HIPdb: a database of experimentally validated HIV inhibiting peptides

IAMPDB: A Knowledgebase of Manually Curated Insects-Derived Antimicrobial Peptides

Insects, a majority of animal species, rely on innate immunity and antimicrobial peptides (AMPs), which are a part of their innate immunity, to combat diverse parasites and pathogens. These peptides have applications ranging from agriculture to antimicrobial resistance (AMR). However, there is a lack of a specialized database, prompting the development of the Insect Antimicrobial Peptide Database (IAMPDB) as a pioneering comprehensive Knowledgebase dedicated to insect-derived antimicrobial peptides (IAMPs), serving as a resource for researchers and industry professionals. Curated from UniProt and associated literature(s), IAMPDB currently houses 438 curated entries of IAMPs from various insect species, spanning 10 taxonomical orders of insects. Each entry is meticulously annotated with details on peptide sequence, source organism, activities, physicochemical properties, and more. IAMPDB offers a user-friendly interface with diverse search options, interactive visualizations, and links to external databases; advanced tools, including a peptide sequence alignment toolbox and a peptide feature calculation toolbox, facilitating sequence alignment, physicochemical property calculation, and in-depth analysis. The knowledgebase is accessible online (at URL https://bblserver.org.in/iampdb/).

The anti-MRSA resource: a comprehensive archive of anti-MRSA peptides and essential oils

Methicillin-resistant Staphylococcus aureus (MRSA), a major cause of fatalities due to Antimicrobial Resistance (AMR), can act as an opportunistic pathogen despite being part of the normal human flora. MRSA infections, such as skin infections, pneumonia, sepsis, and surgical site infections, have risen significantly, with bloodstream infection cases increasing from 21% in 2016 to 35% in 2020. This surge has prompted research into alternative treatments like nanomaterials, photodynamic therapy, antimicrobial peptides (AMPs), and essential oils (EOs). AMPs and EOs have shown higher success rates compared to other alternatives, gaining significant attention for their effectiveness against MRSA. In this perspective, we have created a database for peptides and EOs that have been discovered to treat MRSA. Manual data curation was done to get related information on each of the anti-MRSA EOs and AMPs from the PubMed articles. This led to the curation of 1789 peptides (1029 unique) and 863 EOs (671 unique) that have been reported against MRSA. This was followed by database creation and the development of tools for sequence analysis and determination of physiochemical properties. This resource has been named 'The Anti-MRSA Resource' or 'TAMRSAR' which we believe will aid in future drug development efforts to combat the diseases caused by MRSA. The database is accessible on any web browser at the URL: https://bblserver.org.in/tamrsar/.

AVR/I/SSAPDB: a comprehensive & specialised knowledgebase of antimicrobial peptides to combat VRSA, VISA, and VSSA

The rise of multi-drug resistant (MDR) bacteria, especially strains of Staphylococcus aureus like Vancomycin-resistant S. aureus (VRSA), Vancomycin-intermediate S. aureus (VISA), and Vancomycin-susceptible S. aureus (VSSA), poses a severe threat to global health. This situation underscores the urgent need for novel antimicrobial agents to combat these resistant strains effectively. Here, we are introducing the Anti-Vancomycin-Resistant/Intermediate/Susceptible Staphylococcus aureus Peptide Database (AVR/I/SSAPDB), a manually curated comprehensive and specialised knowledgebase dedicated to antimicrobial peptides (AMPs) that target VRSA, VISA, and VSSA with clinical and non-clinical significance. Our database sources data from PubMed, cataloging 491 experimentally validated AMPs with detailed annotations on peptides, activity, and cross-references to external databases like PubMed, UniProt, PDB, and DrugBank. AVR/I/SSAPDB offers a user-friendly interface with simple to advanced and list-based search capabilities, enabling researchers to explore AMPs against VRSA, VISA, and VSSA. We are hoping that this resource will be helpful to the scientific community in developing targeted peptide-based therapeutics, providing a crucial tool for combating VRSA, VISA, and VSSA, and addressing a major public health concern. AVR/I/SSAPDB is freely accessible via any web-browser at URL: https://bblserver.org.in/avrissa/ .

Structure-Guided Antiviral Peptides Identification Targeting the HIV-1 Integrase

HIV-1 integrase (IN), a major protein in the HIV life cycle responsible for integrating viral cDNA into the host DNA, represents a promising drug target. Small peptides have emerged as antiviral therapeutics for HIV because of their facile synthesis, highly selective nature, and fewer side effects. However, selecting the best candidates from a vast pool of peptides is a daunting task. In this study, multistep virtual screening was employed to identify potential peptides from a list of 280 HIV inhibitory peptides. Initially, 80 peptides were selected based on their minimum inhibitory concentrations (MIC). Then, molecular docking was performed to evaluate their binding scores compared to HIP000 and HIP00N which are experimentally validated HIV-1 integrase binding peptides that were used as a positive and negative control, respectively. The top-scoring docked complexes, namely, IN-HIP1113, IN-HIP1140, IN-HIP1142, IN-HIP678, IN-HIP776, and IN-HIP777, were subjected to initial 500 ns molecular dynamics (MD) simulations. Subsequently, HIP776, HIP777, and HIP1142 were selected for an in-depth mechanistic study of peptide interactions, with multiple simulations conducted for each complex spanning one microsecond. Independent simulations of the peptides, along with comparisons to the bound state, were performed to elucidate the conformational dynamics of the peptides. These peptides exhibit strong interactions with specific residues, as revealed by snapshot interaction analysis. Notably, LYS159, LYS156, VAL150, and GLU69 residues are prominently involved in these interactions. Additionally, residue-based binding free energy (BFE) calculations highlight the significance of HIS67, GLN148, GLN146, and SER147 residues within the binding pocket. Furthermore, the structure-activity relationship (SAR) analysis demonstrated that aromatic amino acids and the overall volume of peptides are the two major contributors to the docking scores. The best peptides will be validated experimentally by incorporating SAR properties, aiming to develop them as therapeutic agents and structural models for future peptide-based HIV-1 drug design, addressing the urgent need for effective HIV treatments.

Computational analysis of spike protein of SARS-CoV-2 (Omicron variant) for development of peptide-based therapeutics and diagnostics

In the last few years, the worldwide population has suffered from the SARS-CoV-2 pandemic. The WHO dashboard indicated that around 504,079,039 people were infected and 6,204,155 died from COVID-19 caused by different variants of SARS-CoV-2. Recently, a new variant of SARS-CoV-2 (B.1.1.529) was reported by South Africa known as Omicron. The high transmissibility rate and resistance towards available anti-SARS-CoV-2 drugs/vaccines/monoclonal antibodies, make Omicron a variant of concern. Because of various mutations in spike protein, available diagnostic and therapeutic treatments are not reliable. Therefore, the present study explored the development of some therapeutic peptides that can inhibit the SARS-CoV-2 virus interaction with host ACE2 receptors and can also be used for diagnostic purposes. The screened linear B cell epitopes derived from receptor-binding domain of spike protein of Omicron variant were evaluated as peptide inhibitor/vaccine candidates through different bioinformatics tools including molecular docking and simulation to analyze the interaction between Omicron peptide and human ACE2 receptor. Overall, in-silico studies revealed that Omicron peptides OP1-P12, OP14, OP20, OP23, OP24, OP25, OP26, OP27, OP28, OP29, and OP30 have the potential to inhibit Omicron interaction with ACE2 receptor. Moreover, Omicron peptides OP20, OP22, OP23, OP24, OP25, OP26, OP27, and OP30 have shown potential antigenic and immunogenic properties that can be used in design and development vaccines against Omicron. Although the in-silico validation was performed by comparative analysis with the control peptide inhibitor, further validation through wet lab experimentation is required before its use as therapeutic peptides.Communicated by Ramaswamy H. Sarma.

