2).

Triazines: Syntheses and Inverse Electron-demand Diels-Alder Reactions.

Triazines are an important class of six-membered aromatic heterocycles possessing three nitrogen atoms, resulting in three types of regio-isomers: 1,2,4-triazines (a-triazines), 1,2,3-triazines (v-triazines), and 1,3,5-triazines (s-triazines). Notably, the application of triazines as cyclic aza-dienes in inverse electron-demand Diels-Alder (IEDDA) cycloaddition reactions has been established as a unique and powerful method in N-heterocycle synthesis, natural product preparation, and bioorthogonal chemistry. In this review, we comprehensively summarize the advances in the construction of these triazines via annulation and ring-expansion reactions, especially emphasizing recent developments and challenges. The synthetic transformations of triazines are focused on IEDDA cycloaddition reactions, which have allowed access to a wide scope of heterocycles, including pyridines, carbolines, azepines, pyridazines, pyrazines, and pyrimidines. The utilization of triazine IEDDA reactions as key steps in natural product synthesis is also discussed. More importantly, a particular attention is paid on the bioorthogonal application of triazines in fast click ligation with various strained alkenes and alkynes, which opens a new opportunity for studying biomolecules in chemical biology.

In vivo antibacterial activity of medicinal plant Sophora flavescens against Streptococcus agalactiae infection.

Streptococcosis disease caused by Streptococcus agalactiae (Group B Streptococcus, GBS) results in a huge economic loss of tilapia culture. It is urgent to find new antimicrobial agents against streptococcosis. In this study, 20 medicinal plants were evaluated in vitro and in vivo to obtain medicinal plants and potential bioactive compounds against GBS infection. The results showed that the ethanol extracts of 20 medicinal plants had low or no antibacterial properties in vitro, with a minimal inhibitory concentration ≥256 mg/L. Interestingly, in vivo tests showed that 7 medicinal plants could significantly inhibit GBS infection in tilapia, and Sophora flavescens (SF) had the strongest anti-GBS activity in tilapia, reaching 92.68%. SF could significantly reduce the bacterial loads of GBS in different tissues (liver, spleen and brain) of tilapia after treated with different tested concentrations (12.5, 25.0, 50.0 and 100.0 mg/kg) for 24 h. Moreover, 50 mg/kg SF could significantly improve the survival rate of GBS-infected tilapia by inhibiting GBS replication. Furthermore, the expression of antioxidant gene cat, immune-related gene c-type lysozyme and anti-inflammatory cytokine il-10 in liver tissue of GBS-infected tilapia significantly increased after treated with SF for 24 h. Meanwhile, SF significantly reduced the expression of immune-related gene myd88 and pro-inflammatory cytokines il-8 and il-1ß in liver tissue of GBS-infected tilapia. The negative and positive models of UPLC-QE-MS, respectively, identified 27 and 57 components of SF. The major components of SF extract in the negative model were α, α-trehalose, DL-malic acid, D- (-)-fructose and xanthohumol, while in the positive model were oxymatrine, formononetin, (-)-maackiain and xanthohumol. Interestingly, oxymatrine and xanthohumol could significantly inhibit GBS infection in tilapia. Taken together, these results suggest that SF can inhibit GBS infection in tilapia, and it has potential for the development of anti-GBS agents.

RETRACTED: Parallel bimodal single-cell sequencing of transcriptome and methylome provides molecular and translational insights on oocyte maturation and maternal aging.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief. It has been brought to our attention that the authors of the article "Parallel bimodal single-cell sequencing of transcriptome and methylome provides molecular and translational insights on oocyte maturation and maternal aging" cannot agree on who should be listed as an author of the article. Further inquiry by the journal revealed that the authorship was also changed at the revision stages of the article without notifying the handling Editor, which is contrary to the journal policy on changes to authorship. The journal considers this unacceptable practice, and the Editor-in-Chief decided to retract the article.

Neuronal Scaffold Protein ARMS Interacts with Synaptotagmin-4 C2AB through the Ankyrin Repeat Domain with an Unexpected Mode.

The ankyrin repeat-rich membrane spanning (ARMS), a transmembrane neuronal scaffold protein, plays a fundamental role in neuronal physiology, including neuronal development, polarity, differentiation, survival and angiogenesis, through interactions with diverse partners. Previous studies have shown that the ARMS negatively regulates brain-derived neurotrophic factor (BDNF) secretion by interacting with Synaptotagmin-4 (Syt4), thereby affecting neurogenesis and the development and function of the nervous system. However, the molecular mechanisms of the ARMS/Syt4 complex assembly remain unclear. Here, we confirmed that the ARMS directly interacts with Syt4 through its N-terminal ankyrin repeats 1-8. Unexpectedly, both the C2A and C2B domains of Syt4 are necessary for binding with the ARMS. We then combined the predicted complex structural models from AlphaFold2 with systematic biochemical analyses using point mutagenesis to underline the molecular basis of ARMS/Syt4 complex formation and to identify two conserved residues, E15 and W72, of the ARMS, as essential residues mediating the assembly of the complex. Furthermore, we showed that ARMS proteins are unable to interact with Syt1 or Syt3, indicating that the interaction between ARMS and Syt4 is specific. Taken together, the findings from this study provide biochemical details on the interaction between the ARMS and Syt4, thereby offering a biochemical basis for the further understanding of the potential mechanisms and functional implications of the ARMS/Syt4 complex formation, especially with regard to the modulation of BDNF secretion and associated neuropathies.