MIL-101(CuFe) Nanozymes with Excellent Peroxidase-like Activity for Simple, Accurate, and Visual Naked-Eye Detection of SARS-CoV-2.

The COVID-19 pandemic has spread to every corner of the world and seriously affected our health and daily activities in the past three years; thereby, it is still urgent to develop various simple, quick, and accurate methods for early detection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. Nanozymes, a kind of nanomaterial with intrinsic enzyme-mimicking activity, have emerged as a suitable alternative for both therapy and diagnosis of SARS-CoV-2. Here, ultrasensitive and ultrafast MIL-101(CuFe)-CD147 biosensors are established for the detection of SARS-CoV-2 by a simple colorimetric method. A MIL-101(CuFe) metal-organic framework has excellent peroxidase-like activity due to the synergistic effect of Fe and Cu atoms. In addition, the MIL-101(CuFe)-CD147 biosensor shows great potential to detect the various variants of SARS-CoV-2 due to the universal receptor of CD147. The enzyme-based biosensor for the detection of SARS-CoV-2 achieves a very low limit of detection (about 3 PFU/mL) within 30 min. Therefore, the present method provides a new generation of an alternative approach for highly sensitive and visual diagnosis of COVID-19.

Low-cost and flexible paper-based plasmonic nanostructure for a highly sensitive SERS substrate.

The application of a noble-metal-based plasmon-enhanced substrate to detect low-concentration analytes has attracted extensive attention. Most of the substrates used in recently reported researches are based on two-dimensional structures. Hence, we prepared a higher efficiency Raman activity substrate with a filter paper structure, which not only provides more plasmonic "hot spots," but also facilitates analyte extraction and detection due to the flexibility of the paper. The preparation of the plasmonic paper substrate adopted centrifugation to deposit the alloy nanoparticles onto the paper base. The optimal particle deposition condition was found by adjusting the centrifugal force and centrifugation time. Then, the surface-enhanced Raman spectroscopy (SERS) performance of the substrate was enhanced by altering the plasmon resonance peak on the surface of the nanoparticles. The enhancement factor of this paper-based substrate was 1.55×107, with high detection uniformity (10-6 M, rhodamine 6G) and a low detection limit (10-11 M, rhodamine 6G). Then, we applied the SERS substrate to pesticide detection; the detection limit of the thiram reached 10-6 M. As a result, the simple and cost-effective paper-based SERS substrate obtained in this way has high detection performance for pesticides and can be used for rapid detection in the field, which is beneficial to food safety and environmental safety.

Dissecting the Structural Dynamics of Authentic Cholesteryl Ester Transfer Protein for the Discovery of Potential Lead Compounds: A Theoretical Study.

Current structural and functional investigations of cholesteryl ester transfer protein (CETP) inhibitor design are nearly entirely based on a fully active mutation (CETPMutant) constructed for protein crystallization, limiting the study of the dynamic structural features of authentic CETP involved in lipid transport under physiological conditions. In this study, we conducted comprehensive molecular dynamics (MD) simulations of both authentic CETP (CETPAuthentic) and CETPMutant. Considering the structural differences between the N- and C-terminal domains of CETPAuthentic and CETPMutant, and their crucial roles in lipid transfer, we identified the two domains as binding pockets of the ligands for virtual screening to discover potential lead compounds targeting CETP. Our results revealed that CETPAuthentic displays greater flexibility and pronounced curvature compared to CETPMutant. Employing virtual screening and MD simulation strategies, we found that ZINC000006242926 has a higher binding affinity for the N- and C-termini, leading to reduced N- and C-opening sizes, disruption of the continuous tunnel, and increased curvature of CETP. In conclusion, CETPAuthentic facilitates the formation of a continuous tunnel in the "neck" region, while CETPMutant does not exhibit such characteristics. The ligand ZINC000006242926 screened for binding to the N- and C-termini induces structural changes in the CETP unfavorable to lipid transport. This study sheds new light on the relationship between the structural and functional mechanisms of CETP. Furthermore, it provides novel ideas for the precise regulation of CETP functions.

Identification of Potential Lead Compounds Targeting Novel Druggable Cavity of SARS-CoV-2 Spike Trimer by Molecular Dynamics Simulations.