HybAVPnet: A Novel Hybrid Network Architecture for Antiviral Peptides Prediction

Viruses pose a great threat to human production and life, thus the research and development of antiviral drugs is urgently needed. Antiviral peptides play an important role in drug design and development. Compared with the time-consuming and laborious wet chemical experiment methods, it is critical to use computational methods to predict antiviral peptides accurately and rapidly. However, due to limited data, accurate prediction of antiviral peptides is still challenging and extracting effective feature representations from sequences is crucial for creating accurate models. This study introduces a novel two-step approach, named HybAVPnet, to predict antiviral peptides with a hybrid network architecture based on neural networks and traditional machine learning methods. We adopted a stacking-like structure to capture both the long-term dependencies and local evolution information to achieve a comprehensive and diverse prediction using the predicted labels and probabilities. Using an ensemble technique with the different kinds of features can reduce the variance without increasing the bias. The experimental result shows HybAVPnet can achieve better and more robust performance compared with the state-of-the-art methods, which makes it useful for the research and development of antiviral drugs. Meanwhile, it can also be extended to other peptide recognition problems because of its generalization ability.

Innovative Alignment-Based Method for Antiviral Peptide Prediction

Antiviral peptides (AVPs) represent a promising strategy for addressing the global challenges of viral infections and their growing resistances to traditional drugs. Lab-based AVP discovery methods are resource-intensive, highlighting the need for efficient computational alternatives. In this study, we developed five non-trained but supervised multi-query similarity search models (MQSSMs) integrated into the StarPep toolbox. Rigorous testing and validation across diverse AVP datasets confirmed the models' robustness and reliability. The top-performing model, M13+, demonstrated impressive results, with an accuracy of 0.969 and a Matthew's correlation coefficient of 0.71. To assess their competitiveness, the top five models were benchmarked against 14 publicly available machine-learning and deep-learning AVP predictors. The MQSSMs outperformed these predictors, highlighting their efficiency in terms of resource demand and public accessibility. Another significant achievement of this study is the creation of the most comprehensive dataset of antiviral sequences to date. In general, these results suggest that MQSSMs are promissory tools to develop good alignment-based models that can be successfully applied in the screening of large datasets for new AVP discovery.

Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides

Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this problem continues to be a task that consumes time and financial resources. Currently, artificial intelligence (AI) has revolutionized many areas of the biological sciences, making it possible to decipher patterns in amino acid sequences that encode different functions and activities. Within the field of AI, machine learning, and deep learning algorithms have been used to discover antimicrobial peptides. Due to their effectiveness and specificity, antimicrobial peptides (AMPs) hold excellent promise for treating various infections caused by pathogens. Antiviral peptides (AVPs) are a specific type of AMPs that have activity against certain viruses. Unlike the research focused on the development of tools and methods for the prediction of antimicrobial peptides, those related to the prediction of AVPs are still scarce. Given the significance of AVPs as potential pharmaceutical options for human and animal health and the ongoing AI revolution, we have reviewed and summarized the current machine learning and deep learning-based tools and methods available for predicting these types of peptides.

iDVEIP: A computer-aided approach for the prediction of viral entry inhibitory peptides

With the notable surge in therapeutic peptide development, various peptides have emerged as potential agents against virus-induced diseases. Viral entry inhibitory peptides (VEIPs), a subset of antiviral peptides (AVPs), offer a promising avenue as entry inhibitors (EIs) with distinct advantages over chemical counterparts. Despite this, a comprehensive analytical platform for characterizing these peptides and their effectiveness in blocking viral entry remains lacking. In this study, we introduce a groundbreaking in silico approach that leverages bioinformatics analysis and machine learning to characterize and identify novel VEIPs. Cross-validation results demonstrate the efficacy of a model combining sequence-based features in predicting VEIPs with high accuracy, validated through independent testing. Additionally, an EI type model has been developed to distinguish peptides specifically acting as Eis from AVPs with alternative activities. Notably, we present iDVEIP, a web-based tool accessible at http://mer.hc.mmh.org.tw/iDVEIP/, designed for automatic analysis and prediction of VEIPs. Emphasizing its capabilities, the tool facilitates comprehensive analyses of peptide characteristics, providing detailed amino acid composition data for each prediction. Furthermore, we showcase the tool's utility in identifying EIs against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).

A two-stage computational framework for identifying antiviral peptides and their functional types based on contrastive learning and multi-feature fusion strategy

Antiviral peptides (AVPs) have shown potential in inhibiting viral attachment, preventing viral fusion with host cells and disrupting viral replication due to their unique action mechanisms. They have now become a broad-spectrum, promising antiviral therapy. However, identifying effective AVPs is traditionally slow and costly. This study proposed a new two-stage computational framework for AVP identification. The first stage identifies AVPs from a wide range of peptides, and the second stage recognizes AVPs targeting specific families or viruses. This method integrates contrastive learning and multi-feature fusion strategy, focusing on sequence information and peptide characteristics, significantly enhancing predictive ability and interpretability. The evaluation results of the model show excellent performance, with accuracy of 0.9240 and Matthews correlation coefficient (MCC) score of 0.8482 on the non-AVP independent dataset, and accuracy of 0.9934 and MCC score of 0.9869 on the non-AMP independent dataset. Furthermore, our model can predict antiviral activities of AVPs against six key viral families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight viruses (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). Finally, to facilitate user accessibility, we built a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼dbAMP/AVP/.

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.

Identification of oral therapeutics using an AI platform against the virus responsible for COVID-19, SARS-CoV-2

