Prediction of anti-inflammatory proteins/peptides: an insilico approach

Milk peptides alleviate irritable bowel syndrome by suppressing colonic mast cell activation and prostaglandin E2 production in mice

This study aimed to investigate the effect of milk peptides on irritable bowel syndrome (IBS). The mice were intragastrally administered with casein or whey protein hydrolysates at a dose of 1 g/kg body weight/day for 24 days and were subjected to Citrobacter rodentium infection and water avoidance stress from day 7 to 24. Results indicated that casein and whey protein hydrolysates effectively reduced diarrhea, anxiety, and visceral hypersensitivity in IBS mice. Casein and whey protein hydrolysates regulated gut microbiota composition and increased the abundance of short-chain fatty acid-producing bacteria, such as Alloprevotella and Alistipes. Whey protein hydrolysate significantly increased the mRNA levels of zonula occludens-1 (ZO-1) and claudin-1 in the colon, while casein hydrolysate significantly improved the mRNA levels of occludin. Casein and whey protein hydrolysates both decreased the levels of pro-inflammatory cytokines including interleukin-6 (IL-6), interleukin-1β (IL-1β), and tumor necrosis factor-α (TNF-α), while increased the level of anti-inflammatory cytokine interleukin-10 (IL-10). Importantly, casein and whey protein hydrolysates significantly reduced colonic mast cell activation and decreased prostaglandin E2 (PGE2) production. Moreover, three novel casein-derived cyclooxygenase-2 (COX2)-inhibitory peptides RGPF, FPK, and NPW were identified with IC50 values of 0.36 ± 0.03, 0.64 ± 0.01, and 1.10 ± 0.09 mM, respectively and predicted to form hydrogen bonds and hydrophobic interactions with the residues of the active site of COX2. This study highlighted the potential of milk peptides as bioactive ingredients in functional foods for managing IBS.

Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models

Protein sequence analysis examines the order of amino acids within protein sequences to unlock diverse types of a wealth of knowledge about biological processes and genetic disorders. It helps in forecasting disease susceptibility by finding unique protein signatures, or biomarkers that are linked to particular disease states. Protein Sequence analysis through wet-lab experiments is expensive, time-consuming and error prone. To facilitate large-scale proteomics sequence analysis, the biological community is striving for utilizing AI competence for transitioning from wet-lab to computer aided applications. However, Proteomics and AI are two distinct fields and development of AI-driven protein sequence analysis applications requires knowledge of both domains. To bridge the gap between both fields, various review articles have been written. However, these articles focus revolves around few individual tasks or specific applications rather than providing a comprehensive overview about wide tasks and applications. Following the need of a comprehensive literature that presents a holistic view of wide array of tasks and applications, contributions of this manuscript are manifold: It bridges the gap between Proteomics and AI fields by presenting a comprehensive array of AI-driven applications for 63 distinct protein sequence analysis tasks. It equips AI researchers by facilitating biological foundations of 63 protein sequence analysis tasks. It enhances development of AI-driven protein sequence analysis applications by providing comprehensive details of 68 protein databases. It presents a rich data landscape, encompassing 627 benchmark datasets of 63 diverse protein sequence analysis tasks. It highlights the utilization of 25 unique word embedding methods and 13 language models in AI-driven protein sequence analysis applications. It accelerates the development of AI-driven applications by facilitating current state-of-the-art performances across 63 protein sequence analysis tasks.

Peptide classification landscape: An in-depth systematic literature review on peptide types, databases, datasets, predictors architectures and performance

Peptides are gaining significant attention in diverse fields such as the pharmaceutical market has seen a steady rise in peptide-based therapeutics over the past six decades. Peptides have been utilized in the development of distinct applications including inhibitors of SARS-COV-2 and treatments for conditions like cancer and diabetes. Distinct types of peptides possess unique characteristics, and development of peptide-specific applications require the discrimination of one peptide type from others. To the best of our knowledge, approximately 230 Artificial Intelligence (AI) driven applications have been developed for 22 distinct types of peptides, yet there remains significant room for development of new predictors. A Comprehensive review addresses the critical gap by providing a consolidated platform for the development of AI-driven peptide classification applications. This paper offers several key contributions, including presenting the biological foundations of 22 unique peptide types and categorizes them into four main classes: Regulatory, Therapeutic, Nutritional, and Delivery Peptides. It offers an in-depth overview of 47 databases that have been used to develop peptide classification benchmark datasets. It summarizes details of 288 benchmark datasets that are used in development of diverse types AI-driven peptide classification applications. It provides a detailed summary of 197 sequence representation learning methods and 94 classifiers that have been used to develop 230 distinct AI-driven peptide classification applications. Across 22 distinct types peptide classification tasks related to 288 benchmark datasets, it demonstrates performance values of 230 AI-driven peptide classification applications. It summarizes experimental settings and various evaluation measures that have been employed to assess the performance of AI-driven peptide classification applications. The primary focus of this manuscript is to consolidate scattered information into a single comprehensive platform. This resource will greatly assist researchers who are interested in developing new AI-driven peptide classification applications.

DeepAIPs-Pred: Predicting Anti-Inflammatory Peptides Using Local Evolutionary Transformation Images and Structural Embedding-Based Optimal Descriptors with Self-Normalized BiTCNs

Inflammation is a biological response to harmful stimuli, playing a crucial role in facilitating tissue repair by eradicating pathogenic microorganisms. However, when inflammation becomes chronic, it leads to numerous serious disorders, particularly in autoimmune diseases. Anti-inflammatory peptides (AIPs) have emerged as promising therapeutic agents due to their high specificity, potency, and low toxicity. However, identifying AIPs using traditional in vivo methods is time-consuming and expensive. Recent advancements in computational-based intelligent models for peptides have offered a cost-effective alternative for identifying various inflammatory diseases, owing to their selectivity toward targeted cells with low side effects. In this paper, we propose a novel computational model, namely, DeepAIPs-Pred, for the accurate prediction of AIP sequences. The training samples are represented using LBP-PSSM- and LBP-SMR-based evolutionary image transformation methods. Additionally, to capture contextual semantic features, we employed attention-based ProtBERT-BFD embedding and QLC for structural features. Furthermore, differential evolution (DE)-based weighted feature integration is utilized to produce a multiview feature vector. The SMOTE-Tomek Links are introduced to address the class imbalance problem, and a two-layer feature selection technique is proposed to reduce and select the optimal features. Finally, the novel self-normalized bidirectional temporal convolutional networks (SnBiTCN) are trained using optimal features, achieving a significant predictive accuracy of 94.92% and an AUC of 0.97. The generalization of our proposed model is validated using two independent datasets, demonstrating higher performance with the improvement of ∼2 and ∼10% of accuracies than the existing state-of-the-art model using Ind-I and Ind-II, respectively. The efficacy and reliability of DeepAIPs-Pred highlight its potential as a valuable and promising tool for drug development and research academia.