The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become an urgent public health problem. Spike (S) protein mediates the fusion between the virus and the host cell membranes, consequently emerging as an important target of drug design. The lack of comparisons of in situ full-length S homotrimer structures in different states hinders understanding the structures and revealing the function, thereby limiting the discovery and development of therapeutic agents. Here, the steady-state structures of the in situ full-length S trimer in closed and open states (Sclosed and Sopen) were modeled with the constraints of density maps, associated with the analysis of the dynamic structural differences. Subsequently, we identified various regions with structure and property differences as potential binding pockets for ligands that promote the formation of inactive trimeric protein complexes. By using virtual screening strategy and a newly defined druggable cavity, five ligands were screened with potential bioactivities. Then molecular dynamic (MD) simulations were performed on apo protein structures and ligand bound complexes to reveal the conformational changes upon ligand binding. Our simulation results revealed that sulforaphane (SFN), which has the best binding affinity, could inhibit the conformational changes of S homotrimer that would occur during the viral membrane fusion. Our results could aid in the understanding of the regulation mechanism of S trimer aggregation and the structure-activity relationship, facilitating the development of potential antiviral agents.

Electrically controllable self-assembly of gold nanorods into a plasmonic nanostructure for highly efficiency SERS.

In this Letter, a method for the rapid and efficient preparation of ultrasensitive detection substrates by assembling gold nanorod suspensions with the application of an alternating current (AC) field is proposed, and it is found that frequency and voltage are the effective means of regulation. A sandwich structure (parallel SiO2 plate) not only effectively slows down the evaporation rate, but also visually reveals the changes in the assembly process. Under the optimal assembly conditions, the sensitivity and uniformity of the substrate to different probe molecules are tested. The Raman detection results experimentally show that the detection limits of Rhodamine 6G (Rh6G), crystal violet (CV), and Aspartame (APM) molecular solutions are 10-14 M, 10-10 M, and 62.5 mg/L, respectively, and the mixed dye molecular solutions can also be effectively distinguished. Furthermore, Rh6G and CV characteristic peaks at 1647 cm-1 and 1619 cm-1 were measured at randomly selected positions, and their relative standard deviations (RSDs) were 5.63% and 8.45%, respectively, indicating that the substrate has good uniformity. The effective regulation of the self-assembly results of nanoparticles will further enhance the practical application effect of surface-enhanced Raman technology and expand the application prospects of this technology.

Prediction of low Apgar score at five minutes following labor induction intervention in vaginal deliveries: machine learning approach for imbalanced data at a tertiary hospital in North Tanzania.

BACKGROUND: Prediction of low Apgar score for vaginal deliveries following labor induction intervention is critical for improving neonatal health outcomes. We set out to investigate important attributes and train popular machine learning (ML) algorithms to correctly classify neonates with a low Apgar scores from an imbalanced learning perspective. METHODS: We analyzed 7716 induced vaginal deliveries from the electronic birth registry of the Kilimanjaro Christian Medical Centre (KCMC). 733 (9.5%) of which constituted of low (< 7) Apgar score neonates. The 'extra-tree classifier' was used to assess features' importance. We used Area Under Curve (AUC), recall, precision, F-score, Matthews Correlation Coefficient (MCC), balanced accuracy (BA), bookmaker informedness (BM), and markedness (MK) to evaluate the performance of the selected six (6) machine learning classifiers. To address class imbalances, we examined three widely used resampling techniques: the Synthetic Minority Oversampling Technique (SMOTE) and Random Oversampling Examples (ROS) and Random undersampling techniques (RUS). We applied Decision Curve Analysis (DCA) to evaluate the net benefit of the selected classifiers. RESULTS: Birth weight, maternal age, and gestational age were found to be important predictors for the low Apgar score following induced vaginal delivery. SMOTE, ROS and and RUS techniques were more effective at improving "recalls" among other metrics in all the models under investigation. A slight improvement was observed in the F1 score, BA, and BM. DCA revealed potential benefits of applying Boosting method for predicting low Apgar scores among the tested models. CONCLUSION: There is an opportunity for more algorithms to be tested to come up with theoretical guidance on more effective rebalancing techniques suitable for this particular imbalanced ratio. Future research should prioritize a debate on which performance indicators to look up to when dealing with imbalanced or skewed data.

Land-use transition and its driving forces in a minority mountainous area: a case study from Mao County, Sichuan Province, China.

