HPEPDOCK: a web server for blind peptide-protein docking based on a hierarchical algorithm

Investigation into potential allergenicity of DBD plasma-treated casein digestion products based on immunoglobulin E linear epitopes and the sensitized-cell model

The study aimed to investigate the allergenicity change in casein treated with dielectric barrier discharge (DBD) plasma during in vitro simulated digestion, focusing on the immunoglobulin E (IgE) linear epitopes and utilizing a sensitized-cell model. Results indicated that prior treatment with DBD plasma treatment (4 min) before simulated digestion led to a 10.5% reduction in the IgE-binding capacity of casein digestion products. Moreover, the release of biologically active substances induced from KU812 cells, including β-HEX release rate, human histamine, IL-4, IL-6, and TNF-α, decreased by 2.1, 28.1, 20.6, 11.6, and 17.3%, respectively. Through a combined analysis of LC-MS/MS and immunoinformatics tools, it was revealed that DBD plasma treatment promoted the degradation of the IgE linear epitopes of casein during digestion, particularly those located in the α-helix region of αs1-CN and αs2-CN. These findings suggest that DBD plasma treatment prior to digestion may alleviate casein allergic reactions.

Establishing Order Through Disorder by the Hsp90 Molecular Chaperone

The Heat Shock Protein 90 (Hsp90) molecular chaperone is a key driver of protein homeostasis (proteostasis) under physiologically normal and stress conditions. In eukaryotes, Hsp90 is essential and is one of the most abundant proteins in a cell where the chaperone shuttles between the cytoplasm and nucleus to fold, stabilize, and regulate client proteins and protein complexes. Numerous high-throughput screens have mapped the Hsp90 interactome, building a vast network comprising ∼25% of the proteome in budding yeast. How Hsp90 is able to associate with this diverse and large cadre of targets is critical to comprehending how the proteostatic process works. Here, we review recent progress on our understanding of the molecular underpinnings driving Hsp90-client interactions from both the perspective of the targets and Hsp90. In addition to considering the available Hsp90-client structures, we also assessed recently identified Hsp90-client peptide complexes to build a model that justifies how Hsp90 might recognize a wide spectrum of target proteins. In brief, Hsp90 either directly recognizes a site within an intrinsically disordered region (IDR) of a client protein to transiently regulate that client or it associates with an unstructured polypeptide section created by the concerted efforts of multiple chaperones and cochaperones to stably associate with a client. Overall, Hsp90 exploits a common recognition property (i.e., IDR) within diverse clients to support chaperone-actionthereby enabling its central role in proteostasis.

Characterization of myoinhibitory peptide signaling system and its implication in larval metamorphosis and spawning behavior in Pacific abalone

Myoinhibitory peptides (MIPs) affect various physiological functions, including juvenile hormone signaling, muscle contraction, larval development, and reproduction in invertebrates. Although MIPs are ligands for MIP and/or sex peptide receptors (MIP/SPRs) in diverse arthropods and model organisms belonging to Lophotrochozoa, the MIP signaling system has not yet been fully investigated in mollusks. In this study, we identified the MIP signaling system in the Pacific abalone Haliotis discus hannai (Hdh). Similar to the invertebrate MIPs, a total of eight paracopies of MIPs (named Hdh-MIP1 to Hdh-MIP8), harboring a WX5-7Wamide motif, except for Hdh-MIP2, were found in the Hdh-MIP precursor. Furthermore, we characterized a functional Hdh-MIPR, which responded to the Hdh-MIPs, except for Hdh-MIP2, possibly linked with the PKC/Ca2+ and PKA/cAMP signaling pathways. Hdh-MIPs delayed larval metamorphosis but increased the spawning behavior. These results suggest that the Hdh-MIP signaling system provides insights into the unique function of MIP in invertebrates.

Microarray Chip-Based High-Throughput Screening of Neurofilament Light Chain Self-Assembling Peptide for Noninvasive Monitoring of Alzheimer's Disease

Alzheimer's disease (AD) starts decades before cognitive symptoms develop. Easily accessible and cost-effective biomarkers that accurately reflect AD pathology are essential for both monitoring and therapeutics of AD. Neurofilament light chain (NfL) levels in blood and cerebrospinal fluid are increased in AD more than a decade before the expected onset, thus providing one of the most promising blood biomarkers for monitoring of AD. The clinical practice of employing single-molecule array (Simoa) technology for routine use in patient care is limited by the high costs. Herein, we developed a microarray chip-based high-throughput screening method and screened an attractive self-assembling peptide targeting NfL. Through directly "imprinting" and further analyzing the sequences, morphology, and affinity of the identified self-assembling peptides, the Pep-NfL peptide nanosheet with high binding affinity toward NfL (KD = 1.39 × 10-9 mol/L), high specificity, and low cost was characterized. The superior binding ability of Pep-NfL was confirmed in AD mouse models and cell lines. In the clinical setting, the Pep-NfL peptide nanosheets hold great potential for discriminating between patients with AD (P < 0.001, n = 37), mild cognitive impairment (P < 0.05, n = 26), and control groups (n = 30). This work provides a high-throughput, high-sensitivity, and economical system for noninvasive tracking of AD to monitor neurodegeneration at different stages of disease. The obtained Pep-NfL peptide nanosheet may be useful for assessing dynamic changes in plasma NfL concentrations to evaluate disease-modifying therapies as a surrogate end point of neurodegeneration in clinical trials.

Bioinformatics design of a peptide vaccine containing sarcoma antigen NY-SAR-35 epitopes against breast cancer and evaluation of its immunological function in BALB/c mouse model

The development of a cancer vaccine has become an essential focus in the field of medical biotechnology and immunology. In our study, the NY-SAR-35 cancer/testis antigen was targeted to design a novel peptide vaccine using bioinformatics tools, and BALB/c mice were used to evaluate the vaccine's immunological function. This evaluation involved assessing peptide-specific IgG levels in the serum via ELISA and measuring the levels of IFN-γ, IL-4, and granzyme B in the supernatant of cultured splenocytes. The final vaccine construct consisted of two T lymphocyte epitopes linked by the AAY linker. This construct displayed high antigenicity, non-allergenicity, non-toxicity, stability, and ability to induce IFN-γ and IL-4. It showed stable dynamics with both human MHC-I and II molecules, as well as mouse MHC-II molecules, and revealed strong Van der Waals and electrostatic energies. Emulsifying our peptide vaccine in incomplete Freund's adjuvant resulted in a remarkable increase in the levels of IgG. The splenocytes of mice that received the combination of peptide and adjuvant displayed a noteworthy increase in IFN-γ, IL-4, and granzyme B secretion. Additionally, their lymphocytes exhibited higher proliferation rates compared to the control group. Our data demonstrated that our vaccine could stimulate a robust immune response, making it a promising candidate for cancer prevention. However, clinical trials are necessary to assess its efficacy in humans.

Exploring the Potential Impact of GLP-1 Receptor Agonists on Substance Use, Compulsive Behavior, and Libido: Insights from Social Media Using a Mixed-Methods Approach

Glucagon-like peptide-1 (GLP-1) is involved in a range of central and peripheral pathways related to appetitive behavior. Hence, this study explored the effects of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) on substance and behavioral addictions, including alcohol, caffeine, nicotine, cannabis, psychostimulants, compulsive shopping, and sex drive/libido. Data were collected from various social platforms. Keywords related to GLP-1 RAs and substance/behavioral addiction were used to extract relevant comments. The study employed a mixed-methods approach to analyze online discussions posted from December 2019 to June 2023 and collected using a specialized web application. Reddit entries were the focus here due to limited data from other platforms, such as TikTok and YouTube. A total of 5859 threads and related comments were extracted from six subreddits, which included threads about GLP-1 RAs drugs and associated brand names. To obtain relevant posts, keywords related to potential substance use and compulsive behavior were selected. Further analysis involved two main steps: (1) manually coding posts based on users' references to the potential impact of GLP-1 RAs on substance use and non-substance habits, excluding irrelevant or unclear comments; (2) performing a thematic analysis on the dataset of keywords, using AI-assisted techniques followed by the manual revision of the generated themes. Second, a thematic analysis was performed on the keyword-related dataset, using AI-assisted techniques followed by the manual revision of the generated themes. In total, 29.75% of alcohol-related; 22.22% of caffeine-related; and 23.08% of nicotine-related comments clearly stated a cessation of the intake of these substances following the start of GLP-1 RAs prescription. Conversely, mixed results were found for cannabis intake, and only limited, anecdotal data were made available for cocaine, entactogens, and dissociative drugs' misuse. Regarding behavioral addictions, 21.35% of comments reported a compulsive shopping interruption, whilst the sexual drive/libido elements reportedly increased in several users. The current mixed-methods approach appeared to be a useful tool in gaining insight into complex topics such as the effects of GLP-1 RAs on substance and non-substance addiction-related disorders; some GLP-1 RA-related mental health benefits could also be inferred from here. Overall, it appeared that GLP-1 RAs may show the potential to target both substance craving and maladaptive/addictive behaviors, although further empirical research is needed.