Purpose: This study introduces a sophisticated computational pipeline, eVir, designed for the discovery of antiviral drugs based on their interactions within the human protein network. There is a pressing need for cost-effective therapeutics for infectious diseases (e.g., COVID-19), particularly in resource-limited countries. Therefore, our team devised an Artificial Intelligence (AI) system to explore repurposing opportunities for currently used oral therapies. The eVir system operates by identifying pharmaceutical compounds that mirror the effects of antiviral peptides (AVPs)-fragments of human proteins known to interfere with fundamental phases of the viral life cycle: entry, fusion, and replication. eVir extrapolates the probable antiviral efficacy of a given compound by analyzing its established and predicted impacts on the human protein-protein interaction network. This innovative approach provides a promising platform for drug repurposing against SARS-CoV-2 or any virus for which peptide data is available. Methods: The eVir AI software pipeline processes drug-protein and protein-protein interaction networks generated from open-source datasets. eVir uses Node2Vec, a graph embedding technique, to understand the nuanced connections among drugs and proteins. The embeddings are input a Siamese Network (SNet) and MLPs, each tailored for the specific mechanisms of entry, fusion, and replication, to evaluate the similarity between drugs and AVPs. Scores generated from the SNet and MLPs undergo a Platt probability calibration and are combined into a unified score that gauges the potential antiviral efficacy of a drug. This integrated approach seeks to boost drug identification confidence, offering a potential solution for detecting therapeutic candidates with pronounced antiviral potency. Once identified a number of compounds were tested for efficacy and toxicity in lung carcinoma cells (Calu-3) infected with SARS-CoV-2. A lead compound was further identified to determine its efficacy and toxicity in K18-hACE2 mice infected with SARS-CoV-2. Computational Predictions: The SNet confidently differentiated between similar and dissimilar drug pairs with an accuracy of 97.28% and AUC of 99.47%. Key compounds identified through these networks included Zinc, Mebendazole, Levomenol, Gefitinib, Niclosamide, and Imatinib. Notably, Mebendazole and Zinc showcased the highest similarity scores, while Imatinib, Levemenol, and Gefitinib also ranked within the top 20, suggesting their significant pharmacological potentials. Further examination of protein binding analysis using explainable AI focused on reverse engineering the causality of the networks. Protein interaction scores for Mebendazole and Imatinib revealed their effects on notable proteins such as CDPK1, VEGF2, ABL1, and several tyrosine protein kinases. Laboratory Studies: This study determined that Mebendazole, Gefitinib, Topotecan and to some extent Carfilzomib showed conventional drug-response curves, with IC50 values near or below that of Remdesivir with excellent confidence all above R2>0.91, and no cytotoxicity at the IC50 concentration in Calu-3 cells. Cyclosporine A showed antiviral activity, but also unconventional drug-response curves and low R2 which are explained by the non-dose dependent toxicity of the compound. Additionally, Niclosamide demonstrated a conventional drug-response curve with high confidence; however, its inherent cytotoxicity may be a confounding element that misrepresents true antiviral efficacy, by reflecting cellular damage rather than a genuine antiviral action. Remdesivir was used as a control compound and was evaluated in parallel with the submitted test article and had conventional drug-response curves validating the overall results of the assay. Mebendazole was identified from the cell studies to have efficacy at non-toxic concentrations and were further evaluated in mice infected with SARS-CoV-2. Mebendazole administered to K18-hACE2 mice infected with SARS-CoV-2, resulted in a 44.2% reduction in lung viral load compared to non-treated placebo control respectively. There were no significant differences in body weight and all clinical chemistry determinations evaluated (i.e., kidney and liver enzymes) between the different treatment groups. Conclusion: This research underscores the potential of repurposing existing compounds for treating COVID-19. Our preliminary findings underscore the therapeutic promise of several compounds, notably Mebendazole, in both in vitro and in vivo settings against SARS-CoV-2. Several of the drugs explored, especially Mebendazole, are off-label medication; their cost-effectiveness position them as economical therapies against SARS-CoV-2.

Physicochemical and sequence determinants of antiviral peptides

Antiviral peptides (AVPs) open new possibilities as an effective antiviral therapeutic in the current scenario of evolving drug-resistant viruses. Knowledge about the sequence and structure activity relationship in AVPs is still largely unknown. AVPs and antimicrobial peptides (AMPs) share several common features but as they target different life forms (living organisms and viruses), exploring the differential sequence features may facilitate in designing specific AVPs. The current work developed accurate prediction models for discriminating (a) AVPs from AMPs, (b) Coronaviridae AVPs from other virus family specific AVPs and (c) highly active AVPs (HAA) from lowly active AVPs (LAA). Further explainable machine learning methods (using model agnostic global interpretable methods) are utilized for exploring and interpreting the physicochemical spaces of AVPs, Coronaviridae AVPs and highly active AVPs. To further understand the association of physicochemical space distribution with pIC50 values, regression models were developed and analyzed using accumulated local effects and interaction strength analysis. An independent sample t-test is used to filter out the significant compositional differences between the smaller length HAA and longer length HAA groups. AVPs prefer lower charge/length ratio and basic residues in comparison with AMPs. Coronaviridae family-specific AVPs have lower propensities for basic amino acids, charge and preference for aspartic acid. Further there is prevalence for basic residues in lowly active AVPs as compared to highly active AVPs. Sequence order effects captured in terms of average amino acid pair distances proved to be more constructive in deciphering the sequences of AVPs.

Developing anti-microbial peptide database version 1 to provide comprehensive and exhaustive resource of manually curated AMPs

Anti-Microbial Peptide Database version 1 (AMPDB v1) is a meticulously curated resource that aims to address the limitations of existing databases in the field of antimicrobial research. We have utilized the latest technology and put our best efforts into adding all relevant tools to cater to the needs of our users. AMPDB v1 is a derived database, built upon information gathered from the available resources and boasts a significant size of 59,122 entries which are classified into 88 classes. All the information in this resource was curated manually. Sequence alignment and protein feature calculation tools were integrated into the database in the form of web applications, to make them easy to use, quick, and responsive in real-time. We have included multiple types of browsing and searching options to enhance the user experience, from simple text search to a completely customizable advanced search page with intuitive options that let the user combine multiple options together to make a powerful search query. The database is accessible by a web browser at https://bblserver.org.in/ampdb/ .

FFMAVP: a new classifier based on feature fusion and multitask learning for identifying antiviral peptides and their subclasses

Antiviral peptides (AVPs) are widely found in animals and plants, with high specificity and strong sensitivity to drug-resistant viruses. However, due to the great heterogeneity of different viruses, most of the AVPs have specific antiviral activities. Therefore, it is necessary to identify the specific activities of AVPs on virus types. Most existing studies only identify AVPs, with only a few studies identifying subclasses by training multiple binary classifiers. We develop a two-stage prediction tool named FFMAVP that can simultaneously predict AVPs and their subclasses. In the first stage, we identify whether a peptide is AVP or not. In the second stage, we predict the six virus families and eight species specifically targeted by AVPs based on two multiclass tasks. Specifically, the feature extraction module in the two-stage task of FFMAVP adopts the same neural network structure, in which one branch extracts features based on amino acid feature descriptors and the other branch extracts sequence features. Then, the two types of features are fused for the following task. Considering the correlation between the two tasks of the second stage, a multitask learning model is constructed to improve the effectiveness of the two multiclass tasks. In addition, to improve the effectiveness of the second stage, the network parameters trained through the first-stage data are used to initialize the network parameters in the second stage. As a demonstration, the cross-validation results, independent test results and visualization results show that FFMAVP achieves great advantages in both stages.