A Comprehensive Review on Haemonchus contortus Excretory and Secretory Proteins (HcESPs): TH-9 stimulated ESPs as a potential candidate for Vaccine Development and Diagnostic Antigen

Haemonchus contortus (Barber pole worm) is one of the dominant helminth parasitic infections in small ruminants which is economically important and causes severe losses in the livestock industry, particularly in tropical and subtropical regions. This parasite resides in the abomasum and is responsible for severe blood loss, leading to anemia, emaciation, hypoproteinemia, weight loss, and potentially death. The economic impact of H. contortus on the livestock industry necessitates effective control measures, including early diagnosis and the development of effective vaccines. H. contortus secretes a variety of excretory and secretory proteins (ESPs), which are glycoproteins that play a crucial role in modulating the host's immune response. These ESPs are not only vital for understanding the immunological interactions between the parasite and the host but also serve as potential diagnostic tools and vaccine candidates. Similar ESPs have been identified in other parasitic species such as Cooperia spp, Ostertagia ostertagia, Teladorsagia circumcincta, Ascaris sum, Schistosoma japonicum, and Echinococcus multilocularis, underscoring their importance in both detection and vaccine development. In addition, there is a lack of highly potential specific proteins which having immunogenic properties that can be used for the accurate, early diagnosis serologically and serve as a potential candidate for the vaccine development against H. contortus. Recent research highlights that TH-9 stimulated proteins from H. contortus are emerging as promising candidates for vaccine development due to their immunomodulatory effects. These proteins have been shown to induce a TH-9 immune response, characterized by increased production of interleukin-9 (IL-9), which is critical for enhancing protective immunity against helminth infections. It is suggested to investigate TH-9 stimulated protein as potential candidates for vaccine development and diagnostic antigen.

Interpretable molecular encodings and representations for machine learning tasks

Molecular encodings and their usage in machine learning models have demonstrated significant breakthroughs in biomedical applications, particularly in the classification of peptides and proteins. To this end, we propose a new encoding method: Interpretable Carbon-based Array of Neighborhoods (iCAN). Designed to address machine learning models' need for more structured and less flexible input, it captures the neighborhoods of carbon atoms in a counting array and improves the utility of the resulting encodings for machine learning models. The iCAN method provides interpretable molecular encodings and representations, enabling the comparison of molecular neighborhoods, identification of repeating patterns, and visualization of relevance heat maps for a given data set. When reproducing a large biomedical peptide classification study, it outperforms its predecessor encoding. When extended to proteins, it outperforms a lead structure-based encoding on 71% of the data sets. Our method offers interpretable encodings that can be applied to all organic molecules, including exotic amino acids, cyclic peptides, and larger proteins, making it highly versatile across various domains and data sets. This work establishes a promising new direction for machine learning in peptide and protein classification in biomedicine and healthcare, potentially accelerating advances in drug discovery and disease diagnosis.

Role of Peptide Associations in Enhancing the Antimicrobial Activity of Adepantins: Comparative Molecular Dynamics Simulations and Design Assessments

Adepantins are peptides designed to optimize antimicrobial biological activity through the choice of specific amino acid residues, resulting in helical and amphipathic structures. This paper focuses on revealing the atomistic details of the mechanism of action of Adepantins and aligning design concepts with peptide behavior through simulation results. Notably, Adepantin-1a exhibits a broad spectrum of activity against both Gram-positive and Gram-negative bacteria, while Adepantin-1 has a narrow spectrum of activity against Gram-negative bacteria. The simulation results showed that one of the main differences is the extent of aggregation. Both peptides exhibit a strong tendency to cluster due to the amphipathicity embedded during design process. However, the more potent Adepantin-1a forms smaller aggregates than Adepantin-1, confirming the idea that the optimal aggregations, not the strongest aggregations, favor activity. Additionally, we show that incorporation of the cell penetration region affects the mechanisms of action of Adepantin-1a and promotes stronger binding to anionic and neutral membranes.

DeepAIP: Deep learning for anti-inflammatory peptide prediction using pre-trained protein language model features based on contextual self-attention network

Non-steroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, and other immunosuppressants are commonly used medications for treating inflammation. However, these drugs often come with numerous side effects. Therefore, finding more effective methods for inflammation treatment has become more necessary. The study of anti-inflammatory peptides can effectively address these issues. In this work, we propose a contextual self-attention deep learning model, coupled with features extracted from a pre-trained protein language model, to predict Anti-inflammatory Peptides (AIP). The contextual self-attention module can effectively enhance and learn the features extracted from the pre-trained protein language model, resulting in high accuracy to predict AIP. Additionally, we compared the performance of features extracted from popular pre-trained protein language models available in the market. Finally, Prot-T5 features demonstrated the best comprehensive performance as the input for our deep learning model named DeepAIP. Compared with existing methods on benchmark test dataset, DeepAIP gets higher Matthews Correlation Coefficient and Accuracy score than the second-best method by 16.35 % and 6.91 %, respectively. Performance comparison analysis was conducted using a dataset of 17 novel anti-inflammatory peptide sequences. DeepAIP demonstrates outstanding accuracy, correctly identifying all 17 peptide types as AIP and predicting values closer to the true ones. Data and code are available at https://github.com/YangQingGuoCCZU/DeepAIP.

Antimicrobial Peptide with a Bent Helix Motif Identified in Parasitic Flatworm Mesocestoides corti

The urgent need for antibiotic alternatives has driven the search for antimicrobial peptides (AMPs) from many different sources, yet parasite-derived AMPs remain underexplored. In this study, three novel potential AMP precursors (mesco-1, -2 and -3) were identified in the parasitic flatworm Mesocestoides corti, via a genome-wide mining approach, and the most promising one, mesco-2, was synthesized and comprehensively characterized. It showed potent broad-spectrum antibacterial activity at submicromolar range against E. coli and K. pneumoniae and low micromolar activity against A. baumannii, P. aeruginosa and S. aureus. Mechanistic studies indicated a membrane-related mechanism of action, and circular dichroism spectroscopy confirmed that mesco-2 is unstructured in water but forms stable helical structures on contact with anionic model membranes, indicating strong interactions and helix stacking. It is, however, unaffected by neutral membranes, suggesting selective antimicrobial activity. Structure prediction combined with molecular dynamics simulations suggested that mesco-2 adopts an unusual bent helix conformation with the N-terminal sequence, when bound to anionic membranes, driven by a central GRGIGRG motif. This study highlights mesco-2 as a promising antibacterial agent and emphasizes the importance of structural motifs in modulating AMP function.

PepNet: an interpretable neural network for anti-inflammatory and antimicrobial peptides prediction using a pre-trained protein language model

Identifying anti-inflammatory peptides (AIPs) and antimicrobial peptides (AMPs) is crucial for the discovery of innovative and effective peptide-based therapies targeting inflammation and microbial infections. However, accurate identification of AIPs and AMPs remains a computational challenge mainly due to limited utilization of peptide sequence information. Here, we propose PepNet, an interpretable neural network for predicting both AIPs and AMPs by applying a pre-trained protein language model to fully utilize the peptide sequence information. It first captures the information of residue arrangements and physicochemical properties using a residual dilated convolution block, and then seizes the function-related diverse information by introducing a residual Transformer block to characterize the residue representations generated by a pre-trained protein language model. After training and testing, PepNet demonstrates great superiority over other leading AIP and AMP predictors and shows strong interpretability of its learned peptide representations. A user-friendly web server for PepNet is freely available at http://liulab.top/PepNet/server .