Land-use change is an important research topic in global environmental change. Analyzing land-use change and its driving factors can aid in the evaluation of the current and the determination of future land-use policies. This study took Mao County, Southwest China, as the study area and used the land-use change and statistical data surveyed in 2009 and 2019. With the help of geographic information system technology, a land-use transfer matrix was used to comprehensively analyze the characteristics of spatiotemporal differentiation of land use, while the driving mechanism was analyzed by constructing the influencing factors using a geographical detector model. The results showed that the change in land use in Mao County was drastic. The increasing land types included orchards, grasslands, built-up lands, and water bodies, whereas the decreasing land types included croplands, forestlands, and unused lands. The main driving factors of land-use transition depended on the type of land-use change. Elevation, distance from the county government, and population were the main driving factors of land-use change. Road density, distance from the river, distance from the town/township government, and gross domestic product also affected land-use change to a certain extent, whereas relief and slope had less impact.

Molecular Insights into the Recruiting Between UCP2 and DDX5/UBAP2L in the Metabolic Plasticity of Non-Small-Cell Lung Cancer.

Mitochondrial uncoupling protein 2 (UCP2) is distributed in tumor cells with a link to the support of systemic metabolic deregulation, and the downregulation of UCP2 has been unveiled as a biomarker of oncogenesis and chemoresistance in non-small-cell lung cancer (NSCLC) cells. However, the underlying mechanism of how UCP2 cooperates with other proteins in this metabolic reprogramming remains largely unsolved. We employed a combined computational and experimental strategy to explore into the recruiting of DDX5 with other proteins, and we unraveled the underlying structural mechanisms. We found that recruiting by ATP-dependent RNA helicase DDX5 (DDX5)/ubiquitin-associated protein 2-like (UBAP2L) might help UCP2 to play the pathological roles in NSCLC cells. According to the view of thermodynamics in physics, UCP2 tends to recruit DDX5 rather than UBAP2L, as shown by the ensemble-based docking, molecular dynamics simulations and molecular mechanics generalized Born surface area (MM/GBSA) approach. Cellular immunofluorescence assays further demonstrated that UCP2 associate with DDX5, and the recruiting of DDX5 with UCP2 at least partially contribute to the metabolic plasticity of NSCLCs via the AKT/mTOR pathway. Our study proposed an efficient way for detecting the protein-protein association via the experimentally validated molecular simulation. Our results shed light on the functional annotation of UCP and DDX family proteins in dysregulated metabolism, and the identification of candidate therapeutic targets for NSCLC.

Molecular insights into the binding variance of the SARS-CoV-2 spike with human, cat and dog ACE2 proteins.

SARS-CoV-2 has recently caused an epidemic in humans and poses a huge threat to global public health. As a primary receptor of SARS-CoV-2, angiotensin-converting enzyme 2 (ACE2) exists in different hosts that are in close contact with humans, especially cats and dogs. However, the underlying mechanism of how the spike receptor binding domain (RBD) of SARS-CoV-2 cooperates with human ACE2 (hACE2), cat ACE2 (cACE2) and dog ACE2 (dACE2) and the variation in binding remains largely unsolved. Therefore, we explored the binding behavior of the spike RBD with cACE2, dACE2 and hACE2 via all-atom molecular dynamics simulations. In accordance with the binding free energies and residue interactions, the spike RBD has respective binding specificities with cACE2, dACE2 and hACE2, and the binding affinities decrease in the order of hACE2, cACE2, dACE2, mainly due to changes in the amino acids Q24L, H34Y, and M82T in cACE2 or dACE2. Furthermore, alanine scanning analysis results validated some key residues of the spike RBD interact with ACE2 and provided clues to the variation of amino acid that could influence the transmissibility or immune responses of SARS-CoV-2. Decreasing dynamic correlations strengths of ACE2 with the RBD were found in all hACE2-RBD, cACE2-RBD and dACE2-RBD systems. The ACE2 protein shows variable motion modes across the zinc metallopeptidase domain, which induces different interactions between ACE2 and the RBD. Our studies reveal that the motion pattern of the zinc metallopeptidase domain is critical to the binding behavior of RBD with ACE2. These findings could aid our understanding of selective recognition involving various ACE2 with the SARS-CoV-2 spike and shed further light on the binding mechanisms.

