In silico approaches for predicting the half-life of natural and modified peptides in blood

Production, characterisation, and biological properties of Tenebrio molitor-derived oligopeptides

Three protein hydrolysates from Tenebrio molitor were obtained by enzymatic hydrolysis employing two food-grade proteases (i.e. Alcalase and Flavourzyme), and a complete characterisation of their composition was done. The digestion-derived products were obtained using the INFOGEST protocol. In vitro antioxidant activity and anti-inflammatory activities were evaluated. Tenebrio molitor flour and the protein hydrolysates showed a high ability to scavenge the DPPH radical (EC50 values from 0.30 to 0.87 mg/mL). The hydrolysate obtained with a combination of the two food-grade proteases could decrease the gene expression of pro-inflammatory genes after being digested. Furthermore, the peptidome was fully determined for the first time for T. molitor hydrolysates and digests, and 40 peptides were selected based on their bioactivity to be evaluated by in silico tools, including prediction tools and molecular docking. These results provide new perspectives on the use of edible insects as sustainable and not nutritionally disadvantageous food for human consumption.

ToxinPred 3.0: An improved method for predicting the toxicity of peptides

Toxicity emerges as a prominent challenge in the design of therapeutic peptides, causing the failure of numerous peptides during clinical trials. In 2013, our group developed ToxinPred, a computational method that has been extensively adopted by the scientific community for predicting peptide toxicity. In this paper, we propose a refined variant of ToxinPred that showcases improved reliability and accuracy in predicting peptide toxicity. Initially, we utilized a similarity/alignment-based approach employing BLAST to predict toxic peptides, which yielded satisfactory accuracy; however, the method suffered from inadequate coverage. Subsequently, we employed a motif-based approach using MERCI software to uncover specific patterns or motifs that are exclusively observed in toxic peptides. The search for these motifs in peptides allowed us to predict toxic peptides with a high level of specificity with poor sensitivity. To overcome the coverage limitations, we developed alignment-free methods using machine/deep learning techniques to balance sensitivity and specificity of prediction. Deep learning model (ANN - LSTM with fixed sequence length) developed using one-hot encoding achieved a maximum AUROC of 0.93 with MCC of 0.71 on an independent dataset. Machine learning model (extra tree) developed using compositional features of peptides achieved a maximum AUROC of 0.95 with MCC of 0.78. We also developed large language models and achieved maximum AUC of 0.93 using ESM2-t33. Finally, we developed hybrid or ensemble methods combining two or more methods to enhance performance. Our specific hybrid method, which combines a motif-based approach with a machine learning-based model, achieved a maximum AUROC of 0.98 with MCC 0.81 on an independent dataset. In this study, all models were trained and tested on 80 % of data using five-fold cross-validation and evaluated on the remaining 20 % of data called independent dataset. The evaluation of all methods on an independent dataset revealed that the method proposed in this study exhibited better performance than existing methods. To cater to the needs of the scientific community, we have developed a standalone software, pip package and web-based server ToxinPred3 (https://github.com/raghavagps/toxinpred3 and https://webs.iiitd.edu.in/raghava/toxinpred3/).

Targeting connexins: possible game changer in managing neuropathic pain?

Neuropathic pain is a chronic debilitating condition caused by nerve injury or a variety of diseases. At the core of neuropathic pain lies the aberrant neuronal excitability in the peripheral and/or central nervous system (PNS and CNS). Enhanced connexin expression and abnormal activation of connexin-assembled gap junctional channels are prominent in neuropathic pain along with reactive gliosis, contributing to neuronal hypersensitivity and hyperexcitability. In this review, we delve into the current understanding of how connexin expression and function contribute to the pathogenesis and pathophysiology of neuropathic pain and argue for connexins as potential therapeutic targets for neuropathic pain management.

THPdb2: compilation of FDA approved therapeutic peptides and proteins

During the past 20 years, there has been a significant increase in the number of protein-based drugs approved by the US Food and Drug Administration (FDA). This paper presents THPdb2, an updated version of the THPdb database, which holds information about all types of protein-based drugs, including peptides, antibodies, and biosimilar proteins. THPdb2 contains a total of 6,385 entries, providing comprehensive information about 894 FDA-approved therapeutic proteins, including 354 monoclonal antibodies and 85 peptides or polypeptides. Each entry includes the name of therapeutic molecule, the amino acid sequence, physical and chemical properties, and route of drug administration. The therapeutic molecules that are included in the database target a wide range of biological molecules, such as receptors, factors, and proteins, and have been approved for the treatment of various diseases, including cancers, infectious diseases, and immune disorders.

Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides

With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.

Introducing enzymatic cleavage features and transfer learning realizes accurate peptide half-life prediction across species and organs

Peptide drugs are becoming star drug agents with high efficiency and selectivity which open up new therapeutic avenues for various diseases. However, the sensitivity to hydrolase and the relatively short half-life have severely hindered their development. In this study, a new generation artificial intelligence-based system for accurate prediction of peptide half-life was proposed, which realized the half-life prediction of both natural and modified peptides and successfully bridged the evaluation possibility between two important species (human, mouse) and two organs (blood, intestine). To achieve this, enzymatic cleavage descriptors were integrated with traditional peptide descriptors to construct a better representation. Then, robust models with accurate performance were established by comparing traditional machine learning and transfer learning, systematically. Results indicated that enzymatic cleavage features could certainly enhance model performance. The deep learning model integrating transfer learning significantly improved predictive accuracy, achieving remarkable R2 values: 0.84 for natural peptides and 0.90 for modified peptides in human blood, 0.984 for natural peptides and 0.93 for modified peptides in mouse blood, and 0.94 for modified peptides in mouse intestine on the test set, respectively. These models not only successfully composed the above-mentioned system but also improved by approximately 15% in terms of correlation compared to related works. This study is expected to provide powerful solutions for peptide half-life evaluation and boost peptide drug development.

A Reliable Multifaceted Solution against Foodborne Viral Infections: The Case of RiLK1 Decapeptide

Food-borne transmission is a recognized route for many viruses associated with gastrointestinal, hepatic, or neurological diseases. Therefore, it is essential to identify new bioactive compounds with broad-spectrum antiviral activity to exploit innovative solutions against these hazards. Recently, antimicrobial peptides (AMPs) have been recognized as promising antiviral agents. Indeed, while the antibacterial and antifungal effects of these molecules have been widely reported, their use as potential antiviral agents has not yet been fully investigated. Herein, the antiviral activity of previously identified or newly designed AMPs was evaluated against the non-enveloped RNA viruses, hepatitis A virus (HAV) and murine norovirus (MNV), a surrogate for human norovirus. Moreover, specific assays were performed to recognize at which stage of the viral infection cycle the peptides could function. The results showed that almost all peptides displayed virucidal effects, with about 90% of infectivity reduction in HAV or MNV. However, the decapeptide RiLK1 demonstrated, together with its antibacterial and antifungal properties, a notable reduction in viral infection for both HAV and MNV, possibly through direct interaction with viral particles causing their damage or hindering the recognition of cellular receptors. Hence, RiLK1 could represent a versatile antimicrobial agent effective against various foodborne pathogens including viruses, bacteria, and fungi.