Navigating the Landscape: A Comprehensive Review of Current Virus Databases

Viruses are abundant and diverse entities that have important roles in public health, ecology, and agriculture. The identification and surveillance of viruses rely on an understanding of their genome organization, sequences, and replication strategy. Despite technological advancements in sequencing methods, our current understanding of virus diversity remains incomplete, highlighting the need to explore undiscovered viruses. Virus databases play a crucial role in providing access to sequences, annotations and other metadata, and analysis tools for studying viruses. However, there has not been a comprehensive review of virus databases in the last five years. This study aimed to fill this gap by identifying 24 active virus databases and included an extensive evaluation of their content, functionality and compliance with the FAIR principles. In this study, we thoroughly assessed the search capabilities of five database catalogs, which serve as comprehensive repositories housing a diverse array of databases and offering essential metadata. Moreover, we conducted a comprehensive review of different types of errors, encompassing taxonomy, names, missing information, sequences, sequence orientation, and chimeric sequences, with the intention of empowering users to effectively tackle these challenges. We expect this review to aid users in selecting suitable virus databases and other resources, and to help databases in error management and improve their adherence to the FAIR principles. The databases listed here represent the current knowledge of viruses and will help aid users find databases of interest based on content, functionality, and scope. The use of virus databases is integral to gaining new insights into the biology, evolution, and transmission of viruses, and developing new strategies to manage virus outbreaks and preserve global health.

A separable temporal convolutional networks based deep learning technique for discovering antiviral medicines

An alarming number of fatalities caused by the COVID-19 pandemic has forced the scientific community to accelerate the process of therapeutic drug discovery. In this regard, the collaboration between biomedical scientists and experts in artificial intelligence (AI) has led to a number of in silico tools being developed for the initial screening of therapeutic molecules. All living organisms produce antiviral peptides (AVPs) as a part of their first line of defense against invading viruses. The Deep-AVPiden model proposed in this paper and its corresponding web app, deployed at https://deep-avpiden.anvil.app , is an effort toward discovering novel AVPs in proteomes of living organisms. Apart from Deep-AVPiden, a computationally efficient model called Deep-AVPiden (DS) has also been developed using the same underlying network but with point-wise separable convolutions. The Deep-AVPiden and Deep-AVPiden (DS) models show an accuracy of 90% and 88%, respectively, and both have a precision of 90%. Also, the proposed models were statistically compared using the Student's t-test. On comparing the proposed models with the state-of-the-art classifiers, it was found that they are much better than them. To test the proposed model, we identified some AVPs in the natural defense proteins of plants, mammals, and fishes and found them to have appreciable sequence similarity with some experimentally validated antimicrobial peptides. These AVPs can be chemically synthesized and tested for their antiviral activity.

DRAVP: A Comprehensive Database of Antiviral Peptides and Proteins

Viruses with rapid replication and easy mutation can become resistant to antiviral drug treatment. With novel viral infections emerging, such as the recent COVID-19 pandemic, novel antiviral therapies are urgently needed. Antiviral proteins, such as interferon, have been used for treating chronic hepatitis C infections for decades. Natural-origin antimicrobial peptides, such as defensins, have also been identified as possessing antiviral activities, including direct antiviral effects and the ability to induce indirect immune responses to viruses. To promote the development of antiviral drugs, we constructed a data repository of antiviral peptides and proteins (DRAVP). The database provides general information, antiviral activity, structure information, physicochemical information, and literature information for peptides and proteins. Because most of the proteins and peptides lack experimentally determined structures, AlphaFold was used to predict each antiviral peptide's structure. A free website for users (http://dravp.cpu-bioinfor.org/, accessed on 30 August 2022) was constructed to facilitate data retrieval and sequence analysis. Additionally, all the data can be accessed from the web interface. The DRAVP database aims to be a useful resource for developing antiviral drugs.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

iDVIP: identification and characterization of viral integrase inhibitory peptides

Antiretroviral peptides are a kind of bioactive peptides that present inhibitory activity against retroviruses through various mechanisms. Among them, viral integrase inhibitory peptides (VINIPs) are a class of antiretroviral peptides that have the ability to block the action of integrase proteins, which is essential for retroviral replication. As the number of experimentally verified bioactive peptides has increased significantly, the lack of in silico machine learning approaches can effectively predict the peptides with the integrase inhibitory activity. Here, we have developed the first prediction model for identifying the novel VINIPs using the sequence characteristics, and the hybrid feature set was considered to improve the predictive ability. The performance was evaluated by 5-fold cross-validation based on the training dataset, and the result indicates the proposed model is capable of predicting the VINIPs, with a sensitivity of 85.82%, a specificity of 88.81%, an accuracy of 88.37%, a balanced accuracy of 87.32% and a Matthews correlation coefficient value of 0.64. Most importantly, the model also consistently provides effective performance in independent testing. To sum up, we propose the first computational approach for identifying and characterizing the VINIPs, which can be considered novel antiretroviral therapy agents. Ultimately, to facilitate further research and development, iDVIP, an automatic computational tool that predicts the VINIPs has been developed, which is now freely available at http://mer.hc.mmh.org.tw/iDVIP/.

CAMPR4: a database of natural and synthetic antimicrobial peptides

There has been an exponential increase in the design of synthetic antimicrobial peptides (AMPs) for its use as novel antibiotics. Synthetic AMPs are substantially enriched in residues with physicochemical properties known to be critical for antimicrobial activity; such as positive charge, hydrophobicity, and higher alpha helical propensity. The current prediction algorithms for AMPs have been developed using AMP sequences from natural sources and hence do not perform well for synthetic peptides. In this version of CAMP database, along with updating sequence information of AMPs, we have created separate prediction algorithms for natural and synthetic AMPs. CAMPR4 holds 24243 AMP sequences, 933 structures, 2143 patents and 263 AMP family signatures. In addition to the data on sequences, source organisms, target organisms, minimum inhibitory and hemolytic concentrations, CAMPR4 provides information on N and C terminal modifications and presence of unusual amino acids, as applicable. The database is integrated with tools for AMP prediction and rational design (natural and synthetic AMPs), sequence (BLAST and clustal omega), structure (VAST) and family analysis (PRATT, ScanProsite, CAMPSign). The data along with the algorithms of CAMPR4 will aid to enhance AMP research. CAMPR4 is accessible at http://camp.bicnirrh.res.in/.

Identification of antiviral peptide inhibitors for receptor binding domain of SARS-CoV-2 omicron and its sub-variants: an in-silico approach

Omicron, a variant of concern (VOC) of SARS-CoV-2, emerged in South Africa in November 2021. Omicron has been continuously acquiring a series of new mutations, especially in the spike (S) protein that led to high infectivity and transmissibility. Peptides targeting the receptor-binding domain (RBD) of the spike protein by which omicron and its variants attach to the host receptor, angiotensin-converting enzyme (ACE2) can block the viral infection at the first step. This study aims to identify antiviral peptides from the Antiviral peptide database (AVPdb) and HIV-inhibitory peptide database (HIPdb) against the RBD of omicron by using a molecular docking approach. The lead RBD binder peptides obtained through molecular docking were screened for allergenicity and physicochemical criteria (isoelectric point (pI) and net charge) required for peptide-based drugs. The binding affinity of the best five peptide inhibitors with the RBD of omicron was validated further by molecular dynamics (MD) simulation. Our result introduces five antiviral peptides, including AVP1056, AVP1059, AVP1225, AVP1801, and HIP755, that may effectively hinder omicron-host interactions. It is worth mentioning that all the three major sub-variants of omicron, BA.1 (B.1.1.529.1), BA.2 (B.1.1.529.2), and BA.3 (B.1.1.529.3), exhibits conserved ACE-2 interacting residues. Hence, the screened antiviral peptides with similar affinity can also interrupt the RBD-mediated invasion of different major sub-variants of omicron. Altogether, these peptides can be considered in the peptide-based therapeutics development for omicron treatment after further experimentation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03258-4.