In vitro and in silico investigation of effects of antimicrobial peptides from Solanaceae plants against rice sheath blight pathogen Rhizoctinia solani

Rhizoctonia solani, the causative agent of sheath blight disease in rice, poses a significant threat to agricultural productivity. Traditional management approaches involving chemical fungicides have been effective but come with detrimental consequences for the ecosystem. This study aimed to investigate sustainable alternatives in the form of antifungal peptides derived from Solanaceous plant species as potential agents against R. solani. Peptide extracts were obtained using an optimized antimicrobial peptide (AMP) extraction method and desalted using the solid-phase extraction technique. The antifungal potential of peptide-rich extracts from Solanum tuberosum and Capsicum annum was assessed through in vitro tests employing the agar well diffusion method. Furthermore, peptide-protein docking analysis was performed on HPEPDOCK and HDOCK server; and molecular dynamics simulations (MDS) of 100 ns period were performed using the Gromacs 2020.4. The results demonstrated significant inhibition zones for both extracts at concentrations of 100 mg/mL. Additionally, the extracts of Solanum tuberosum and Capsicum annum had minimum inhibitory concentrations of 50 mg/mL and 25 mg/mL, respectively with minimum fungicidal concentrations of 25 mg/mL. Insights into the potential mechanisms of key peptides inhibiting R. solani targets were gleaned from in-silico studies. Notably, certain AMPs exhibited favorable free energy of binding against pathogenicity-related targets, including histone demethylase, sortin nexin, and squalene synthase, in protein-peptide docking simulations. Extended molecular dynamics simulations lasting 100 ns and MM-PBSA calculations were performed on select protein-peptide complexes. AMP10 displayed the most favorable binding free energy against all target proteins, with AMP3, AMP12b, AMP6, and AMP15 also exhibiting promising results against specific targets of R. solani. These findings underscore the potential of peptide extracts from S. tuberosum and C. annum as effective antifungal agents against rice sheath blight caused by R. solani.

Investigation of the MDM2-binding potential of de novo designed peptides using enhanced sampling simulations

The tumor suppressor p53 plays a crucial role in cellular responses to various stresses, regulating key processes such as apoptosis, senescence, and DNA repair. Dysfunctional p53, prevalent in approximately 50 % of human cancers, contributes to tumor development and resistance to treatment. This study employed deep learning-based protein design and structure prediction methods to identify novel high-affinity peptide binders (Pep1 and Pep2) targeting MDM2, with the aim of disrupting its interaction with p53. Extensive all-atom molecular dynamics simulations highlighted the stability of the designed peptide in complex with the target, supported by several structural analyses, including RMSD, RMSF, Rg, SASA, PCA, and free energy landscapes. Using the steered molecular dynamics and umbrella sampling simulations, we elucidate the dissociation dynamics of p53, Pep1, and Pep2 from MDM2. Notable differences in interaction profiles were observed, emphasizing the distinct dissociation patterns of each peptide. In conclusion, the results of our umbrella sampling simulations suggest Pep1 as a higher-affinity MDM2 binder compared to p53 and Pep2, positioning it as a potential inhibitor of the MDM2-p53 interaction. Using state-of-the-art protein design tools and advanced MD simulations, this study provides a comprehensive framework for rational in silico design of peptide binders with therapeutic implications in disrupting MDM2-p53 interactions for anticancer interventions.

The role and future prospects of artificial intelligence algorithms in peptide drug development

Peptide medications have been more well-known in recent years due to their many benefits, including low side effects, high biological activity, specificity, effectiveness, and so on. Over 100 peptide medications have been introduced to the market to treat a variety of illnesses. Most of these peptide medications are developed on the basis of endogenous peptides or natural peptides, which frequently required expensive, time-consuming, and extensive tests to confirm. As artificial intelligence advances quickly, it is now possible to build machine learning or deep learning models that screen a large number of candidate sequences for therapeutic peptides. Therapeutic peptides, such as those with antibacterial or anticancer properties, have been developed by the application of artificial intelligence algorithms.The process of finding and developing peptide drugs is outlined in this review, along with a few related cases that were helped by AI and conventional methods. These resources will open up new avenues for peptide drug development and discovery, helping to meet the pressing needs of clinical patients for disease treatment. Although peptide drugs are a new class of biopharmaceuticals that distinguish them from chemical and small molecule drugs, their clinical purpose and value cannot be ignored. However, the traditional peptide drug research and development has a long development cycle and high investment, and the creation of peptide medications will be substantially hastened by the AI-assisted (AI+) mode, offering a new boost for combating diseases.

Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives

Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world's population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

Computational Screening for the Dipeptidyl Peptidase-IV Inhibitory Peptides from Putative Hemp Seed Hydrolyzed Peptidome as a Potential Antidiabetic Agent

Dipeptidyl peptidase-IV (DPPIV) inhibitory peptides are a class of antihyperglycemic drugs used in the treatment of type 2 diabetes mellitus, a metabolic disorder resulting from reduced levels of the incretin hormone GLP-1. Given that DPPIV degrades incretin, a key regulator of blood sugar levels, various antidiabetic medications that inhibit DPPIV, such as vildagliptin, sitagliptin, and linagliptin, are employed. However, the potential side effects of these drugs remain a matter of debate. Therefore, we aimed to investigate food-derived peptides from Cannabis sativa (hemp) seeds. Our developed bioinformatics pipeline was used to identify the putative hydrolyzed peptidome of three highly abundant proteins: albumin, edestin, and vicilin. These proteins were subjected to in silico digestion by different proteases (trypsin, chymotrypsin, and pepsin) and then screened for DPPIV inhibitory peptides using IDPPIV-SCM. To assess potential adverse effects, several prediction tools, namely, TOXINpred, AllerCatPro, and HemoPred, were employed to evaluate toxicity, allergenicity, and hemolytic effects, respectively. COPID was used to determine the amino acid composition. Molecular docking was performed using GalaxyPepDock and HPEPDOCK, 3D visualizations were conducted using the UCSF Chimera program, and MD simulations were carried out with AMBER20 MD software. Based on the predictive outcomes, FNVDTE from edestin and EAQPST from vicilin emerged as promising candidates for DPPIV inhibitors. We anticipate that our findings may pave the way for the development of alternative DPPIV inhibitors.

GAPS: a geometric attention-based network for peptide binding site identification by the transfer learning approach

Protein-peptide interactions (PPepIs) are vital to understanding cellular functions, which can facilitate the design of novel drugs. As an essential component in forming a PPepI, protein-peptide binding sites are the basis for understanding the mechanisms involved in PPepIs. Therefore, accurately identifying protein-peptide binding sites becomes a critical task. The traditional experimental methods for researching these binding sites are labor-intensive and time-consuming, and some computational tools have been invented to supplement it. However, these computational tools have limitations in generality or accuracy due to the need for ligand information, complex feature construction, or their reliance on modeling based on amino acid residues. To deal with the drawbacks of these computational algorithms, we describe a geometric attention-based network for peptide binding site identification (GAPS) in this work. The proposed model utilizes geometric feature engineering to construct atom representations and incorporates multiple attention mechanisms to update relevant biological features. In addition, the transfer learning strategy is implemented for leveraging the protein-protein binding sites information to enhance the protein-peptide binding sites recognition capability, taking into account the common structure and biological bias between proteins and peptides. Consequently, GAPS demonstrates the state-of-the-art performance and excellent robustness in this task. Moreover, our model exhibits exceptional performance across several extended experiments including predicting the apo protein-peptide, protein-cyclic peptide and the AlphaFold-predicted protein-peptide binding sites. These results confirm that the GAPS model is a powerful, versatile, stable method suitable for diverse binding site predictions.

Identification and Evaluation of Olive Phenolics in the Context of Amine Oxidase Enzyme Inhibition and Depression: In Silico Modelling and In Vitro Validation

The Mediterranean diet well known for its beneficial health effects, including mood enhancement, is characterised by the relatively high consumption of extra virgin olive oil (EVOO), which is rich in bioactive phenolic compounds. Over 200 phenolic compounds have been associated with Olea europaea, and of these, only a relatively small fraction have been characterised. Utilising the OliveNetTM library, phenolic compounds were investigated as potential inhibitors of the epigenetic modifier lysine-specific demethylase 1 (LSD1). Furthermore, the compounds were screened for inhibition of the structurally similar monoamine oxidases (MAOs) which are directly implicated in the pathophysiology of depression. Molecular docking highlighted that olive phenolics interact with the active site of LSD1 and MAOs. Protein-peptide docking was also performed to evaluate the interaction of the histone H3 peptide with LSD1, in the presence of ligands bound to the substrate-binding cavity. To validate the in silico studies, the inhibitory activity of phenolic compounds was compared to the clinically approved inhibitor tranylcypromine. Our findings indicate that olive phenolics inhibit LSD1 and the MAOs in vitro. Using a cell culture model system with corticosteroid-stimulated human BJ fibroblast cells, the results demonstrate the attenuation of dexamethasone- and hydrocortisone-induced MAO activity by phenolic compounds. The findings were further corroborated using human embryonic stem cell (hESC)-derived neurons stimulated with all-trans retinoic acid. Overall, the results indicate the inhibition of flavin adenine dinucleotide (FAD)-dependent amine oxidases by olive phenolics. More generally, our findings further support at least a partial mechanism accounting for the antidepressant effects associated with EVOO and the Mediterranean diet.

