C-terminal deletion-induced condensation sequesters AID from IgH targets in immunodeficiency.

In activated B cells, activation-induced cytidine deaminase (AID) generates programmed DNA lesions required for antibody class switch recombination (CSR), which may also threaten genome integrity. AID dynamically shuttles between cytoplasm and nucleus, and the majority stays in the cytoplasm due to active nuclear export mediated by its C-terminal peptide. In immunodeficient-patient cells expressing mutant AID lacking its C-terminus, a catalytically active AID-delC protein accumulates in the nucleus but nevertheless fails to support CSR. To resolve this apparent paradox, we dissected the function of AID-delC proteins in the CSR process and found that they cannot efficiently target antibody genes. We demonstrate that AID-delC proteins form condensates both in vivo and in vitro, dependent on its N-terminus and on a surface arginine-rich patch. Co-expression of AID-delC and wild-type AID leads to an unbalanced nuclear AID-delC/AID ratio, with AID-delC proteins able to trap wild-type AID in condensates, resulting in a dominant-negative phenotype that could contribute to immunodeficiency. The co-condensation model of mutant and wild-type proteins could be an alternative explanation for the dominant-negative effect in genetic disorders.

Improving protein-protein interaction prediction using protein language model and protein network features.

Interactions between proteins are ubiquitous in a wide variety of biological processes. Accurately identifying the protein-protein interaction (PPI) is of significant importance for understanding the mechanisms of protein functions and facilitating drug discovery. Although the wet-lab technological methods are the best way to identify PPI, their major constraints are their time-consuming nature, high cost, and labor-intensiveness. Hence, lots of efforts have been made towards developing computational methods to improve the performance of PPI prediction. In this study, we propose a novel hybrid computational method (called KSGPPI) that aims at improving the prediction performance of PPI via extracting the discriminative information from protein sequences and interaction networks. The KSGPPI model comprises two feature extraction modules. In the first feature extraction module, a large protein language model, ESM-2, is employed to exploit the global complex patterns concealed within protein sequences. Subsequently, feature representations are further extracted through CKSAAP, and a two-dimensional convolutional neural network (CNN) is utilized to capture local information. In the second feature extraction module, the query protein acquires its similar protein from the STRING database via the sequence alignment tool NW-align and then captures the graph embedding feature for the query protein in the protein interaction network of the similar protein using the algorithm of Node2vec. Finally, the features of these two feature extraction modules are efficiently fused; the fused features are then fed into the multilayer perceptron to predict PPI. The results of five-fold cross-validation on the used benchmarked datasets demonstrate that KSGPPI achieves an average prediction accuracy of 88.96 %. Additionally, the average Matthews correlation coefficient value (0.781) of KSGPPI is significantly higher than that of those state-of-the-art PPI prediction methods. The standalone package of KSGPPI is freely downloaded at https://github.com/rickleezhe/KSGPPI.

Protein-peptide binding residue prediction based on protein language models and cross-attention mechanism.

Accurate identifications of protein-peptide binding residues are essential for protein-peptide interactions and advancing drug discovery. To address this problem, extensive research efforts have been made to design more discriminative feature representations. However, extracting these explicit features usually depend on third-party tools, resulting in low computational efficacy and suffering from low predictive performance. In this study, we design an end-to-end deep learning-based method, E2EPep, for protein-peptide binding residue prediction using protein sequence only. E2EPep first employs and fine-tunes two state-of-the-art pre-trained protein language models that can extract two different high-latent feature representations from protein sequences relevant for protein structures and functions. A novel feature fusion module is then designed in E2EPep to fuse and optimize the above two feature representations of binding residues. In addition, we have also design E2EPep+, which integrates E2EPep and PepBCL models, to improve the prediction performance. Experimental results on two independent testing data sets demonstrate that E2EPep and E2EPep + could achieve the average AUC values of 0.846 and 0.842 while achieving an average Matthew's correlation coefficient value that is significantly higher than that of existing most of sequence-based methods and comparable to that of the state-of-the-art structure-based predictors. Detailed data analysis shows that the primary strength of E2EPep lies in the effectiveness of feature representation using cross-attention mechanism to fuse the embeddings generated by two fine-tuned protein language models. The standalone package of E2EPep and E2EPep + can be obtained at https://github.com/ckx259/E2EPep.git for academic use only.

E2EATP: Fast and High-Accuracy Protein-ATP Binding Residue Prediction via Protein Language Model Embedding.