AntiDMPpred: a web service for identifying anti-diabetic peptides

Diabetes mellitus (DM) is a chronic metabolic disease that has been a major threat to human health globally, causing great economic and social adversities. The oral administration of anti-diabetic peptide drugs has become a novel route for diabetes therapy. Numerous bioactive peptides have demonstrated potential anti-diabetic properties and are promising as alternative treatment measures to prevent and manage diabetes. The computational prediction of anti-diabetic peptides can help promote peptide-based drug discovery in the process of searching newly effective therapeutic peptide agents for diabetes treatment. Here, we resorted to random forest to develop a computational model, named AntiDMPpred, for predicting anti-diabetic peptides. A benchmark dataset with 236 anti-diabetic and 236 non-anti-diabetic peptides was first constructed. Four types of sequence-derived descriptors were used to represent the peptide sequences. We then combined four machine learning methods and six feature scoring methods to select the non-redundant features, which were fed into diverse machine learning classifiers to train the models. Experimental results show that AntiDMPpred reached an accuracy of 77.12% and area under the receiver operating curve (AUCROC) of 0.8193 in the nested five-fold cross-validation, yielding a satisfactory performance and surpassing other classifiers implemented in the study. The web service is freely accessible at http://i.uestc.edu.cn/AntiDMPpred/cgi-bin/AntiDMPpred.pl. We hope AntiDMPpred could improve the discovery of anti-diabetic bioactive peptides.

Bioactive peptides of whey: obtaining, activity, mechanism of action, and further applications

Bioactive peptides derived from diverse food proteins have been part of diverse investigations. Whey is a rich source of proteins and components related to biological activity. It is known that proteins have effects that promote health benefits. Peptides derived from whey proteins are currently widely studied. These bioactive peptides are amino acid sequences that are encrypted within the first structure of proteins, which required hydrolysis for their release. The hydrolysis could be through in vitro or in vivo enzymatic digestion and using microorganisms in fermented systems. The biological activities associated with bio-peptides include immunomodulatory properties, antibacterial, antihypertensive, antioxidant and opioid, etc. These functions are related to general conditions of health or reduced risk of certain chronic illnesses. To determine the suitability of these peptides/ingredients for applications in food technology, clinical studies are required to evaluate their bioavailability, health claims, and safety of them. This review aimed to describe the biological importance of whey proteins according to the incidence in human health, their role as bioactive peptides source, describing methods, and obtaining technics. In addition, the paper exposes biochemical mechanisms during the activity exerted by biopeptides of whey, and their application trends.

An Overview of Antiviral Peptides and Rational Biodesign Considerations

Viral diseases have contributed significantly to worldwide morbidity and mortality throughout history. Despite the existence of therapeutic treatments for many viral infections, antiviral resistance and the threat posed by novel viruses highlight the need for an increased number of effective therapeutics. In addition to small molecule drugs and biologics, antimicrobial peptides (AMPs) represent an emerging class of potential antiviral therapeutics. While AMPs have traditionally been regarded in the context of their antibacterial activities, many AMPs are now known to be antiviral. These antiviral peptides (AVPs) have been shown to target and perturb viral membrane envelopes and inhibit various stages of the viral life cycle, from preattachment inhibition through viral release from infected host cells. Rational design of AMPs has also proven effective in identifying highly active and specific peptides and can aid in the discovery of lead peptides with high therapeutic selectivity. In this review, we highlight AVPs with strong antiviral activity largely curated from a publicly available AMP database. We then compile the sequences present in our AVP database to generate structural predictions of generic AVP motifs. Finally, we cover the rational design approaches available for AVPs taking into account approaches currently used for the rational design of AMPs.

Biological Functions and Applications of Antimicrobial Peptides

Abstract:

Despite antimicrobial resistance, which is attributed to the misuse of broad-spectrum antibiotics, antibiotics can indiscriminately kill pathogenic and beneficial microorganisms. These events disrupt the delicate microbial balance in both humans and animals, leading to secondary infections and other negative effects. Antimicrobial peptides (AMPs) are functional natural biopolymers in plants and animals. Due to their excellent antimicrobial activities and absence of microbial resistance, AMPs have attracted enormous research attention. We reviewed the antibacterial, antifungal, antiviral, antiparasitic, as well as antitumor properties of AMPs and research progress on AMPs. In addition, we highlighted various recommendations and potential research areas for their progress and challenges in practical applications.

Rough Set Based Classification and Feature Selection Using Improved Harmony Search for Peptide Analysis and Prediction of Anti-HIV-1 Activities

AIDS, which is caused by the most widespread HIV-1 virus, attacks the immune system of the human body, and despite the incredible endeavors for finding proficient medication strategies, the continuing spread of AIDS and claiming subsequent infections has not yet been decreased. Consequently, the discovery of innovative medicinal methodologies is highly in demand. Some available therapies, based on peptides, proclaim the treatment for several deadly diseases such as AIDS and cancer. Since many experimental types of research are restricted by the analysis period and expenses, computational methods overcome the issues effectually. In computational technique, the peptide residues with anti-HIV-1 activity are predicted by classification method, and the learning process of the classification is improved with significant features. Rough set-based algorithms are capable of dealing with the gaps and imperfections present in real-time data. In this work, feature selection using Rough Set Improved Harmony Search Quick Reduct and Rough Set Improved Harmony Search Relative Reduct with Rough Set Classification framework is implemented to classify Anti-HIV-1 peptides. The primary objective of the proposed methodology is to predict the peptides with an anti-HIV-1 activity using effective feature selection and classification algorithms incorporated in the proposed framework. The results of the proposed algorithms are comparatively studied with existing rough set feature selection algorithms and benchmark classifiers, and the reliability of the algorithms implemented in the proposed framework is measured by validity measures, such as Precision, Recall, F-measure, Kulczynski Index, and Fowlkes–Mallows Index. The final results show that the proposed framework analyzed and classified the peptides with a high predictive accuracy of 96%. In this study, we have investigated the ability of a rough set-based framework with sequence-based numeric features to classify anti-HIV-1 peptides, and the experimentation results show that the proposed framework discloses the most satisfactory solutions, where it rapidly congregates in the problem space and finds the best reduct, which improves the prediction accuracy of the given dataset.

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.

A student led computational screening of peptide inhibitors against main protease of SARS-CoV-2

The main protease of SARS-CoV-2 is a promising drug target due to its functional role as a catalytic dyad in mediating proteolysis during the viral life cycle. In this study, experimentally proven 14 HIV protease peptides were screened against the main protease of SARS-CoV-2. Fourteen middle and high school "student researchers" were trained on relevant computational tools, provided with necessary biological and chemical background and scientific article writing. They performed the primary screening via molecular docking and the best performing complexes were subjected to molecular dynamics simulations. Molecular docking revealed that HIP82 and HIP1079 can bind with the catalytic residues, however after molecular dynamics simulation only HIP1079 retained its interaction with the catalytic sites. The student researchers were also trained to write scientific article and were involved with drafting of the manuscript. This project provided the student researchers an insight into multi-disciplinary research in biology and chemistry, inspired them about practical approaches of computational chemistry in solving a real-world problem like a global pandemic. This project also serves as an example to introduce scientific inquiry, research methodology, critical thinking, scientific writing, and communication for high school students.