Protecting Orthopaedic Implants from Infection: Antimicrobial Peptide Mel4 Is Non-Toxic to Bone Cells and Reduces Bacterial Colonisation When Bound to Plasma Ion-Implanted 3D-Printed PAEK Polymers

Even with the best infection control protocols in place, the risk of a hospital-acquired infection of the surface of an implanted device remains significant. A bacterial biofilm can form and has the potential to escape the host immune system and develop resistance to conventional antibiotics, ultimately causing the implant to fail, seriously impacting patient well-being. Here, we demonstrate a 4 log reduction in the infection rate by the common pathogen S. aureus of 3D-printed polyaryl ether ketone (PAEK) polymeric surfaces by covalently binding the antimicrobial peptide Mel4 to the surface using plasma immersion ion implantation (PIII) treatment. The surfaces with added texture created by 3D-printed processes such as fused deposition-modelled polyether ether ketone (PEEK) and selective laser-sintered polyether ketone (PEK) can be equally well protected as conventionally manufactured materials. Unbound Mel4 in solution at relevant concentrations is non-cytotoxic to osteoblastic cell line Saos-2. Mel4 in combination with PIII aids Saos-2 cells to attach to the surface, increasing the adhesion by 88% compared to untreated materials without Mel4. A reduction in mineralisation on the Mel4-containing surfaces relative to surfaces without peptide was found, attributed to the acellular portion of mineral deposition.

In silico screening of potential plant peptides against the non-structural proteins of dengue virus

BACKGROUND OBJECTIVES

Peptides isolated from different sources of plants have the advantages of specificity, lower toxicity, and increased therapeutic effects; hence, it is necessary to search for newer antivirals from plant sources for the treatment of dengue viral infections.

METHODS

In silico screening of selected plant peptides against the non-structural protein 1, NS3 protease domain (NS2B-NS3Pro) with the cofactor and ATPase/helicase domain (NS3 helicase domain/NS3hel) of dengue virus was performed. The physicochemical characteristics of the peptides were calculated using Protparam tools, and the allergenicity and toxicity profiles were assessed using allergenFP and ToxinPred, respectively.

RESULTS

Among the tested compounds, Ginkbilobin demonstrated higher binding energy against three tested nonstructural protein targets. Kalata B8 demonstrated maximum binding energy against NSP-1 and NSP-2, whereas Circulin A acted against the NSP3 protein of dengue virus.

INTERPRETATION CONCLUSION

The three compounds identified by in silico screening can be tested in vitro, which could act as potential leads as they are involved in hampering the replication of the dengue virus by interacting with the three prime non-structural proteins.

Derivation of a novel antimicrobial peptide from the Red Sea Brine Pools modified to enhance its anticancer activity against U2OS cells

Cancer associated drug resistance is a major cause for cancer aggravation, particularly as conventional therapies have presented limited efficiency, low specificity, resulting in long term deleterious side effects. Peptide based drugs have emerged as potential alternative cancer treatment tools due to their selectivity, ease of design and synthesis, safety profile, and low cost of manufacturing. In this study, we utilized the Red Sea metagenomics database, generated during AUC/KAUST Red Sea microbiome project, to derive a viable anticancer peptide (ACP). We generated a set of peptide hits from our library that shared similar composition to ACPs. A peptide with a homeodomain was selected, modified to improve its anticancer properties, verified to maintain high anticancer properties, and processed for further in-silico prediction of structure and function. The peptide's anticancer properties were then assessed in vitro on osteosarcoma U2OS cells, through cytotoxicity assay (MTT assay), scratch-wound healing assay, apoptosis/necrosis detection assay (Annexin/PI assay), RNA expression analysis of Caspase 3, KI67 and Survivin, and protein expression of PARP1. L929 mouse fibroblasts were also assessed for cytotoxicity treatment. In addition, the antimicrobial activity of the peptide was also examined on E coli and S. aureus, as sample representative species of the human bacterial microbiome, by examining viability, disk diffusion, morphological assessment, and hemolytic analysis. We observed a dose dependent cytotoxic response from peptide treatment of U2OS, with a higher tolerance in L929s. Wound closure was debilitated in cells exposed to the peptide, while annexin fluorescent imaging suggested peptide treatment caused apoptosis as a major mode of cell death. Caspase 3 gene expression was not altered, while KI67 and Survivin were both downregulated in peptide treated cells. Additionally, PARP-1 protein analysis showed a decrease in expression with peptide exposure. The peptide exhibited minimal antimicrobial activity on critical human microbiome species E. coli and S. aureus, with a low inhibition rate, maintenance of structural morphology and minimal hemolytic impact. These findings suggest our novel peptide displayed preliminary ACP properties against U2OS cells, through limited specificity, while triggering apoptosis as a primary mode of cell death and while having minimal impact on the microbiological species E. coli and S. aureus.

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design

Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.

Elucidation of conserved multi-epitope vaccine against Leishmania donovani using reverse vaccinology

Visceral leishmaniasis (VL) is a tropical disease that causes severe public health problems in humans when untreated. As no licensed vaccine exists against VL, we aimed to formulate a potential MHC-restricted chimeric vaccine construct against this dreadful parasitic disease. Amastin-like protein derived from L. donovani is considered to be stable, immunogenic and non-allergic. A comprehensive established framework was used to explore the set of immunogenic epitopes with estimated population coverage of 96.08% worldwide. The rigorous assessment revealed 6 promiscuous T-epitopes which can plausibly be presented by more than 66 diverse HLA alleles. Further docking and simulation study of peptide receptor complexes identified a strong and stable binding interaction with better structural compactness. The predicted epitopes were combined with appropriate linkers and adjuvant molecules and their translation efficiency was evaluated in pET28+(a), an bacterial expression vector using in-silico cloning. Molecular docking followed by MD simulation study revealed a stable interaction between chimeric vaccine construct with TLRs. Immune simulation of the chimeric vaccine constructs showed an elevated Th1 immune response against both B and T epitopes. With this, the detailed computational analysis suggested that the chimeric vaccine construct can evoke a robust immune response against Leishmania donovani infection. Future studies are required to validate the role of amastin as a promising vaccine target.Communicated by Ramaswamy H. Sarma.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Multi-CGAN: Deep Generative Model-Based Multiproperty Antimicrobial Peptide Design

Antimicrobial peptides are peptides that are effective against bacteria and viruses, and the discovery of new antimicrobial peptides is of great importance to human life and health. Although the design of antimicrobial peptides using machine learning methods has achieved good results in recent years, it remains a challenge to learn and design novel antimicrobial peptides with multiple properties of interest from peptide data with certain property labels. To this end, we propose Multi-CGAN, a deep generative model-based architecture that can learn from single-attribute peptide data and generate antimicrobial peptide sequences with multiple attributes that we need, which may have a potentially wide range of uses in drug discovery. In particular, we verified that our Multi-CGAN generated peptides with the desired properties have good performance in terms of generation rate. Moreover, a comprehensive statistical analysis demonstrated that our generated peptides are diverse and have a low probability of being homologous to the training data. Interestingly, we found that the performance of many popular deep learning methods on the antimicrobial peptide prediction task can be improved by using Multi-CGAN to expand the data on the training set of the original task, indicating the high quality of our generated peptides and the robust ability of our method. In addition, we also investigated whether it is possible to directionally generate peptide sequences with specified properties by controlling the input noise sampling for our model.