Machine learning tools for peptide bioactivity evaluation - Implications for cell culture media optimization and the broader cultivated meat industry

Although bioactive peptides have traditionally been studied for their health-promoting qualities in the context of nutrition and medicine, the past twenty years have seen a steady increase in their application to cell culture media optimization. Complex natural sources of bioactive peptides, such as hydrolysates, offer a sustainable and cost-effective means of promoting cellular growth, making them an essential component of scaling-up cultivated meat production. However, the sheer diversity of hydrolysates makes product selection difficult, highlighting the need for functional characterization. Traditional wet-lab techniques for isolating and estimating peptide bioactivity cannot keep pace with peptide identification using high-throughput tools such as mass spectrometry, requiring the development and use of machine learning-based classifiers. This review provides a comprehensive list of available software tools to evaluate peptide bioactivity, classified and compared based on the algorithm, training set, functionality, and limitations of the underlying models. We curated independent test sets to compare the predictive performance of different models based on specific bioactivity classification relevant to promoting cell culture growth: antioxidant and anti-inflammatory. A comprehensive screening of all bioactivity classifiers revealed that while there are approximately fifty tools to elucidate antimicrobial activity and sixteen that predict anti-inflammatory activity, fewer tools are available for other functionalities related to cell growth - five that predict antioxidant activity and two for growth factor and/or cell signaling prediction. A thorough evaluation of the available tools revealed significant issues with sensitivity, specificity, and overall accuracy. Despite the overall interest in estimating peptide bioactivity, our work highlights key gaps in the broader adoption of existing software for the specific application of cell culture media optimization in the context of cultivated meat and beyond.

PredIL13: Stacking a variety of machine and deep learning methods with ESM-2 language model for identifying IL13-inducing peptides

Interleukin (IL)-13 has emerged as one of the recently identified cytokine. Since IL-13 causes the severity of COVID-19 and alters crucial biological processes, it is urgent to explore novel molecules or peptides capable of including IL-13. Computational prediction has received attention as a complementary method to in-vivo and in-vitro experimental identification of IL-13 inducing peptides, because experimental identification is time-consuming, laborious, and expensive. A few computational tools have been presented, including the IL13Pred and iIL13Pred. To increase prediction capability, we have developed PredIL13, a cutting-edge ensemble learning method with the latest ESM-2 protein language model. This method stacked the probability scores outputted by 168 single-feature machine/deep learning models, and then trained a logistic regression-based meta-classifier with the stacked probability score vectors. The key technology was to implement ESM-2 and to select the optimal single-feature models according to their absolute weight coefficient for logistic regression (AWCLR), an indicator of the importance of each single-feature model. Especially, the sequential deletion of single-feature models based on the iterative AWCLR ranking (SDIWC) method constructed the meta-classifier consisting of the top 16 single-feature models, named PredIL13, while considering the model's accuracy. The PredIL13 greatly outperformed the-state-of-the-art predictors, thus is an invaluable tool for accelerating the detection of IL13-inducing peptide within the human genome.

Native characterization and QC profiling of human amniotic mesenchymal stromal cell vesicular fractions for secretome-based therapy

Human amniotic mesenchymal stromal cells (hAMSCs) have unique immunomodulatory properties making them attractive candidates for regenerative applications in inflammatory diseases. Most of their beneficial properties are mediated through their secretome. The bioactive factors concurring to its therapeutic activity are still unknown. Evidence suggests synergy between the two main components of the secretome, soluble factors and vesicular fractions, pivotal in shifting inflammation and promoting self-healing. Biological variability and the absence of quality control (QC) protocols hinder secretome-based therapy translation to clinical applications. Moreover, vesicular secretome contains a multitude of particles with varying size, cargos and functions whose complexity hinders full characterization and comprehension. This study achieved a significant advancement in secretome characterization by utilizing native, FFF-based separation and characterizing extracellular vesicles derived from hAMSCs. This was accomplished by obtaining dimensionally homogeneous fractions then characterized based on their protein content, potentially enabling the identification of subpopulations with diverse functionalities. This method proved to be successful as an independent technique for secretome profiling, with the potential to contribute to the standardization of a qualitative method. Additionally, it served as a preparative separation tool, streamlining populations before ELISA and LC-MS characterization. This approach facilitated the categorization of distinctive and recurring proteins, along with the identification of clusters associated with vesicle activity and functions. However, the presence of proteins unique to each fraction obtained through the FFF separation tool presents a challenge for further analysis of the protein content within these cargoes.

A deep learning model for anti-inflammatory peptides identification based on deep variational autoencoder and contrastive learning

As a class of biologically active molecules with significant immunomodulatory and anti-inflammatory effects, anti-inflammatory peptides have important application value in the medical and biotechnology fields due to their unique biological functions. Research on the identification of anti-inflammatory peptides provides important theoretical foundations and practical value for a deeper understanding of the biological mechanisms of inflammation and immune regulation, as well as for the development of new drugs and biotechnological applications. Therefore, it is necessary to develop more advanced computational models for identifying anti-inflammatory peptides. In this study, we propose a deep learning model named DAC-AIPs based on variational autoencoder and contrastive learning for accurate identification of anti-inflammatory peptides. In the sequence encoding part, the incorporation of multi-hot encoding helps capture richer sequence information. The autoencoder, composed of convolutional layers and linear layers, can learn latent features and reconstruct features, with variational inference enhancing the representation capability of latent features. Additionally, the introduction of contrastive learning aims to improve the model's classification ability. Through cross-validation and independent dataset testing experiments, DAC-AIPs achieves superior performance compared to existing state-of-the-art models. In cross-validation, the classification accuracy of DAC-AIPs reached around 88%, which is 7% higher than previous models. Furthermore, various ablation experiments and interpretability experiments validate the effectiveness of DAC-AIPs. Finally, a user-friendly online predictor is designed to enhance the practicality of the model, and the server is freely accessible at http://dac-aips.online .

MMDB: Multimodal dual-branch model for multi-functional bioactive peptide prediction

Bioactive peptides can hinder oxidative processes and microbial spoilage in foodstuffs and play important roles in treating diverse diseases and disorders. While most of the methods focus on single-functional bioactive peptides and have obtained promising prediction performance, it is still a significant challenge to accurately detect complex and diverse functions simultaneously with the quick increase of multi-functional bioactive peptides. In contrast to previous research on multi-functional bioactive peptide prediction based solely on sequence, we propose a novel multimodal dual-branch (MMDB) lightweight deep learning model that designs two different branches to effectively capture the complementary information of peptide sequence and structural properties. Specifically, a multi-scale dilated convolution with Bi-LSTM branch is presented to effectively model the different scales sequence properties of peptides while a multi-layer convolution branch is proposed to capture structural information. To the best of our knowledge, this is the first effective extraction of peptide sequence features using multi-scale dilated convolution without parameter increase. Multimodal features from both branches are integrated via a fully connected layer for multi-label classification. Compared to state-of-the-art methods, our MMDB model exhibits competitive results across metrics, with a 9.1% Coverage increase and 5.3% and 3.5% improvements in Precision and Accuracy, respectively.