Machine Learning Prediction of Antimicrobial 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.

An overview of databases and bioinformatics tools for plant antimicrobial peptides

Antimicrobial peptides (AMPs) are small, ribosomally synthesized proteins found in nearly all forms of life. In plants, AMPs play a central role in plant defense due to their distinct physicochemical properties. Due to their broad-spectrum antimicrobial activity and rapid killing action, plant AMPs have become important candidates for the development of new drugs to control plant and animal pathogens that are resistant to multiple drugs. Further research is required to explore the potential uses of these natural compounds. Computational strategies have been increasingly used to understand key aspects of antimicrobial peptides. These strategies will help to minimize the time and cost of "wet-lab" experimentation. Researchers have developed various tools and databases to provide updated information on AMPs. However, despite the increased availability of antimicrobial peptide resources in biological databases, finding AMPs from plants can still be a difficult task. The number of plant AMP sequences in current databases is still small and yet often redundant. To facilitate further characterization of plant AMPs, we have summarized information on the location, distribution, and annotations of plant AMPs available in the most relevant databases for AMPs research. We also mapped and categorized the bioinformatics tools available in these databases. We expect that this will allow researchers to advance in the discovery and development of new plant AMPs with potent biological properties. We hope to provide insights to further expand the application of AMPs in the fields of biotechnology, pharmacy, and agriculture.

Antimicrobial Peptides: An Update on Classifications and Databases

Antimicrobial peptides (AMPs) are distributed across all kingdoms of life and are an indispensable component of host defenses. They consist of predominantly short cationic peptides with a wide variety of structures and targets. Given the ever-emerging resistance of various pathogens to existing antimicrobial therapies, AMPs have recently attracted extensive interest as potential therapeutic agents. As the discovery of new AMPs has increased, many databases specializing in AMPs have been developed to collect both fundamental and pharmacological information. In this review, we summarize the sources, structures, modes of action, and classifications of AMPs. Additionally, we examine current AMP databases, compare valuable computational tools used to predict antimicrobial activity and mechanisms of action, and highlight new machine learning approaches that can be employed to improve AMP activity to combat global antimicrobial resistance.

Prediction for understanding the effectiveness of antiviral peptides

The low efficacy of current antivirals in conjunction with the resistance of viruses against existing antiviral drugs has resulted in the demand for the development of novel antiviral agents. Antiviral peptides (AVPs) are those bioactive peptides having virucidal activity and they can be developed into promising antiviral drugs. They are shorter length peptides having the ability to cease the progression of viral infections. The use of antiviral peptides in therapeutics has recently attracted the attention of the research community. The development and identification of AVPs is imperative for the discovery of novel therapeutics for viral infections. In the present work, a meta classifier (stacking) based approach is implemented for the prediction of IC₅₀ (half maximal inhibitory concentration) and pIC₅₀ (negative log of half maximal inhibitory concentration) values. The best prediction model with evolutionary information and local alignment scores as features achieved a correlation coefficient values of 0.670 and 0.753 on the training and testing sets respectively for IC₅₀. Further, the prediction of pIC₅₀ reached a correlation coefficient value of 0.797 and 0.789 for training and testing sets respectively. For the development of machine learning models involved in the prediction of IC₅₀, the use of pIC₅₀ over IC₅₀ is recommended as the target variable. Further on a systematic comparison of AVPs with high IC₅₀ values and Low IC₅₀ values, it is revealed that higher mean charge and tiny amino acids are preferred and higher length and consecutive hydrophilic amino acids are avoided in the former.

AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches

Antiviral peptide (AVP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against virus infection. Machine learning-based prediction with a computational biology approach can facilitate the development of the novel therapeutic agents. In this study, we proposed a double-stage classification scheme, named AVPIden, for predicting the AVPs and their functional activities against different viruses. The first stage is to distinguish the AVP from a broad-spectrum peptide collection, including not only the regular peptides (non-AMP) but also the AMPs without antiviral functions (non-AVP). The second stage is responsible for characterizing one or more virus families or species that the AVP targets. Imbalanced learning is utilized to improve the performance of prediction. The AVPIden uses multiple descriptors to precisely demonstrate the peptide properties and adopts explainable machine learning strategies based on Shapley value to exploit how the descriptors impact the antiviral activities. Finally, the evaluation performance of the proposed model suggests its ability to predict the antivirus activities and their potential functions against six virus families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight kinds of virus (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). The AVPIden gives an option for reinforcing the development of AVPs with the computer-aided method and has been deployed at http://awi.cuhk.edu.cn/AVPIden/.

LAMP2: a major update of the database linking antimicrobial peptides

Antimicrobial peptides (AMPs) have been regarded as a potential weapon to fight against drug-resistant bacteria, which is threating the globe. Thus, more and more AMPs had been designed or identified. There is a need to integrate them into a platform for researchers to facilitate investigation and analyze existing AMPs. The AMP database has become an important tool for the discovery and transformation of AMPs as agents. A database linking antimicrobial peptides (LAMPs), launched in 2013, serves as a comprehensive tool to supply exhaustive information of AMP on a single platform. LAMP2, an updated version of LAMP, holds 23 253 unique AMP sequences and expands to link 16 public AMP databases. In the current version, there are more than 50% (12 236) sequences only linking a single database and more than 45% of AMPs linking two or more database links. Additionally, updated categories based on primary structure, collection, composition, source and function have been integrated into LAMP2. Peptides in LAMP2 have been integrated in 8 major functional classes and 38 functional activities. More than 89% (20 909) of the peptides are experimentally validated peptides. A total of 1924 references were extracted and regarded as the evidence for supporting AMP activity and cytotoxicity. The updated version will be helpful to the scientific community.

Unveiling Putative Functions of Mucus Proteins and Their Tryptic Peptides in Seven Gastropod Species Using Comparative Proteomics and Machine Learning-Based Bioinformatics Predictions

Gastropods are among the most diverse animals. Gastropod mucus contains several glycoproteins and peptides that vary by species and habitat. Some bioactive peptides from gastropod mucus were identified only in a few species. Therefore, using biochemical, mass spectrometric, and bioinformatics approaches, this study aimed to comprehensively identify putative bioactive peptides from the mucus proteomes of seven commonly found or commercially valuable gastropods. The mucus was collected in triplicate samples, and the proteins were separated by 1D-SDS-PAGE before tryptic digestion and peptide identification by nano LC-MS/MS. The mucus peptides were subsequently compared with R scripts. A total of 2818 different peptides constituting 1634 proteins from the mucus samples were identified, and 1218 of these peptides (43%) were core peptides found in the mucus of all examined species. Clustering and correspondence analyses of 1600 variable peptides showed unique mucous peptide patterns for each species. The high-throughput k-nearest neighbor and random forest-based prediction programs were developed with more than 95% averaged accuracy and could identify 11 functional categories of putative bioactive peptides and 268 peptides (9.5%) with at least five to seven bioactive properties. Antihypertensive, drug-delivering, and antiparasitic peptides were predominant. These peptides provide an understanding of gastropod mucus, and the putative bioactive peptides are expected to be experimentally validated for further medical, pharmaceutical, and cosmetic applications.

