Multifaceted roles of t6A biogenesis in efficiency and fidelity of mitochondrial gene expression.

N 6-Threonylcarbamoyladenosine at A37 (t6A37) of ANN-decoding transfer RNAs (tRNAs) is a universal modification whose functions have been well documented in bacteria and lower eukaryotes; however, its role in organellar translation is not completely understood. In this study, we deleted the mitochondrial t6A37-modifying enzyme OSGEPL1 in HEK293T cells. OSGEPL1 is dispensable for cell viability. t6A37 hypomodification selectively stimulated N1-methyladenosine at A9 (m1A9) and N2-methylguanosine at G10 (m2G10) modifications and caused a substantial reduction in the aminoacylation of mitochondrial tRNAThr and tRNALys, resulting in impaired translation efficiency. Multiple types of amino acid misincorporation due to the misreading of near-cognate codons by t6A37-unmodified tRNAs were detected, indicating a triggered translational infidelity. Accordingly, the alterations in mitochondrial structure, function, and the activated mitochondrial unfolded protein response were observed. Mitochondrial function was efficiently restored by wild-type, but not by tRNA-binding-defective OSGEPL1. Lastly, in Osgepl1 deletion mice, disruption to mitochondrial translation was evident but resulted in no observable deficiency under physiological conditions in heart, which displays the highest Osgepl1 expression. Taken together, our data delineate the multifaceted roles of mitochondrial t6A37 modification in translation efficiency and quality control in mitochondria.

Structure-aware protein self-supervised learning.

MOTIVATION: Protein representation learning methods have shown great potential to many downstream tasks in biological applications. A few recent studies have demonstrated that the self-supervised learning is a promising solution to addressing insufficient labels of proteins, which is a major obstacle to effective protein representation learning. However, existing protein representation learning is usually pretrained on protein sequences without considering the important protein structural information. RESULTS: In this work, we propose a novel structure-aware protein self-supervised learning method to effectively capture structural information of proteins. In particular, a graph neural network model is pretrained to preserve the protein structural information with self-supervised tasks from a pairwise residue distance perspective and a dihedral angle perspective, respectively. Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning. Specifically, we identify the relation between the sequential information in the protein language model and the structural information in the specially designed graph neural network model via a novel pseudo bi-level optimization scheme. We conduct experiments on three downstream tasks: the binary classification into membrane/non-membrane proteins, the location classification into 10 cellular compartments, and the enzyme-catalyzed reaction classification into 384 EC numbers, and these experiments verify the effectiveness of our proposed method. AVAILABILITY AND IMPLEMENTATION: The Alphafold2 database is available in https://alphafold.ebi.ac.uk/. The PDB files are available in https://www.rcsb.org/. The downstream tasks are available in https://github.com/phermosilla/IEConv\_proteins/tree/master/Datasets. The code of the proposed method is available in https://github.com/GGchen1997/STEPS_Bioinformatics.

Commonality and diversity in tRNA substrate recognition in t6A biogenesis by eukaryotic KEOPSs.

N 6-Threonylcarbamoyladenosine (t6A) is a universal and pivotal tRNA modification. KEOPS in eukaryotes participates in its biogenesis, whose mutations are connected with Galloway-Mowat syndrome. However, the tRNA substrate selection mechanism by KEOPS and t6A modification function in mammalian cells remain unclear. Here, we confirmed that all ANN-decoding human cytoplasmic tRNAs harbor a t6A moiety. Using t6A modification systems from various eukaryotes, we proposed the possible coevolution of position 33 of initiator tRNAMet and modification enzymes. The role of the universal CCA end in t6A biogenesis varied among species. However, all KEOPSs critically depended on C32 and two base pairs in the D-stem. Knockdown of the catalytic subunit OSGEP in HEK293T cells had no effect on the steady-state abundance of cytoplasmic tRNAs but selectively inhibited tRNAIle aminoacylation. Combined with in vitro aminoacylation assays, we revealed that t6A functions as a tRNAIle isoacceptor-specific positive determinant for human cytoplasmic isoleucyl-tRNA synthetase (IARS1). t6A deficiency had divergent effects on decoding efficiency at ANN codons and promoted +1 frameshifting. Altogether, our results shed light on the tRNA recognition mechanism, revealing both commonality and diversity in substrate recognition by eukaryotic KEOPSs, and elucidated the critical role of t6A in tRNAIle aminoacylation and codon decoding in human cells.