Identification, characterization, and molecular docking of immunomodulatory oligopeptides from bioavailable hempseed protein hydrolysates

Promoting dietary patterns in which the content of vegetables is higher than the current consumption of them is one of the strategies to achieve a sustainable food system while promoting health in humans. Hemp (Cannabis sativa L.) protein contains bioactive peptides that can be released via enzymatic hydrolysis. These peptides must reach the target organ in order to potentially exert bioactivity and regulate specific metabolic pathways. The peptides contained in two bioavailable hempseed protein hydrolysates (bioHPHs) showing anti-inflammatory activity were identified using a transwell system employing CACO-2 cell culture as absorption model and subjected to in silico analysis to select 10 unique peptides. These sequences were chemically synthetized to verify their activity in primary human monocytes (assessing gene expression of IL-1β, IL-6, TNF-α, IL-4, IL-10, and TLR4), in addition to evaluate the interaction with TRL4/MD2 by molecular docking. Six peptides (DDNPRRF, SRRFHLA, RNIFKGF, VREPVFSF, QADIFNPR and SAERGFLY) showed high immunomodulatory activity in in vitro and the mechanisms of interaction with TLR4/MD2 were described. Bioavailable anti-inflammatory hempseed-derived peptides were identified, and their activity verified, suggesting the health benefits that the ingestion of HPHs could exert in humans. These findings open new opportunities for developing nutritional strategies with hemp as a dietary source of biopeptides to prevent the development and progression of inflammatory-related diseases.

Bioinformatics analysis of multi-epitope peptide vaccines against Hepatitis C virus: a molecular docking study

BACKGROUND

Hepatitis C Virus (HCV) infection is one of the causal agents of liver disease burden. Six multiple antigenic peptides were synthesized including (P315, P412, and P517) plus (P1771, P2121, and P2941) to induce humoral and cellular responses, respectively against HCV infection.

AIM

This paper aimed to employ computational tools to evaluate the efficacy of each peptide individually and to determine the most effective one for better vaccine development and/or immunotherapy.

METHODS

VaxiJen web and AllerTOP servers were used for antigenicity and allergenicity prediction, respectively. The ToxinPred web server was used to investigate the peptide toxicity. Each peptide was docked with its corresponding receptors.

RESULTS

No peptides were expected to be toxic. P315 and P2941 are predicted to have robust antigenic properties, lowest allergenicity, and minimal sOPEP energies. In turn, P315 (derived from gpE1) formed the highest hydrophobic bonds with the BCR and CD81 receptors that will elicit B cell function. P2941 (derived from NS5B) was shown to strongly bind to both CD4 and CD8 receptors that will elicit T cell function.

CONCLUSION

P315 successfully bound to B cell (BCR and CD81) receptors. Also, P2941 is strongly bound to T cell (CD4 and CD8) receptors.

Antimicrobial activity, membrane interaction and structural features of short arginine-rich antimicrobial peptides

Antimicrobial activity of many AMPs can be improved by lysine-to-arginine substitution due to a more favourable interaction of arginine guanidinium moiety with bacterial membranes. In a previous work, the structural and functional characterization of an amphipathic antimicrobial peptide named RiLK1, including lysine and arginine as the positively charged amino acids in its sequence, was reported. Specifically, RiLK1 retained its β-sheet structure under a wide range of environmental conditions (temperature, pH, and ionic strength), and exhibited bactericidal activity against Gram-positive and Gram-negative bacteria and fungal pathogens with no evidence of toxicity on mammalian cells. To further elucidate the influence of a lysine-to-arginine replacement on RiLK1 conformational properties, antimicrobial activity and peptide-liposome interaction, a new RiLK1-derivative, named RiLK3, in which the lysine is replaced with an arginine residue, was projected and characterised in comparison with its parental compound. The results evidenced that lysine-to-arginine mutation not only did not assure an improvement in the antimicrobial potency of RiLK1 in terms of bactericidal, virucidal and fungicidal activities, but rather it was completely abolished against the hepatitis A virus. Therefore, RiLK1 exhibited a wide range of antimicrobial activity like other cationic peptides, although the exact mechanisms of action are not completely understood. Moreover, tryptophan fluorescence measurements confirmed that RiLK3 bound to negatively charged lipid vesicles with an affinity lower than that of RiLK1, although no substantial differences from the structural and self-assembled point of view were evidenced. Therefore, our findings imply that antimicrobial efficacy and selectivity are affected by several complex and interrelated factors related to substitution of lysine with arginine, such as their relative proportion and position. In this context, this study could provide a better rationalisation for the optimization of antimicrobial peptide sequences, paving the way for the development of novel AMPs with broad applications.

Characterization of a novel peptide mined from the Red Sea brine pools and modified to enhance its anticancer activity

Drug resistance is a major cause of the inefficacy of conventional cancer therapies, and often accompanied by severe side effects. Thus, there is an urgent need to develop novel drugs with low cytotoxicity, high selectivity and minimal acquired chemical resistance. Peptide-based drugs (less than 0.5 kDa) have emerged as a potential approach to address these issues due to their high specificity and potent anticancer activity. In this study, we developed a support vector machine model (SVM) to detect the potential anticancer properties of novel peptides by scanning the American University in Cairo (AUC) Red Sea metagenomics library. We identified a novel 37-mer antimicrobial peptide through SVM pipeline analysis and characterized its anticancer potential through in silico cross-examination. The peptide sequence was further modified to enhance its anticancer activity, analyzed for gene ontology, and subsequently synthesized. To evaluate the anticancer properties of the modified 37-mer peptide, we assessed its effect on the viability and morphology of SNU449, HepG2, SKOV3, and HeLa cells, using an MTT assay. Additionally, we evaluated the migration capabilities of SNU449 and SKOV3 cells using a scratch-wound healing assay. The targeted selectivity of the modified peptide was examined by evaluating its hemolytic activity on human erythrocytes. Treatment with the peptide significantly reduced cell viability and had a critical impact on the morphology of hepatocellular carcinoma (SNU449 and HepG2), and ovarian cancer (SKOV3) cells, with a marginal effect on cervical cancer cell lines (HeLa). The viability of a human fibroblast cell line (1Br-hTERT) was also significantly reduced by peptide treatment, as were the proliferation and migration abilities of SNU449 and SKOV3 cells. The annexin V assay revealed programmed cell death (apoptosis) as one of the potential cellular death pathways in SNU449 cells upon peptide treatment. Finally, the peptide exhibited antimicrobial effects on both gram-positive and gram-negative bacterial strains. The findings presented here suggest the potential of our novel peptide as a potent anticancer and antimicrobial agent.

Molecular Representations in Machine-Learning-Based Prediction of PK Parameters for Insulin Analogs

Therapeutic peptides and proteins derived from either endogenous hormones, such as insulin, or de novo design via display technologies occupy a distinct pharmaceutical space in between small molecules and large proteins such as antibodies. Optimizing the pharmacokinetic (PK) profile of drug candidates is of high importance when it comes to prioritizing lead candidates, and machine-learning models can provide a relevant tool to accelerate the drug design process. Predicting PK parameters of proteins remains difficult due to the complex factors that influence PK properties; furthermore, the data sets are small compared to the variety of compounds in the protein space. This study describes a novel combination of molecular descriptors for proteins such as insulin analogs, where many contained chemical modifications, e.g., attached small molecules for protraction of the half-life. The underlying data set consisted of 640 structural diverse insulin analogs, of which around half had attached small molecules. Other analogs were conjugated to peptides, amino acid extensions, or fragment crystallizable regions. The PK parameters clearance (CL), half-life (T1/2), and mean residence time (MRT) could be predicted by using classical machine-learning models such as Random Forest (RF) and Artificial Neural Networks (ANN) with root-mean-square errors of CL of 0.60 and 0.68 (log units) and average fold errors of 2.5 and 2.9 for RF and ANN, respectively. Both random and temporal data splittings were employed to evaluate ideal and prospective model performance with the best models, regardless of data splitting, achieving a minimum of 70% of predictions within a twofold error. The tested molecular representations include (1) global physiochemical descriptors combined with descriptors encoding the amino acid composition of the insulin analogs, (2) physiochemical descriptors of the attached small molecule, (3) protein language model (evolutionary scale modeling) embedding of the amino acid sequence of the molecules, and (4) a natural language processing inspired embedding (mol2vec) of the attached small molecule. Encoding the attached small molecule via (2) or (4) significantly improved the predictions, while the benefit of using the protein language model-based encoding (3) depended on the used machine-learning model. The most important molecular descriptors were identified as descriptors related to the molecular size of both the protein and protraction part using Shapley additive explanations values. Overall, the results show that combining representations of proteins and small molecules was key for PK predictions of insulin analogs.