Multi-epitopevaccines, from design to expression; an in silico approach

The development of vaccines against a wide range of infectious diseases and pathogens often relies on multi-epitope strategies that can effectively stimulate both humoral and cellular immunity. Immunoinformatics tools play a pivotal role in designing such vaccines, enhancing immune response potential, and minimizing the risk of failure. This review presents a comprehensive overview of practical tools for epitope prediction and the associated immune responses. These immunoinformatics tools facilitate the selection of epitopes based on parameters such as antigenicity, absence of toxic and allergenic sequences, secondary and tertiary structures, sequence conservation, and population coverage. The chosen epitopes can be tailored for B-cells or T-cells, both of which require further assessments covered in this study. We offer a range of suitable linkers that effectively separate cytotoxic T lymphocyte and helper T lymphocyte epitopes while preserving their functionality. Additionally, we identify various adjuvants for specific purposes. We delve into the evaluation of MHC-epitope interactions, MHC clusters, and the simulation of final constructs through molecular docking techniques. We provide diverse linkers and adjuvants optimized for epitope functions to bolster immune responses through epitope attachment. By leveraging these comprehensive tools, the development of multi-epitope vaccines holds the promise of robust immunity and a significant reduction in experimental costs.

Molecular docking studies on α-amylase inhibitory peptides from milk of different farm animals

Milk-derived peptides have emerged as a popular mean to manage various lifestyle disorders such as diabetes. Fermentation is being explored as one of the faster and efficient way of producing peptides with antidiabetic potential. Therefore, in this study, an attempt was made to comparatively investigate the pancreatic α-amylase (PAA) inhibitory properties of peptides derived from milk of different farm animals through probiotic fermentation. Peptide's identification was carried out using liquid chromatography-quadrupole time-of-flight mass spectrometry and inhibition mechanisms were characterized by molecular docking. Results obtained showed a PAA-IC50 value (the amount of protein equivalent needed to inhibit 50% of enzymes) between 2.39 and 36.1 µg protein equivalent for different fermented samples. Overall, Pediococcus pentosaceus MF000957-derived fermented milk from all animals indicated higher PAA inhibition than other probiotic derived fermented milk (PAA-IC50 values of 6.01, 3.53, 15.6, and 10.8 µg protein equivalent for bovine, camel, goat, and sheep fermented milk). Further, molecular docking analysis indicated that camel milk-derived peptide IMEQQQTEDEQQDK and goat milk-derived peptide DQHQKAMKPWTQPK were the most potent PAA inhibitory peptides. Overall, the study concluded that fermentation derived peptides may prove useful in for managing diabetes via inhibition of carbohydrate digesting enzyme PAA.

Characterization of host receptor interaction with envelop protein of Kyasanur forest disease virus and predicting suitable epitopes for vaccine candidate

Kyasanur forest disease (KFD) is a zoonotic disease that is endemic to southern India and caused by KFD virus (KFDV) belonging to the family Flaviviridae. Humans are the dead-end host of the KFDV life cycle. The absence of effective treatment strategies against KFD can be attributed to a lack of studies on the mechanistic part of the spread of the disease. Hypothesizing molecular etiological similarity of KFDV to other well characterized flaviviruses, such as dengue virus (DENV), we focused on predicting the target receptor protein(s) in host and provided molecular basis of receptor-mediated recognition of the human host by KFDV envelop protein (EKFDV), drawing from the extant knowledge on the dengue counterpart, EDENV. Indeed, in silico approach helped to identify that the EKFDV structure closely resembles the EDENV structure and indicated DC-SIGN and/or Mannose receptors to be the plausible target host receptors. Immune-informatics approach aided in predicting 10 epitopes from E, NS1, NS2A, and NS2B proteins of the KFDV-P9605 genotype for vaccine design against KFDV. Further, molecular dynamics simulation (MDS) analyses of their complexes with human leukocyte antigens (HLAs) identified the epitopes DISLTCRVT and YAMEIRPVH as two high ranking candidates for vaccine design.Communicated by Ramaswamy H. Sarma.

Exploring the inhibitory potential of KPHs-AL-derived GLLF peptide on pancreatic lipase and cholesterol esterase activities

The effective modulation of pancreatic lipase and cholesterol esterase activities proves critical in maintaining circulatory triglycerides and cholesterol levels within physiological boundaries. In this study, peptides derived from KPHs-AL, produced through the enzymatic hydrolysis of skipjack tuna dark muscle using alkaline protease, have a specific inhibitory effect on pancreatic lipase and cholesterol esterase. It is hypothesized that these peptides target and modulate the activities of enzymes by inducing conformational changes within their binding pockets, potentially impacting the catalytic functions of both pancreatic lipase and cholesterol esterase. Results revealed these peptides including AINDPFIDL, FLGM, GLLF and WGPL, were found to nestle into the binding site groove of pancreatic lipase and cholesterol esterase. Among these, GLLF stood out, demonstrating potent inhibition with IC50 values of 0.1891 mg/mL and 0.2534 mg/mL for pancreatic lipase and cholesterol esterase, respectively. The kinetics studies suggested that GLLF competed effectively with substrates for the enzyme active sites. Spectroscopic analyses, including ultraviolet-visible, fluorescence quenching, and circular dichroism, indicated that GLLF binding induced conformational changes within the enzymes, likely through hydrogen bond formation and hydrophobic interactions, thereby increasing structural flexibility. Molecular docking and molecular dynamics simulations supported these findings, showing GLLF's stable interaction with vital active site residues. These findings position GLLF as a potent inhibitor of key digestive enzymes, offering insights into its role in regulating lipid metabolism and highlighting its potential as functional ingredient.

Peptidomics-based identification of antihypertensive and antidiabetic peptides from sheep milk fermented using Limosilactobacillus fermentum KGL4 MTCC 25515 with anti-inflammatory activity: in silico, in vitro, and molecular docking studies

This study investigated the synthesis of bioactive peptides from sheep milk through fermentation with Limosilactobacillus fermentum KGL4 MTCC 25515 strain and assessed lipase inhibition, ACE inhibition, α-glucosidase inhibition, and α-amylase inhibition activities during the fermentation process. The study observed the highest activities, reaching 74.82%, 70.02%, 72.19%, and 67.08% (lipase inhibition, ACE inhibition, α-glucosidase inhibition, and α-amylase inhibition) after 48 h at 37°C, respectively. Growth optimization experiments revealed that a 2.5% inoculation rate after 48 h of fermentation time resulted in the highest proteolytic activity at 9.88 mg/mL. Additionally, fractions with less than 3 kDa of molecular weight exhibited superior ACE-inhibition and anti-diabetic activities compared to other fractions. Fermentation of sheep milk with KGL4 led to a significant reduction in the excessive production of NO, TNF-α, IL-6, and IL-1β produced in RAW 267.4 cells upon treatment with LPS. Peptides were purified utilizing SDS-PAGE and electrophoresis on 2D gels, identifying a maximum number of proteins bands ranging 10-70 kDa. Peptide sequences were cross-referenced with AHTPDB and BIOPEP databases, confirming potential antihypertensive and antidiabetic properties. Notably, the peptide (GPFPILV) exhibited the highest HPEPDOCK score against both α-amylase and ACE.

Radiolabeled 15-mer peptide internalization is mediated by megalin (LRP2 receptor) in a CRISPR/Cas9-based LRP2 knockout human kidney cell model

BACKGROUND

Megalin (LRP2 receptor) mediates the endocytosis of radiolabeled peptides into proximal tubular kidney cells, which may cause nephrotoxicity due to the accumulation of a radioactive tracer. The study aimed to develop a cellular model of human kidney HK2 cells with LRP2 knockout (KO) using CRISPR/Cas9 technique. This model was employed for the determination of the megalin-mediated accumulation of 68Ga- and 99mTc-labeled 15-mer peptide developed to target the vascular endothelial growth factor (VEGF) receptor in oncology radiodiagnostics.

RESULTS

The gene editing in the LRP2 KO model was verified by testing two well-known megalin ligands when higher viability of KO cells was observed after gentamicin treatment at cytotoxic concentrations and lower FITC-albumin internalization by the KO cells was detected in accumulation studies. Fluorescent-activated cell sorting was used to separate genetically modified LRP2 KO cell subpopulations. Moreover, flow cytometry with a specific antibody against megalin confirmed LRP2 knockout. The verified KO model identified both 68Ga- and 99mTc-radiolabeled 15-mer peptides as megalin ligands in accumulation studies. We found that both radiolabeled 15-mers enter LRP2 KO HK2 cells to a lesser extent compared to parent cells. Differences in megalin-mediated cellular uptake depending on the radiolabeling were not observed. Using biomolecular docking, the interaction site of the 15-mer with megalin was also described.