Identifying the ATP-binding sites of proteins is fundamentally important to uncover the mechanisms of protein functions and explore drug discovery. Many computational methods are proposed to predict ATP-binding sites. However, due to the limitation of the quality of feature representation, the prediction performance still has a big room for improvement. In this study, we propose an end-to-end deep learning model, E2EATP, to dig out more discriminative information from a protein sequence for improving the ATP-binding site prediction performance. Concretely, we employ a pretrained deep learning-based protein language model (ESM2) to automatically extract high-latent discriminative representations of protein sequences relevant for protein functions. Based on ESM2, we design a residual convolutional neural network to train a protein-ATP binding site prediction model. Furthermore, a weighted focal loss function is used to reduce the negative impact of imbalanced data on the model training stage. Experimental results on the two independent testing data sets demonstrate that E2EATP could achieve higher Matthew's correlation coefficient and AUC values than most existing state-of-the-art prediction methods. The speed (about 0.05 s per protein) of E2EATP is much faster than the other existing prediction methods. Detailed data analyses show that the major advantage of E2EATP lies at the utilization of the pretrained protein language model that extracts more discriminative information from the protein sequence only. The standalone package of E2EATP is freely available for academic at https://github.com/jun-csbio/e2eatp/.

Integrative Analysis of Bioinformatics and Machine Learning Algorithms Identifies a Novel Diagnostic Model Based on Costimulatory Molecule for Predicting Immune Microenvironment Status in Lung Adenocarcinoma.

Costimulatory molecules are an indispensable signal for activating immune cells. However, the features of many costimulatory molecule genes (CMGs) in lung adenocarcinoma (LUAD) are poorly understood. This study systematically explored expression patterns of CMGs in the tumor immune microenvironment (TIME) status of patients with LUAD. Their expression profiles were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Two robust TIME subtypes ("hot" and "cold") were classified by K-means clustering and estimation of stromal and immune cells in malignant tumor tissues using expression data. The "hot" subtype presented higher infiltration in activated immune cells and enrichments in the immune cell receptor signaling pathway and adaptive immune response. Three CMGs (CD80, LTB, and TNFSF8) were screened as final diagnostic markers by means of Least Absolute Shrinkage Selection Operator and Support Vector Machine-Recursive Feature Elimination algorithms. Accordingly, the diagnostic nomogram for predicting individualized TIME status showed satisfactory diagnostic accuracy in The Cancer Genome Atlas training cohort as well as GSE31210 and GSE180347 validation cohorts. Immunohistochemistry staining of 16 specimens revealed an apparently positive correlation between the expression of CMG biomarkers and pathologic response to immunotherapy. Thus, this diagnostic nomogram provided individualized predictions in TIME status of LUAD patients with good predictive accuracy, which could serve as a potential tool for identifying ideal candidates for immunotherapy.

Improving DNA 6mA Site Prediction via Integrating Bidirectional Long Short-Term Memory, Convolutional Neural Network, and Self-Attention Mechanism.

Identifying DNA N6-methyladenine (6mA) sites is significantly important to understanding the function of DNA. Many deep learning-based methods have been developed to improve the performance of 6mA site prediction. In this study, to further improve the performance of 6mA site prediction, we propose a new meta method, called Co6mA, to integrate bidirectional long short-term memory (BiLSTM), convolutional neural networks (CNNs), and self-attention mechanisms (SAM) via assembling two different deep learning-based models. The first model developed in this study is called CBi6mA, which is composed of CNN, BiLSTM, and fully connected modules. The second model is borrowed from LA6mA, which is an existing 6mA prediction method based on BiLSTM and SAM modules. Experimental results on two independent testing sets of different model organisms, i.e., Arabidopsis thaliana and Drosophila melanogaster, demonstrate that Co6mA can achieve an average accuracy of 91.8%, covering 89% of all 6mA samples while achieving an average Matthews correlation coefficient value (0.839), which is higher than the second-best method DeepM6A.

A novel combined metallurgy-beneficiation method for the facile and low-cost comprehensive resource utilization of low-grade kaolin solid wastes.

