Human Gain-of-Function MC4R Variants Show Signaling Bias and Protect against Obesity.

The melanocortin 4 receptor (MC4R) is a G protein-coupled receptor whose disruption causes obesity. We functionally characterized 61 MC4R variants identified in 0.5 million people from UK Biobank and examined their associations with body mass index (BMI) and obesity-related cardiometabolic diseases. We found that the maximal efficacy of ß-arrestin recruitment to MC4R, rather than canonical Gαs-mediated cyclic adenosine-monophosphate production, explained 88% of the variance in the association of MC4R variants with BMI. While most MC4R variants caused loss of function, a subset caused gain of function; these variants were associated with significantly lower BMI and lower odds of obesity, type 2 diabetes, and coronary artery disease. Protective associations were driven by MC4R variants exhibiting signaling bias toward ß-arrestin recruitment and increased mitogen-activated protein kinase pathway activation. Harnessing ß-arrestin-biased MC4R signaling may represent an effective strategy for weight loss and the treatment of obesity-related cardiometabolic diseases.

Derivation of Pluripotent Stem Cells with In Vivo Embryonic and Extraembryonic Potency.

Of all known cultured stem cell types, pluripotent stem cells (PSCs) sit atop the landscape of developmental potency and are characterized by their ability to generate all cell types of an adult organism. However, PSCs show limited contribution to the extraembryonic placental tissues in vivo. Here, we show that a chemical cocktail enables the derivation of stem cells with unique functional and molecular features from mice and humans, designated as extended pluripotent stem (EPS) cells, which are capable of chimerizing both embryonic and extraembryonic tissues. Notably, a single mouse EPS cell shows widespread chimeric contribution to both embryonic and extraembryonic lineages in vivo and permits generating single-EPS-cell-derived mice by tetraploid complementation. Furthermore, human EPS cells exhibit interspecies chimeric competency in mouse conceptuses. Our findings constitute a first step toward capturing pluripotent stem cells with extraembryonic developmental potentials in culture and open new avenues for basic and translational research. VIDEO ABSTRACT.

Transcription factor Hoxb5 reprograms B cells into functional T lymphocytes.

Deletion of master regulators of the B cell lineage reprograms B cells into T cells. Here we found that the transcription factor Hoxb5, which is expressed in uncommitted hematopoietic progenitor cells but is not present in cells committed to the B cell or T cell lineage, was able to reprogram pro-pre-B cells into functional early T cell lineage progenitors. This reprogramming started in the bone marrow and was completed in the thymus and gave rise to T lymphocytes with transcriptomes, hierarchical differentiation, tissue distribution and immunological functions that closely resembled those of their natural counterparts. Hoxb5 repressed B cell 'master genes', activated regulators of T cells and regulated crucial chromatin modifiers in pro-pre-B cells and ultimately drove the B cell fate-to-T cell fate conversion. Our results provide a de novo paradigm for the generation of functional T cells through reprogramming in vivo.

Publisher Correction: Transcription factor Hoxb5 reprograms B cells into functional T lymphocytes.

In the version of this article initially published, some identification of the supplementary information was incorrect. The items originally called Supplementary Tables 1, 2, 3, 4 and 5 should be Source Data Figures 1, 2, 4, 5 and 7, respectively; those originally called Supplementary Tables 6, 7 and 8 should be Supplementary Tables 1, 2 and 3, respectively; and those originally called Source Data Figures 1, 2, 4, 5 and 7 should be Supplementary Tables 4, 5, 6, 7 and 8, respectively. The errors have been corrected in the HTML version of the article.

iRhom2 is essential for innate immunity to DNA viruses by mediating trafficking and stability of the adaptor STING.

STING is a central adaptor in the innate immune response to DNA viruses. However, the manner in which STING activity is regulated remains unclear. We identified iRhom2 ('inactive rhomboid protein 2') as a positive regulator of DNA-virus-triggered induction of type I interferons. iRhom2 deficiency markedly impaired DNA-virus- and intracellular-DNA-induced signaling in cells, and iRhom2-deficient mice were more susceptible to lethal herpes simplex virus type 1 (HSV-1) infection. iRhom2 was constitutively associated with STING and acted in two distinct processes to regulate STING activity. iRhom2 recruited the translocon-associated protein TRAPß to the STING complex to facilitate trafficking of STING from the endoplasmic reticulum to perinuclear microsomes. iRhom2 also recruited the deubiquitination enzyme EIF3S5 to maintain the stability of STING through removal of its K48-linked polyubiquitin chains. These results suggest that iRhom2 is essential for STING activity, as it regulates TRAPß-mediated translocation and EIF3S5-mediated deubiquitination of STING.