Computer simulation approach to the identification of visfatin-derived angiogenic peptides

Angiogenesis plays an essential role in various normal physiological processes, such as embryogenesis, tissue repair, and skin regeneration. Visfatin is a 52 kDa adipokine secreted by various tissues including adipocytes. It stimulates the expression of vascular endothelial growth factor (VEGF) and promotes angiogenesis. However, there are several issues in developing full-length visfatin as a therapeutic drug due to its high molecular weight. Therefore, the purpose of this study was to develop peptides, based on the active site of visfatin, with similar or superior angiogenic activity using computer simulation techniques.Initially, the active site domain (residues 181∼390) of visfatin was first truncated into small peptides using the overlapping technique. Subsequently, the 114 truncated small peptides were then subjected to molecular docking analysis using two docking programs (HADDOCK and GalaxyPepDock) to generate small peptides with the highest affinity for visfatin. Furthermore, molecular dynamics simulations (MD) were conducted to investigate the stability of the protein-ligand complexes by computing root mean square deviation (RSMD) and root mean square fluctuation(RMSF) plots for the visfatin-peptide complexes. Finally, peptides with the highest affinity were examined for angiogenic activities, such as cell migration, invasion, and tubule formation in human umbilical vein endothelial cells (HUVECs). Through the docking analysis of the 114 truncated peptides, we screened nine peptides with a high affinity for visfatin. Of these, we discovered two peptides (peptide-1: LEYKLHDFGY and peptide-2: EYKLHDFGYRGV) with the highest affinity for visfatin. In an in vitrostudy, these two peptides showed superior angiogenic activity compared to visfatin itself and stimulated mRNA expressions of visfatin and VEGF-A. These results show that the peptides generated by the protein-peptide docking simulation have a more efficient angiogenic activity than the original visfatin.

ALA-A2 Is a Novel Anticancer Peptide Inspired by Alpha-Lactalbumin: A Discovery from a Computational Peptide Library, In Silico Anticancer Peptide Screening and In Vitro Experimental Validation

Anticancer peptides (ACPs) are rising as a new strategy for cancer therapy. However, traditional laboratory screening to find and identify novel ACPs from hundreds to thousands of peptides is costly and time consuming. Here, a sequential procedure is applied to identify candidate ACPs from a computer-generated peptide library inspired by alpha-lactalbumin, a milk protein with known anticancer properties. A total of 2688 distinct peptides, 5-25 amino acids in length, are generated from alpha-lactalbumin. In silico ACP screening using the physicochemical and structural filters and three machine learning models lead to the top candidate peptides ALA-A1 and ALA-A2. In vitro screening against five human cancer cell lines supports ALA-A2 as the positive hit. ALA-A2 selectively kills A549 lung cancer cells in a dose-dependent manner, with no hemolytic side effects, and acts as a cell penetrating peptide without membranolytic effects. Sequential window acquisition of all theorical fragment ions-proteomics and functional validation reveal that ALA-A2 induces autophagy to mediate lung cancer cell death. This approach to identify ALA-A2 is time and cost-effective. Further investigations are warranted to elucidate the exact intracellular targets of ALA-A2. Moreover, these findings support the use of larger computational peptide libraries built upon multiple proteins to further advance ACP research and development.

Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models

In the last three decades, a wide range of protein features have been discovered to annotate a protein. Numerous attempts have been made to integrate these features in a software package/platform so that the user may compute a wide range of features from a single source. To complement the existing methods, we developed a method, Pfeature, for computing a wide range of protein features. Pfeature allows to compute more than 200,000 features required for predicting the overall function of a protein, residue-level annotation of a protein, and function of chemically modified peptides. It has six major modules, namely, composition, binary profiles, evolutionary information, structural features, patterns, and model building. Composition module facilitates to compute most of the existing compositional features, plus novel features. The binary profile of amino acid sequences allows to compute the fraction of each type of residue as well as its position. The evolutionary information module allows to compute evolutionary information of a protein in the form of a position-specific scoring matrix profile generated using Position-Specific Iterative Basic Local Alignment Search Tool (PSI-BLAST); fit for annotation of a protein and its residues. A structural module was developed for computing of structural features/descriptors from a tertiary structure of a protein. These features are suitable to predict the therapeutic potential of a protein containing non-natural or chemically modified residues. The model-building module allows to implement various machine learning techniques for developing classification and regression models as well as feature selection. Pfeature also allows the generation of overlapping patterns and features from a protein. A user-friendly Pfeature is available as a web server python library and stand-alone package.

Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides

Antimicrobial peptides (AMPs) have appeared as promising compounds to treat a wide range of diseases. Their clinical potentialities reside in the wide range of mechanisms they can use for both killing microbes and modulating immune responses. However, the hugeness of the AMPs' chemical space (AMPCS), represented by more than 10⁶⁵ unique sequences, has represented a big challenge for the discovery of new promising therapeutic peptides and for the identification of common structural motifs. Here, we introduce network science and a similarity searching approach to discover new promising AMPs, specifically antiparasitic peptides (APPs). We exploited the network-based representation of APPs' chemical space (APPCS) to retrieve valuable information by using three network types: chemical space (CSN), half-space proximal (HSPN), and metadata (METN). Some centrality measures were applied to identify in each network the most important and nonredundant peptides. Then, these central peptides were considered as queries (Qs) in group fusion similarity-based searches against a comprehensive collection of known AMPs, stored in the graph database StarPepDB, to propose new potential APPs. The performance of the resulting multiquery similarity-based search models (mQSSMs) was evaluated in five benchmarking data sets of APP/non-APPs. The predictions performed by the best mQSSM showed a strong-to-very-strong performance since their external Matthews correlation coefficient (MCC) values ranged from 0.834 to 0.965. Outstanding MCC values (>0.85) were attained by the mQSSM with 219 Qs from both networks CSN and HSPN with 0.5 as similarity threshold in external data sets. Then, the performance of our best mQSSM was compared with the APPs prediction servers AMPDiscover and AMPFun. The proposed model showed its relevance by outperforming state-of-the-art machine learning models to predict APPs. After applying the best mQSSM and additional filters on the non-APP space from StarPepDB, 95 AMPs were repurposed as potential APP hits. Due to the high sequence diversity of these peptides, different computational approaches were applied to identify relevant motifs for searching and designing new APPs. Lastly, we identified 11 promising APP lead candidates by using our best mQSSMs together with diversity-based network analyses, and 24 web servers for activity/toxicity and drug-like properties. These results support that network-based similarity searches can be an effective and reliable strategy to identify APPs. The proposed models and pipeline are freely available through the StarPep toolbox software at http://mobiosd-hub.com/starpep.