A BERT-based approach for identifying anti-inflammatory peptides using sequence information

The use of anti-inflammatory peptides (AIPs) as an alternative therapeutic approach for inflammatory diseases holds great research significance. Due to the high cost and difficulty in identifying AIPs with experimental methods, the discovery and design of peptides by computational methods before the experimental stage have become promising technology. In this study, we present BertAIP, a bidirectional encoder representation from transformers (BERT)-based method for predicting AIPs directly from their amino acid sequence without using any other information. BertAIP implements a BERT model to extract features of a protein, and uses a fully connected feed-forward network for AIP classification. It was constructed and evaluated using the AIP datasets that were reconstructed from the latest Immune Epitope Database. The experimental results showed that BertAIP achieved an accuracy of 0.751 and a Matthews correlation coefficient of 0.451, which were higher than other commonly used methods. The results of the independent test suggested that BertAIP outperformed the existing AIP predictors. In addition, to enhance the interpretability of BertAIP, we explored and visualized the amino acids that the model considered important for AIP prediction. We believe that the BertAIP proposed herein will be a useful tool for large-scale screening and identifying novel AIPs for drug development and therapeutic research related to inflammatory diseases.

Machine learning for antimicrobial peptide identification and design

Artificial intelligence (AI) and machine learning (ML) models are being deployed in many domains of society and have recently reached the field of drug discovery. Given the increasing prevalence of antimicrobial resistance, as well as the challenges intrinsic to antibiotic development, there is an urgent need to accelerate the design of new antimicrobial therapies. Antimicrobial peptides (AMPs) are therapeutic agents for treating bacterial infections, but their translation into the clinic has been slow owing to toxicity, poor stability, limited cellular penetration and high cost, among other issues. Recent advances in AI and ML have led to breakthroughs in our abilities to predict biomolecular properties and structures and to generate new molecules. The ML-based modelling of peptides may overcome some of the disadvantages associated with traditional drug discovery and aid the rapid development and translation of AMPs. Here, we provide an introduction to this emerging field and survey ML approaches that can be used to address issues currently hindering AMP development. We also outline important limitations that can be addressed for the broader adoption of AMPs in clinical practice, as well as new opportunities in data-driven peptide design.

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Synergistic Inhibitory Effect of Lactobacillus Cell Lysates and Butyrate on Poly I:C-Induced IL-8 Production in Human Intestinal Epithelial Cells

Postbiotics include cell lysates (CLs), enzymes, cell wall fragments, and heat-killed bacteria derived from probiotics. Although postbiotics are increasingly being considered for their potential health-promoting properties, the effects of postbiotics on virus-mediated inflammatory responses in the intestine have not been elucidated. Hence, the present study aimed to examine whether CLs of Lactipantibacillus plantarum (LP CL) and Lacticaseibacillus rhamnosus GG (LR CL) could inhibit virus-mediated inflammatory responses in the human intestinal epithelial cell line HT-29 in vitro. Pretreatment with LP CL and LR CL significantly inhibited interleukin (IL)-8 production, which was induced by poly I:C, a synthetic analog of double-stranded RNA (dsRNA) viruses, at the mRNA and protein levels in HT-29 cells. However, peptidoglycans and heat-killed L. plantarum and L. rhamnosus GG did not effectively inhibit IL-8 production. LP CL and LR CL attenuated the poly I:C-induced phosphorylation of ERK and JNK and the activation of NF-κB, suggesting that these CLs could inhibit poly I:C-induced IL-8 production by regulating intracellular signaling pathways in HT-29 cells. Furthermore, among the short-chain fatty acids, butyrate enhanced the inhibitory effect of CLs on poly I:C-induced IL-8 production at the mRNA and protein levels in HT-29 cells, while acetate and propionate did not. Taken together, these results suggest that both LP CL and LR CL could act as potent effector molecules that can inhibit virus-mediated inflammatory responses and confer synergistic inhibitory effects with butyrate in human intestinal epithelial cells.

Adipose tissue inflammation linked to obesity: A review of current understanding, therapies and relevance of phyto-therapeutics

Obesity is a current global challenge affecting all ages and is characterized by the up-regulated secretion of bioactive factors/pathways which result in adipose tissue inflammation (ATI). Current obesity therapies are mainly focused on lifestyle (diet/nutrition) changes. This is because many chemosynthetic anti-obesogenic medications cause adverse effects like diarrhoea, dyspepsia, and faecal incontinence, among others. As such, it is necessary to appraise the efficacies and mechanisms of action of safer, natural alternatives like plant-sourced compounds, extracts [extractable phenol (EP) and macromolecular antioxidant (MA) extracts], and anti-inflammatory peptides, among others, with a view to providing a unique approach to obesity care. These natural alternatives may constitute potent therapies for ATI linked to obesity. The potential of MA compounds (analysed for the first time in this review) and extracts in ATI and obesity management is elucidated upon, while also highlighting research gaps and future prospects. Furthermore, immune cells, signalling pathways, genes, and adipocyte cytokines play key roles in ATI responses and are targeted in certain therapies. As a result, this review gives an in-depth appraisal of ATI linked to obesity, its causes, mechanisms, and effects of past, present, and future therapies for reversal and alleviation of ATI. Achieving a significant decrease in morbidity and mortality rates attributed to ATI linked to obesity and related comorbidities is possible as research improves our understanding over time.

IF-AIP: A machine learning method for the identification of anti-inflammatory peptides using multi-feature fusion strategy

BACKGROUND

The most commonly used therapy currently for inflammatory and autoimmune diseases is nonspecific anti-inflammatory drugs, which have various hazardous side effects. Recently, some anti-inflammatory peptides (AIPs) have been found to be a substitute therapy for inflammatory diseases like rheumatoid arthritis and Alzheimer's. Therefore, the identification of these AIPs is an emerging topic that is equally important.

METHODS

In this work, we have proposed an identification model for AIPs using a voting classifier. We used eight different feature descriptors and five conventional machine-learning classifiers. The eight feature encodings were concatenated to get a hybrid feature set. The five baseline models trained on the hybrid feature set were integrated via a voting classifier. Finally, a feature selection algorithm was used to select the optimal feature set for the construction of our final model, named IF-AIP.

RESULTS

We tested the proposed model on two independent datasets. On independent data 1, the IF-AIP model shows an improvement of 3%-5.6% in terms of accuracies and 6.7%-10.8% in terms of MCC compared to the existing methods. On the independent dataset 2, our model IF-AIP shows an overall improvement of 2.9%-5.7% in terms of accuracy and 8.3%-8.6% in terms of MCC score compared to the existing methods. A comparative performance analysis was conducted between the proposed model and existing methods using a set of 24 novel peptide sequences. Notably, the IF-AIP method exhibited exceptional accuracy, correctly identifying all 24 peptides as AIPs. The source code, pre-trained models, and all datasets are made available at https://github.com/Mir-Saima/IF-AIP.

Predicting Anti-inflammatory Peptides by Ensemble Machine Learning and Deep Learning

Inflammation is a biological response to harmful stimuli, aiding in the maintenance of tissue homeostasis. However, excessive or persistent inflammation can precipitate a myriad of pathological conditions. Although current treatments such as NSAIDs, corticosteroids, and immunosuppressants are effective, they can have side effects and resistance issues. In this backdrop, anti-inflammatory peptides (AIPs) have emerged as a promising therapeutic approach against inflammation. Leveraging machine learning methods, we have the opportunity to accelerate the discovery and investigation of these AIPs more effectively. In this study, we proposed an advanced framework by ensemble machine learning and deep learning for AIP prediction. Initially, we constructed three individual models with extremely randomized trees (ET), gated recurrent unit (GRU), and convolutional neural networks (CNNs) with attention mechanism and then used stacking architecture to build the final predictor. By utilizing various sequence encodings and combining the strengths of different algorithms, our predictor demonstrated exemplary performance. On our independent test set, our model achieved an accuracy, MCC, and F1-score of 0.757, 0.500, and 0.707, respectively, clearly outperforming other contemporary AIP prediction methods. Additionally, our model offers profound insights into the feature interpretation of AIPs, establishing a valuable knowledge foundation for the design and development of future anti-inflammatory strategies.

