Discovery Of Peptides Research Articles

Anti‑angiogenic effect of Bryopsis plumosa‑derived peptide via aquaporin 3 in non‑small cell lung cancer.

Developing novel anti‑angiogenic agents with minimal toxicity is notably challenging for cancer therapeutics. The discovery and development of peptides, whether derived from natural sources or synthesized, has potential for developing anti‑angiogenic agents characterized by their ability to penetrate cancer cells, high specificity and low toxicity. The present study identified a Bryopsis plumose‑derived anticancer and anti‑angiogenesis marine‑derived peptide 06 (MP06). A 22‑amino acid peptide was synthesized and conjugated with fluorescein isothiocyanate (FITC‑MP06) for intracellular localization in H1299 non‑small cell lung cancer cells. Regulatory effects of this peptide on the viability, migration and self‑renewal of lung cancer cells was assessed. Furthermore, anti‑angiogenic effect of MP06 was investigated by monitoring vascular tube formation in human umbilical vein endothelial cells and a zebrafish model. Aquaporin (AQP)3, a membrane channel in various tissues, is involved in regulating stemness, epithelial‑mesenchymal transition (EMT) and angiogenesis. MP06 downregulated AQP3 expression. Consistently, AQP3 knockdown by RNA silencing downregulated its gene expression, leading to a decrease in stemness, EMT and angiogenesis properties in H1299 cells. MP06 could thus serve as a novel therapeutic target with anticancer and angiogenesis properties for non‑small cell lung cancer.

IMFP-LG: Identification of Novel Multi-Functional Peptides by Using Protein Language Models and Graph-Based Deep Learning.

Functional peptides are short amino acid fragments that have a wide range of beneficial functions for living organisms. The majority of previous research focused on mono-functional peptides, but a growing number of multi-functional peptides have been discovered. Although there have been enormous experimental efforts to assay multi-functional peptides, only a small fraction of millions of known peptides have been explored. Effective and precise techniques for identifying multi-functional peptides can facilitate their discovery and mechanistic understanding. In this article, we presented a method iMFP-LG for identifying multi-functional peptides based on protein language models (pLMs) and graph attention networks (GATs). Comparison results showed that iMFP-LG outperforms state-of-the-art methods on both multi-functional bioactive peptides and multi-functional therapeutic peptides datasets. The interpretability of iMFP-LG was also illustrated by visualizing attention patterns in pLMs and GATs. Regarding the outstanding performance of iMFP-LG on the identification of multi-functional peptides, we employed iMFP-LG to screen novel candidate peptides with both ACP and AMP functions from millions of known peptides in the UniRef90. As a result, 8 candidate peptides were identified, and 1 candidate that exhibits both antibacterial and anticancer effects was confirmed through molecular structure alignment and biological experiments. We anticipate that iMFP-LG can assist in the discovery of multi-functional peptides and contribute to the advancement of peptide drug design.

Discovery and Characterization of an Atypical Crustin Antimicrobial Peptide from Pollicipes pollicipes

Discovery and Characterization of an Atypical Crustin Antimicrobial Peptide from Pollicipes pollicipes

UniBioPAN: A Novel Universal Classification Architecture for Bioactive Peptides Inspired by Video Action Recognition.

The classification of bioactive peptides is of great importance in protein biology, but there is still a lack of a universal and effective classifier. Inspired by video action recognition, we developed the UniBioPAN architecture to create a universal peptide classifier to solve this problem. The architecture treats the peptide sequence as a video sequence and the molecular image of each amino acid in the peptide sequence as a video frame, enabling feature extraction and classification using convolutional neural networks, bidirectional long short-term memory networks, and fully connected networks. As a novel peptide classification architecture, UniBioPAN significantly outperforms other universal architecture in ACC, AUC and MCC across 11 data sets, and F1 score in 9 data sets. UniBioPAN is available in three ways: python script, jupyter notebook script and web server (https://gzliang.cqu.edu.cn/software/UniBioPAN.html). In summary, UniBioPAN is a universal, convenient, and high-performance peptide classification architecture. UniBioPAN holds significant importance in the discovery of bioactive peptides and the advancement of peptide classifiers. All the codes and data sets are publicly available at https://github.com/sanwrh/UniBioPAN.