Low-grade kaolin is the largest emissions of industrial solid waste that is difficult to dispose of and pollutes the environment seriously. From the perspective of harmless and complete resource utilization, we proposed a novel strategy that combines the wet leaching under mild conditions and physical beneficiation for the facile and low-cost high-valued utilization of low-grade kaolin that involves high-efficiency recovery of aluminum (Al), silicon (Si), and titanium (Ti). The key to successful implementation of this method lies in the new discovery that the residual SiO2 after Al extraction of kaolinite by acid leaching under specific conditions could be rapidly dissolved in dilute NaOH solution at room temperature 25 °C. This highly reactive SiO2 challenges the conventional notions of various silica species are usually chemically stable. By adjusting the key technical parameters of the thermal activation-acid leaching process, the selective and efficient extraction of Al2O3 from low-grade kaolin was realized. The acid leaching residue was then subjected to selective recovery of SiO2 by alkaline leaching at 25 °C to obtain high-quality sodium silicate. Finally, the alkali leaching residue as titanium coarse concentrate was separated by centrifugal concentrator to obtain artificial rutile (TiO2 >91.06%). The key mechanism for the formation of the highly reactive silica was also systematically studied and confirmed.

ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides.

Fast and accurate identification of the peptides with anticancer activity potential from large-scale proteins is currently a challenging task. In this study, we propose a new machine learning predictor, namely, ACPred-Fuse, that can automatically and accurately predict protein sequences with or without anticancer activity in peptide form. Specifically, we establish a feature representation learning model that can explore class and probabilistic information embedded in anticancer peptides (ACPs) by integrating a total of 29 different sequence-based feature descriptors. In order to make full use of various multiview information, we further fused the class and probabilistic features with handcrafted sequential features and then optimized the representation ability of the multiview features, which are ultimately used as input for training our prediction model. By comparing the multiview features and existing feature descriptors, we demonstrate that the fused multiview features have more discriminative ability to capture the characteristics of ACPs. In addition, the information from different views is complementary for the performance improvement. Finally, our benchmarking comparison results showed that the proposed ACPred-Fuse is more precise and promising in the identification of ACPs than existing predictors. To facilitate the use of the proposed predictor, we built a web server, which is now freely available via http://server.malab.cn/ACPred-Fuse.

PepFormer: End-to-End Transformer-Based Siamese Network to Predict and Enhance Peptide Detectability Based on Sequence Only.

The detectability of peptides is fundamentally important in shotgun proteomics experiments. At present, there are many computational methods to predict the detectability of peptides based on sequential composition or physicochemical properties, but they all have various shortcomings. Here, we present PepFormer, a novel end-to-end Siamese network coupled with a hybrid architecture of a Transformer and gated recurrent units that is able to predict the peptide detectability based on peptide sequences only. Specially, we, for the first time, use contrastive learning and construct a new loss function for model training, greatly improving the generalization ability of our predictive model. Comparative results demonstrate that our model performs significantly better than state-of-the-art methods on benchmark data sets in two species (Homo sapiens and Mus musculus). To make the model more interpretable, we further investigate the embedded representations of peptide sequences automatically learnt from our model, and the visualization results indicate that our model can efficiently capture high-latent discriminative information, improving the predictive performance. In addition, our model shows a strong ability of cross-species transfer learning and adaptability, demonstrating that it has great potential in robust prediction of peptides detectability on different species. The source code of our proposed method can be found via https://github.com/WLYLab/PepFormer.

Miniaturization of the Whole Process of Protein Crystallographic Analysis by a Microfluidic Droplet Robot: From Nanoliter-Scale Purified Proteins to Diffraction-Quality Crystals.

To obtain diffraction-quality crystals is one of the largest barriers to analyze the protein structure using X-ray crystallography. Here we describe a microfluidic droplet robot that enables successful miniaturization of the whole process of crystallization experiments including large-scale initial crystallization screening, crystallization optimization, and crystal harvesting. The combination of the state-of-the-art droplet-based microfluidic technique with the microbatch crystallization mode dramatically reduces the volumes of droplet crystallization reactors to tens nanoliter range, allowing large-scale initial screening of 1536 crystallization conditions and multifactor crystallization condition optimization with extremely low protein consumption, and on-chip harvesting of diffraction-quality crystals directly from the droplet reactors. We applied the droplet robot in miniaturized crystallization experiments of seven soluble proteins and two membrane proteins, and on-chip crystal harvesting of six proteins. The X-ray diffraction data sets of these crystals were collected using synchrotron radiation for analyzing the structures with similar diffraction qualities as conventional crystallization methods.