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions

Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

Antimicrobial/anticancer peptides: bioactive molecules and therapeutic agents

Antimicrobial peptides (AMPs) have been known as host-defense peptides. These cationic and amphipathic peptides are relatively short (∼5-50 L-amino acids) with molecular weight less than 10 kDa. AMPs have various roles including immunomodulatory, angiogenic and antitumor activities. Anticancer peptides (ACPs) are a main subset of AMPs as a novel therapeutic approach against tumor cells. The physicochemical properties of the ACPs influence their cell penetration, stability and efficiency of targeting. Up to now, several databases and web servers for in silico prediction of AMPs/ACPs have been established prior to the lab analysis. The present review focuses on the recent advancement about AMPs/ACPs activities including their in silico prediction by computational tools and their potential applications as therapeutic agents especially in cancer.

Modeling novel Anti-Viral peptides (AVPs) with in-silico docking simulations against corona virus

The havoc created by Corona virus has been dealt with using various integrative approaches adopted by laboratories through-out the world. Use of anti-viral peptides (AVPs) although new but has shown tremendous potential against many pathogens.

Previously AVPs have been designed against spike protein of corona virus which is the major entry mediating molecule. Using various in-silico strategies, in this research work AVPs have been modeled against lesser studied viral proteins namely ORF7a protein, Envelope protein (E), Nucleoprotein (N), and Non-Structural protein (Nsp1 and Nsp2). The predicted AVPs have been docked against various host as well as viral proteins. The interaction of small AVPs seems capable of interfering with binding between viral protein and its host counterpart. Therefore, these AVPs can act as a deterrent against novel corona virus, which requires further validation through laboratory techniques.

Anti-HIV-1 Activity of pepRF1, a Proteolysis-Resistant CXCR4 Antagonist Derived from Dengue Virus Capsid Protein

There is an urgent need for the development of new anti-HIV drugs that can complement existing medicines to be used against resistant strains. Here, we report the anti-HIV-1 peptide pepRF1, a human serum-resistant peptide derived from the Dengue virus capsid protein. In vitro, pepRF1 shows a 50% inhibitory concentration of 1.5 nM with a potential therapeutic window higher than 53 000. This peptide is specific for CXCR4-tropic strains, preventing viral entry into target cells by binding to the viral coreceptor CXCR4, acting as an antagonist of this receptor. pepRF1 is more effective than T20, the only peptide-based HIV-1 entry inhibitor approved, and excels in inhibiting a HIV-1 strain resistant to T20. Potentially, pepRF1 can be used alone or in combination with other anti-HIV drugs. Furthermore, one can also envisage its use as a novel therapeutic strategy for other CXCR4-related diseases.

Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance

The emergence of viral epidemics throughout the world is of concern due to the scarcity of available effective antiviral therapeutics. The discovery of new antiviral therapies is imperative to address this challenge, and antiviral peptides (AVPs) represent a valuable resource for the development of novel therapies to combat viral infection. We present a new machine learning model to distinguish AVPs from non-AVPs using the most informative features derived from the physicochemical and structural properties of their amino acid sequences. To focus on those features that are most likely to contribute to antiviral performance, we filter potential features based on their importance for classification. These feature selection analyses suggest that secondary structure is the most important peptide sequence feature for predicting AVPs. Our Feature-Informed Reduced Machine Learning for Antiviral Peptide Prediction (FIRM-AVP) approach achieves a higher accuracy than either the model with all features or current state-of-the-art single classifiers. Understanding the features that are associated with AVP activity is a core need to identify and design new AVPs in novel systems. The FIRM-AVP code and standalone software package are available at https://github.com/pmartR/FIRM-AVP with an accompanying web application at https://msc-viz.emsl.pnnl.gov/AVPR.

Marine Bioactive Peptides-An Overview of Generation, Structure and Application with a Focus on Food Sources

The biggest obstacles in the application of marine peptides are two-fold, as in the case of non-marine plant and animal-derived bioactive peptides: elucidating correlation between the peptide structure and its effect and demonstrating its stability in vivo. The structures of marine bioactive peptides are highly variable and complex and dependent on the sources from which they are isolated. They can be cyclical, in the form of depsipeptides, and often contain secondary structures. Because of steric factors, marine-derived peptides can be resistant to proteolysis by gastrointestinal proteases, which presents an advantage over other peptide sources. Because of heterogeneity, amino acid sequences as well as preferred mechanisms of peptides showing specific bioactivities differ compared to their animal-derived counterparts. This review offers insights on the extreme diversity of bioactivities, effects, and structural features, analyzing 253 peptides, mainly from marine food sources. Similar to peptides in food of non-marine animal origin, a significant percentage (52.7%) of the examined sequences contain one or more proline residues, implying that proline might play a significant role in the stability of bioactive peptides. Additional problems with analyzing marine-derived bioactive peptides include their accessibility, extraction, and purification; this review considers the challenges and proposes possible solutions.

A proposed treatment for pathogenic enveloped viruses having high rates of mutation or replication

Several enveloped viruses, particularly some RNA viruses, have high rates of mutation or replication, which can make them virulent pathogens in humans and other mammals. A proposed treatment could use synthesized proteins to mask pathogenic viral surface proteins to quickly induce an immune attack on specific enveloped viruses by using existing immune cells. One treatment could inject dual‐protein ligand masks into patients' bloodstreams to mask pathogenic surface proteins used to infect mammalian cells. The mammalian immune system already uses an analogous, more complex structure called a pentraxin to neutralize some pathogens by connecting their surface proteins to immune cells. And several types of antiviral peptides have already experimentally demonstrated effectiveness in blocking various viral pathogen infections. These treatments offer advantages, especially for currently untreatable viral pathogens. Furthermore, using dual‐protein ligands and the antigenic memory of some sub‐populations of NK cells would also allow the creation of defacto vaccines based on a host's NK cells, instead of vaccines utilizing CD4 and CD8 α:β T cells, which are limited by the requirement of MHC presentation of the target antigens to α:β T cells. Targeted NK cell vaccines could attack host cells latently or actively infected by intracellular pathogens, even host cells having pathogen downregulated MHC antigen presentation. Eight postulates concerning the effects of pathogen mutation, or change in phenotype from genetic recombination or rearrangement, and replication rates on pathogen vs host dominance are also listed, which should be applicable to viral and non‐viral pathogens.

Graph-based data integration from bioactive peptide databases of pharmaceutical interest: toward an organized collection enabling visual network analysis

MOTIVATION

Bioactive peptides have gained great attention in the academy and pharmaceutical industry since they play an important role in human health. However, the increasing number of bioactive peptide databases is causing the problem of data redundancy and duplicated efforts. Even worse is the fact that the available data is non-standardized and often dirty with data entry errors. Therefore, there is a need for a unified view that enables a more comprehensive analysis of the information on this topic residing at different sites.