CONCLUSION

The CRISPR/Cas9 knockout of LRP2 in human kidney HK2 cells is an effective approach for the determination of radiopeptide internalization mediated by megalin. This in vitro method provided direct molecular evidence for the cellular uptake of radiolabeled anti-VEGFR 15-mer peptides via megalin.

Engineering Antigen-Specific Tolerance to an Artificial Protein Hydrogel

Artificial protein hydrogels are an emerging class of biomaterials with numerous prospective applications in tissue engineering and regenerative medicine. These materials are likely to be immunogenic due to their frequent incorporation of novel amino acid sequence domains, which often serve a functional role within the material itself. We engineered injectable "self" and "nonself" artificial protein hydrogels, which were predicted to have divergent immune outcomes in vivo on the basis of their primary amino acid sequence. Following implantation in mouse, the nonself gels raised significantly higher antigel antibody titers than the corresponding self gels. Prophylactic administration of a fusion antibody targeting the nonself hydrogel epitopes to DEC-205, an endocytic receptor involved in Treg induction, fully suppressed the elevated antibody titer against the nonself gels. These results suggest that the clinical immune response to artificial protein biomaterials, including those that contain highly antigenic sequence domains, can be tuned through the induction of antigen-specific tolerance.

Computational Insights into the Interaction of the Conserved Cysteine-Noose Domain of the Human Respiratory Syncytial Virus G Protein with the Canonical Fractalkine Binding site of Transmembrane Receptor CX3CR1 Isoforms

The human Respiratory Syncytial Virus (hRSV) stands as one of the most common causes of acute respiratory diseases. The infectivity of this virus is intricately linked to its membrane proteins, notably the attachment glycoprotein (G protein). The latter plays a key role in facilitating the attachment of hRSV to respiratory tract epithelial cells, thereby initiating the infection process. The present study aimed to characterize the interaction of the conserved cysteine-noose domain of hRSV G protein (cndG) with the transmembrane CX3C motif chemokine receptor 1 (CX3CR1) isoforms using computational tools of molecular modeling, docking, molecular dynamics simulations, and binding free energy calculations. From MD simulations of the molecular system embedded in the POPC lipid bilayer, we showed a stable interaction of cndG with the canonical fractalkine binding site in the N-terminal cavity of the CX3CR1 isoforms and identified that residues in the extracellular loop 2 (ECL2) region and Glu279 of this receptor are pivotal for the stabilization of CX3CR1/cndG binding, corroborating what was reported for the interaction of the chemokine fractalkine with CX3CR1 and its structure homolog US28. Therefore, the results presented here contribute by revealing key structural points for the CX3CR1/G interaction, allowing us to better understand the biology of hRSV from its attachment process and to develop new strategies to combat it.

Peptide-based allosteric inhibitor targets TNFR1 conformationally active region and disables receptor-ligand signaling complex

Tumor necrosis factor (TNF) receptor 1 (TNFR1) plays a pivotal role in mediating TNF induced downstream signaling and regulating inflammatory response. Recent studies have suggested that TNFR1 activation involves conformational rearrangements of preligand assembled receptor dimers and targeting receptor conformational dynamics is a viable strategy to modulate TNFR1 signaling. Here, we used a combination of biophysical, biochemical, and cellular assays, as well as molecular dynamics simulation to show that an anti-inflammatory peptide (FKCRRWQWRMKK), which we termed FKC, inhibits TNFR1 activation allosterically by altering the conformational states of the receptor dimer without blocking receptor-ligand interaction or disrupting receptor dimerization. We also demonstrated the efficacy of FKC by showing that the peptide inhibits TNFR1 signaling in HEK293 cells and attenuates inflammation in mice with intraperitoneal TNF injection. Mechanistically, we found that FKC binds to TNFR1 cysteine-rich domains (CRD2/3) and perturbs the conformational dynamics required for receptor activation. Importantly, FKC increases the frequency in the opening of both CRD2/3 and CRD4 in the receptor dimer, as well as induces a conformational opening in the cytosolic regions of the receptor. This results in an inhibitory conformational state that impedes the recruitment of downstream signaling molecules. Together, these data provide evidence on the feasibility of targeting TNFR1 conformationally active region and open new avenues for receptor-specific inhibition of TNFR1 signaling.

Novel Antimicrobial Peptide SAAP Mutant as a Better Adjuvant to Sulbactam-Based Treatments Against Clinical Strains of XDR Acinetobacter baumannii

The production of extended spectrum β-lactamases (ESBLs) in extensively drug-resistant (XDR) strains of Acinetobacter baumannii has created havoc amongst clinicians making the treatment procedure challenging. Carbapenem-resistant strains have displayed total ineffectiveness towards newer combinations of β-lactam-β-lactamase inhibitors (βL-βLI) in tertiary healthcare settings. Therefore, the present study was aimed to design potential β-lactamase antimicrobial peptide (AMP) inhibitors against ESBLs produced by the strains. We have constructed an AMP mutant library with higher antimicrobial efficacy (range: ~ 15 to 27%) than their parent peptides. The mutants were thoroughly screened based on different physicochemical and immunogenic properties revealing three peptides, namely SAAP-148, HFIAP-1, myticalin-C6 and their mutants with safe pharmacokinetics profile. Molecular docking highlighted SAAP-148_M15 displaying maximum inhibitory potential with lowest binding energies against NDM1 (- 1148.7 kcal/mol), followed by OXA23 (- 1032.5 kcal/mol) and OXA58 (- 925.3 kcal/mol). The intermolecular interaction profiles displayed SAAP-148_M15 exhibiting hydrogen bonds and van der Waals hydrophobic interactions with the crucial residues of metallo β-lactamase [IPR001279] and penicillin-binding transpeptidase [IPR001460] domains. Coarse-grained clustering and molecular dynamics simulations (MDS) further validated the stable backbone profile and minimal residue-level fluctuations of the protein-peptide complex that were maintained throughout the simulation timeframe. The present study hypothesised that the combination of sulbactam (βL) with SAAP-148_M15 (βLI) holds immense potential in inhibiting the ESBLs alongside restoration of sulbactam activity. The current in silico findings upon further experimental validations can pave path towards designing of successful therapeutic strategy against XDR strains of A. baumannii.

Antioxidant and Angiotensin-Converting Enzyme Inhibitory Activity of Faba Bean-Derived Peptides After In Vitro Gastrointestinal Digestion: Insight into Their Mechanism of Action

Faba bean flour, after in vitro gastrointestinal digestion, showed important antioxidant and angiotensin-converting enzyme (ACE) inhibitory activities. In the present study, 11 faba bean- derived peptides were synthesized to confirm their bioactivities and provide a deeper understanding of their mechanisms of action. The results revealed that 7 peptides were potent antioxidants, namely, NYDEGSEPR, TETWNPNHPEL, TETWNPNHPE, VIPTEPPH, VIPTEPPHA, VVIPTEPPHA, and VVIPTEPPH. Among them, TETWNPNHPEL had the highest activity in the ABTS (EC50 = 0.5 ± 0.2 mM) and DPPH (EC50 = 2.1 ± 0.1 mM) assays (p < 0.05), whereas TETWNPNHPE had the highest activity (p < 0.05) in the ORAC assay (2.84 ± 0.08 mM Trolox equivalent/mM). Synergistic and/or additive effects were found when selected peptides (TETWNPNHPEL, NYDEGSEPR, and VVIPTEPPHA) were combined. Four peptides were potent ACE inhibitors, where VVIPTEPPH (IC50 = 43 ± 1 μM) and VVIPTEPPHA (IC50 = 50 ± 5 μM) had the highest activity (p < 0.05), followed by VIPTEPPH (IC50 = 90 ± 10 μM) and then VIPTEPPHA (IC50 = 123 ± 5 μM) (p < 0.05). These peptides were noncompetitive inhibitors, as supported by kinetic studies and a molecular docking investigation. This study demonstrated that peptides derived from faba beans have multifunctional bioactivities, making them a promising food-functional and nutraceutical ingredient.