Surgical treatment to multiple primary lung cancer patients: a systematic review and meta-analysis.

BACKGROUND: As there is no consensus on the optimal surgery strategy for multiple primary lung cancer (MPLC), we conducted this study to address this issue by comparing the prognosis of MPLC patients underwent different surgical strategies including sublobar resection and the standard resection through a systemic review and meta-analysis. METHODS: Relevant literature was obtained from three databases including PubMed, Embase and Web of Science. Inclusion and exclusion criteria were set for the screening of articles to be selected for further conduction of systemic review and meta-analysis. The HRs of OS of the sublobar group compared with standard resection group were extracted directly or calculated indirectly from included researches. RESULTS: Ten researches published from 2000 to 2017 were included in this study, with 468 and 445 MPLC cases for the standard resection group and sublobar resection group respectively. The result suggested that OS of MPLC patients underwent sublobar resection (segmentectomy or wedge resection for at least one lesion) was comparable with those underwent standard resection approach (lobectomy or pneumonectomy for all lesions), with HR 1.07, 95% CI 0.67-1.71, p = 0.784. Further analysis found no difference in subgroups of synchronous and metachronous (from second metachronous lesion), different population region and dominant sex type. CONCLUSIONS: This study may reveal that sublobar resection is acceptable for patients with MPLC at an early stage, because of the equivalent prognosis to the standard resection and better pulmonary function preservation. Further research is needed to validate these findings.

Learning Spatio-Temporal Pulse Representation With Global-Local Interaction and Supervision for Remote Prediction of Heart Rate.

Recent studies have demonstrated the benefit of extracting and fusing pulse signals from multi-scale region-of-interests (ROIs). However, these methods suffer from heavy computational load. This paper aims to effectively utilize multi-scale rPPG features with a more compact architecture. Inspired by recent research works exploring two-path architecture that leverages global and local information with bidirectional bridge in between. This paper designs a novel architecture Global-Local Interaction and Supervision Network (GLISNet), which uses a local path to learn representations in the original scale and a global path to learn representations in the other scale capturing multi-scale information. A light-weight rPPG signal generation block is attached to the output of each path that maps the pulse representation to the pulse output. A hybrid loss function is utilized enabling the local and global representations to learn directly from the training data. Extensive experiments are conducted on two publicly available datasets, and results demonstrate that GLISNet achieves superior performance in terms of signal-to-noise ratio (SNR), mean absolute error (MAE), and root mean squared error (RMSE). In terms of SNR, GLISNet has an increase of 4.41% compared with the second best algorithm PhysNet on PURE dataset. The MAE has a decrease of 13.16% compared with the second best algorithm DeeprPPG on UBFC-rPPG dataset. The RMSE has a decrease of 26.29% compared with the second best algorithm PhysNet on UBFC-rPPG dataset. Experiments on MIHR dataset demonstrates the robustness of GLISNet under low-light environment.

The structural basis for the collagen processing by human P3H1/CRTAP/PPIB ternary complex.

Collagen posttranslational processing is crucial for its proper assembly and function. Disruption of collagen processing leads to tissue development and structure disorders like osteogenesis imperfecta (OI). OI-related collagen processing machinery includes prolyl 3-hydroxylase 1 (P3H1), peptidyl-prolyl cis-trans isomerase B (PPIB), and cartilage-associated protein (CRTAP), with their structural organization and mechanism unclear. We determine cryo-EM structures of the P3H1/CRTAP/PPIB complex. The active sites of P3H1 and PPIB form a face-to-face bifunctional reaction center, indicating a coupled modification mechanism. The structure of the P3H1/CRTAP/PPIB/collagen peptide complex reveals multiple binding sites, suggesting a substrate interacting zone. Unexpectedly, a dual-ternary complex is observed, and the balance between ternary and dual-ternary states can be altered by mutations in the P3H1/PPIB active site and the addition of PPIB inhibitors. These findings provide insights into the structural basis of collagen processing by P3H1/CRTAP/PPIB and the molecular pathology of collagen-related disorders.

Cryo-EM structure of human sphingomyelin synthase and its mechanistic implications for sphingomyelin synthesis.

