Bioactive peptide therapeutics has been a long-standing research topic. Notably, the antimicrobial peptides (AMPs) have been extensively studied for its therapeutic potential. Meanwhile, the demand for annotating other therapeutic peptides, such as antiviral peptides (AVPs) and anticancer peptides (ACPs), also witnessed an increase in recent years. However, we conceive that the structure of peptide chains and the intrinsic information between the amino acids is not fully investigated among the existing protocols. Therefore, we develop a new graph deep learning model, namely TP-LMMSG, which offers lightweight and easy-to-deploy advantages while improving the annotation performance in a generalizable manner. The results indicate that our model can accurately predict the properties of different peptides. The model surpasses the other state-of-the-art models on AMP, AVP and ACP prediction across multiple experimental validated datasets. Moreover, TP-LMMSG also addresses the challenges of time-consuming pre-processing in graph neural network frameworks. With its flexibility in integrating heterogeneous peptide features, our model can provide substantial impacts on the screening and discovery of therapeutic peptides. The source code is available at https://github.com/NanjunChen37/TP_LMMSG.

BACKGROUND: Inflammatory mediators play havoc in several diseases including the novel Coronavirus disease 2019 (COVID-19) and generally correlate with the severity of the disease. Interleukin-13 (IL-13), is a pleiotropic cytokine that is known to be associated with airway inflammation in asthma and reactive airway diseases, in neoplastic and autoimmune diseases. Interestingly, the recent association of IL-13 with COVID-19 severity has sparked interest in this cytokine. Therefore characterization of new molecules which can regulate IL-13 induction might lead to novel therapeutics.
RESULTS: Here, we present an improved prediction of IL-13-inducing peptides. The positive and negative datasets were obtained from a recent study (IL13Pred) and the Pfeature algorithm was used to compute features for the peptides. As compared to the state-of-the-art which used the regularization based feature selection technique (linear support vector classifier with the L1 penalty), we used a multivariate feature selection technique (minimum redundancy maximum relevance) to obtain non-redundant and highly relevant features. In the proposed study (improved IL-13 prediction (iIL13Pred)), the use of the mRMR feature selection method is instrumental in choosing the most discriminatory features of IL-13-inducing peptides with improved performance. We investigated seven common machine learning classifiers including Decision Tree, Gaussian Naïve Bayes, k-Nearest Neighbour, Logistic Regression, Support Vector Machine, Random Forest, and extreme gradient boosting to efficiently classify IL-13-inducing peptides. We report improved AUC, and MCC scores of 0.83 and 0.33 on validation data as compared to the current method.
CONCLUSIONS: Extensive benchmarking experiments suggest that the proposed method (iIL13Pred) could provide improved performance metrics in terms of sensitivity, specificity, accuracy, the area under the curve - receiver operating characteristics (AUCROC) and Matthews correlation coefficient (MCC) than the existing state-of-the-art approach (IL13Pred) on the validation dataset and an external dataset comprising of experimentally validated IL-13-inducing peptides. Additionally, the experiments were performed with an increased number of experimentally validated training datasets to obtain a more robust model. A user-friendly web server ( www.soodlab.com/iil13pred ) is also designed to facilitate rapid screening of IL-13-inducing peptides.

Antibiotic resistance constitutes a global threat and could lead to a future pandemic. One strategy is to develop a new generation of antimicrobials. Naturally occurring antimicrobial peptides (AMPs) are recognized templates and some are already in clinical use. To accelerate the discovery of new antibiotics, it is useful to predict novel AMPs from the sequenced genomes of various organisms. The antimicrobial peptide database (APD) provided the first empirical peptide prediction program. It also facilitated the testing of the first machine-learning algorithms. This chapter provides an overview of machine-learning predictions of AMPs. Most of the predictors, such as AntiBP, CAMP, and iAMPpred, involve a single-label prediction of antimicrobial activity. This type of prediction has been expanded to antifungal, antiviral, antibiofilm, anti-TB, hemolytic, and anti-inflammatory peptides. The multiple functional roles of AMPs annotated in the APD also enabled multi-label predictions (iAMP-2L, MLAMP, and AMAP), which include antibacterial, antiviral, antifungal, antiparasitic, antibiofilm, anticancer, anti-HIV, antimalarial, insecticidal, antioxidant, chemotactic, spermicidal activities, and protease inhibiting activities. Also considered in predictions are peptide posttranslational modification, 3D structure, and microbial species-specific information. We compare important amino acids of AMPs implied from machine learning with the frequently occurring residues of the major classes of natural peptides. Finally, we discuss advances, limitations, and future directions of machine-learning predictions of antimicrobial peptides. Ultimately, we may assemble a pipeline of such predictions beyond antimicrobial activity to accelerate the discovery of novel AMP-based antimicrobials.