AutoPepVax, a Novel Machine-Learning-Based Program for Vaccine Design: Application to a Pan-Cancer Vaccine Targeting EGFR Missense Mutations

The current epitope selection methods for peptide vaccines often rely on epitope binding affinity predictions, prompting the need for the development of more sophisticated in silico methods to determine immunologically relevant epitopes. Here, we developed AutoPepVax to expedite and improve the in silico epitope selection for peptide vaccine design. AutoPepVax is a novel program that automatically identifies non-toxic and non-allergenic epitopes capable of inducing tumor-infiltrating lymphocytes by considering various epitope characteristics. AutoPepVax employs random forest classification and linear regression machine-learning-based models, which are trained with datasets derived from tumor samples. AutoPepVax, along with documentation on how to run the program, is freely available on GitHub. We used AutoPepVax to design a pan-cancer peptide vaccine targeting epidermal growth factor receptor (EGFR) missense mutations commonly found in lung adenocarcinoma (LUAD), colorectal adenocarcinoma (CRAD), glioblastoma multiforme (GBM), and head and neck squamous cell carcinoma (HNSCC). These mutations have been previously targeted in clinical trials for EGFR-specific peptide vaccines in GBM and LUAD, and they show promise but lack demonstrated clinical efficacy. Using AutoPepVax, our analysis of 96 EGFR mutations identified 368 potential MHC-I-restricted epitope-HLA pairs from 49,113 candidates and 430 potential MHC-II-restricted pairs from 168,669 candidates. Notably, 19 mutations presented viable epitopes for MHC I and II restrictions. To evaluate the potential impact of a pan-cancer vaccine composed of these epitopes, we used our program, PCOptim, to curate a minimal list of epitopes with optimal population coverage. The world population coverage of our list ranged from 81.8% to 98.5% for MHC Class II and Class I epitopes, respectively. From our list of epitopes, we constructed 3D epitope-MHC models for six MHC-I-restricted and four MHC-II-restricted epitopes, demonstrating their epitope binding potential and interaction with T-cell receptors. AutoPepVax's comprehensive approach to in silico epitope selection addresses vaccine safety, efficacy, and broad applicability. Future studies aim to validate the AutoPepVax-designed vaccines with murine tumor models that harbor the studied mutations.

Design and functional characterization of Salmo salar TLR5 agonist peptides derived from high mobility group B1 acidic tail

Toll-like receptor 5 (TLR5) responds to the monomeric form of flagellin and induces the MyD88-depending signaling pathway, activating proinflammatory transcription factors such as NF-κB and the consequent induction of cytokines. On the other hand, HMGB1 is a highly conserved non-histone chromosomal protein shown to interact with and activate TLR5. The present work aimed to design and characterize TLR5 agonist peptides derived from the acidic tail of Salmo salar HMGB1 based on the structural knowledge of the TLR5 surface using global molecular docking platforms. Peptide binding poses complexed on TLR5 ectodomain model from each algorithm were filtrated based on docking scoring functions and predicted theoretical binding affinity of the complex. Circular dichroism spectra were recorded for each peptide selected for synthesis. Only intrinsically disordered peptides (6W, 11W, and SsOri) were selected for experimental functional assay. The functional characterization of the peptides was performed by NF-κB activation assays, RT-qPCR gene expression assays, and Piscirickettsia salmonis challenge in SHK-1 cells. The 6W and 11W peptides increased the nuclear translation of p65 and phosphorylation. In addition, the peptides induced the expression of genes related to the TLR5 pathway activation, pro- and anti-inflammatory response, and differentiation and activation of T lymphocytes towards phenotypes such as TH1, TH17, and TH2. Finally, it was shown that the 11W peptide protects immune cells against infection with P. salmonis bacteria. Overall, the results indicate the usefulness of novel peptides as potential immunostimulants in salmonids.

SLAM (CD150) receptor homologous peptides block the peste des petits ruminants virus entry into B95a cells

The fusion of haemagglutinin-neuraminidase (HN) protein of peste des petits ruminant (PPR) virus with signaling lymphocyte activation molecules (SLAM) host cell receptor consequences the virus entry and multiplication inside the host cell. The use of synthetic SLAM homologous peptides (i.e., molecular decoy for HN protein of PPR virus) may check PPR infection at the preliminary stage. Hence, the predicted SLAM homologous peptides using bioinformatics tools were synthesized by solid phase chemistry with standard Merrifield's 9-fluorenylmethoxycarbonyl (Fmoc) chemistry and were purified by reverse phase high performance liquid chromatography. The secondary structures of synthesized peptides were elucidated by circular dichroism spectroscopy. The in vitro interactions of these peptides were studied through indirect Enzyme Linked Immuno Sorbent Assay (ELISA) and visual surface plasmon UV-visible spectroscopy. The SLAM homologous peptides were able to interact with the peste des petits ruminant virus (PPRV) with varying binding efficiency. The interaction of SLAM homologous peptide with the PPR virus was ascertained by the change in the plasmon color from red wine to purple during visual detection and also by bathochromic shift in absorbance spectra under UV-visible spectrophotometry. The cytotoxic and anti-PPRV effect of these peptides were also evaluated in B95a cell line using PPR virus (Sungri/96). The cytotoxic concentration 50 (CC50 ) value of each peptide was greater than 1000 μg mL-1 . The anti-PPRV efficiency of SLAM-22 was relatively high among SLAM homologous peptides, SLAM-22 at 25 μg mL-1 concentration showed a reduction of more than log10 3 virus titer by priming of B95a cell line while the use of SLAM-15 and Muco-17 at the same concentration dropped virus titer from log10 4.8 to log10 2.5 and log10 3.1 respectively. The concentration of SLAM homologous peptide (25 μg mL-1 ) to exert its anti-PPRV effect was much less than its CC50 level (>1000 μg mL-1 ). Therefore, the synthetic SLAM homologous peptides may prove to be better agents to target PPRV.

TopBP1 utilises a bipartite GINS binding mode to support genome replication

Activation of the replicative Mcm2-7 helicase by loading GINS and Cdc45 is crucial for replication origin firing, and as such for faithful genetic inheritance. Our biochemical and structural studies demonstrate that the helicase activator GINS interacts with TopBP1 through two separate binding surfaces, the first involving a stretch of highly conserved amino acids in the TopBP1-GINI region, the second a surface on TopBP1-BRCT4. The two surfaces bind to opposite ends of the A domain of the GINS subunit Psf1. Mutation analysis reveals that either surface is individually able to support TopBP1-GINS interaction, albeit with reduced affinity. Consistently, either surface is sufficient for replication origin firing in Xenopus egg extracts and becomes essential in the absence of the other. The TopBP1-GINS interaction appears sterically incompatible with simultaneous binding of DNA polymerase epsilon (Polε) to GINS when bound to Mcm2-7-Cdc45, although TopBP1-BRCT4 and the Polε subunit PolE2 show only partial competitivity in binding to Psf1. Our TopBP1-GINS model improves the understanding of the recently characterised metazoan pre-loading complex. It further predicts the coordination of three molecular origin firing processes, DNA polymerase epsilon arrival, TopBP1 ejection and GINS integration into Mcm2-7-Cdc45.

Global fungal-host interactome mapping identifies host targets of candidalysin

Candidalysin, a cytolytic peptide toxin secreted by the human fungal pathogen Candida albicans, is critical for fungal pathogenesis. Yet, its intracellular targets have not been extensively mapped. Here, we performed a high-throughput enhanced yeast two-hybrid (HT-eY2H) screen to map the interactome of all eight Ece1 peptides with their direct human protein targets and identified a list of potential interacting proteins, some of which were shared between the peptides. CCNH, a regulatory subunit of the CDK-activating kinase (CAK) complex involved in DNA damage repair, was identified as one of the host targets of candidalysin. Mechanistic studies revealed that candidalysin triggers a significantly increased double-strand DNA breaks (DSBs), as evidenced by the formation of γ-H2AX foci and colocalization of CCNH and γ-H2AX. Importantly, candidalysin binds directly to CCNH to activate CAK to inhibit DNA damage repair pathway. Loss of CCNH alleviates DSBs formation under candidalysin treatment. Depletion of candidalysin-encoding gene fails to induce DSBs and stimulates CCNH upregulation in a murine model of oropharyngeal candidiasis. Collectively, our study reveals that a secreted fungal toxin acts to hijack the canonical DNA damage repair pathway by targeting CCNH and to promote fungal infection.

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.

Structural, functional, molecular docking analysis of a hypothetical protein from Talaromyces marneffei and its molecular dynamic simulation: an in-silico approach

Telaromyces marneffei (formerly Penicillium marneffei) is an endemic pathogenic fungus in Southern China and Southeast Asia. It can cause disease in patients with travel-related exposure to this organism and high morbidity and mortality in acquired immune deficiency syndrome (AIDS). In this study, we analyzed the structure and function of a hypothetical protein from T. marneffei using several bioinformatics tools and servers to unveil novel pharmacological targets and design a peptide vaccine against specific epitopes. A total of seven functional epitopes were screened on the protein, and 'STGVDMWSV' was the most antigenic, non-allergenic and non-toxic. Molecular docking showed stronger affinity between the CTL epitope 'STGVDMWSV' and the MHC I allele HLA-A*02:01, a higher docking score -234.98 kcal/mol, revealed stable interactions during a 100 ns molecular dynamic simulation. Overall, the results of this study revealed that this hypothetical protein is crucial for comprehending biochemical, physiological pathways and identifying novel therapeutic targets for human health. Communicated by Ramaswamy H. Sarma.

COSMIC-based mutation database enhances identification efficiency of HLA-I immunopeptidome

BACKGROUND

Neoantigens have emerged as a promising area of focus in tumor immunotherapy, with several established strategies aiming to enhance their identification. Human leukocyte antigen class I molecules (HLA-I), which present intracellular immunopeptides to T cells, provide an ideal source for identifying neoantigens. However, solely relying on a mutation database generated through commonly used whole exome sequencing (WES) for the identification of HLA-I immunopeptides, may result in potential neoantigens being missed due to limitations in sequencing depth and sample quality.