Sphingomyelin (SM) has key roles in modulating mammalian membrane properties and serves as an important pool for bioactive molecules. SM biosynthesis is mediated by the sphingomyelin synthase (SMS) family, comprising SMS1, SMS2 and SMS-related (SMSr) members. Although SMS1 and SMS2 exhibit SMS activity, SMSr possesses ceramide phosphoethanolamine synthase activity. Here we determined the cryo-electron microscopic structures of human SMSr in complexes with ceramide, diacylglycerol/phosphoethanolamine and ceramide/phosphoethanolamine (CPE). The structures revealed a hexameric arrangement with a reaction chamber located between the transmembrane helices. Within this structure, a catalytic pentad E-H/D-H-D was identified, situated at the interface between the lipophilic and hydrophilic segments of the reaction chamber. Additionally, the study unveiled the two-step synthesis process catalyzed by SMSr, involving PE-PLC (phosphatidylethanolamine-phospholipase C) hydrolysis and the subsequent transfer of the phosphoethanolamine moiety to ceramide. This research provides insights into the catalytic mechanism of SMSr and expands our understanding of sphingolipid metabolism.

The cryo-EM structure of the human ERAD retrotranslocation complex.

Endoplasmic reticulum-associated degradation (ERAD) maintains protein homeostasis by retrieving misfolded proteins from the endoplasmic reticulum (ER) lumen into the cytosol for degradation. The retrotranslocation of misfolded proteins across the ER membrane is an energy-consuming process, with the detailed transportation mechanism still needing clarification. We determined the cryo-EM structures of the hetero-decameric complex formed by the Derlin-1 tetramer and the p97 hexamer. It showed an intriguing asymmetric complex and a putative coordinated squeezing movement in Derlin-1 and p97 parts. With the conformational changes of p97 induced by its ATP hydrolysis activities, the Derlin-1 channel could be torn into a "U" shape with a large opening to the lipidic environment, thereby forming an entry for the substrates in the ER membrane. The EM analysis showed that p97 formed a functional protein complex with Derlin-1, revealing the coupling mechanism between the ERAD retrotranslocation and the ATP hydrolysis activities.

The structural basis of the pH-homeostasis mediated by the Cl-/HCO3- exchanger, AE2.

The cell maintains its intracellular pH in a narrow physiological range and disrupting the pH-homeostasis could cause dysfunctional metabolic states. Anion exchanger 2 (AE2) works at high cellular pH to catalyze the exchange between the intracellular HCO3- and extracellular Cl-, thereby maintaining the pH-homeostasis. Here, we determine the cryo-EM structures of human AE2 in five major operating states and one transitional hybrid state. Among those states, the AE2 shows the inward-facing, outward-facing, and intermediate conformations, as well as the substrate-binding pockets at two sides of the cell membrane. Furthermore, critical structural features were identified showing an interlock mechanism for interactions among the cytoplasmic N-terminal domain and the transmembrane domain and the self-inhibitory effect of the C-terminal loop. The structural and cell-based functional assay collectively demonstrate the dynamic process of the anion exchange across membranes and provide the structural basis for the pH-sensitive pH-rebalancing activity of AE2.

The structural pathology for hypophosphatasia caused by malfunctional tissue non-specific alkaline phosphatase.

Hypophosphatasia (HPP) is a metabolic bone disease that manifests as developmental abnormalities in bone and dental tissues. HPP patients exhibit hypo-mineralization and osteopenia due to the deficiency or malfunction of tissue non-specific alkaline phosphatase (TNAP), which catalyzes the hydrolysis of phosphate-containing molecules outside the cells, promoting the deposition of hydroxyapatite in the extracellular matrix. Despite the identification of hundreds of pathogenic TNAP mutations, the detailed molecular pathology of HPP remains unclear. Here, to address this issue, we determine the crystal structures of human TNAP at near-atomic resolution and map the major pathogenic mutations onto the structure. Our study reveals an unexpected octameric architecture for TNAP, which is generated by the tetramerization of dimeric TNAPs, potentially stabilizing the TNAPs in the extracellular environments. Moreover, we use cryo-electron microscopy to demonstrate that the TNAP agonist antibody (JTALP001) forms a stable complex with TNAP by binding to the octameric interface. The administration of JTALP001 enhances osteoblast mineralization and promoted recombinant TNAP-rescued mineralization in TNAP knockout osteoblasts. Our findings elucidate the structural pathology of HPP and highlight the therapeutic potential of the TNAP agonist antibody for osteoblast-associated bone disorders.

Pan-Immune-Inflammatory Value in Patients with Non-Small-Cell Lung Cancer Undergoing Neoadjuvant Immunochemotherapy.