Computational Simulation of HIV Protease Inhibitors to the Main Protease (Mpro) of SARS-CoV-2: Implications for COVID-19 Drugs Design.

SARS-CoV-2 is highly homologous to SARS-CoV. To date, the main protease (Mpro) of SARS-CoV-2 is regarded as an important drug target for the treatment of Coronavirus Disease 2019 (COVID-19). Some experiments confirmed that several HIV protease inhibitors present the inhibitory effects on the replication of SARS-CoV-2 by inhibiting Mpro. However, the mechanism of action has still not been studied very clearly. In this work, the interaction mechanism of four HIV protease inhibitors Darunavir (DRV), Lopinavir (LPV), Nelfinavir (NFV), and Ritonavire (RTV) targeting SARS-CoV-2 Mpro was explored by applying docking, molecular dynamics (MD) simulations, and MM-GBSA methods using the broad-spectrum antiviral drug Ribavirin (RBV) as the negative and nonspecific control. Our results revealed that LPV, RTV, and NFV have higher binding affinities with Mpro, and they all interact with catalytic residues His41 and the other two key amino acids Met49 and Met165. Pharmacophore model analysis further revealed that the aromatic ring, hydrogen bond donor, and hydrophobic group are the essential infrastructure of Mpro inhibitors. Overall, this study applied computational simulation methods to study the interaction mechanism of HIV-1 protease inhibitors with SARS-CoV-2 Mpro, and the findings provide useful insights for the development of novel anti-SARS-CoV-2 agents for the treatment of COVID-19.

Four Parallel Pathways in T4 Ligase-Catalyzed Repair of Nicked DNA with Diverse Bending Angles.

The structural diversity of biological macromolecules in different environments contributes complexity to enzymological processes vital for cellular functions. Fluorescence resonance energy transfer and electron microscopy are used to investigate the enzymatic reaction of T4 DNA ligase catalyzing the ligation of nicked DNA. The data show that both the ligase-AMP complex and the ligase-AMP-DNA complex can have four conformations. This finding suggests the parallel occurrence of four ligation reaction pathways, each characterized by specific conformations of the ligase-AMP complex that persist in the ligase-AMP-DNA complex. Notably, these complexes have DNA bending angles of ≈0°, 20°, 60°, or 100°. The mechanism of parallel reactions challenges the conventional notion of simple sequential reaction steps occurring among multiple conformations. The results provide insights into the dynamic conformational changes and the versatile attributes of T4 DNA ligase and suggest that the parallel multiple reaction pathways may correspond to diverse T4 DNA ligase functions. This mechanism may potentially have evolved as an adaptive strategy across evolutionary history to navigate complex environments.

Molecular insights into the inhibition mechanism of harringtonine against essential proteins associated with SARS-CoV-2 entry.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has recently posed a serious threat to global public health. Harringtonine (HT), as a small-molecule antagonist, has antiviral activity against a variety of viruses. There is evidence that HT can inhibit the SARS-CoV-2 entry into host cells by blocking the Spike protein and transmembrane protease serine 2 (TMPRSS2). However, the molecular mechanism underlying the inhibition effect of HT is largely elusive. Here, docking and all-atom molecular dynamics simulations were used to investigate the mechanism of HT against the receptor binding domain (RBD) of Spike, TMPRSS2, as well as the complex of RBD and angiotensin-converting enzyme 2 complex (RBD-ACE2). The results reveal that HT binds to all proteins primarily through hydrogen bond and hydrophobic interactions. Binding with HT influences the structural stability and dynamic motility processes of each protein. The interactions of HT with residues N33, H34 and K353 of ACE2, and residue K417 and Y453 of RBD contribute to disrupting the binding affinity between RBD and ACE2, which may hinder the virus entry into host cells. Our research provides molecular insights into the inhibition mechanism of HT against SARS-CoV-2 associated proteins, which will help for the novel antiviral drugs development.

Accounting Conformational Dynamics into Structural Modeling Reflected by Cryo-EM with Deep Learning.