AIPs-SnTCN: Predicting Anti-Inflammatory Peptides Using fastText and Transformer Encoder-Based Hybrid Word Embedding with Self-Normalized Temporal Convolutional Networks

Inflammation is a biologically resistant response to harmful stimuli, such as infection, damaged cells, toxic chemicals, or tissue injuries. Its purpose is to eradicate pathogenic micro-organisms or irritants and facilitate tissue repair. Prolonged inflammation can result in chronic inflammatory diseases. However, wet-laboratory-based treatments are costly and time-consuming and may have adverse side effects on normal cells. In the past decade, peptide therapeutics have gained significant attention due to their high specificity in targeting affected cells without affecting healthy cells. Motivated by the significance of peptide-based therapies, we developed a highly discriminative prediction model called AIPs-SnTCN to predict anti-inflammatory peptides accurately. The peptide samples are encoded using word embedding techniques such as skip-gram and attention-based bidirectional encoder representation using a transformer (BERT). The conjoint triad feature (CTF) also collects structure-based cluster profile features. The fused vector of word embedding and sequential features is formed to compensate for the limitations of single encoding methods. Support vector machine-based recursive feature elimination (SVM-RFE) is applied to choose the ranking-based optimal space. The optimized feature space is trained by using an improved self-normalized temporal convolutional network (SnTCN). The AIPs-SnTCN model achieved a predictive accuracy of 95.86% and an AUC of 0.97 by using training samples. In the case of the alternate training data set, our model obtained an accuracy of 92.04% and an AUC of 0.96. The proposed AIPs-SnTCN model outperformed existing models with an ∼19% higher accuracy and an ∼14% higher AUC value. The reliability and efficacy of our AIPs-SnTCN model make it a valuable tool for scientists and may play a beneficial role in pharmaceutical design and research academia.

Walnut Protein Peptides Ameliorate DSS-Induced Ulcerative Colitis Damage in Mice: An in Silico Analysis and in Vivo Investigation

Walnut (Juglans regia L.) is a food with food-medicine homology, whose derived protein peptides have been shown to have anti-inflammatory activity in vitro. However, the effects and mechanisms of walnut protein peptides on ulcerative colitis (UC) in vivo have not been systematically and thoroughly investigated. In this study, we applied virtual screening and network pharmacology screening of bioactive peptides to obtain three novel WPPs (SHTLP, HYNLN, and LGTYP) that may alleviate UC through TLR4-MAPK signaling. In vivo studies have shown that WPPs improve intestinal mucosal barrier dysfunction and reduce inflammation by inhibiting activation of the TLR4-MAPK pathway. In addition, WPPs restore intestinal microbial homeostasis by reducing harmful bacteria (Helicobacter and Bacteroides) and increasing the relative abundance of beneficial bacteria (Candidatus_Saccharimonas). Our study showed that the WPPs obtained by virtual screening were effective in ameliorating colitis, which has important implications for future screening of bioactive peptides from medicinal food homologues as drugs or dietary supplements.

DeepTPpred: A Deep Learning Approach With Matrix Factorization for Predicting Therapeutic Peptides by Integrating Length Information

The abuse of traditional antibiotics has led to increased resistance of bacteria and viruses. Efficient therapeutic peptide prediction is critical for peptide drug discovery. However, most of the existing methods only make effective predictions for one class of therapeutic peptides. It is worth noting that currently no predictive method considers sequence length information as a distinct feature of therapeutic peptides. In this article, a novel deep learning approach with matrix factorization for predicting therapeutic peptides (DeepTPpred) by integrating length information are proposed. The matrix factorization layer can learn the potential features of the encoded sequence through the mechanism of first compression and then restoration. And the length features of the sequence of therapeutic peptides are embedded with encoded amino acid sequences. To automatically learn therapeutic peptide predictions, these latent features are input into the neural networks with self-attention mechanism. On eight therapeutic peptide datasets, DeepTPpred achieved excellent prediction results. Based on these datasets, we first integrated eight datasets to obtain a full therapeutic peptide integration dataset. Then, we obtained two functional integration datasets based on the functional similarity of the peptides. Finally, we also conduct experiments on the latest versions of the ACP and CPP datasets. Overall, the experimental results show that our work is effective for the identification of therapeutic peptides.

From the updated landscape of the emerging biologics for IBDs treatment to the new delivery systems

Inflammatory bowel diseases (IBDs) treatments have shifted from small-molecular therapeutics to the oncoming biologics. The first-line biologics against the moderate-to-severe IBDs are mainly involved in antibodies against integrins, cytokines and cell adhesion molecules. Besides, other biologics including growth factors, antioxidative enzyme, anti-inflammatory peptides, nucleic acids, stem cells and probiotics have also been explored at preclinical or clinical studies. Biologics with variety of origins have their unique potentials in attenuating immune inflammation or gut mucosa healing. Great advances in use of biologics for IBDs treatments have been archived in recent years. But delivering issues for biologic have also been confronted due to their liable nature. In this review, we will focus on biologics for IBDs treatments in the recent publications; summarize the current landscapes of biologics and their promise to control disease progress. Alternatively, the confronted challenges for delivering biologics will also be analyzed. To combat these drawbacks, some new delivering strategies are provided: firstly, designing the functional materials with high affinity toward biologics; secondly, the delivering vehicle systems to encapsulate the liable biologics; thirdly, the topical adhering delivery systems as enema. To our knowledge, this review is the first study to summarize the updated usage of the oncoming biologics for IBDs, their confronted challenges in term of delivery and the potential combating strategies.

Peptidomics Analysis Reveals the Buccal Gland of Jawless Vertebrate Lamprey as a Source of Multiple Bioactive Peptides

Various proteins with antibacterial, anticoagulant, and anti-inflammatory properties have been identified in the buccal glands of jawless blood-sucking vertebrate lampreys. However, studies on endogenous peptides in the buccal gland of lampreys are limited. In this study, 4528 endogenous peptides were identified from 1224 precursor proteins using peptidomics and screened for bioactivity in the buccal glands of the lamprey, Lethenteron camtschaticum. We synthesized four candidate bioactive peptides (VSLNLPYSVVRGEQFVVQA, DIPVPEVPILE, VVQLPPVVLGTFG, and VPPPPLVLPPASVK), calculated their secondary structures, and validated their bioactivity. The results showed that the peptide VSLNLPYSVVRGEQFVVQA possessed anti-inflammatory activity, which significantly increased the expression of anti-inflammatory factors and decreased the expression of inflammatory factors in THP-1 cells. The peptide VVQLPPVVLGTFG showed antibacterial activity against some gram-positive bacteria. The peptide VSLNLPYSVVRGEQFVQA possessed good ACE inhibitory activity at low concentrations, but no dose-related correlation was observed. Our study revealed that the buccal glands of the jawless vertebrate lamprey are a source of multiple bioactive peptides, which will provide new insights into the blood-sucking mechanism of lamprey.