Molecular basis for human mitochondrial tRNA m3C modification by alternatively spliced METTL8.

METTL8 has recently been identified as the methyltransferase catalyzing 3-methylcytidine biogenesis at position 32 (m3C32) of mitochondrial tRNAs. METTL8 also potentially participates in mRNA methylation and R-loop biogenesis. How METTL8 plays multiple roles in distinct cell compartments and catalyzes mitochondrial tRNA m3C formation remain unclear. Here, we discovered that alternative mRNA splicing generated several isoforms of METTL8. One isoform (METTL8-Iso1) was targeted to mitochondria via an N-terminal pre-sequence, while another one (METTL8-Iso4) mainly localized to the nucleolus. METTL8-Iso1-mediated m3C32 modification of human mitochondrial tRNAThr (hmtRNAThr) was not reliant on t6A modification at A37 (t6A37), while that of hmtRNASer(UCN) critically depended on i6A modification at A37 (i6A37). We clarified the hmtRNAThr substrate recognition mechanism, which was obviously different from that of hmtRNASer(UCN), in terms of requiring a G35 determinant. Moreover, SARS2 (mitochondrial seryl-tRNA synthetase) interacted with METTL8-Iso1 in an RNA-independent manner and modestly accelerated m3C modification activity. We further elucidated how nonsubstrate tRNAs in human mitochondria were efficiently discriminated by METTL8-Iso1. In summary, our results established the expression pattern of METTL8, clarified the molecular basis for m3C32 modification by METTL8-Iso1 and provided the rationale for the involvement of METTL8 in tRNA modification, mRNA methylation or R-loop biogenesis.

Adaptive Weighted Data Fusion for Line Structured Light and Photometric Stereo Measurement System.

Line structured light (LSL) measurement systems can obtain high accuracy profiles, but the overall clarity relies greatly on the sampling interval of the scanning process. Photometric stereo (PS), on the other hand, is sensitive to tiny features but has poor geometrical accuracy. Cooperative measurement with these two methods is an effective way to ensure precision and clarity results. In this paper, an LSL-PS cooperative measurement system is brought out. The calibration methods used in the LSL and PS measurement system are given. Then, a data fusion algorithm with adaptive weights is proposed, where an error function that contains the 3D point cloud matching error and normal vector error is established. The weights, which are based on the angles of adjacent normal vectors, are also added to the error function. Afterward, the fusion results can be obtained by solving linear equations. From the experimental results, it can be seen that the proposed method has the advantages of both the LSL and PS methods. The 3D reconstruction results have the merits of high accuracy and high clarity.

Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validation.

Drug combination therapy is a highly effective approach for enhancing the therapeutic efficacy of anti-cancer drugs and overcoming drug resistance. However, the innumerable possible drug combinations make it impractical to screen all synergistic drug pairs. Moreover, biological insights into synergistic drug pairs are still lacking. To address this challenge, we systematically analyzed drug combination datasets curated from multiple databases to identify drug pairs more likely to show synergy. We classified drug pairs based on their MoA and discovered that 110 MoA pairs were significantly enriched in synergy in at least one type of cancer. To improve the accuracy of predicting synergistic effects of drug pairs, we developed a suite of machine learning models that achieve better predictive performance. Unlike most previous methods that were rarely validated by wet-lab experiments, our models were validated using two-dimensional cell lines and three-dimensional tumor slice culture (3D-TSC) models, implying their practical utility. Our prediction and validation results indicated that the combination of the RTK inhibitors Lapatinib and Pazopanib exhibited a strong therapeutic effect in breast cancer by blocking the downstream PI3K/AKT/mTOR signaling pathway. Furthermore, we incorporated molecular features to identify potential biomarkers for synergistic drug pairs, and almost all potential biomarkers found connections between drug targets and corresponding molecular features using protein-protein interaction network. Overall, this study provides valuable insights to complement and guide rational efforts to develop drug combination treatments.

Human lysyl-tRNA synthetase evolves a dynamic structure that can be stabilized by forming complex.