Application of a deep generative model produces novel and diverse functional peptides against microbial resistance

Antimicrobial resistance could threaten millions of lives in the immediate future. Antimicrobial peptides (AMPs) are an alternative to conventional antibiotics practice against infectious diseases. Despite the potential contribution of AMPs to the antibiotic's world, their development and optimization have encountered serious challenges. Cutting-edge methods with novel and improved selectivity toward resistant targets must be established to create AMPs-driven treatments. Here, we present AMPTrans-lstm, a deep generative network-based approach for the rational design of AMPs. The AMPTrans-lstm pipeline involves pre-training, transfer learning, and module identification. The AMPTrans-lstm model has two sub-models, namely, (long short-term memory) LSTM sampler and Transformer converter, which can be connected in series to make full use of the stability of LSTM and the novelty of Transformer model. These elements could generate AMPs candidates, which can then be tailored for specific applications. By analyzing the generated sequence and trained AMPs, we prove that AMPTrans-lstm can expand the design space of the trained AMPs and produce reasonable and brand-new AMPs sequences. AMPTrans-lstm can generate functional peptides for antimicrobial resistance with good novelty and diversity, so it is an efficient AMPs design tool.

Polypharmacological Cell-Penetrating Peptides from Venomous Marine Animals Based on Immunomodulating, Antimicrobial, and Anticancer Properties

Complex pathological diseases, such as cancer, infection, and Alzheimer's, need to be targeted by multipronged curative. Various omics technologies, with a high rate of data generation, demand artificial intelligence to translate these data into druggable targets. In this study, 82 marine venomous animal species were retrieved, and 3505 cryptic cell-penetrating peptides (CPPs) were identified in their toxins. A total of 279 safe peptides were further analyzed for antimicrobial, anticancer, and immunomodulatory characteristics. Protease-resistant CPPs with endosomal-escape ability in Hydrophis hardwickii, nuclear-localizing peptides in Scorpaena plumieri, and mitochondrial-targeting peptides from Synanceia horrida were suitable for compartmental drug delivery. A broad-spectrum S. horrida-derived antimicrobial peptide with a high binding-affinity to bacterial membranes was an antigen-presenting cell (APC) stimulator that primes cytokine release and naïve T-cell maturation simultaneously. While antibiofilm and wound-healing peptides were detected in Synanceia verrucosa, APC epitopes as universal adjuvants for antiviral vaccination were in Pterois volitans and Conus monile. Conus pennaceus-derived anticancer peptides showed antiangiogenic and IL-2-inducing properties with moderate BBB-permeation and were defined to be a tumor-homing peptide (THP) with the ability to inhibit programmed death ligand-1 (PDL-1). Isoforms of RGD-containing peptides with innate antiangiogenic characteristics were in Conus tessulatus for tumor targeting. Inhibitors of neuropilin-1 in C. pennaceus are proposed for imaging probes or therapeutic delivery. A Conus betulinus cryptic peptide, with BBB-permeation, mitochondrial-targeting, and antioxidant capacity, was a stimulator of anti-inflammatory cytokines and non-inducer of proinflammation proposed for Alzheimer's. Conclusively, we have considered the dynamic interaction of cells, their microenvironment, and proportional-orchestrating-host- immune pathways by multi-target-directed CPPs resembling single-molecule polypharmacology. This strategy might fill the therapeutic gap in complex resistant disorders and increase the candidates' clinical-translation chance.

Targeting gliovascular connexins prevents inflammatory blood-brain barrier leakage and astrogliosis

The blood-brain barrier is formed by capillary endothelial cells expressing Cx37, Cx40 and Cx43, and is joined by closely apposed astrocytes expressing Cx43 and Cx30. We investigated whether connexin-targeting peptides could limit barrier leakage triggered by LPS-induced systemic inflammation in mice. Intraperitoneal LPS increased endothelial and astrocytic Cx43 expression, elevated TNFα, IL1β, IFNγ and IL6 in plasma and IL6 in the brain, and induced barrier leakage recorded over 24h. Barrier leakage was largely prevented by global Cx43 knockdown and Cx43/Cx30 double-knockout in astrocytes, slightly diminished by endothelial Cx43 knockout and not protected by global Cx30 knockout. Intravenous administration of Gap27 or Tat-Gap19 just before LPS also prevented barrier leakage, and intravenous BAPTA-AM to chelate intracellular calcium was equally effective. Patch-clamp experiments demonstrated LPS-induced Cx43 hemichannel opening in endothelial cells, which was suppressed by Gap27, Gap19 and BAPTA. LPS additionally triggered astrogliosis that was prevented by intravenous Tat-Gap19 or BAPTA-AM. Cortically applied Tat-Gap19 or BAPTA-AM to primarily target astrocytes, also strongly diminished barrier leakage. In vivo dye uptake and in vitro patch-clamp showed Cx43 hemichannel opening in astrocytes that was induced by IL6 in a calcium-dependent manner. We conclude that targeting endothelial and astrocytic connexins is a powerful approach to limit barrier failure and astrogliosis.

Characterization and impact of peptide physicochemical properties on oral and subcutaneous delivery

Peptides, an emerging modality within the biopharmaceutical industry, are often delivered subcutaneously with evolving prospects on oral delivery. Barrier biology within the subcutis or gastrointestinal tract is a significant challenge in limiting absorption or otherwise disrupting peptide disposition. Aspects of peptide pharmacokinetic performance and ADME can be mitigated with careful molecular design that tailors for properties such as effective size, hydrophobicity, net charge, proteolytic stability, and albumin binding. In this review, we endeavor to highlight effective techniques in qualifying physicochemical properties of peptides and discuss advancements of in vitro models of subcutaneous and oral delivery. Additionally, we will delineate empirical findings around the relationship of these physicochemical properties and in vivo (animal or human) impact. We conclude that robust peptide characterization methods and in vitro techniques with demonstrated correlations to in vivo data are key routines to incorporate in the drug discovery and development to improve the probability of technical and commercial success of peptide therapeutics.

Experimental Study of Potential CD8+ Trivalent Synthetic Peptides for Liver Cancer Vaccine Development Using Sprague Dawley Rat Models

Background. Liver cancer (LC) is the most devastating disease affecting a large set of populations in the world. The mortality due to LC is escalating, indicating the lack of effective therapeutic options. Immunotherapeutic agents may play an important role against cancer cells. As immune cells, especially T lymphocytes, which are part of cancer immunology, the design of vaccine candidates for cytotoxic T lymphocytes may be an effective strategy for curing liver cancer. Results. In our study, based on an immunoinformatics approach, we predicted potential T cell epitopes of MHC class I molecules using integrated steps of data retrieval, screening of antigenic proteins, functional analysis, peptide synthesis, and experimental in vivo investigations. We predicted the binding affinity of epitopes LLECADDRADLAKY, VSEHRIQDKDGLFY, and EYILSLEELVNGMY of LC membrane-bounded extracellular proteins including butyrophilin-like protein-2 (BTNL2), glypican-3 (GPC3), and serum albumin (ALB), respectively, with MHC class I molecules (allele: HLA-A $\ast$ 01:01). These T cell epitopes rely on the level of their binding energy and antigenic properties. We designed and constructed a trivalent immunogenic model by conjugating these epitopes with linkers to activate cytotoxic T cells. For validation, the nonspecific hematological assays showed a significant rise in the count of white blood cells ( $5\times {10}^9/\text{l}$ ), lymphocytes ( $13\times {10}^9/\text{l}$ ), and granulocytes ( $5\times {10}^9/\text{l}$ ) compared to the control after administration of trivalent peptides. Specific immunoassays including granzyme B and IgG ELISA exhibited the significant concentration of these effector molecules in blood serum, indicating the activity of cytotoxic T cells. Granzyme concentration increased to 1050 pg/ml at the second booster dose compared to the control (95 pg/ml), while the concentration of IgG raised to 6 g/l compared to the control (2 g/l). Conclusion. We concluded that a potential therapeutic trivalent vaccine can activate and modulate the immune system to cure liver cancer on the basis of significant outcomes of specific and nonspecific assays.