Bioactive Peptides and Protein Hydrolysates as Lipoxygenase Inhibitors

Lipoxygenases are non-heme iron-containing enzymes that catalyze the oxidation of polyunsaturated fatty acids, resulting in the production of lipid hydroperoxides, which are precursors of inflammatory lipid mediators. These enzymes are widely distributed in humans, other eukaryotes, and cyanobacteria. Lipoxygenases hold promise as therapeutic targets for several human diseases, including cancer and inflammation-related disorders. Inhibitors of lipoxygenase have potential applications in pharmaceuticals, cosmetics, and food. Bioactive peptides are short amino acid sequences embedded within parent proteins, which can be released by enzymatic hydrolysis, microbial fermentation, and gastrointestinal digestion. A wide variety of bioactivities have been documented for protein hydrolysates and peptides derived from different biological sources. Recent findings indicate that protein hydrolysates and peptides derived from both edible and non-edible bioresources can act as lipoxygenase inhibitors. This review aims to provide an overview of the current knowledge regarding the production of anti-lipoxygenase protein hydrolysates and peptides from millet grains, chia seeds, insects, milk proteins, fish feed, velvet antler blood, fish scales, and feather keratins. The anti-lipoxygenase activities and modes of action of these protein hydrolysates and peptides are discussed. The strengths and shortcomings of previous research in this area are emphasized. Additionally, potential research directions and areas for improvement are suggested to accelerate the discovery of anti-lipoxygenase peptides in the near future.

Evaluation of Antioxidant Activities from a Sustainable Source of Okara Protein Hydrolysate Using Enzymatic Reaction

Okara is a solid byproduct created during the processing of soy milk. The production of protein hydrolysates utilizing enzymatic tests such as papain can result in the production of bioactive peptides (BPs), which are amino acid sequences that can also be produced from the okara protein by hydrolysis. The objective of this study was to investigate the antioxidant activities of okara hydrolysates using papain, based on the in silico and in vitro assays using the papain enzyme. We found that using the in silico assessment, the antioxidant peptides can be found from the precursor (glycinin and conglycinin) in okara. When used as a protease, papain provides the maximum degree of hydrolysis for antioxidative peptides. The highest-peptide-rank peptide sequence was predicted using peptide ranks such as proline-histidine-phenylalanine (PHF), alanine-aspartic acid-phenylalanine (ADF), tyrosine-tyrosine-leucine (YYL), proline-histidine-histidine (PHH), isoleucine-arginine (IR), and serine-valine-leucine (SVL). Molecular docking studies revealed that all peptides generated from the parent protein impeded substrate access to the active site of xanthine oxidase (XO). They have antioxidative properties and are employed in the in silico approach to the XO enzyme. We also use papain to evaluate the antioxidant activity by using in vitro tests for protein hydrolysate following proteolysis. The antioxidant properties of okara protein hydrolysates have been shown in vitro, utilizing DPPH and FRAP experiments. This study suggests that okara hydrolysates generated by papain can be employed as natural antioxidants in food and for further applications, such as active ingredients for antioxidants in packaging.

Protein Biocargo and Anti-Inflammatory Effect of Tomato Fruit-Derived Nanovesicles Separated by Density Gradient Ultracentrifugation and Loaded with Curcumin

Plant-derived nanovesicles (PDNVs) have become attractive alternatives to mammalian cell-derived extracellular vesicles (EVs) both as therapeutic approaches and drug-delivery vehicles. In this study, we isolated tomato fruit-derived NVs and separated them by the iodixanol density gradient ultracentrifugation (DGUC) into twelve fractions. Three visible bands were observed at densities 1.064 ± 0.007 g/mL, 1.103 ± 0.006 g/mL and 1.122 ± 0.012 g/mL. Crude tomato PDNVs and DGUC fractions were characterized by particle size-distribution, concentration, lipid and protein contents as well as protein composition using mass spectrometry-based proteomics. Cytotoxicity and anti-inflammatory activity of the DGUC fractions associated to these bands were assessed in the lipopolysaccharide (LPS)-stimulated human monocytic THP-1 cell culture. The middle and the low-density visible DGUC fractions of tomato PDNVs showed a significant reduction in LPS-induced inflammatory IL-1β cytokine mRNA production. Functional analysis of proteins identified in these fractions reveals the presence of 14-3-3 proteins, endoplasmic reticulum luminal binding proteins and GTP binding proteins associated to gene ontology (GO) term GO:0050794 and the regulation of several cellular processes including inflammation. The most abundant middle-density DGUC fraction was loaded with curcumin using direct loading, sonication and extrusion methods and anti-inflammatory activity was compared. The highest entrapment efficiency and drug loading capacity was obtained by direct loading. Curcumin loaded by sonication increased the basal anti-inflammatory activity of tomato PDNVs.

Bioactive and Sensory Di- and Tripeptides Generated during Dry-Curing of Pork Meat

Dry-cured pork products, such as dry-cured ham, undergo an extensive proteolysis during manufacturing process which determines the organoleptic properties of the final product. As a result of endogenous pork muscle endo- and exopeptidases, many medium- and short-chain peptides are released from muscle proteins. Many of them have been isolated, identified, and characterized, and some peptides have been reported to exert relevant bioactivity with potential benefit for human health. However, little attention has been given to di- and tripeptides, which are far less known, although they have received increasing attention in recent years due to their high potential relevance in terms of bioactivity and role in taste development. This review gathers the current knowledge about di- and tripeptides, regarding their bioactivity and sensory properties and focusing on their generation during long-term processing such as dry-cured pork meats.

Identification and virtual screening of novel anti-inflammatory peptides from broccoli fermented by Lactobacillus strains

Lactobacillus strains fermentation of broccoli as a good source of bioactive peptides has not been fully elucidated. In this work, the peptide composition of broccoli fermented by L. plantarum A3 and L. rhamnosus ATCC7469 was analyzed by peptidomics to study the protein digestion patterns after fermentation by different strains. Results showed that water-soluble proteins such as rubisco were abundant sources of peptides, which triggered the sustained release of peptides as the main target of hydrolysis. In addition, 17 novel anti-inflammatory peptides were identified by virtual screening. Among them, SIWYGPDRP had the strongest ability to inhibit the release of NO from inflammatory cells at a concentration of 25 μM with an inhibition rate of 52.32 ± 1.48%. RFR and KASFAFAGL had the strongest inhibitory effects on the secretion of TNF-α and IL-6, respectively. At a concentration of 25 μM, the corresponding inhibition rates were 74.61 ± 1.68% and 29.84 ± 0.63%, respectively. Molecular docking results showed that 17 peptides formed hydrogen bonds and hydrophobic interactions with inducible nitric oxide synthase (iNOS). This study is conducive to the high-value utilization of broccoli and reduction of the antibiotic use.