The evolutionary necessity of aminoacyl-tRNA synthetases being associated into complex is unknown. Human lysyl-tRNA synthetase (LysRS) is one component of the multi-tRNA synthetase complex (MSC), which is not only critical for protein translation but also involved in multiple cellular pathways such as immune response, cell migration, etc. Here, combined with crystallography, CRISPR/Cas9-based genome editing, biochemistry, and cell biology analyses, we show that the structures of LysRSs from metazoan are more dynamic than those from single-celled organisms. Without the presence of MSC scaffold proteins, such as aminoacyl-tRNA synthetase complex-interacting multifunctional protein 2 (AIMP2), human LysRS is free from the MSC. The interaction with AIMP2 stabilizes the closed conformation of LysRS, thereby protects the essential aminoacylation activity under stressed conditions. Deleting AIMP2 from the human embryonic kidney 293 cells leads to retardation in cell growth in nutrient deficient mediums. Together, these results suggest that the evolutionary emergence of the MSC in metazoan might be to protect the aminoacyl-tRNA synthetase components from being modified or recruited for use in other cellular pathways.

Mutually exclusive substrate selection strategy by human m3C RNA transferases METTL2A and METTL6.

tRNAs harbor the most diverse posttranscriptional modifications. The 3-methylcytidine (m3C) is widely distributed at position C32 (m3C32) of eukaryotic tRNAThr and tRNASer species. m3C32 is decorated by the single methyltransferase Trm140 in budding yeasts; however, two (Trm140 and Trm141 in fission yeasts) or three enzymes (METTL2A, METTL2B and METTL6 in mammals) are involved in its biogenesis. The rationale for the existence of multiple m3C32 methyltransferases and their substrate discrimination mechanism is hitherto unknown. Here, we revealed that both METTL2A and METTL2B are expressed in vivo. We purified human METTL2A, METTL2B, and METTL6 to high homogeneity. We successfully reconstituted m3C32 modification activity for tRNAThr by METT2A and for tRNASer(GCU) by METTL6, assisted by seryl-tRNA synthetase (SerRS) in vitro. Compared with METTL2A, METTL2B exhibited dramatically lower activity in vitro. Both G35 and t6A at position 37 (t6A37) are necessary but insufficient prerequisites for tRNAThr m3C32 formation, while the anticodon loop and the long variable arm, but not t6A37, are key determinants for tRNASer(GCU) m3C32 biogenesis, likely being recognized synergistically by METTL6 and SerRS, respectively. Finally, we proposed a mutually exclusive substrate selection model to ensure correct discrimination among multiple tRNAs by multiple m3C32 methyltransferases.

High-Precision Calibration of a Monocular-Vision-Guided Handheld Line-Structured-Light Measurement System.

Due to the advantages of simple construction, easy application and good environmental suitability, handheld structured-light measurement systems have broad application prospects in 3D measurements. Here, a monocular-vision-guided line-structured-light measurement system is developed, and the posture of the handheld device can be obtained via a specifically designed target attached to it. No more marker points need to be adhered onto the object under inspection. The key for the system calibration is to obtain the coordinate transformation matrix from the sensor to the featured target coordinate system. The mathematical model of the system is first established. Then, an improved multi-view calibration method is proposed, where a selection process for the image pairs is conducted for accuracy improvement. With this method, the maximum relative error of the measured stair heights can be reduced from 0.48% to 0.16%. The measurement results for the specific parts further verified the effectiveness of the proposed system and the calibration method.

Modifications of the human tRNA anticodon loop and their associations with genetic diseases.

Transfer RNAs (tRNAs) harbor the most diverse posttranscriptional modifications. Among such modifications, those in the anticodon loop, either on nucleosides or base groups, compose over half of the identified posttranscriptional modifications. The derivatives of modified nucleotides and the crosstalk of different chemical modifications further add to the structural and functional complexity of tRNAs. These modifications play critical roles in maintaining anticodon loop conformation, wobble base pairing, efficient aminoacylation, and translation speed and fidelity as well as mediating various responses to different stress conditions. Posttranscriptional modifications of tRNA are catalyzed mainly by enzymes and/or cofactors encoded by nuclear genes, whose mutations are firmly connected with diverse human diseases involving genetic nervous system disorders and/or the onset of multisystem failure. In this review, we summarize recent studies about the mechanisms of tRNA modifications occurring at tRNA anticodon loops. In addition, the pathogenesis of related disease-causing mutations at these genes is briefly described.

Molecular basis for t6A modification in human mitochondria.