Humanizing plant-derived snakins and their encrypted antimicrobial peptides

Due to safety restrictions, plant-derived antimicrobial peptides (AMPs) need optimization to be consumed beyond preservatives. Herein, 175 GASA-domain-containing snakins were analyzed. Factors including charge, hydrophobicity, helicity, hydrophobic moment (μH), folding enthalpy, folding heat capacity, folding free energy, therapeutic index, allergenicity, and bitterness were considered. The most optimal snakins for oral consumption as preservatives were from Cajanus cajan, Cucumis melo, Durio zibethinus, Glycine soja, Herrania umbratica, and Ziziphus jujuba. Virtual digestion of snakins predicted ACE1 and DPPIV inhibitory as dominant effects upon oral use with antihypertensive and antidiabetic properties. To be applied as a therapeutic in parenteral administration, snakins were browsed for short 20-mer encrypted fragments that were non-toxic or with eliminated toxicity using directed mutagenesis yet retaining the AMP property. The most promising 20-mer AMPs were Mr-SNK2-1a in Morella rubra with BBB permeation, Na-SNK2-2a(C¹⁸W), and Na-SNK2-2b(C¹⁶F) from Nicotiana attenuata. These AMPs were cell-penetrating peptides (CPPs), with a charge of +6, a μH of about 0.40, and a Boman-index higher than 2.48 Kcalmol^-1. Na-SNK2-2a(C¹⁸W) had putative activity against gram-negative bacteria with MIC lower than 25 μgml^-1, and Na-SNK2-2b(C¹⁶F) was a potential anti-HIV with an IC₅₀ of 3.04 μM. Other 20-mer AMPs, such as Cc-SNK1-2a from Cajanus cajan displayed an anti-HCV property with an IC₅₀ of 13.91 μM. While Si-SNK2-3a(C¹⁷P) from Sesamum indicum was a cationic anti-angiogenic CPP targeting the acidic microenvironment of tumors, Cme-SNK2-1a(C¹¹F) from Cucumis melo was an immunomodulator CPP applicable as a vaccine adjuvant. Because of combined mechanisms, investigating cysteine-rich peptides can nominate effective biotherapeutics.

Population balance modelling captures host cell protein dynamics in CHO cell cultures

Monoclonal antibodies (mAbs) have been extensively studied for their wide therapeutic and research applications. Increases in mAb titre has been achieved mainly by cell culture media/feed improvement and cell line engineering to increase cell density and specific mAb productivity. However, this improvement has shifted the bottleneck to downstream purification steps. The higher accumulation of the main cell-derived impurities, host cell proteins (HCPs), in the supernatant can negatively affect product integrity and immunogenicity in addition to increasing the cost of capture and polishing steps. Mathematical modelling of bioprocess dynamics is a valuable tool to improve industrial production at fast rate and low cost. Herein, a single stage volume-based population balance model (PBM) has been built to capture Chinese hamster ovary (CHO) cell behaviour in fed-batch bioreactors. Using cell volume as the internal variable, the model captures the dynamics of mAb and HCP accumulation extracellularly under physiological and mild hypothermic culture conditions. Model-based analysis and orthogonal measurements of lactate dehydrogenase activity and double-stranded DNA concentration in the supernatant show that a significant proportion of HCPs found in the extracellular matrix is secreted by viable cells. The PBM then served as a platform for generating operating strategies that optimise antibody titre and increase cost-efficiency while minimising impurity levels.

Preliminary safety assessment of CIGB-210, an investigational peptide for HIV infection

Current human immunodeficiency virus treatments need to be periodically administered lifelong. In this study we assess the effect of repeated doses of an anti-HIV peptide drug candidate in C57BL6 strain. Two schemes of up to 15 administrations and one of 30, daily dosing for 5 days per week, all by the subcutaneous route were evaluated. Different dose concentrations of the peptide were assayed. CIGB-210 treated animals showed no symptoms or abnormal behavior as compared with placebo. All the animals gained weight during the study. Macroscopic evaluation showed no alterations in any of the organs studied. Microscopic analysis of the tissues did not show morphological changes in thymus, stomach, small and large intestines, kidney, brain, or cerebellum. The proliferative response of splenocytes and their capacity to secrete gamma interferon were not compromised by the repeated administration of CIGB-210. There were not statistically significant differences for any of the parameters evaluated during the study among treated and non-treated groups. We can conclude that CIGB-210 is well tolerated in C57BL6 mice in the dose concentration range explored and merits subsequent toxicological studies.

Integration of Molecular Docking Analysis and Molecular Dynamics Simulations for Studying Food Proteins and Bioactive Peptides

In silico tools, such as molecular docking, are widely applied to study interactions and binding affinity of biological activity of proteins and peptides. However, restricted sampling of both ligand and receptor conformations and use of approximated scoring functions can produce results that do not correlate with actual experimental binding affinities. Molecular dynamics simulations (MDS) can provide valuable information in deciphering functional mechanisms of proteins/peptides and other biomolecules, overcoming the rigid sampling limitations in docking analysis. This review will discuss the information related to the traditional use of in silico models, such as molecular docking, and its application for studying food proteins and bioactive peptides, followed by an in-depth introduction to the theory of MDS and description of why these molecular simulation techniques are important in the theoretical prediction of structural and functional dynamics of food proteins and bioactive peptides. Applications, limitations, and future prospects of MDS will also be discussed.

Viral Prefusion Targeting Using Entry Inhibitor Peptides: The Case of SARS-CoV-2 and Influenza A virus

In this study, peptide entry inhibitors against the fusion processes of severe acute respiratory syndrome coronavirus-2 (SCV2) and influenza A virus (IAV) were designed and evaluated. Fusion inhibitor peptides targeting the conformational shift of the viral fusion protein were designed based on the relatively conserved sequence of HR2 from SCV2 spike protein and the conserved fusion peptide from hemagglutinin (HA) of IAV. Helical HR2 peptides bind more efficiently to HR1 trimer, while helical amphipathic anti-IAV peptides have higher cell penetration and endosomal uptake. The initial sequences were mutated by increasing the amphipathicity, using helix favoring residues, and residues likely to form salt- and disulfide-bridges. After docking against their targets, all anti-SCV2 designed peptides bonded with the HR1 3-helical bundle's hydrophobic crevice, while AntiSCV2P1, AntiSCV2P3, AntiSCV2P7, and AntiSCV2P8 expected to form coiled coils with at least one of the HR1 strands. Four of the designed anti-IAV peptides were cell-penetrating (AntiIAVP2, AntiIAVP3, AntiIAVP4, AntiIAVP7). All of them interacted with the fusion peptide of HA and some of the residues in the conserved hydrophobic pocket of HA2 in H1N1, H3N1, and H5N1 subtypes of IAV. AntiIAVP3 and AntiIAVP4 peptides had the best binding to HA2 conserved hydrophobic pocket, while, AntiIAVP2 and AntiIAVP6 showed the best binding to the fusion peptide region. According to analyses for in-vivo administration, AntiSCV2P1, AntiSCV2P7, AntiIAVP2, and AntiIAVP7 were the best candidates. AntiSCV2 and AntiIAV peptides were also conjugated using an in vivo cleavable linker sensitive to TMPRSS2 applicable as a single therapeutic in coinfections or uncertain diagnosis.