In Silico Prospecting for Novel Bioactive Peptides from Seafoods: A Case Study on Pacific Oyster (Crassostrea gigas)

Pacific oyster (Crassostrea gigas), an abundant bivalve consumed across the Pacific, is known to possess a wide range of bioactivities. While there has been some work on its bioactive hydrolysates, the discovery of bioactive peptides (BAPs) remains limited due to the resource-intensive nature of the existing discovery pipeline. To overcome this constraint, in silico-based prospecting is employed to accelerate BAP discovery. Major oyster proteins were digested virtually under a simulated gastrointestinal condition to generate virtual peptide products that were screened against existing databases for peptide bioactivities, toxicity, bitterness, stability in the intestine and in the blood, and novelty. Five peptide candidates were shortlisted showing antidiabetic, anti-inflammatory, antihypertensive, antimicrobial, and anticancer potential. By employing this approach, oyster BAPs were identified at a faster rate, with a wider applicability reach. With the growing market for peptide-based nutraceuticals, this provides an efficient workflow for candidate scouting and end-use investigation for targeted functional product preparation.

A Web-Based Method for the Identification of IL6-Based Immunotoxicity in Vaccine Candidates

Interleukin 6 (IL6) is a major pro-inflammatory cytokine that plays a pivotal role in both innate and adaptive immune responses. In the past, a number of studies reported that high level of IL6 promotes the proliferation of cancer, autoimmune disorders, and cytokine storm in COVID-19 patients. Thus, it is extremely important to identify and remove the antigenic regions from a therapeutic protein or vaccine candidate that may induce IL6-associated immunotoxicity. In order to overcome this challenge, our group has developed a computational tool, IL6pred, for discovering IL6-inducing peptides in a vaccine candidate. The aim of this chapter is to describe the potential applications and methodology of IL6pred. It sheds light on the prediction, designing, and scanning modules of IL6pred webserver and standalone package ( https://webs.iiitd.edu.in/raghava/il6pred/ ).

In Silico Tool for Identification, Designing, and Searching of IL13-Inducing Peptides in Antigens

Interleukins are a distinctive class of molecules exhibiting various immune signaling functions. Immunoregulatory cytokine, Interleukin 13 (IL13), is primarily synthesized by activated T-helper 2 cells, mast cells, and basophils. IL13, is known to stimulate many allergic and autoimmune diseases, such as asthma, rheumatoid arthritis, systemic sclerosis, ulcerative colitis, airway hyperresponsiveness, glycoprotein hypersecretion, and goblet cell hyperplasia. In addition to such disorders, IL13 also leads to carcinogenesis by inhibiting tumor immunosurveillance. Due to its role in various diseases, predicting IL13-inducing peptides or regions in a protein is vital to designing safe protein vaccines and therapeutics. IL13pred is an in silico tool which aids in identifying, predicting, and designing IL13-inducing peptides. The IL13pred web server and standalone package is easily accessible at ( https://webs.iiitd.edu.in/raghava/il13pred/ ).

Cytoskeletal Responses and Aif-1 Expression in Caco-2 Monolayers Exposed to Phorbol-12-Myristate-13-Acetate and Carnosine

The dis(re)organization of the cytoskeletal actin in enterocytes mediates epithelial barrier dys(re)function, playing a key role in modulating epithelial monolayer's integrity and remodeling under transition from physiological to pathological states. Here, by fluorescence-based morphological and morphometric analyses, we detected differential responses of cytoskeletal actin in intestinal epithelial Caco-2 cell monolayers at two different stages of their spontaneous differentiation, i.e., undifferentiated cells at 7 days post-seeding (dps) and differentiated enterocyte-like cells at 21 dps, upon challenge in vitro with the inflammation-mimicking stimulus of phorbol-12-myristate-13-acetate (PMA). In addition, specific responses were found in the presence of the natural dipeptide carnosine detecting its potential counteraction against PMA-induced cytoskeletal alterations and remodeling in differentiated Caco-2 monolayers. In such an experimental context, by both immunocytochemistry and Western blot assays in Caco-2 monolayers, we identified the expression of the allograft inflammatory factor 1 (AIF-1) as protein functionally related to both inflammatory and cytoskeletal pathways. In 21 dps monolayers, particularly, we detected variations of its intracellular localization associated with the inflammatory stimulus and its mRNA/protein increase associated with the differentiated 21 dps enterocyte-like monolayer compared to the undifferentiated cells.

In vitro anti-inflammatory and antibiofilm activities of bacterial lysates from lactobacilli against oral pathogenic bacteria

Postbiotics are functional biological compounds, such as bacterial lysates (BLs) released from probiotic bacteria. Although postbiotics exert various bioactivities, the anti-inflammatory and antibiofilm activities of BLs against oral pathogenic bacteria have not been investigated. In the present study, pretreatment with BLs extracted from Lactobacillus plantarum and L. rhamnosus GG suppressed the mRNA and protein expression levels of inflammatory mediators induced by the lipopolysaccharide (LPS) of Porphyromonas gingivalis in RAW 264.7 cells. Both BLs attenuated P. gingivalis LPS-induced phosphorylation of mitogen-activated protein kinases (MAPKs) and activation of nuclear factor-κB (NF-κB), suggesting that BLs inhibit periodontal inflammatory responses by regulating the MAPK and NF-κB signaling pathways. Moreover, both BLs interfered with biofilm formation by Streptococcus mutans; however, they did not eradicate the established S. mutans biofilm. Furthermore, both BLs downregulated gtfB, gtfC, and gtfD responsible for biofilm formation by S. mutans, suggesting that BLs reduce the synthesis of extracellular polysaccharide and thereby reduce S. mutans biofilm. Taken together, these results suggest that BLs of L. plantarum and L. rhamnosus GG can attenuate periodontal inflammation and dental caries and thus contribute to the improvement of oral health.

Anti-Cancer and Anti-Inflammatory Activities of a Short Molecule, PS14 Derived from the Virulent Cellulose Binding Domain of Aphanomyces invadans, on Human Laryngeal Epithelial Cells and an In Vivo Zebrafish Embryo Model

In this study, the anti-cancer and anti-inflammatory activities of PS14, a short peptide derived from the cellulase binding domain of pathogenic fungus, Aphanomyces invadans, have been evaluated, in vitro and in vivo. Bioinformatics analysis of PS14 revealed the physicochemical properties and the web-based predictions, which indicate that PS14 is non-toxic, and it has the potential to elicit anti-cancer and anti-inflammatory activities. These in silico results were experimentally validated through in vitro (L6 or Hep-2 cells) and in vivo (zebrafish embryo or larvae) models. Experimental results showed that PS14 is non-toxic in L6 cells and the zebrafish embryo, and it elicits an antitumor effect Hep-2 cells and zebrafish embryos. Anticancer activity assays, in terms of MTT, trypan blue and LDH assays, showed a dose-dependent inhibitory effect on cell proliferation. Moreover, in the epithelial cancer cells and zebrafish embryos, the peptide challenge (i) caused significant changes in the cytomorphology and induced apoptosis; (ii) triggered ROS generation; and (iii) showed a significant up-regulation of anti-cancer genes including BAX, Caspase 3, Caspase 9 and down-regulation of Bcl-2, in vitro. The anti-inflammatory activity of PS14 was observed in the cell-free in vitro assays for the inhibition of proteinase and lipoxygenase, and heat-induced hemolysis and hypotonicity-induced hemolysis. Together, this study has identified that PS14 has anti-cancer and anti-inflammatory activities, while being non-toxic, in vitro and in vivo. Future experiments can focus on the clinical or pharmacodynamics aspects of PS14.