Ultrathin ferroic HfO2-ZrO2 superlattice gate stack for advanced transistors.

With the scaling of lateral dimensions in advanced transistors, an increased gate capacitance is desirable both to retain the control of the gate electrode over the channel and to reduce the operating voltage1. This led to a fundamental change in the gate stack in 2008, the incorporation of high-dielectric-constant HfO2 (ref. 2), which remains the material of choice to date. Here we report HfO2-ZrO2 superlattice heterostructures as a gate stack, stabilized with mixed ferroelectric-antiferroelectric order, directly integrated onto Si transistors, and scaled down to approximately 20 ångströms, the same gate oxide thickness required for high-performance transistors. The overall equivalent oxide thickness in metal-oxide-semiconductor capacitors is equivalent to an effective SiO2 thickness of approximately 6.5 ångströms. Such a low effective oxide thickness and the resulting large capacitance cannot be achieved in conventional HfO2-based high-dielectric-constant gate stacks without scavenging the interfacial SiO2, which has adverse effects on the electron transport and gate leakage current3. Accordingly, our gate stacks, which do not require such scavenging, provide substantially lower leakage current and no mobility degradation. This work demonstrates that ultrathin ferroic HfO2-ZrO2 multilayers, stabilized with competing ferroelectric-antiferroelectric order in the two-nanometre-thickness regime, provide a path towards advanced gate oxide stacks in electronic devices beyond conventional HfO2-based high-dielectric-constant materials.

MHCpLogics: an interactive machine learning-based tool for unsupervised data visualization and cluster analysis of immunopeptidomes.

The major histocompatibility complex (MHC) encodes a range of immune response genes, including the human leukocyte antigens (HLAs) in humans. These molecules bind peptide antigens and present them on the cell surface for T cell recognition. The repertoires of peptides presented by HLA molecules are termed immunopeptidomes. The highly polymorphic nature of the genres that encode the HLA molecules confers allotype-specific differences in the sequences of bound ligands. Allotype-specific ligand preferences are often defined by peptide-binding motifs. Individuals express up to six classical class I HLA allotypes, which likely present peptides displaying different binding motifs. Such complex datasets make the deconvolution of immunopeptidomic data into allotype-specific contributions and further dissection of binding-specificities challenging. Herein, we developed MHCpLogics as an interactive machine learning-based tool for mining peptide-binding sequence motifs and visualization of immunopeptidome data across complex datasets. We showcase the functionalities of MHCpLogics by analyzing both in-house and published mono- and multi-allelic immunopeptidomics data. The visualization modalities of MHCpLogics allow users to inspect clustered sequences down to individual peptide components and to examine broader sequence patterns within multiple immunopeptidome datasets. MHCpLogics can deconvolute large immunopeptidome datasets enabling the interrogation of clusters for the segregation of allotype-specific peptide sequence motifs, identification of sub-peptidome motifs, and the exportation of clustered peptide sequence lists. The tool facilitates rapid inspection of immunopeptidomes as a resource for the immunology and vaccine communities. MHCpLogics is a standalone application available via an executable installation at: https://github.com/PurcellLab/MHCpLogics.

Thrombocytopenia Independently Leads to Changes in Monocyte Immune Function.