METHOD

In this study, we constructed and evaluated an extended database for neoantigen identification, based on COSMIC mutation database. This study utilized mass spectrometry-based proteogenomic profiling to identify the HLA-I immunopeptidome enriched from HepG2 cell. HepG2 WES-based and the COSMIC-based mutation database were generated and utilized to identify HepG2-specific mutant immunopeptides.

RESULT

The results demonstrated that COSMIC-based database identified 5 immunopeptides compared to only 1 mutant peptide identified by HepG2 WES-based database, indicating its effectiveness in identifying mutant immunopeptides. Furthermore, HLA-I affinity of the mutant immunopeptides was evaluated through NetMHCpan and peptide-docking modeling to validate their binding to HLA-I molecules, demonstrating the potential of mutant peptides identified by the COSMIC-based database as neoantigens.

CONCLUSION

Utilizing the COSMIC-based mutation database is a more efficient strategy for identifying mutant peptides from HLA-I immunopeptidome without significantly increasing the false positive rate. HepG2 specific WES-based database may exclude certain mutant peptides due to WES sequencing depth or sample heterogeneity. The COSMIC-based database can effectively uncover potential neoantigens within the HLA-I immunopeptidomes.

Antimicrobial Peptide Recognition Motif of the Substrate Binding Protein SapA from Nontypeable Haemophilus influenzae

Nontypeable Haemophilus influenzae (NTHi) is an opportunistic pathogen associated with respiratory diseases, including otitis media and exacerbations of chronic obstructive pulmonary disease. NTHi exhibits resistance to killing by host antimicrobial peptides (AMPs) mediated by SapA, the substrate binding protein of the sensitivity to antimicrobial peptides (Sap) transporter. However, the specific mechanisms by which SapA selectively binds various AMPs such as defensins and cathelicidin are unknown. In this study, we report mutational analyses of both defensin AMPs and the SapA binding pocket to define the specificity of AMP recognition. Bactericidal assays revealed that NTHi lacking SapA are more susceptible to human beta defensins and LL-37, while remaining highly resistant to a human alpha defensin. In contrast to homologues, our research underscores the distinct specificity of NTHi SapA, which selectively recognizes and binds to peptides containing the charged-hydrophobic motif PKE and RRY. These findings provide valuable insight into the divergence of SapA among bacterial species and NTHi SapA's ability to selectively interact with specific AMPs to mediate resistance.

Invited review: Camel milk-derived bioactive peptides and diabetes-Molecular view and perspectives

In dairy science, camel milk (CM) constitutes a center of interest for scientists due to its known beneficial effect on diabetes as demonstrated in many in vitro, in vivo, and clinical studies and trials. Overall, CM had positive effects on various parameters related to glucose transport and metabolism as well as the structural and functional properties of the pancreatic β-cells and insulin secretion. Thus, CM consumption may help manage diabetes; however, such a recommendation will become rationale and clinically conceivable only if the exact molecular mechanisms and pathways involved at the cellular levels are well understood. Moreover, the application of CM as an alternative antidiabetic tool may first require the identification of the exact bioactive molecules behind such antidiabetic properties. In this review, we describe the advances in our knowledge of the molecular mechanisms reported to be involved in the beneficial effects of CM in managing diabetes using different in vitro and in vivo models. This mainly includes the effects of CM on the different molecular pathways controlling (1) insulin receptor signaling and glucose uptake, (2) the pancreatic β-cell structure and function, and (3) the activity of key metabolic enzymes in glucose metabolism. Moreover, we described the current status of the identification of CM-derived bioactive peptides and their structure-activity relationship study and characterization in the context of molecular markers related to diabetes. Such an overview will not only enrich our scientific knowledge of the plausible mode of action of CM in diabetes but should ultimately rationalize the claim of the potential application of CM against diabetes. This will pave the way toward new directions and ideas for developing a new generation of antidiabetic products taking benefits from the chemical composition of CM.

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).

Crustacean cardioactive peptide signaling system in the gastropod mollusk Pacific abalone

Crustacean cardioactive peptide (CCAP) signaling systems have been characterized in a diverse range of protostomes, representatively in arthropods. The cyclic CX5C-type CCAP regulates various biological activities through CCAP receptors (CCAPRs), which are orthologous to neuropeptide S receptors (NPSRs) in deuterostomes. However, the CCAPRs of the lophotrochozoa remain poorly characterized; therefore, the relationship between the CCAP, NPS, and CX4C-type oxytocin/vasopressin (OT/VP) signaling systems is unclear. In this study, we identified a CCAP precursor and two CCAPR isoforms in the Pacific abalone (Haliotis discus hannai; Hdh). The Hdh-CCAP precursor was found to harbor three CX5C-type and one CX4C-type CCAPs. The Hdh-CCAPRs displayed homology with protostome CCAPRs and deuterostome NPSRs, having characteristics of the rhodopsin-type G protein-coupled receptors. Phylogenetic analysis showed that lophotrochozoan CCAPRs, including Hdh-CCAPRs, form a monophyletic group distinct from arthropod CCAPRs. Reporter assays demonstrated that all examined Hdh-CCAPs and insect CCAP-induced intracellular Ca2+ mobilization and cAMP accumulation in Hdh-CCAPR-expressing HEK293 cells, whereas none of the CCAP peptides inhibited the forskolin-stimulated cAMP signaling pathway even at micromolar concentrations. In silico ligand-receptor docking models showed that the N-terminal FCN motifs of Hdh-CCAPs are deeply inserted inside the binding pocket of Hdh-CCAPR, forming extensive hydrophobic interactions. In mature Pacific abalone, the transcripts for Hdh-CCAP precursor and Hdh-CCAPR were highly expressed in the neural ganglia compared to the peripheral tissues. Collectively, this study characterized the first CCAP signaling system linked to both Ca2+/PKC and cAMP/PKA signal transduction pathways in gastropod mollusks and gives insights into the evolutional origins of deuterostomian NPS and OT/VP signaling systems.

Host serine protease ACOT2 assists DENV proliferation by hydrolyzing viral polyproteins

Dengue fever is a mosquito-borne tropical disease caused by the dengue virus (DENV). The replication of DENV relies on the processing of its genome-encoded polyprotein by both viral protease NS3 (NS3pro) and host proteases. However, the impact of host proteases on DENV proliferation is not well understood. In this study, we utilized fluorophosphonate-based probes (FPs) to investigate the up-regulation of host serine proteases during DENV infection in detail. Among the identified proteases, acyl-CoA thioesterase 2 (ACOT2), an enzyme that hydrolyzes acyl-CoA molecules to generate fatty acids and free CoA, exhibited cleavage activity against DENV polypeptide substrates. Enzymatic assays and virological experiments confirmed that ACOT2 contributes to DENV propagation during the replication stage by cleaving the viral polyprotein. Docking models provided insights into the binding pocket of viral polypeptides and the catalytic mechanism of ACOT2. Notably, this study is the first to demonstrate that ACOT2 functions as a serine protease to hydrolyze protein substrates. These findings offer novel insights into DENV infection, host response, as well as the potential development of innovative antiviral strategies.IMPORTANCEDENV, one of the major pathogens of Dengue fever, remains a significant public health concern in tropical and subtropical regions worldwide. How DENV efficiently hijacks the host and accesses its life cycle with delicate interaction remains to be elucidated. Here, we deconvoluted that the host protease ACOT2 assists the DENV replication and characterized the ACOT2 as a serine protease involved in the hydrolysis of the DENV polypeptide substrate. Our results not only further the understanding of the DENV life cycle but also provide a possibility for the usage of activity-based proteomics to reveal host-virus interactions.

Targeted Degradation of Signal Transduction and Activator of Transcription 3 by Chaperone-Mediated Autophagy Targeting Chimeric Nanoplatform

Chaperone-mediated autophagy (CMA) is a lysosomal-dependent proteolysis pathway for the degradation of cytosolic proteins. However, exploiting CMA-mediated proteolysis to degrade proteins of interest in cancer therapy has not been widely applied. In this study, we develop a CMA-targeting chimera (CMATAC) to efficiently and specifically degrade signal transduction and activator of transcription 3 (STAT3) in tumor cells. CMATAC consists of STAT3 and heat shock cognate 70 kDa protein (HSC70) targeting peptides connected by a linker. To efficiently deliver CMATACs into tumor cells, lipid nanoparticles (LNPs) are used to encapsulate CMATACs (nCMATACs) and decorated with an insulin-like growth factor 2 receptor (IGF2R) targeting peptide (InCMATACs) to achieve tumor targeting and precise delivery. The CMA pathway is activated in tumor cells by a fasting-mimicking diet (FMD). Furthermore, FMD treatment strongly enhances the cellular uptake and tumor accumulation of InCMATACs by upregulating the IGF2R expression. As a result, InCMATACs efficiently degrade STAT3 protein in both A549 and HCC827 tumor cells and inhibit tumor growths in vivo. This study demonstrates that InCMATACs can be used for selective proteolysis in cancer therapy.