RESULTS

After collecting web pages from a large variety of bioactive peptide databases, we organized the web content into an integrated graph database (starPepDB) that holds a total of 71 310 nodes and 348 505 relationships. In this graph structure, there are 45 120 nodes representing peptides, and the rest of the nodes are connected to peptides for describing metadata. Additionally, to facilitate a better understanding of the integrated data, a software tool (starPep toolbox) has been developed for supporting visual network analysis in a user-friendly way; providing several functionalities such as peptide retrieval and filtering, network construction and visualization, interactive exploration and exporting data options.

AVAILABILITY AND IMPLEMENTATION

Both starPepDB and starPep toolbox are freely available at http://mobiosd-hub.com/starpep/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Potential chimeric peptides to block the SARS-CoV-2 spike receptor-binding domain

Background: There are no known medicines or vaccines to control the COVID-19 pandemic caused by SARS-CoV-2 (nCoV). Antiviral peptides are superior to conventional drugs and may also be effective against COVID-19. Hence, we investigated the SARS-CoV-2 Spike receptor-binding domain (nCoV-RBD) that interacts with hACE2 for viral attachment and entry.

Methods: Three strategies and bioinformatics approaches were employed to design potential nCoV-RBD - hACE2 interaction-blocking peptides that may restrict viral attachment and entry. Firstly, the key residues interacting with nCoV-RBD - hACE2 are identified and hACE2 sequence-based peptides are designed. Second, peptides from five antibacterial peptide databases that block nCoV-RBD are identified; finally, a chimeric peptide design approach is used to design peptides that can bind to key nCoV-RBD residues. The final peptides are selected based on their physiochemical properties, numbers and positions of key residues binding, binding energy, and antiviral properties.

Results: We found that: (i) three amino acid stretches in hACE2 interact with nCoV-RBD; (ii) effective peptides must bind to three key positions of nCoV-RBD (Gly485/Phe486/Asn487, Gln493, and Gln498/Thr500/Asn501); (iii) Phe486, Gln493, and Asn501 are critical residues; (iv) AC20 and AC23 derived from hACE2 may block two key critical positions; (iv) DBP6 identified from databases can block the three sites of the nCoV-RBD and interacts with one critical position, Gln498; (v) seven chimeric peptides were considered promising, among which cnCoVP-3, cnCoVP-4, and cnCoVP-7 are the top three; and (vi) cnCoVP-4 meets all the criteria and is the best peptide.

Conclusions: To conclude, using three different bioinformatics approaches, we identified 17 peptides that can potentially bind to the nCoV-RBD that interacts with hACE2. Binding these peptides to nCoV-RBD may potentially inhibit the virus to access hACE2 and thereby may prevent the infection. Out of 17, 10 peptides have promising potential and need further experimental validation.

BioDADPep: A Bioinformatics database for anti diabetic peptides

The increasing number of cases for diabetes worldwide is a concern. Therefore, it is of interest to design therapeutic peptides to overcome side effects caused by the available drugs. It should be noted that data on several known anti-diabetic peptides is available in the literature in an organized manner. Hence, it is of interest to collect, glean and store such data in form of a searchable database supported by RDBMS. Data on anti-diabetic peptides and their related data are collected from the literature using manual search. Data on related peptides from other databases (THPdb, ADP3, LAMP, AHTPDB, AVPdb, BioPepDB, CancerPPD, CPPsite, DRAMP, SATPdb, CAMPR3 and MBPDB) are also included after adequate curation. Thus, we describe the development and utility of BioDADPep, a Bioinformatics database for anti-diabetic peptides. The database has cross-reference for antidiabetic peptides. The database is enabled with a web-based GUI using a simple Google-like search function. Data presented in BioDADPep finds application in the design of an effective anti-diabetic peptide.

ADAPTABLE: a comprehensive web platform of antimicrobial peptides tailored to the user's research

Antimicrobial peptides (AMPs) are part of the innate immune response to pathogens in all of the kingdoms of life. They have received significant attention because of their extraordinary variety of activities, in particular, as candidate drugs against the threat of super-bacteria. A systematic study of the relation between the sequence and the mechanism of action is urgently needed, given the thousands of sequences already in multiple web resources. ADAPTABLE web platform (http://gec.u-picardie.fr/adaptable) introduces the concept of "property alignment" to create families of property and sequence-related peptides (SR families). This feature provides the researcher with a tool to select those AMPs meaningful to their research from among more than 40,000 nonredundant sequences. Selectable properties include the target organism and experimental activity concentration, allowing selection of peptides with multiple simultaneous actions. This is made possible by ADAPTABLE because it not only merges sequences of AMP databases but also merges their data, thereby standardizing values and handling non-proteinogenic amino acids. In this unified platform, SR families allow the creation of peptide scaffolds based on common traits in peptides with similar activity, independently of their source.

Antiviral peptides as promising therapeutic drugs

While scientific advances have led to large-scale production and widespread distribution of vaccines and antiviral drugs, viruses still remain a major cause of human diseases today. The ever-increasing reports of viral resistance and the emergence and re-emergence of viral epidemics pressure the health and scientific community to constantly find novel molecules with antiviral potential. This search involves numerous different approaches, and the use of antimicrobial peptides has presented itself as an interesting alternative. Even though the number of antimicrobial peptides with antiviral activity is still low, they already show immense potential to become pharmaceutically available antiviral drugs. Such peptides can originate from natural sources, such as those isolated from mammals and from animal venoms, or from artificial sources, when bioinformatics tools are used. This review aims to shed some light on antimicrobial peptides with antiviral activities against human viruses and update the data about the already well-known peptides that are still undergoing studies, emphasizing the most promising ones that may become medicines for clinical use.

Natural ACE inhibitory peptides discovery from Spirulina (Arthrospira platensis) strain C1

Bioactive peptides from natural sources are utilized as food supplements for disease prevention and are increasingly becoming targets for drug discovery due to their specificity, efficacy and the absence of undesirable side effects, among others. Hence, the 'SpirPep' platform was developed to facilitate the in silico-based bioactive peptide discovery of these highly sought-after biomolecules from Spirulina(Arthrospira platensis) and to select the protease (thermolysin) used for in vitro digestion. Analysis of the predicted and experimentally-derived peptides suggested that they were mainly involved in ACE inhibition; thus, an ACEi assay was used to study the ACE inhibitory activity of five candidate peptides (SpirPep1-5), chosen from common peptides with multifunctional bioactivity and 100% bioactive peptide coverage, originating from phycobiliproteins. Results showed that SpirPep1 inhibited the activity of ACE with IC₅₀ of 1.748 mM and was non-toxic to fibroblasts of African green monkey kidney and human dermal skin. The molecular docking and MD simulation analysis revealed SpirPep1 had significantly lower binding scores than others and showed greater specificity to ACE. The non-bonded interaction energy of SpirPep1 and ACE was -883 kJ/mol. The SpirPep1 indirectly bound to ACE via the ACE substrate binding sites residues (D121, E123, S516, and S517) found in natural ACE inhibitory peptides (angiotensin II and bradykinin potentiating peptides). In addition, two unreported substrate binding sites including R124 and S219 were found. These results indicate that 'SpirPep' platform could increase the success rate for natural bioactive peptide discovery.