The antimicrobial peptide database (APD) has served the antimicrobial peptide field for 18 years. Because it is widely used in research and education, this article documents database milestones and key events that have transformed it into the current form. A comparison is made for the APD peptide statistics between 2010 and 2020, validating the major database findings to date. We also describe new additions ranging from peptide entries to search functions. Of note, the APD also contains antimicrobial peptides from host microbiota, which are important in shaping immune systems and could be linked to a variety of human diseases. Finally, the database has been re-programmed to the web branding and latest security compliance of the University of Nebraska Medical Center. The reprogrammed APD can be accessed at https://aps.unmc.edu.

Although mutated HLA ligands are considered ideal cancer-specific immunotherapy targets, evidence for their presentation is lacking in hepatocellular carcinomas (HCCs). Employing a unique multi-omics approach comprising a neoepitope identification pipeline, we assessed exome-derived mutations naturally presented as HLA class I ligands in HCCs.
In-depth multi-omics analyses included whole exome and transcriptome sequencing to define individual patient-specific search spaces of neoepitope candidates. Evidence for the natural presentation of mutated HLA ligands was investigated through an in silico pipeline integrating proteome and HLA ligandome profiling data.
The approach was successfully validated in a state-of-the-art dataset from malignant melanoma, and despite multi-omics evidence for somatic mutations, mutated naturally presented HLA ligands remained elusive in HCCs. An analysis of extensive cancer datasets confirmed fundamental differences of tumor mutational burden in HCC and malignant melanoma, challenging the notion that exome-derived mutations contribute relevantly to the expectable neoepitope pool in malignancies with only few mutations.
This study suggests that exome-derived mutated HLA ligands appear to be rarely presented in HCCs, inter alia resulting from a low mutational burden as compared to other malignancies such as malignant melanoma. Our results therefore demand widening the target scope for personalized immunotherapy beyond this limited range of mutated neoepitopes, particularly for malignancies with similar or lower mutational burden.

Thrombosis represents a major cause of morbidity and mortality around the world. Peptides isolated from natural sources have been proven to have anticoagulant and antithrombotic properties. VITPOR AI, a 16-mer peptide, isolated from Porphyra yezoensis was reported to have anticoagulant property. In this study, the coagulation factor XIIa activity in the presence of VITPOR AI was determined. Molecular modelling was performed to investigate the interaction between peptide and FXIIa. The structure of the peptide was predicted using PEP-FOLD3 server and simulated by molecular dynamics (MD) using GROMACS package. Molecular docking was carried out using peptide-protein docking software, pepATTRACT and its stability was confirmed by MD simulations. The chromogenic substrate assay revealed that the peptide inhibited the amidolytic activity of FXIIa with IC50 of 70.24 μM. The docking result showed peptide interactions through hydrogen bonds with Pro 96, Tyr 99, Glu 146, Gly 193 and Ser 195 of FXIIa. The MD simulation demonstrated that the peptide\'s binding with the FXIIa was stable as it did not move away from its binding region throughout the simulation period of 100 ns Moreover, MM/PBSA analysis also indicated a stable binding between the protein and peptide. These results suggest that the inhibition of the FXIIa activity might be due to binding of the peptide to oxyanion hole of the catalytic site. Thus, VITPOR AI could be explored as a potent anticoagulant which inhibits only intrinsic pathway of coagulation cascade but does not affect the extrinsic pathway.