A New Epitope Selection Method: Application to Design a Multi-Valent Epitope Vaccine Targeting HRAS Oncogene in Squamous Cell Carcinoma

We developed an epitope selection method for the design of MHC targeting peptide vaccines. The method utilizes predictions for several clinical checkpoint filters, including binding affinity, immunogenicity, antigenicity, half-life, toxicity, IFNγ release, and instability. The accuracy of the prediction tools for these filter variables was confirmed using experimental data obtained from the Immune Epitope Database (IEDB). We also developed a graphical user interface computational tool called 'PCOptim' to assess the success of an epitope filtration method. To validate the filtration methods, we used a large data set of experimentally determined, immunogenic SARS-CoV-2 epitopes, which were obtained from a meta-analysis. The validation process proved that placing filters on individual parameters was the most effective method to select top epitopes. For a proof-of-concept, we designed epitope-based vaccine candidates for squamous cell carcinoma, selected from the top mutated epitopes of the HRAS gene. By comparing the filtered epitopes to PCOptim's output, we assessed the success of the epitope selection method. The top 15 mutations in squamous cell carcinoma resulted in 16 CD8 epitopes which passed the clinical checkpoints filters. Notably, the identified HRAS epitopes are the same as the clinical immunogenic HRAS epitope-based vaccine candidates identified by the previous studies. This indicates further validation of our filtration method. We expect a similar turn-around for the other designed HRAS epitopes as a vaccine candidate for squamous cell carcinoma. Furthermore, we obtained a world population coverage of 89.45% for the top MHC Class I epitopes and 98.55% population coverage in the absence of the IFNγ release clinical checkpoint filter. We also identified some of the predicted human epitopes to be strong binders to murine MHC molecules, which provides insight into studying their immunogenicity in preclinical models. Further investigation in murine models could warrant the application of these epitopes for treatment or prevention of squamous cell carcinoma.

In-Silico Tool for Predicting, Scanning, and Designing Defensins

Defensins are host defense peptides present in nearly all living species, which play a crucial role in innate immunity. These peptides provide protection to the host, either by killing microbes directly or indirectly by activating the immune system. In the era of antibiotic resistance, there is a need to develop a fast and accurate method for predicting defensins. In this study, a systematic attempt has been made to develop models for predicting defensins from available information on defensins. We created a dataset of defensins and non-defensins called the main dataset that contains 1,036 defensins and 1,035 AMPs (antimicrobial peptides, or non-defensins) to understand the difference between defensins and AMPs. Our analysis indicates that certain residues like Cys, Arg, and Tyr are more abundant in defensins in comparison to AMPs. We developed machine learning technique-based models on the main dataset using a wide range of peptide features. Our SVM (support vector machine)-based model discriminates defensins and AMPs with MCC of 0.88 and AUC of 0.98 on the validation set of the main dataset. In addition, we created an alternate dataset that consists of 1,036 defensins and 1,054 non-defensins obtained from Swiss-Prot. Models were also developed on the alternate dataset to predict defensins. Our SVM-based model achieved maximum MCC of 0.96 with AUC of 0.99 on the validation set of the alternate dataset. All models were trained, tested, and validated using standard protocols. Finally, we developed a web-based service "DefPred" to predict defensins, scan defensins in proteins, and design the best defensins from their analogs. The stand-alone software and web server of DefPred are available at https://webs.iiitd.edu.in/raghava/defpred.

Natural Peptides Inducing Cancer Cell Death: Mechanisms and Properties of Specific Candidates for Cancer Therapeutics

Nowadays, cancer has become the second highest leading cause of death, and it is expected to continue to affect the population in forthcoming years. Additionally, treatment options will become less accessible to the public as cases continue to grow and disease mechanisms expand. Hence, specific candidates with confirmed anticancer effects are required to develop new drugs. Among the novel therapeutic options, proteins are considered a relevant source, given that they have bioactive peptides encrypted within their sequences. These bioactive peptides, which are molecules consisting of 2–50 amino acids, have specific activities when administered, producing anticancer effects. Current databases report the effects of peptides. However, uncertainty is found when their molecular mechanisms are investigated. Furthermore, analyses addressing their interaction networks or their directly implicated mechanisms are needed to elucidate their effects on cancer cells entirely. Therefore, relevant peptides considered as candidates for cancer therapeutics with specific sequences and known anticancer mechanisms were accurately reviewed. Likewise, those features which turn certain peptides into candidates and the mechanisms by which peptides mediate tumor cell death were highlighted. This information will make robust the knowledge of these candidate peptides with recognized mechanisms and enhance their non-toxic capacity in relation to healthy cells and further avoid cell resistance.

Natural Peptides Inducing Cancer Cell Death: Mechanisms and Properties of Specific Candidates for Cancer Therapeutics

Nowadays, cancer has become the second highest leading cause of death, and it is expected to continue to affect the population in forthcoming years. Additionally, treatment options will become less accessible to the public as cases continue to grow and disease mechanisms expand. Hence, specific candidates with confirmed anticancer effects are required to develop new drugs. Among the novel therapeutic options, proteins are considered a relevant source, given that they have bioactive peptides encrypted within their sequences. These bioactive peptides, which are molecules consisting of 2–50 amino acids, have specific activities when administered, producing anticancer effects. Current databases report the effects of peptides. However, uncertainty is found when their molecular mechanisms are investigated. Furthermore, analyses addressing their interaction networks or their directly implicated mechanisms are needed to elucidate their effects on cancer cells entirely. Therefore, relevant peptides considered as candidates for cancer therapeutics with specific sequences and known anticancer mechanisms were accurately reviewed. Likewise, those features which turn certain peptides into candidates and the mechanisms by which peptides mediate tumor cell death were highlighted. This information will make robust the knowledge of these candidate peptides with recognized mechanisms and enhance their non-toxic capacity in relation to healthy cells and further avoid cell resistance.

Computational Screening for the Anticancer Potential of Seed-Derived Antioxidant Peptides: A Cheminformatic Approach

Some seed-derived antioxidant peptides are known to regulate cellular modulators of ROS production, including those proposed to be promising targets of anticancer therapy. Nevertheless, research in this direction is relatively slow owing to the inevitable time-consuming nature of wet-lab experimentations. To help expedite such explorations, we performed structure-based virtual screening on seed-derived antioxidant peptides in the literature for anticancer potential. The ability of the peptides to interact with myeloperoxidase, xanthine oxidase, Keap1, and p47^phox was examined. We generated a virtual library of 677 peptides based on a database and literature search. Screening for anticancer potential, non-toxicity, non-allergenicity, non-hemolyticity narrowed down the collection to five candidates. Molecular docking found LYSPH as the most promising in targeting myeloperoxidase, xanthine oxidase, and Keap1, whereas PSYLNTPLL was the best candidate to bind stably to key residues in p47^phox. Stability of the four peptide-target complexes was supported by molecular dynamics simulation. LYSPH and PSYLNTPLL were predicted to have cell- and blood-brain barrier penetrating potential, although intolerant to gastrointestinal digestion. Computational alanine scanning found tyrosine residues in both peptides as crucial to stable binding to the targets. Overall, LYSPH and PSYLNTPLL are two potential anticancer peptides that deserve deeper exploration in future.