N 6-Threonylcarbamoyladenosine (t6A) is a universal tRNA modification essential for translational accuracy and fidelity. In human mitochondria, YrdC synthesises an l-threonylcarbamoyl adenylate (TC-AMP) intermediate, and OSGEPL1 transfers the TC-moiety to five tRNAs, including human mitochondrial tRNAThr (hmtRNAThr). Mutation of hmtRNAs, YrdC and OSGEPL1, affecting efficient t6A modification, has been implicated in various human diseases. However, little is known about the tRNA recognition mechanism in t6A formation in human mitochondria. Herein, we showed that OSGEPL1 is a monomer and is unique in utilising C34 as an anti-determinant by studying the contributions of individual bases in the anticodon loop of hmtRNAThr to t6A modification. OSGEPL1 activity was greatly enhanced by introducing G38A in hmtRNAIle or the A28:U42 base pair in a chimeric tRNA containing the anticodon stem of hmtRNASer(AGY), suggesting that sequences of specific hmtRNAs are fine-tuned for different modification levels. Moreover, using purified OSGEPL1, we identified multiple acetylation sites, and OSGEPL1 activity was readily affected by acetylation via multiple mechanisms in vitro and in vivo. Collectively, we systematically elucidated the nucleotide requirement in the anticodon loop of hmtRNAs, and revealed mechanisms involving tRNA sequence optimisation and post-translational protein modification that determine t6A modification levels.

IDRMutPred: predicting disease-associated germline nonsynonymous single nucleotide variants (nsSNVs) in intrinsically disordered regions.

MOTIVATION: Despite of the lack of folded structure, intrinsically disordered regions (IDRs) of proteins play versatile roles in various biological processes, and many nonsynonymous single nucleotide variants (nsSNVs) in IDRs are associated with human diseases. The continuous accumulation of nsSNVs resulted from the wide application of NGS has driven the development of disease-association prediction methods for decades. However, their performance on nsSNVs in IDRs remains inferior, possibly due to the domination of nsSNVs from structured regions in training data. Therefore, it is highly demanding to build a disease-association predictor specifically for nsSNVs in IDRs with better performance. RESULTS: We present IDRMutPred, a machine learning-based tool specifically for predicting disease-associated germline nsSNVs in IDRs. Based on 17 selected optimal features that are extracted from sequence alignments, protein annotations, hydrophobicity indices and disorder scores, IDRMutPred was trained using three ensemble learning algorithms on the training dataset containing only IDR nsSNVs. The evaluation on the two testing datasets shows that all the three prediction models outperform 17 other popular general predictors significantly, achieving the ACC between 0.856 and 0.868 and MCC between 0.713 and 0.737. IDRMutPred will prioritize disease-associated IDR germline nsSNVs more reliably than general predictors. AVAILABILITY AND IMPLEMENTATION: The software is freely available at http://www.wdspdb.com/IDRMutPred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hyperoside ameliorates diabetic nephropathy induced by STZ via targeting the miR-499-5p/APC axis.

Diabetic nephropathy is a serious complication of diabetes. Hyperoside has been widely reported to ameliorate diabetes-associated disease. The current study is designed to explore the mechanism of hyperoside in diabetic nephropathy. In the present study, high glucose was used to treat podocytes. Diabetic nephropathy mice models were established by high-fat feeding followed by multiple low dose injections of streptozocin. Western blot analysis was conducted for detection of extracellular matrix accumulation, inflammatory response and cell apoptosis. We found out that hyperoside improved high glucose-induced cell injury. Additionally, hyperoside prevented mice with diabetic nephropathy from diabetic symptoms and renal dysfunction. Mechanistically, hyperoside inhibited the mRNA and protein expression of APC. MiR-499-5p was found to be an upstream negative mediator of APC, and hyperoside induced the upregulation of miR-499-5p. MiR-499-5p bound with the 3' untranslated region of APC to inhibit its expression. Finally, rescue assays revealed that the suppressive effects of miR-499-5p overexpression on renal dysfunction were rescued by upregulation of APC in mice with diabetic nephropathy. In conclusion, these findings indicated that hyperoside ameliorates diabetic nephropathy via targeting the miR-499-5p/APC axis, suggesting that hyperoside may offer a potential tactic for diabetic nephropathy treatment.

The G3-U70-independent tRNA recognition by human mitochondrial alanyl-tRNA synthetase.