Background: We aimed to investigate the predictive value of a systematic serum inflammation index, pan-immune-inflammatory value (PIV), in pathological complete response (pCR) of patients treated with neoadjuvant immunotherapy to further promote ideal patients' selection. Methods: The clinicopathological and baseline laboratory information of 128 NSCLC patients receiving neoadjuvant immunochemotherapy between October 2019 and April 2022 were retrospectively reviewed. We performed least absolute shrinkage and selection operator (LASSO) algorithm to screen candidate serum biomarkers for predicting pCR, which further entered the multivariate logistic regression model to determine final biomarkers. Accordingly, a diagnostic model for predicting individual pCR was established. Kaplan-Meier method was utilized to estimate curves of disease-free survival (DFS), and the Log rank test was analyzed to compare DFS differences between patients with and without pCR. Results: Patients with NSCLC heterogeneously responded to neoadjuvant immunotherapy, and those with pCR had a significant longer DFS than patients without pCR. Through LASSO and the multivariate logistic regression model, PIV was identified as a predictor for predicting pCR of patients. Subsequently, a diagnostic model integrating with PIV, differentiated degree and histological type was constructed to predict pCR, which presented a satisfactory predictive power (AUC, 0.736), significant agreement between actual and our nomogram-predicted pathological response. Conclusion: Baseline PIV was an independent predictor of pCR for NSCLC patients receiving neoadjuvant immunochemotherapy. A significantly longer DFS was achieved in patients with pCR rather than those without pCR; thus, the PIV-based diagnostic model might serve as a practical tool to identify ideal patients for neoadjuvant immunotherapeutic guidance.

An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma.

Aging is an inevitable process characterized by a decline in many physiological activities, and has been known as a significant risk factor for many kinds of malignancies, but there are few studies about aging-related genes (ARGs) in lung squamous carcinoma (LUSC). We designed this study to explore the prognostic value of ARGs and establish an ARG-based prognosis signature for LUSC patients. RNA-sequencing and corresponding clinicopathological data of patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The ARG risk signature was developed on the basis of results of LASSO and multivariate Cox analysis in the TCGA training dataset (n = 492). Furthermore, the GSE73403 dataset (n = 69) validated the prognostic performance of this ARG signature. Immunohistochemistry (IHC) staining was used to verify the expression of the ARGs in the signature. A five ARG-based signature, including A2M, CHEK2, ELN, FOS, and PLAU, was constructed in the TCGA dataset, and stratified patients into low- and high-risk groups with significantly different overall survival (OS) rates. The ARG risk score remained to be considered as an independent indicator of OS in the multivariate Cox regression model for LUSC patients. Then, a prognostic nomogram incorporating the ARG risk score with T-, N-, and M-classification was established. It achieved a good discriminative ability with a C-index of 0.628 (95% confidence interval [CI]: 0.586-0.671) in the TCGA cohort and 0.648 (95% CI: 0.535-0.762) in the GSE73403 dataset. Calibration curves displayed excellent agreement between the actual observations and the nomogram-predicted survival. The IHC staining discovered that these five ARGs were overexpression in LUSC tissues. Besides, the immune infiltration analysis in the TCGA cohort represented a distinctly differentiated infiltration of anti-tumor immune cells between the low- and high-risk groups. We identified a novel ARG-related prognostic signature, which may serve as a potential biomarker for individualized survival predictions and personalized therapeutic recommendation of anti-tumor immunity for patients with LUSC.

The cryo-EM structure of an ERAD protein channel formed by tetrameric human Derlin-1.

Endoplasmic reticulum-associated degradation (ERAD) is a process directing misfolded proteins from the ER lumen and membrane to the degradation machinery in the cytosol. A key step in ERAD is the translocation of ER proteins to the cytosol. Derlins are essential for protein translocation in ERAD, but the mechanism remains unclear. Here, we solved the structure of human Derlin-1 by cryo-electron microscopy. The structure shows that Derlin-1 forms a homotetramer that encircles a large tunnel traversing the ER membrane. The tunnel has a diameter of about 12 to 15 angstroms, large enough to allow an α helix to pass through. The structure also shows a lateral gate within the membrane, providing access of transmembrane proteins to the tunnel, and thus, human Derlin-1 forms a protein channel for translocation of misfolded proteins. Our structure is different from the monomeric yeast Derlin structure previously reported, which forms a semichannel with another protein.