Preliminary Characterization of NP339, a Novel Polyarginine Peptide with Broad Antifungal Activity

Fungi cause disease in nearly one billion individuals worldwide. Only three classes of antifungal agents are currently available in mainstream clinical use. Emerging and drug-resistant fungi, toxicity, and drug-drug interactions compromise their efficacy and applicability. Consequently, new and improved antifungal therapies are urgently needed. In response to that need, we have developed NP339, a 2-kDa polyarginine peptide that is active against pathogenic fungi from the genera Candida, Aspergillus, and Cryptococcus, as well as others. NP339 was designed based on endogenous cationic human defense peptides, which are constituents of the cornerstone of immune defense against pathogenic microbes. NP339 specifically targets the fungal cell membrane through a charge-charge-initiated membrane interaction and therefore possesses a differentiated safety and toxicity profile to existing antifungal classes. NP339 is rapidly fungicidal and does not elicit resistance in target fungi upon extensive passaging in vitro. Preliminary analyses in murine models indicate scope for therapeutic application of NP339 against a range of systemic and mucocutaneous fungal infections. Collectively, these data indicate that NP339 can be developed into a highly differentiated, first-in-class antifungal candidate for poorly served invasive and other serious fungal diseases.

Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations

The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrobials with desired attributes. The method leverages guidance from classifiers trained on an informative latent space of molecules modelled using a deep generative autoencoder, and screens the generated molecules using deep-learning classifiers as well as physicochemical features derived from high-throughput molecular dynamics simulations. Within 48 days, we identified, synthesized and experimentally tested 20 candidate antimicrobial peptides, of which two displayed high potency against diverse Gram-positive and Gram-negative pathogens (including multidrug-resistant Klebsiella pneumoniae) and a low propensity to induce drug resistance in Escherichia coli. Both peptides have low toxicity, as validated in vitro and in mice. We also show using live-cell confocal imaging that the bactericidal mode of action of the peptides involves the formation of membrane pores. The combination of deep learning and molecular dynamics may accelerate the discovery of potent and selective broad-spectrum antimicrobials.

Intestinal guard: Human CXCL17 modulates protective response against mycotoxins and CXCL17-mimetic peptides development

Mycotoxin contamination is an ongoing and growing issue that can create health risks and even cause death. Unfortunately, there is currently a lack of specific therapy against mycotoxins with few side effects. On the other hand, the strategic expression of CXCL17 in mucosal tissues suggests that it may be involved in immune response when exposed to mycotoxins, but the exact role of CXCL17 remains largely unknown. Using Caco-2 as a cell model of the intestinal epithelial barrier (the first line of defense against mycotoxins), we showed that a strong production of ROS-dependent CXCL17 was triggered by mycotoxins via p38 and JNK pathways. Under the mycotoxins stress, CXCL17 modulated enhanced immuno-protective response with a remission of inflammation and apoptosis through PI3K/AKT/mTOR. Based on our observed feedback of CXCL17 to the mycotoxins, we developed the CXCL17-mimetic peptides in silico (CX1 and CX2) that possessed the safety and the capability to ameliorate mycotoxins-inducible inflammation and apoptosis. In this study, the identification of detoxifying feature of CXCL17 is a prominent addition to the chemokine field, pointing out a new direction for curing the mycotoxins-caused damage.

SAPdb: A database of short peptides and the corresponding nanostructures formed by self-assembly

Nanostructures generated by self-assembly of peptides yield nanomaterials that have many therapeutic applications, including drug delivery and biomedical engineering, due to their low cytotoxicity and higher uptake by targeted cells owing to their high affinity and specificity towards cell surface receptors. Despite the promising implications of this rapidly expanding field, there is no dedicated resource to study peptide nanostructures. This study endeavours to create a repository of short peptides, which may prove to be the best models to study ordered nanostructures formed by peptide self-assembly. SAPdb has a repertoire of 1049 entries of experimentally validated nanostructures formed by the self-assembly of small peptides. It consists of 328 tripeptides, 701 dipeptides, and 20 single amino acids with some conjugate partners. Each entry encompasses comprehensive information about the peptide, such as chemical modifications, the type of nanostructure formed, experimental conditions like pH, temperature, solvent required for the self-assembly, etc. Our analysis indicates that peptides containing aromatic amino acids favour the formation of self-assembling nanostructures. Additionally, we observed that these peptides form different nanostructures under different experimental conditions. SAPdb provides this comprehensive information in a hassle-free tabulated manner at a glance. User-friendly browsing, searching, and analysis modules have been integrated for easy data retrieval, data comparison, and examination of properties. We anticipate SAPdb to be a valuable repository for researchers engaged in the burgeoning arena of nanobiotechnology. It is freely available at https://webs.iiitd.edu.in/raghava/sapdb.

Therapeutic Potential of Tuna Backbone Peptide and Its Analogs: An In Vitro and In Silico Study

Tuna backbone peptide (TBP) has been reported to exert potent inhibitory activity against lipid peroxidation in vitro. Since this bears relevant physiological implications, this study was undertaken to assess the impact of peptide modifications on its bioactivity and other therapeutic potential using in vitro and in silico approach. Some TBP analogs, despite lower purity than the parent peptide, exerted promising antioxidant activities in vitro demonstrated by ABTS radical scavenging assay and cellular antioxidant activity assay. In silico digestion of the peptides resulted in the generation of antioxidant, angiotensin-converting enzyme (ACE), and dipeptidyl peptidase-IV (DPPIV) inhibitory dipeptides. Using bioinformatics platforms, we found five stable TBP analogs that hold therapeutic potential with their predicted multifunctionality, stability, non-toxicity, and low bitterness intensity. This work shows how screening and prospecting for bioactive peptides can be improved with the use of in vitro and in silico approaches.

AMPGAN v2: Machine Learning-Guided Design of Antimicrobial Peptides

Antibiotic resistance is a critical public health problem. Each year ∼2.8 million resistant infections lead to more than 35 000 deaths in the U.S. alone. Antimicrobial peptides (AMPs) show promise in treating resistant infections. However, applications of known AMPs have encountered issues in development, production, and shelf-life. To drive the development of AMP-based treatments, it is necessary to create design approaches with higher precision and selectivity toward resistant targets. Previously, we developed AMPGAN and obtained proof-of-concept evidence for the generative approach to design AMPs with experimental validation. Building on the success of AMPGAN, we present AMPGAN v2, a bidirectional conditional generative adversarial network (BiCGAN)-based approach for rational AMP design. AMPGAN v2 uses generator-discriminator dynamics to learn data-driven priors and controls generation using conditioning variables. The bidirectional component, implemented using a learned encoder to map data samples into the latent space of the generator, aids iterative manipulation of candidate peptides. These elements allow AMPGAN v2 to generate candidates that are novel, diverse, and tailored for specific applications, making it an efficient AMP design tool.

Tools for the Recognition of Sorting Signals and the Prediction of Subcellular Localization of Proteins From Their Amino Acid Sequences

At the time of translation, nascent proteins are thought to be sorted into their final subcellular localization sites, based on the part of their amino acid sequences (i.e., sorting or targeting signals). Thus, it is interesting to computationally recognize these signals from the amino acid sequences of any given proteins and to predict their final subcellular localization with such information, supplemented with additional information (e.g., k-mer frequency). This field has a long history and many prediction tools have been released. Even in this era of proteomic atlas at the single-cell level, researchers continue to develop new algorithms, aiming at accessing the impact of disease-causing mutations/cell type-specific alternative splicing, for example. In this article, we overview the entire field and discuss its future direction.