BACKGROUND: While platelets have well-studied hemostatic functions, platelets are immune cells that circulate at the interface between the vascular wall and white blood cells. The physiological implications of these constant transient interactions are poorly understood. Activated platelets induce and amplify immune responses, but platelets may also maintain immune homeostasis in healthy conditions, including maintaining vascular integrity and T helper cell differentiation, meaning that platelets are central to both immune responses and immune quiescence. Clinical data have shown an association between low platelet counts (thrombocytopenia) and immune dysfunction in patients with sepsis and extracorporeal membrane oxygenation, further implicating platelets as more holistic immune regulators, but studies of platelet immune functions in nondisease contexts have had limited study. METHODS: We used in vivo models of thrombocytopenia and in vitro models of platelet and monocyte interactions, as well as RNA-seq and ATAC-seq (assay for transposase-accessible chromatin with sequencing), to mechanistically determine how resting platelet and monocyte interactions immune program monocytes. RESULTS: Circulating platelets and monocytes interact in a CD47-dependent manner to regulate monocyte metabolism, histone methylation, and gene expression. Resting platelet-monocyte interactions limit TLR (toll-like receptor) signaling responses in healthy conditions in an innate immune training-like manner. In both human patients with sepsis and mouse sepsis models, thrombocytopenia exacerbated monocyte immune dysfunction, including increased cytokine production. CONCLUSIONS: Thrombocytopenia immune programs monocytes in a manner that may lead to immune dysfunction in the context of sepsis. This is the first demonstration that sterile, endogenous cell interactions between resting platelets and monocytes regulate monocyte metabolism and pathogen responses, demonstrating platelets to be immune rheostats in both health and disease.

Monocytes Release Pro-Cathepsin D to Drive Blood-to-Brain Transcytosis in Diabetes.

BACKGROUND: Microvascular complications are the major outcome of type 2 diabetes progression, and the underlying mechanism remains to be determined. METHODS: High-throughput RNA sequencing was performed using human monocyte samples from controls and diabetes. The transgenic mice expressing human CTSD (cathepsin D) in the monocytes was constructed using CD68 promoter. In vivo 2-photon imaging, behavioral tests, immunofluorescence, transmission electron microscopy, Western blot analysis, vascular leakage assay, and single-cell RNA sequencing were performed to clarify the phenotype and elucidate the molecular mechanism. RESULTS: Monocytes expressed high-level CTSD in patients with type 2 diabetes. The transgenic mice expressing human CTSD in the monocytes showed increased brain microvascular permeability resembling the diabetic microvascular phenotype, accompanied by cognitive deficit. Mechanistically, the monocytes release nonenzymatic pro-CTSD to upregulate caveolin expression in brain endothelium triggering caveolae-mediated transcytosis, without affecting the paracellular route of brain microvasculature. The circulating pro-CTSD activated the caveolae-mediated transcytosis in brain endothelial cells via its binding with low-density LRP1 (lipoprotein receptor-related protein 1). Importantly, genetic ablation of CTSD in the monocytes exhibited a protective effect against the diabetes-enhanced brain microvascular transcytosis and the diabetes-induced cognitive impairment. CONCLUSIONS: These findings uncover the novel role of circulatory pro-CTSD from monocytes in the pathogenesis of cerebral microvascular lesions in diabetes. The circulatory pro-CTSD is a potential target for the intervention of microvascular complications in diabetes.

Adipocyte HSL is required for maintaining circulating vitamin A and RBP4 levels during fasting.

Vitamin A (retinol) is distributed via the blood bound to its specific carrier protein, retinol-binding protein 4 (RBP4). Retinol-loaded RBP4 is secreted into the circulation exclusively from hepatocytes, thereby mobilizing hepatic retinoid stores that represent the major vitamin A reserves in the body. The relevance of extrahepatic retinoid stores for circulating retinol and RBP4 levels that are usually kept within narrow physiological limits is unknown. Here, we show that fasting affects retinoid mobilization in a tissue-specific manner, and that hormone-sensitive lipase (HSL) in adipose tissue is required to maintain serum concentrations of retinol and RBP4 during fasting in mice. We found that extracellular retinol-free apo-RBP4 induces retinol release by adipocytes in an HSL-dependent manner. Consistently, global or adipocyte-specific HSL deficiency leads to an accumulation of retinoids in adipose tissue and a drop of serum retinol and RBP4 during fasting, which affects retinoid-responsive gene expression in eye and kidney and lowers renal retinoid content. These findings establish a novel crosstalk between liver and adipose tissue retinoid stores for the maintenance of systemic vitamin A homeostasis during fasting.

Publisher Correction: Enhanced ferroelectricity in ultrathin films grown directly on silicon.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Enhanced ferroelectricity in ultrathin films grown directly on silicon.