Alanyl-tRNA synthetases (AlaRSs) from three domains of life predominantly rely on a single wobble base pair, G3-U70, of tRNAAla as a major determinant. However, this base pair is divergent in human mitochondrial tRNAAla, but instead with a translocated G5-U68. How human mitochondrial AlaRS (hmtAlaRS) recognizes tRNAAla, in particular, in the acceptor stem region, remains unknown. In the present study, we found that hmtAlaRS is a monomer and recognizes mitochondrial tRNAAla in a G3-U70-independent manner, requiring several elements in the acceptor stem. In addition, we found that hmtAlaRS misactivates noncognate Gly and catalyzes strong transfer RNA (tRNA)-independent pre-transfer editing for Gly. A completely conserved residue outside of the editing active site, Arg663, likely functions as a tRNA translocation determinant to facilitate tRNA entry into the editing domain during editing. Finally, we investigated the effects of the severe infantile-onset cardiomyopathy-associated R592W mutation of hmtAlaRS on the canonical enzymatic activities of hmtAlaRS. Overall, our results provide fundamental information about tRNA recognition and deepen our understanding of translational quality control mechanisms by hmtAlaRS.

WDSPdb: an updated resource for WD40 proteins.

SUMMARY: The WD40-repeat proteins are a large family of scaffold molecules that assemble complexes in various cellular processes. Obtaining their structures is the key to understanding their interaction details. We present WDSPdb 2.0, a significantly updated resource providing accurately predicted secondary and tertiary structures and featured sites annotations. Based on an optimized pipeline, WDSPdb 2.0 contains about 600 thousand entries, an increase of 10-fold, and integrates more than 37 000 variants from sources of ClinVar, Cosmic, 1000 Genomes, ExAC, IntOGen, cBioPortal and IntAct. In addition, the web site is largely improved for visualization, exploring and data downloading. AVAILABILITY AND IMPLEMENTATION: http://www.wdspdb.com/wdsp/ or http://wu.scbb.pkusz.edu.cn/wdsp/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Real-Time Stripe Width Computation Using Back Propagation Neural Network for Adaptive Control of Line Structured Light Sensors.

A line structured light sensor (LSLS) is generally constituted of a laser line projector and a camera. With the advantages of simple construction, non-contact, and high measuring speed, it is of great perspective in 3D measurement. For traditional LSLSs, the camera exposure time is usually fixed while the surface properties can be varied for different measurement tasks. This would lead to under/over exposure of the stripe images or even failure of the measurement. To avoid these undesired situations, an adaptive control method was proposed to modulate the average stripe width (ASW) within a favorite range. The ASW is first computed based on the back propagation neural network (BPNN), which can reach a high accuracy result and reduce the runtime dramatically. Then, the approximate linear relationship between the ASW and the exposure time was demonstrated via a series of experiments. Thus, a linear iteration procedure was proposed to compute the optimal camera exposure time. When the optimized exposure time is real-time adjusted, stripe images with the favorite ASW can be obtained during the whole scanning process. The smoothness of the stripe center lines and the surface integrity can be improved. A small proportion of the invalid stripe images further proves the effectiveness of the control method.

Real time analysis of lead-containing atmospheric particles in Guangzhou during wintertime using single particle aerosol mass spectrometry.

The toxic effects of lead on human health and the environment have long been a focus of research. To explore sources of lead in Guangzhou, China, we investigated atmospheric lead-containing particles (LCPs) during wintertime using a single particle aerosol mass spectrometer (SPAMS). Based on mass spectral features, LCPs were classified into eight major particle types, including Pb-Cl and Pb-Cl-Li (coal combustion and waste incineration), Pb-Cl-EC and Pb-Cl-OC (diesel trucks and coal combustion), Pb-Cl-Fe (iron and steel industry), Pb-Cl-AlSi (dust), Pb-Sec (secondary formation), and Pb-Cl-Zn (industrial process); these sources (in parentheses) were identified by comparing atmospheric LCP mass spectra with authentic Pb emission source mass spectra. Sampling periods with LCP number fractions (NFs) more than three times the average LCP NF (APF = 4.35%) and below the APF were defined as high LCP NF periods (HLFPs: H1, H3, and H5) and low LCP NF APF periods (LLFPs: L2 and L4), respectively. Diurnal patterns and high Pb-Sec content during LLFPs indicate that photochemical activity and heterogeneous reactions may have controlled Pb-Sec particle formation. The inverse Pb-Cl and Pb-Sec particle diurnal trends during LLFPs suggest the replacement of Cl by sulfate and nitrate. On average over the five periods, ~ 76% of the LCPs likely arose from coal combustion and/or waste incineration, which were dominant sources during all five periods, followed by diesel trucks during LLFPs and iron- and steel-related sources during HLFPs; HLFP LCPs arose mainly from primary emissions. These results can be used to more efficiently control Pb emission sources and prevent harm to human and environmental health from Pb toxicity.