Study on the Mechanism of Interaction between Dipeptidyl Peptidase 4 and Inhibitory Peptides Based on Gaussian Accelerated Molecular Dynamic Simulation

Dipeptidyl peptidase 4 (DPP4) inhibitors can effectively inhibit the activity of DPP4, increasing the concentrations of glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), which allows for them to effectively contribute to the reduction of blood sugar levels. Leu-Pro-Ala-Val-Thr-Ile-Arg (LPAVTIR) and Leu-Pro-Pro-Glu-His-Asp-Trp-Arg (LPPEHDWR) were the two peptides with the strongest inhibitory activity against DPP4 selected from silkworm pupa proteins. In this study, four systems were established: Apo (ligand-free DPP4), IPI (IPI-bound DPP4), LPAVTIR (LPAVTIR-bound DPP4), LPPEHDWR (LPPEHDWR-bound DPP4), and Gaussian accelerated molecular dynamic (GaMD) simulation was conducted to investigate the mechanism of action of two inhibitory peptides binding to DPP4. Our study revealed that the LPAVTIR peptide possessed a more stable structure and exhibited a tighter binding to the Ser630 active site in DPP4, thus exhibiting a favorable competitive inhibition effect. In contrast, the LPPEHDWR peptide caused the horizontal α-helix (residues 201-215) composed of Glu205 and Glu206 residues in DPP4 to disappear. The spatial arrangement of active sites Ser630 relative to Glu205 and Glu206 was disrupted, resulting in enzyme inactivation. Moreover, the size of the substrate channel and cavity volume was significantly reduced after the binding of the inhibitory peptide to the protein, which was an important factor in the inhibition of the enzyme activity. A similar effect was also found from IPI (our positive control). By stabilizing the active site of DPP4, the IPI peptide induced the disappearance of the horizontal α-helix and a notable reduction in the active cavity volume. In conclusion, our study provided a solid theoretical foundation for the inhibitory mechanisms of IPI, LPAVTIR, and LPPEHDWR on DPP4, offering valuable insights for advancing the development of drug targets for type 2 diabetes.

Activation of Polycystin-1 Signaling by Binding of Stalk-derived Peptide Agonists

Polycystin-1 (PC1) is the membrane protein product of the PKD1 gene whose mutation is responsible for 85% of the cases of autosomal dominant polycystic kidney disease (ADPKD). ADPKD is primarily characterized by the formation of renal cysts and potential kidney failure. PC1 is an atypical G protein-coupled receptor (GPCR) consisting of 11 transmembrane helices and an autocatalytic GAIN domain that cleaves PC1 into extracellular N-terminal (NTF) and membrane-embedded C-terminal (CTF) fragments. Recently, signaling activation of the PC1 CTF was shown to be regulated by a stalk tethered agonist (TA), a distinct mechanism observed in the adhesion GPCR family. A novel allosteric activation pathway was elucidated for the PC1 CTF through a combination of Gaussian accelerated molecular dynamics (GaMD), mutagenesis and cellular signaling experiments. Here, we show that synthetic, soluble peptides with 7 to 21 residues derived from the stalk TA, in particular, peptides including the first 9 residues (p9), 17 residues (p17) and 21 residues (p21) exhibited the ability to re-activate signaling by a stalkless PC1 CTF mutant in cellular assays. To reveal molecular mechanisms of stalk peptide-mediated signaling activation, we have applied a novel Peptide GaMD (Pep-GaMD) algorithm to elucidate binding conformations of selected stalk peptide agonists p9, p17 and p21 to the stalkless PC1 CTF. The simulations revealed multiple specific binding regions of the stalk peptide agonists to the PC1 protein including an "intermediate" bound yet inactive state. Our Pep-GaMD simulation findings were consistent with the cellular assay experimental data. Binding of peptide agonists to the TOP domain of PC1 induced close TOP-putative pore loop interactions, a characteristic feature of the PC1 CTF signaling activation mechanism. Using sequence covariation analysis of PC1 homologs, we further showed that the peptide binding regions were consistent with covarying residue pairs identified between the TOP domain and the stalk TA. Therefore, structural dynamic insights into the mechanisms of PC1 activation by stalk-derived peptide agonists have enabled an in-depth understanding of PC1 signaling. They will form a foundation for development of PC1 as a therapeutic target for the treatment of ADPKD.

Agonistic effect of peptides derived from a truncated HMGB1 acidic tail sequence in TLR5 from Salmo salar

Based on the structural knowledge of TLR5 surface and using blind docking platforms, peptides derived from a truncated HMGB1 acidic tail from Salmo salar was designed as TLR5 agonistic. Additionally, a template peptide with the native N-terminal of the acidic tail sequence as a reference was included (SsOri). Peptide binding poses complexed on TLR5 ectodomain model from each algorithm were filtrated based on docking scoring functions and predicted theoretical binding affinity of the complex. The best peptides, termed 6WK and 5LWK, were selected for chemical synthesis and experimental functional assay. The agonist activity by immunoblotting and immunocytochemistry was determined following the NF-κBp65 phosphorylation (p-NF-κBp65) and the nuclear translocation of the NF-κBp65 subunit from the cytosol, respectively. HeLa cells stably expressing a S. salar TLR5 chimeric form (TLR5c7) showed increased p-NF-κBp65 levels regarding extracts from flagellin-treated cells. No statistically significant differences (p > 0.05) were found in the detected p-NF-κBp65 levels between cellular extracts treated with peptides or flagellin by one-way ANOVA. The image analysis of NF-κBp65 immunolabeled cells obtained by confocal microscopy showed increased nuclear NF-κBp65 co-localization in cells both 5LWK and flagellin stimulated, while 6WK and SsOri showed less effect on p65 nuclear translocation (p < 0.05). Also, an increased transcript expression profile of proinflammatory cytokines such as TNFα, IL-1β, and IL-8 in HKL cells isolated from Salmo salar was evidenced in 5LWK - stimulated by RT-PCR analysis. Overall, the result indicates the usefulness of novel peptides as a potential immunostimulant in S. salar.

Shotgun proteomics and molecular simulations on multifunctional bioactive peptides derived from the whey of unexplored "Gaddi" goat of Himalayas

The very first time, whey protein from the Himalayan goat breed "Gaddi" was hydrolyzed with alcalase, flavourzyme, and a combination of both in this study. The degree of hydrolysis (DH) ranged from 28 to 53%, with sequential hydrolysis by combination achieving the highest DH. The sequential hydrolysis demonstrated antimicrobial activity against all pathogens used with 3 kDa permeate showed significantly higher (p < 0.05) activity against S. aureus, E. coli, B. cereus and C. sakazakii. The antioxidant activity was in the range of IC50 = 0.49 to 2.00 mg protein/mL, flavourzyme and sequential hydrolysates showed significant ABTS radical and FRAP inhibition. The α-amylase inhibitory activity was highest in 3 kDa permeate of flavourzyme with IC50 values of 0.34 mg protein/mL. Bioactive peptides DDSPDLPK, EMPFPK and TPEVDKEALEK were identified most significant in the hydrolysates. In molecular docking, the DDSPDLPK interacted most efficiently with enzymes involved in microbial growth, oxidative stress, and hyperglycemia.

Molecular Docking of Intrinsically Disordered Proteins: Challenges and Strategies

Intrinsically disordered proteins (IDPs) are a novel class of proteins that have established a significant importance and attention within a very short period of time. These proteins are essentially characterized by their inherent structural disorder, encoded mainly by their amino acid sequences. The profound abundance of IDPs and intrinsically disordered regions (IDRs) in the biological world delineates their deep-rooted functionality. IDPs and IDRs convey such extensive functionality through their unique dynamic nature, which enables them to carry out huge number of multifaceted biomolecular interactions and make them "interaction hub" of the cellular systems. Additionally, with such widespread functions, their misfunctioning is also intimately associated with multiple diseases. Thus, understanding the dynamic heterogeneity of various IDPs along with their interactions with respective binding partners is an important field with immense potentials in biomolecular research. In this context, molecular docking-based computational approaches have proven to be remarkable in case of ordered proteins. Molecular docking methods essentially model the biomolecular interactions in both structural and energetic terms and use this information to characterize the putative interactions between the two participant molecules. However, direct applications of the conventional docking methods to study IDPs are largely limited by their structural heterogeneity and demands for unique IDP-centric strategies. Thus, in this chapter, we have presented an overview of current methodologies for successful docking operations involving IDPs and IDRs. These specialized methods majorly include the ensemble-based and fragment-based approaches with their own benefits and limitations. More recently, artificial intelligence and machine learning-assisted approaches are also used to significantly reduce the complexity and computational burden associated with various docking applications. Thus, this chapter aims to provide a comprehensive summary of major challenges and recent advancements of molecular docking approaches in the IDP field for their better utilization and greater applicability.Asp (D).