Ultrathin ferroelectric materials could potentially enable low-power perovskite ferroelectric tetragonality logic and nonvolatile memories1,2. As ferroelectric materials are made thinner, however, the ferroelectricity is usually suppressed. Size effects in ferroelectrics have been thoroughly investigated in perovskite oxides-the archetypal ferroelectric system3. Perovskites, however, have so far proved unsuitable for thickness scaling and integration with modern semiconductor processes4. Here we report ferroelectricity in ultrathin doped hafnium oxide (HfO2), a fluorite-structure oxide grown by atomic layer deposition on silicon. We demonstrate the persistence of inversion symmetry breaking and spontaneous, switchable polarization down to a thickness of one nanometre. Our results indicate not only the absence of a ferroelectric critical thickness but also enhanced polar distortions as film thickness is reduced, unlike in perovskite ferroelectrics. This approach to enhancing ferroelectricity in ultrathin layers could provide a route towards polarization-driven memories and ferroelectric-based advanced transistors. This work shifts the search for the fundamental limits of ferroelectricity to simpler transition-metal oxide systems-that is, from perovskite-derived complex oxides to fluorite-structure binary oxides-in which 'reverse' size effects counterintuitively stabilize polar symmetry in the ultrathin regime.

PRMD: an integrated database for plant RNA modifications.

The scope and function of RNA modifications in model plant systems have been extensively studied, resulting in the identification of an increasing number of novel RNA modifications in recent years. Researchers have gradually revealed that RNA modifications, especially N6-methyladenosine (m6A), which is one of the most abundant and commonly studied RNA modifications in plants, have important roles in physiological and pathological processes. These modifications alter the structure of RNA, which affects its molecular complementarity and binding to specific proteins, thereby resulting in various of physiological effects. The increasing interest in plant RNA modifications has necessitated research into RNA modifications and associated datasets. However, there is a lack of a convenient and integrated database with comprehensive annotations and intuitive visualization of plant RNA modifications. Here, we developed the Plant RNA Modification Database (PRMD; http://bioinformatics.sc.cn/PRMD and http://rnainformatics.org.cn/PRMD) to facilitate RNA modification research. This database contains information regarding 20 plant species and provides an intuitive interface for displaying information. Moreover, PRMD offers multiple tools, including RMlevelDiff, RMplantVar, RNAmodNet and Blast (for functional analyses), and mRNAbrowse, RNAlollipop, JBrowse and Integrative Genomics Viewer (for displaying data). Furthermore, PRMD is freely available, making it useful for the rapid development and promotion of research on plant RNA modifications.

Inactive PARP1 causes embryonic lethality and genome instability in a dominant-negative manner.

PARP1 (poly-ADP ribose polymerase 1) is recruited and activated by DNA strand breaks, catalyzing the generation of poly-ADP-ribose (PAR) chains from NAD+. PAR relaxes chromatin and recruits other DNA repair factors, including XRCC1 and DNA Ligase 3, to maintain genomic stability. Here we show that, in contrast to the normal development of Parp1-null mice, heterozygous expression of catalytically inactive Parp1 (E988A, Parp1+/A) acts in a dominant-negative manner to disrupt murine embryogenesis. As such, all the surviving F1 Parp1+/A mice are chimeras with mixed Parp1+/AN (neoR retention) cells that act similarly to Parp1+/-. Pure F2 Parp1+/A embryos were found at Mendelian ratios at the E3.5 blastocyst stage but died before E9.5. Compared to Parp1-/- cells, genotype and expression-validated pure Parp1+/A cells retain significant ADP-ribosylation and PARylation activities but accumulate markedly higher levels of sister chromatid exchange and mitotic bridges. Despite proficiency for homologous recombination and nonhomologous end-joining measured by reporter assays and supported by normal lymphocyte and germ cell development, Parp1+/A cells are hypersensitive to base damages, radiation, and Topoisomerase I and II inhibition. The sensitivity of Parp1+/A cells to base damages and Topo inhibitors exceed Parp1-/- controls. The findings show that the enzymatically inactive PARP1 dominant negatively blocks DNA repair in selective pathways beyond wild-type PARP1 and establishes a crucial physiological difference between PARP1 inactivation vs. deletion. As a result, the expression of enzymatically inactive PARP1 from one allele is sufficient to abrogate murine embryonic development, providing a mechanism for the on-target side effect of PARP inhibitors used for cancer therapy.

Nesprin-2 is a novel scaffold protein for telethonin and FHL-2 in the cardiomyocyte sarcomere.