Early Fault Detection Method for Rotating Machinery Based on Harmonic-Assisted Multivariate Empirical Mode Decomposition and Transfer Entropy.

It is a difficult task to analyze the coupling characteristics of rotating machinery fault signals under the influence of complex and nonlinear interference signals. This difficulty is due to the strong noise background of rotating machinery fault feature extraction and weaknesses, such as modal mixing problems, in the existing Ensemble Empirical Mode Decomposition (EEMD) time-frequency analysis methods. To quantitatively study the nonlinear synchronous coupling characteristics and information transfer characteristics of rotating machinery fault signals between different frequency scales under the influence of complex and nonlinear interference signals, a new nonlinear signal processing method-the harmonic assisted multivariate empirical mode decomposition method (HA-MEMD)-is proposed in this paper. By adding additional high-frequency harmonic-assisted channels and reducing them, the decomposing precision of the Intrinsic Mode Function (IMF) can be effectively improved, and the phenomenon of mode aliasing can be mitigated. Analysis results of the simulated signals prove the effectiveness of this method. By combining HA-MEMD with the transfer entropy algorithm and introducing signal processing of the rotating machinery, a fault detection method of rotating machinery based on high-frequency harmonic-assisted multivariate empirical mode decomposition-transfer entropy (HA-MEMD-TE) was established. The main features of the mechanical transmission system were extracted by the high-frequency harmonic-assisted multivariate empirical mode decomposition method, and the signal, after noise reduction, was used for the transfer entropy calculation. The evaluation index of the rotating machinery state based on HA-MEMD-TE was established to quantitatively describe the degree of nonlinear coupling between signals to effectively evaluate and diagnose the operating state of the mechanical system. By adding noise to different signal-to-noise ratios, the fault detection ability of HA-MEMD-TE method in the background of strong noise is investigated, which proves that the method has strong reliability and robustness. In this paper, transfer entropy is applied to the fault diagnosis field of rotating machinery, which provides a new effective method for early fault diagnosis and performance degradation-state recognition of rotating machinery, and leads to relevant research conclusions.

Sub-Pixel Extraction of Laser Stripe Center Using an Improved Gray-Gravity Method.

Laser stripe center extraction is a key step for the profile measurement of line structured light sensors (LSLS). To accurately obtain the center coordinates at sub-pixel level, an improved gray-gravity method (IGGM) was proposed. Firstly, the center points of the stripe were computed using the gray-gravity method (GGM) for all columns of the image. By fitting these points using the moving least squares algorithm, the tangential vector, the normal vector and the radius of curvature can be robustly obtained. One rectangular region could be defined around each of the center points. Its two sides that are parallel to the tangential vector could alter their lengths according to the radius of the curvature. After that, the coordinate for each center point was recalculated within the rectangular region and in the direction of the normal vector. The center uncertainty was also analyzed based on the Monte Carlo method. The obtained experimental results indicate that the IGGM is suitable for both the smooth stripes and the ones with sharp corners. The high accuracy center points can be obtained at a relatively low computation cost. The measured results of the stairs and the screw surface further demonstrate the effectiveness of the method.

A Vacuum Vapor Deposition Strategy to Fe Single-Atom Catalysts with Densely Active Sites for High-Performance Zn-Air Battery.

Iron single-atom catalysts (SACs) have garnered increasing attention as highly efficient catalysts for the oxygen reduction reaction (ORR), yet their performance in practical devices remains suboptimal due to the low density of accessible active sites. Anchoring iron single atoms on 2D support is a promising way to increase the accessible active sites but remains difficult attributing to the high aggregation tendency of iron atoms on the 2D support. Herein, a vacuum vapor deposition strategy is presented to fabricate an iron SAC supported on ultrathin N-doped carbon nanosheets with densely active sites (FeSAs-UNCNS). Experimental analyses confirm that the FeSAs-UNCNS achieves densely accessible active sites (1.11 × 1020 sites g-1) in the configuration of Fe─N4O. Consequently, the half-wave potential of FeSAs-UNCNS in 0.1 m KOH reaches a remarkable value of 0.951 V versus RHE. Moreover, when employed as the cathode of various kinds of Zn-air batteries, FeSAs-UNCNS exhibits boosting performances by achieving a maximum power density of 306 mW cm-2 and long cycle life (>180 h) at room temperature, surpassing both Pt/C and reported SACs. Further investigations reveal that FeSAs-UNCNS facilitates the mass and charge transfer during catalysis and the atomic configuration favors the desorption of *OH kinetically.