In many insects, mating induces drastic changes in male and female responses to sex pheromones or host-plant odors. In the male moth Agrotis ipsilon, mating induces a transient inhibition of behavioral and neuronal responses to the female sex pheromone. As neuropeptides and peptide hormones regulate most behavioral processes, we hypothesize that they could be involved in this mating-dependent olfactory plasticity. Here we used next-generation RNA sequencing and a combination of liquid chromatography, matrix assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry, and direct tissue profiling to analyze the transcriptome and peptidome of different brain compartments in virgin and mated males and females of A. ipsilon. We identified 37 transcripts encoding putative neuropeptide precursors and 54 putative bioactive neuropeptides from 23 neuropeptide precursors (70 sequences in total, 25 neuropeptide precursors) in different areas of the central nervous system including the antennal lobes, the gnathal ganglion, and the corpora cardiaca-corpora allata complex. Comparisons between virgin and mated males and females revealed tissue-specific differences in peptide composition between sexes and according to physiological state. Mated males showed postmating differences in neuropeptide occurrence, which could participate in the mating-induced olfactory plasticity.

The accurate prediction of human CD8+ T-cell epitopes has great potential clinical and translational implications in the context of infection, cancer and autoimmunity. Prediction algorithms have traditionally focused on calculated peptide affinity for the binding groove of MHC-I. However, over the years it has become increasingly clear that the ultimate T-cell recognition of MHC-I-bound peptides is governed by many contributing factors within the complex antigen presentation pathway. Recent advances in next-generation sequencing and immunnopeptidomics have increased the precision of HLA-I sub-allele classification, and have led to the discovery of peptide processing events and individual allele-specific binding preferences. Here, we review some of the discoveries that initiated the development of peptide prediction algorithms, and outline some of the current available online tools for CD8+ T-cell epitope prediction.

The bed bug Cimex lectularius is a globally distributed human ectoparasite with fascinating biology. It has recently acquired resistance against a broad range of insecticides, causing a worldwide increase in bed bug infestations. The recent annotation of the bed bug genome revealed a full complement of neuropeptide and neuropeptide receptor genes in this species. With regard to the biology of C. lectularius, neuropeptide signaling is especially interesting because it regulates feeding, diuresis, digestion, as well as reproduction and also provides potential new targets for chemical control. To identify which neuropeptides are translated from the genome-predicted genes, we performed a comprehensive peptidomic analysis of the central nervous system of the bed bug. We identified in total 144 different peptides from 29 precursors, of which at least 67 likely present bioactive mature neuropeptides. C. lectularius corazonin and myosuppressin are unique and deviate considerably from the canonical insect consensus sequences. Several identified neuropeptides likely act as hormones, as evidenced by the occurrence of respective mass signals and immunoreactivity in neurohemal structures. Our data provide the most comprehensive peptidome of a Heteropteran species so far and in comparison suggest that a hematophageous life style does not require qualitative adaptations of the insect peptidome.

Ants show a rich behavioral repertoire and a highly complex organization, which have been attracting behavioral and sociobiological researchers for a long time. The neuronal underpinnings of ant behavior and social organization are, however, much less understood. Neuropeptides are key signals that orchestrate animal behavior and physiology, and it is thus feasible to assume that they play an important role also for the social constitution of ants. Despite the availability of different ant genomes and in silico prediction of ant neuropeptides, a comprehensive biochemical survey of the neuropeptidergic communication possibilities of ants is missing. We therefore combined different mass spectrometric methods to characterize the neuropeptidome of the adult carpenter ant Camponotus floridanus. We also characterized the local neuropeptide complement in different parts of the nervous and neuroendocrine system, including the antennal and optic lobes. Our analysis identifies 39 neuropeptides encoded by different prepropeptide genes, and in silico predicts new prepropeptide genes encoding CAPA peptides, CNMamide as well as homologues of the honey bee IDLSRFYGHFNT- and ITGQGNRIF-containing peptides. Our data provides basic information about the identity and localization of neuropeptides that is required to anatomically and functionally address the role and significance of neuropeptides in ant behavior and physiology.