Nesprins comprise a family of multi-isomeric scaffolding proteins, forming the linker of nucleoskeleton-and-cytoskeleton complex with lamin A/C, emerin and SUN1/2 at the nuclear envelope. Mutations in nesprin-1/-2 are associated with Emery-Dreifuss muscular dystrophy (EDMD) with conduction defects and dilated cardiomyopathy (DCM). We have previously observed sarcomeric staining of nesprin-1/-2 in cardiac and skeletal muscle, but nesprin function in this compartment remains unknown. In this study, we show that specific nesprin-2 isoforms are highly expressed in cardiac muscle and localize to the Z-disc and I band of the sarcomere. Expression of GFP-tagged nesprin-2 giant spectrin repeats 52 to 53, localized to the sarcomere of neonatal rat cardiomyocytes. Yeast two-hybrid screening of a cardiac muscle cDNA library identified telethonin and four-and-half LIM domain (FHL)-2 as potential nesprin-2 binding partners. GST pull-down and immunoprecipitation confirmed the individual interactions between nesprin-2/telethonin and nesprin-2/FHL-2, and showed that nesprin-2 and telethonin binding was dependent on telethonin phosphorylation status. Importantly, the interactions between these binding partners were impaired by mutations in nesprin-2, telethonin, and FHL-2 identified in EDMD with DCM and hypertrophic cardiomyopathy patients. These data suggest that nesprin-2 is a novel sarcomeric scaffold protein that may potentially participate in the maintenance and/or regulation of sarcomeric organization and function.

iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model.

As a kind of small molecule protein that can fight against various microorganisms in nature, antimicrobial peptides (AMPs) play an indispensable role in maintaining the health of organisms and fortifying defenses against diseases. Nevertheless, experimental approaches for AMP identification still demand substantial allocation of human resources and material inputs. Alternatively, computing approaches can assist researchers effectively and promptly predict AMPs. In this study, we present a novel AMP predictor called iAMP-Attenpred. As far as we know, this is the first work that not only employs the popular BERT model in the field of natural language processing (NLP) for AMPs feature encoding, but also utilizes the idea of combining multiple models to discover AMPs. Firstly, we treat each amino acid from preprocessed AMPs and non-AMP sequences as a word, and then input it into BERT pre-training model for feature extraction. Moreover, the features obtained from BERT method are fed to a composite model composed of one-dimensional CNN, BiLSTM and attention mechanism for better discriminating features. Finally, a flatten layer and various fully connected layers are utilized for the final classification of AMPs. Experimental results reveal that, compared with the existing predictors, our iAMP-Attenpred predictor achieves better performance indicators, such as accuracy, precision and so on. This further demonstrates that using the BERT approach to capture effective feature information of peptide sequences and combining multiple deep learning models are effective and meaningful for predicting AMPs.

ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species.

A-to-I editing is the most prevalent RNA editing event, which refers to the change of adenosine (A) bases to inosine (I) bases in double-stranded RNAs. Several studies have revealed that A-to-I editing can regulate cellular processes and is associated with various human diseases. Therefore, accurate identification of A-to-I editing sites is crucial for understanding RNA-level (i.e. transcriptional) modifications and their potential roles in molecular functions. To date, various computational approaches for A-to-I editing site identification have been developed; however, their performance is still unsatisfactory and needs further improvement. In this study, we developed a novel stacked-ensemble learning model, ATTIC (A-To-I ediTing predICtor), to accurately identify A-to-I editing sites across three species, including Homo sapiens, Mus musculus and Drosophila melanogaster. We first comprehensively evaluated 37 RNA sequence-derived features combined with 14 popular machine learning algorithms. Then, we selected the optimal base models to build a series of stacked ensemble models. The final ATTIC framework was developed based on the optimal models improved by the feature selection strategy for specific species. Extensive cross-validation and independent tests illustrate that ATTIC outperforms state-of-the-art tools for predicting A-to-I editing sites. We also developed a web server for ATTIC, which is publicly available at http://web.unimelb-bioinfortools.cloud.edu.au/ATTIC/. We anticipate that ATTIC can be utilized as a useful tool to accelerate the identification of A-to-I RNA editing events and help characterize their roles in post-transcriptional regulation.

TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level.

Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis pipelines. However, manual cell-type annotation heavily relies on the quality of scRNA-seq data and marker genes, and therefore can be laborious and time-consuming. Furthermore, the heterogeneity of scRNA-seq datasets poses another challenge for accurate cell-type annotation, such as the batch effect induced by different scRNA-seq protocols and samples. To overcome these limitations, here we propose a novel pipeline, termed TripletCell, for cross-species, cross-protocol and cross-sample cell-type annotation. We developed a cell embedding and dimension-reduction module for the feature extraction (FE) in TripletCell, namely TripletCell-FE, to leverage the deep metric learning-based algorithm for the relationships between the reference gene expression matrix and the query cells. Our experimental studies on 21 datasets (covering nine scRNA-seq protocols, two species and three tissues) demonstrate that TripletCell outperformed state-of-the-art approaches for cell-type annotation. More importantly, regardless of protocols or species, TripletCell can deliver outstanding and robust performance in annotating different types of cells. TripletCell is freely available at https://github.com/liuyan3056/TripletCell. We believe that TripletCell is a reliable computational tool for accurately annotating various cell types using scRNA-seq data and will be instrumental in assisting the generation of novel biological hypotheses in cell biology.

VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants.

Determining the pathogenicity and functional impact (i.e. gain-of-function; GOF or loss-of-function; LOF) of a variant is vital for unraveling the genetic level mechanisms of human diseases. To provide a 'one-stop' framework for the accurate identification of pathogenicity and functional impact of variants, we developed a two-stage deep-learning-based computational solution, termed VPatho, which was trained using a total of 9619 pathogenic GOF/LOF and 138 026 neutral variants curated from various databases. A total number of 138 variant-level, 262 protein-level and 103 genome-level features were extracted for constructing the models of VPatho. The development of VPatho consists of two stages: (i) a random under-sampling multi-scale residual neural network (ResNet) with a newly defined weighted-loss function (RUS-Wg-MSResNet) was proposed to predict variants' pathogenicity on the gnomAD_NV + GOF/LOF dataset; and (ii) an XGBOD model was constructed to predict the functional impact of the given variants. Benchmarking experiments demonstrated that RUS-Wg-MSResNet achieved the highest prediction performance with the weights calculated based on the ratios of neutral versus pathogenic variants. Independent tests showed that both RUS-Wg-MSResNet and XGBOD achieved outstanding performance. Moreover, assessed using variants from the CAGI6 competition, RUS-Wg-MSResNet achieved superior performance compared to state-of-the-art predictors. The fine-trained XGBOD models were further used to blind test the whole LOF data downloaded from gnomAD and accordingly, we identified 31 nonLOF variants that were previously labeled as LOF/uncertain variants. As an implementation of the developed approach, a webserver of VPatho is made publicly available at http://csbio.njust.edu.cn/bioinf/vpatho/ to facilitate community-wide efforts for profiling and prioritizing the query variants with respect to their pathogenicity and functional impact.

iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities.

Antimicrobial peptides (AMPs) are short peptides that play crucial roles in diverse biological processes and have various functional activities against target organisms. Due to the abuse of chemical antibiotics and microbial pathogens' increasing resistance to antibiotics, AMPs have the potential to be alternatives to antibiotics. As such, the identification of AMPs has become a widely discussed topic. A variety of computational approaches have been developed to identify AMPs based on machine learning algorithms. However, most of them are not capable of predicting the functional activities of AMPs, and those predictors that can specify activities only focus on a few of them. In this study, we first surveyed 10 predictors that can identify AMPs and their functional activities in terms of the features they employed and the algorithms they utilized. Then, we constructed comprehensive AMP datasets and proposed a new deep learning-based framework, iAMPCN (identification of AMPs based on CNNs), to identify AMPs and their related 22 functional activities. Our experiments demonstrate that iAMPCN significantly improved the prediction performance of AMPs and their corresponding functional activities based on four types of sequence features. Benchmarking experiments on the independent test datasets showed that iAMPCN outperformed a number of state-of-the-art approaches for predicting AMPs and their functional activities. Furthermore, we analyzed the amino acid preferences of different AMP activities and evaluated the model on datasets of varying sequence redundancy thresholds. To facilitate the community-wide identification of AMPs and their corresponding functional types, we have made the source codes of iAMPCN publicly available at https://github.com/joy50706/iAMPCN/tree/master. We anticipate that iAMPCN can be explored as a valuable tool for identifying potential AMPs with specific functional activities for further experimental validation.