Molecular Dynamics Simulations in Protein-Protein Docking

Concerted interactions between all the cell components form the basis of biological processes. Protein-protein interactions (PPIs) constitute a tremendous part of this interaction network. Deeper insight into PPIs can help us better understand numerous diseases and lead to the development of new diagnostic and therapeutic strategies. PPI interfaces, until recently, were considered undruggable. However, it is now believed that the interfaces contain "hot spots," which could be targeted by small molecules. Such a strategy would require high-quality structural data of PPIs, which are difficult to obtain experimentally. Therefore, in silico modeling can complement or be an alternative to in vitro approaches. There are several computational methods for analyzing the structural data of the binding partners and modeling of the protein-protein dimer/oligomer structure. The major problem with in silico structure prediction of protein assemblies is obtaining sufficient sampling of protein dynamics. One of the methods that can take protein flexibility and the effects of the environment into account is Molecular Dynamics (MD). While sampling of the whole protein-protein association process with plain MD would be computationally expensive, there are several strategies to harness the method to PPI studies while maintaining reasonable use of resources. This chapter reviews known applications of MD in the PPI investigation workflows.

Computer aided therapeutic tripeptide design, in alleviating the pathogenic proclivities of nocuous α-synuclein fibrils

Parkinson's disorder (PD) exacerbates neuronal degeneration of motor nerves, thereby effectuating uncoordinated movements and tremors. Aberrant alpha-synuclein (α-syn) is culpable of triggering PD, wherein cytotoxic amyloid aggregates of α-syn get deposited in motor neurons to instigate neuro-degeneration. Amyloid aggregates, typically rich in beta sheets are cardinal targets to mitigate their neurotoxic effects. In this analysis, owing to their interaction specificity, we formulated an efficacious tripeptide out of the aggregation-prone region of α-syn protein. With the help of a proficient computational pipeline, systematic peptide shortening and an adept molecular simulation platform, we formulated a tripeptide, VAV from α-syn structure based hexapeptide KISVRV. Indeed, the VAV tripeptide was able to effectively mitigate the α-syn amyloid fibrils' dynamic rate of beta-sheet formation. Additional trajectory analyses of the VAV- α-syn complex indicated that, upon its dynamic interaction, VAV efficiently altered the distinct pathogenic structural dynamics of α-syn, further advocating its potential in alleviating aberrant α-syn's amyloidogenic proclivities. Consistent findings from various computational analyses have led us to surmise that VAV could potentially re-alter the pathogenic conformational orientation of α-syn, essential to mitigate its cytotoxicity. Hence, VAV tripeptide could be an efficacious therapeutic candidate to efficiently ameliorate aberrant α-syn amyloid mediated neurotoxicity, eventually attenuating the nocuous effects of PD.Communicated by Ramaswamy H. Sarma.

Rational design of peptide inhibitors targeting HSP90-CDC37 protein-protein interaction

Background: Specifically blocking HSP90-CDC37 interaction is emerging as a prospective strategy for cancer therapy. Aim: Applying a kinase pseudopeptide rationale to the discovery of HSP90-CDC37 protein-protein interaction (PPI) inhibitors. Methods: Pseudosubstrates were identified through sequence alignment and evaluated by biolayer interferometry assay, co-immunoprecipitation assay and antiproliferation assay. Results: TAT-DDO-59120 was identified to disrupt HSP90-CDC37 PPI through directly binding to HSP90, both extracellularly and intracellularly. In addition, the identified peptide showed ideal antiproliferative activity against the colorectal cancer cell HCT116 (IC50 = 12.82 μM). Conclusion: Compared with the traditional method of screening a large compound library to identify PPI inhibitors, this method is rapid and efficient with strong purpose, which provides a novel strategy for designing HSP90-CDC37 PPI inhibitors.

Applications of peptide-functionalized or unfunctionalized selenium nanoparticles for the passivation of SARS-CoV-2 variants and the respiratory syncytial virus (RSV)

The SARS-CoV-2 Omicron subvariants BA.4, BA. 5 and XBB are currently causing a COVID resurgence due to their increased spreading and infectivity. These latest subvariants have been shown to be somewhat resistant to the most common vaccines even with the third dose. Moreover, it has been well documented that when patients stop taking some commercial therapies (such as Paxlovid), COVID from these variants may return and may even be more contagious. Herein, we tested unfunctionalized and functionalized selenium (Se) nanoparticles with three novel peptides (NapFFTLUFLTUTEKKKK, NapFFMLUFLMUMEKKKK, and NapFFSAVLQSGFKKKK) previously shown by themselves to passivate the Omicron SARS-CoV-2 BA.4, BA.5 and XBB variants. Se is a natural element in our diet and is well known to boost the immune system, thus, providing a complementary approach to viral infections. NapFFMLUFLMUMEKKKK showed a stronger inhibition ability at 98 % for Omicron BA.4 % and 96 % for Omicron BA.5 after just 15 min in vitro. Two types of Se nanoparticles (those made chemically or biogenically by cells) were tested to passivate the new SARS-CoV-2 XBB variant. Results showed that the combination of any peptide and using either type of Se NP, the Omicron subvariant XBB was inhibited by 100 % after just 15 min in vitro. Interestingly, the use of Se NPs alone outperformed the peptides in terms of XBB passivation. Also, in order to determine a mechanism of action, functionalizing Se nanoparticles with the NapFFSAVLQSGFKKKK peptide showed a high binding ability toward the chemotrypsin-like cysteine protease (SARS CoV-2 3CLpro). Further, as a demonstration of their versatility, these functionalized peptides also passivated the Respiratory Syncytial Virus (RSV). NapFFTLUFLTUTEKKKK and NapFFMLUFLMUMEKKKK showed in silico interactions with the fusion glycoprotein of RSV prompting in vitro RSV pseudo virus testing. Compared to the conventionally precipitated synthetic Se nanoparticles, in vitro results showed that biogenic Se functionalized with the peptides enhanced the inhibition RSV to 100 % after just 15 min of incubation. NapFFTLUFLTUTEKKKK and NapFFMLUFLMUMEKKKK also showed no potential genotoxicity or carcinogenic effects. The peptides showed good gastro-intestinal (GI) tract absorption and bioavailability as predicted using the partition coefficient (QP logPo/w), and high-water solubility as detected by QPlogS. According to these promising results, functionalizing biogenic Se nanoparticles with these novel peptides should be further studied in vivo for the improved diagnosis, prevention, and treatment of SARS-CoV-2, RSV, and other respiratory virus infections.

Paradoxical effect of Aβ on protein levels of ABCA1 in astrocytes, microglia, and neurons isolated from C57BL/6 mice: an in vitro and in silico study to elucidate the effect of Aβ on ABCA1 in the brain cells

Impaired cholesterol metabolism has been reported in Alzheimer's disease. Since ABCA1 is one of the main players in the brain's cholesterol homeostasis, here we used the in-vitro and in-silico experiments to investigate the effect of Aβ on ABCA1 protein levels in microglia, astrocytes, and neurons in mice. Microglia, astrocytes, and neurons were cultured and exposed to beta amyloid. ABCA1 in cell lysates was determined by Western blotting, and cholesterol efflux was measured in the conditioned media. Molecular docking, molecular dynamics simulations, and MM-GBSA analysis were conducted to gain a better understanding of the effects of Aβ on ABCA1. In response to Aβ, the protein levels of ABCA1 increase significantly in microglia, astrocytes, and neurons; however, its ability to enhance cholesterol efflux is diminished. Aβ inhibited the function of ABCA1 by obstructing the extracellular tunnel that transports lipids outside the cell, as determined by molecular docking. MD simulation analysis validated these findings. Our results demonstrated that Aβ could increase ABCA1 protein levels in various brain cells, regardless of cell type. Molecular docking, molecular dynamics simulation, and MM-GBSA studies indicate that Aβ has a significant effect on the structural conformation of ABCA1, possibly interfering with its function. We believe that the conformational changes of ABCA1 will inhibit its ability to subsequently release cellular cholesterol. Aβ may obstruct the extracellular tunnel of ABCA1, rendering it less accessible to proteases such as the calpain family, which may explain the increase in ABCA1 levels but decrease in its function.Communicated by Ramaswamy H. Sarma.

A New Advanced Approach: Design and Screening of Affinity Peptide Ligands Using Computer Simulation Techniques

Peptides acquire target affinity based on the combination of residues in their sequences and the conformation formed by their flexible folding, an ability that makes them very attractive biomaterials in therapeutic, diagnostic, and assay fields. With the development of computer technology, computer-aided design and screening of affinity peptides has become a more efficient and faster method. This review summarizes successful cases of computer-aided design and screening of affinity peptide ligands in recent years and lists the computer programs and online servers used in the process. In particular, the characteristics of different design and screening methods are summarized and categorized to help researchers choose between different methods. In addition, experimentally validated sequences are listed, and their applications are described, providing directions for the future development and application of computational peptide screening and design.