With the continuous development of structural biology, the requirement for accurate threedimensional structures during functional modulation of biological macromolecules is increasing. Therefore, determining the dynamic structures of bio-macromolecular at high resolution has been a highpriority task. With the development of cryo-electron microscopy (cryo-EM) techniques, the flexible structures of biomacromolecules at the atomic resolution level grow rapidly. Nevertheless, it is difficult for cryo-EM to produce high-resolution dynamic structures without a great deal of manpower and time. Fortunately, deep learning, belonging to the domain of artificial intelligence, speeds up and simplifies this workflow for handling the high-throughput cryo-EM data. Here, we generalized and summarized some software packages and referred algorithms of deep learning with remarkable effects on cryo-EM data processing, including Warp, user-free preprocessing routines, TranSPHIRE, PARSED, Topaz, crYOLO, and self-supervised workflow, and pointed out the strategies to improve the resolution and efficiency of three-dimensional reconstruction. We hope it will shed some light on the bio-macromolecular dynamic structure modeling with the deep learning algorithms.

High-Throughput Screening for the Potential Inhibitors of SARS-CoV-2 with Essential Dynamic Behavior.

Global health security has been challenged by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. Due to the lengthy process of generating vaccinations, it is vital to reposition currently available drugs in order to relieve anti-epidemic tensions and accelerate the development of therapies for Coronavirus Disease 2019 (COVID-19), the public threat caused by SARS-CoV-2. High throughput screening techniques have established their roles in the evaluation of already available medications and the search for novel potential agents with desirable chemical space and more cost-effectiveness. Here, we present the architectural aspects of highthroughput screening for SARS-CoV-2 inhibitors, especially three generations of virtual screening methodologies with structural dynamics: ligand-based screening, receptor-based screening, and machine learning (ML)-based scoring functions (SFs). By outlining the benefits and drawbacks, we hope that researchers will be motivated to adopt these methods in the development of novel anti- SARS-CoV-2 agents.

Reversible single-crystal to single-crystal transformation between triangular single-molecule toroics.

We report a method for synthesizing single-molecule magnets through a single-crystal to single-crystal transformation. This process yields two single-molecule magnets with similar triangular Dy3 cores but distinct solvents and space groups achieved via solvent exchange. Magnetic properties reveal that both Dy3 molecules exhibit similar toroidal moments but manifest diverse multiple magnetization dynamic behaviors owing to the spin-lattice coupling influence from different solvent molecules.

Efficient inactivation of the contamination with pathogenic microorganisms by a combination of water spray and plasma-activated air.

The global pandemic caused by SARS-CoV-2 has lasted two and a half years and the infections caused by the viral contamination are still occurring. Developing efficient disinfection technology is crucial for the current epidemic or infectious diseases caused by other pathogenic microorganisms. Gas plasma can efficiently inactivate different microorganisms, therefore, in this study, a combination of water spray and plasma-activated air was established for the disinfection of pathogenic microorganisms. The combined treatment efficiently inactivated the Omicron-pseudovirus through caused the nitration modification of the spike proteins and also the pathogenic bacteria. The combined treatment was improved with a funnel-shaped nozzle to form a temporary relatively sealed environment for the treatment of the contaminated area. The improved device could efficiently inactivate the Omicron-pseudovirus and bacteria on the surface of different materials including quartz, metal, leather, plastic, and cardboard and the particle size of the water spray did not affect the inactivation effects. This study supplied a disinfection strategy based on plasma-activated air for the inactivation of contaminated pathogenic microorganisms.

Metformin suppresses foam cell formation, inflammation and ferroptosis via the AMPK/ERK signaling pathway in ox­LDL­induced THP­1 monocytes.

Numerous studies have shown that the formation of foam cells is of vital importance in the process of atherosclerosis. The aim of the present study was to assess the effects of metformin on foam cell formation in oxidized low-density lipoprotein (ox-LDL)-treated THP-1 cells and explore its associated mechanism of action. Human monocytic THP-1 cells were pretreated with metformin for 2 h and subsequently treated with ox-LDL for 24 h. The data indicated that metformin significantly inhibited lipid accumulation in ox-LDL-treated THP-1 cells by decreasing the expression of scavenger receptor A, cluster of differentiation 36 and adipocyte enhancer-binding protein 1. In addition, metformin increased the expression levels of scavenger receptor B1 and ATP binding cassette transporter G1 and suppresses the esterification of free cholesterol. Furthermore, it markedly inhibited ferroptosis (reflected by the upregulation of glutathione peroxidase glutathione peroxidase 4 and the downregulation of Heme oxygenase-1). In addition, it caused a marked suppression in the expression levels of cysteinyl aspartate specific proteinase-1, IL-1ß, NOD-like receptor protein 3, IL-18 secretion and in the levels of oxidative stress. Metformin attenuated the activation of ERK and facilitated the phosphorylation of 5' adenosine monophosphate-activated protein kinase (AMPK). Treatment of THP-1 cells with an ERK inhibitor reversed these effects, while inhibition of AMPK activity exacerbated the effects noted in ox-LDL-treated THP-1 cells. In conclusion, the present study suggested that metformin suppressed foam cell formation, inflammatory responses and inhibited ferroptosis in ox-LDL-treated macrophages via the AMPK/ERK signaling pathway.