CSM-peptides: A computational approach to rapid identification of therapeutic peptides

Peptides are attractive alternatives for the development of new therapeutic strategies due to their versatility and low complexity of synthesis. Increasing interest in these molecules has led to the creation of large collections of experimentally characterized therapeutic peptides, which greatly contributes to development of data-driven computational approaches. Here we propose CSM-peptides, a novel machine learning method for rapid identification of eight different types of therapeutic peptides: anti-angiogenic, anti-bacterial, anti-cancer, anti-inflammatory, anti-viral, cell-penetrating, quorum sensing, and surface binding. Our method has shown to outperform existing approaches, achieving an AUC of up to 0.92 on independent blind tests, and consistent performance on cross-validation. We anticipate CSM-peptides to be of great value in helping screening large libraries to identify novel peptides with therapeutic potential and have made it freely available as a user-friendly web server and Application Programming Interface at https://biosig.lab.uq.edu.au/csm_peptides.

Not all T cell epitopes are equally desired: a review of in silico tools for the prediction of cytokine-inducing potential of T-cell epitopes

Assessment of protective or harmful T cell response induced by any antigenic epitope is important in designing any immunotherapeutic molecule. The understanding of cytokine induction potential also helps us to monitor antigen-specific cellular immune responses and rational vaccine design. The classical immunoinformatics tools served well for prediction of B cell and T cell epitopes. However, in the last decade, the prediction algorithms for T cell epitope inducing specific cytokines have also been developed and appreciated in the scientific community. This review summarizes the current status of such tools, their applications, background algorithms, their use in experimental setup and functionalities available in the tools/web servers.

Designed Multifunctional Peptides for Intracellular Targets

Nature’s way for bioactive peptides is to provide them with several related functions and the ability to cooperate in performing their job. Natural cell-penetrating peptides (CPP), such as penetratins, inspired the design of multifunctional constructs with CPP ability. This review focuses on known and novel peptides that can easily reach intracellular targets with little or no toxicity to mammalian cells. All peptide candidates were evaluated and ranked according to the predictions of low toxicity to mammalian cells and broad-spectrum activity. The final set of the 20 best peptide candidates contains the peptides optimized for cell-penetrating, antimicrobial, anticancer, antiviral, antifungal, and anti-inflammatory activity. Their predicted features are intrinsic disorder and the ability to acquire an amphipathic structure upon contact with membranes or nucleic acids. In conclusion, the review argues for exploring wide-spectrum multifunctionality for novel nontoxic hybrids with cell-penetrating peptides.

Prediction of anti-inflammatory peptides by a sequence-based stacking ensemble model named AIPStack

Accurate and efficient identification of anti-inflammatory peptides (AIPs) is crucial for the treatment of inflammation. Here, we proposed a two-layer stacking ensemble model, AIPStack, to effectively predict AIPs. At first, we constructed a new dataset for model building and validation. Then, peptide sequences were represented by hybrid features, which were fused by two amino acid composition descriptors. Next, the stacking ensemble model was constructed by random forest and extremely randomized tree as the base-classifiers and logistic regression as the meta-classifier to receive the outputs from the base-classifiers. AIPStack achieved an AUC of 0.819, accuracy of 0.755, and MCC of 0.510 on the independent set 3, which were higher than other AIP predictors. Furthermore, the essential sequence features were highlighted by the Shapley Additive exPlanation (SHAP) method. It is anticipated that AIPStack could be used for AIP prediction in a high-throughput manner and facilitate the hypothesis-driven experimental design.

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AIPStack model was developed for the prediction of anti-inflammatory peptides

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The hybrid features were used to describe the peptide sequences

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The proposed model AIPStack outperformed existing ones

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SHAP was used to highlight the essential features required for AIP prediction

MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides

Bioactive peptides are typically small functional peptides with 2-20 amino acid residues and play versatile roles in metabolic and biological processes. Bioactive peptides are multi-functional, so it is vastly challenging to accurately detect all their functions simultaneously. We proposed a convolution neural network (CNN) and bi-directional long short-term memory (Bi-LSTM)-based deep learning method (called MPMABP) for recognizing multi-activities of bioactive peptides. The MPMABP stacked five CNNs at different scales, and used the residual network to preserve the information from loss. The empirical results showed that the MPMABP is superior to the state-of-the-art methods. Analysis on the distribution of amino acids indicated that the lysine preferred to appear in the anti-cancer peptide, the leucine in the anti-diabetic peptide, and the proline in the anti-hypertensive peptide. The method and analysis are beneficial to recognize multi-activities of bioactive peptides.

Designing a Novel Functional Peptide With Dual Antimicrobial and Anti-inflammatory Activities via in Silico Methods

As spider venom is composed of various bioactive substances, it can be utilized as a platform for discovering future therapeutics. Host defense peptides are great candidates for developing novel antimicrobial agents due to their multifunctional properties. In this study, novel functional peptides were rationally designed to have dual antibacterial and anti-inflammatory activities with high cytocompatibility. Based on a template sequence from the transcriptome of spider Agelena koreana, a series of via in silico analysis were conducted, incorporating web-based machine learning tools along with the alteration of amino acid residues. Two peptides, Ak-N’ and Ak-N’m, were designed and were subjected to functional validation. The peptides inhibited gram-negative and gram-positive bacteria by disrupting the outer and bacterial cytoplasmic membrane. Moreover, the peptides down-regulated the expression of pro-inflammatory mediators, tumor necrosis factor-α, interleukin (IL)-1β, and IL6. Along with low cytotoxicity, Ak-N’m was shown to interact with macrophage surface receptors, inhibiting both Myeloid differentiation primary response 88-dependent and TIR-domain-containing adapter-inducing interferon-β-dependent pathways of Toll-like receptor 4 signaling on lipopolysaccharide-stimulated THP-1-derived macrophages. Here, we rationally designed functional peptides based on the suggested in silico strategy, demonstrating new insights for utilizing biological resources as well as developing therapeutic agents with enhanced properties.

Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2

Coronavirus disease 2019 (COVID-19) has impacted public health as well as societal and economic well-being. In the last two decades, various prediction algorithms and tools have been developed for predicting antiviral peptides (AVPs). The current COVID-19 pandemic has underscored the need to develop more efficient and accurate machine learning (ML)-based prediction algorithms for the rapid identification of therapeutic peptides against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Several peptide-based ML approaches, including anti-coronavirus peptides (ACVPs), IL-6 inducing epitopes and other epitopes targeting SARS-CoV-2, have been implemented in COVID-19 therapeutics. Owing to the growing interest in the COVID-19 field, it is crucial to systematically compare the existing ML algorithms based on their performances. Accordingly, we comprehensively evaluated the state-of-the-art IL-6 and AVP predictors against coronaviruses in terms of core algorithms, feature encoding schemes, performance evaluation metrics and software usability. A comprehensive performance assessment was then conducted to evaluate the robustness and scalability of the existing predictors using well-constructed independent validation datasets. Additionally, we discussed the advantages and disadvantages of the existing methods, providing useful insights into the development of novel computational tools for characterizing and identifying epitopes or ACVPs. The insights gained from this review are anticipated to provide critical guidance to the scientific community in the rapid design and development of accurate and efficient next-generation in silico tools against SARS-CoV-2.

In silico tools and databases for designing cancer immunotherapy

Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.