Organelle Interaction and Drug Discovery: Towards Correlative Nanoscopy and Molecular Dynamics Simulation.

The inter-organelle interactions, including the cytomembrane, endoplasmic reticulum, mitochondrion, lysosome, dictyosome, and nucleus, play the important roles in maintaining the normal function and homeostasis of cells. Organelle dysfunction can lead to a range of diseases (e.g., Alzheimer's disease (AD), Parkinson's disease (PD), and cancer), and provide a new perspective for drug discovery. With the development of imaging techniques and functional fluorescent probes, a variety of algorithms and strategies have been developed for the ever-improving estimation of subcellular structures, organelle interaction, and organelle-related drug discovery with accounting for the dynamic structures of organelles, such as the nanoscopy technology and molecular dynamics (MD) simulations. Accordingly, this work summarizes a series of state-of-the-art examples of the recent progress in this rapidly changing field and uncovering the drug screening based on the structures and interactions of organelles. Finally, we propose the future outlook for exciting applications of organelle-related drug discovery, with the cooperation of nanoscopy and MD simulations.

Facilely prepared aggregation-induced emission (AIE) nanocrystals with deep-red emission for super-resolution imaging.

Organic nanocrystals (NCs) with high brightness are highly desirable for biological imaging. However, the preparation of NCs by a facile and fast method is still challenging. Herein, an aggregation-induced emission (AIE) luminogen of 4,4'-(5,6-difluorobenzo[c][1,2,5]thiadiazole-4,7-diyl)bis(N,N-bis(4-methoxyphenyl)aniline) (DTPA-BT-F) in the deep-red region is designed with intensive crystalline features to obtain NCs by kinetically controlled nanoprecipitation. The prepared AIE NCs with high brightness and good photo-stability are then applied in super-resolution imaging via stimulated emission depletion (STED) nanoscopy. As observed, the nanostructures in lysosomes of both fixed and live cells are well visualized with superior lateral resolutions under STED nanoscopy (full width at half maximum values, 107 and 108 nm) in contrast to that in confocal imaging (548 and 740 nm). More importantly, dynamic monitoring and long-term tracking of lysosomal movements in live HeLa cells, such as lysosomal contact, can also be carried out by using DTPA-BT-F NCs at a superior resolution. To the best of our knowledge, this is the first case of AIE NCs prepared by nanoprecipitation for STED nanoscopy, thus providing a new strategy to develop high performance imaging agents for super-resolution imaging.

Sequence Matching between Hemagglutinin and Neuraminidase through Sequence Analysis Using Machine Learning.

To date, many experiments have revealed that the functional balance between hemagglutinin (HA) and neuraminidase (NA) plays a crucial role in viral mobility, production, and transmission. However, whether and how HA and NA maintain balance at the sequence level needs further investigation. Here, we applied principal component analysis and hierarchical clustering analysis on thousands of HA and NA sequences of A/H1N1 and A/H3N2. We discovered significant coevolution between HA and NA at the sequence level, which is closely related to the type of host species and virus epidemic years. Furthermore, we propose a sequence-to-sequence transformer model (S2STM), which mainly consists of an encoder and a decoder that adopts a multi-head attention mechanism for establishing the mapping relationship between HA and NA sequences. The training results reveal that the S2STM can effectively realize the "translation" from HA to NA or vice versa, thereby building a relationship network between them. Our work combines unsupervised and supervised machine learning methods to identify the sequence matching between HA and NA, which will advance our understanding of IAVs' evolution and also provide a novel idea for sequence analysis methods.