Peptide discovery across the spectrum of neuroinflammation; microglia and astrocyte phenotypical targeting, mediation, and mechanistic understanding.

Uncontrolled and chronic inflammatory states in the Central Nervous System (CNS) are the hallmark of neurodegenerative pathology and every injury or stroke-related insult. The key mediators of these neuroinflammatory states are glial cells known as microglia, the resident immune cell at the core of the inflammatory event, and astroglia, which encapsulate inflammatory insults in proteoglycan-rich scar tissue. Since the majority of neuroinflammation is exclusively based on the responses of said glia, their phenotypes have been identified to be on an inflammatory spectrum encompassing developmental, homeostatic, and reparative behaviors as opposed to their ability to affect devastating cell death cascades and scar tissue formation. Recently, research groups have focused on peptide discovery to identify these phenotypes, find novel mechanisms, and mediate or re-engineer their actions. Peptides retain the diverse function of proteins but significantly reduce the activity dependence on delicate 3D structures. Several peptides targeting unique phenotypes of microglia and astroglia have been identified, along with several capable of mediating deleterious behaviors or promoting beneficial outcomes in the context of neuroinflammation. A comprehensive review of the peptides unique to microglia and astroglia will be provided along with their primary discovery methodologies, including top-down approaches using known biomolecules and naïve strategies using peptide and phage libraries.

Reshaping the discovery of self-assembling peptides with generative AI guided by hybrid deep learning

Reshaping the discovery of self-assembling peptides with generative AI guided by hybrid deep learning

Advances in Discovery and Design of Anti-influenza Virus Peptides.

The influenza virus, a well-known pathogen that causes respiratory illness, remains an important global health threat because of the significant morbidity and mortality rates of people infected with the virus annually. The influenza virus undergoes frequent antigenic variation, and with the increasing frequency of resistant influenza strains against existing antiviral drugs, there is an urgent need for the development of new anti- influenza treatment strategies. Peptides have the potential to offer high potency, selectivity, and relatively low drug resistance. As such, the design and screening of novel anti- influenza virus peptides with high potency have become increasingly important in an effort to fight global influenza epidemics. Herein, we introduce three approaches to developing anti-influenza virus peptides: discovery from natural products, library construction for antiviral peptide screening, and rational design based on functional regions of influenza viral proteins. This review summarizes recent progress in the discovery and design of anti-influenza virus peptides over the past 20 years.

Sequence-Activity Relationship of Angiotensin-Converting Enzyme Inhibitory Peptides Derived from Food Proteins, Based on a New Deep Learning Model.

Food-derived peptides are usually safe natural drug candidates that can potentially inhibit the angiotensin-converting enzyme (ACE). The wet experiments used to identify ACE inhibitory peptides (ACEiPs) are time-consuming and costly, making it important and urgent to reduce the scope of experimental validation through bioinformatics methods. Here, we construct an ACE inhibitory peptide predictor (ACEiPP) using optimized amino acid descriptors (AADs) and long- and short-term memory neural networks. Our results show that combined-AAD models exhibit more efficient feature transformation ability than single-AAD models, especially the training model with the optimal descriptors as the feature inputs, which exhibits the highest predictive ability in the independent test (Acc = 0.9479 and AUC = 0.9876), with a significant performance improvement compared to the existing three predictors. The model can effectively characterize the structure-activity relationship of ACEiPs. By combining the model with database mining, we used ACEiPP to screen four ACEiPs with multiple reported functions. We also used ACEiPP to predict peptides from 21,249 food-derived proteins in the Database of Food-derived Bioactive Peptides (DFBP) and construct a library of potential ACEiPs to facilitate the discovery of new anti-ACE peptides.

Antioxidant, Antihypertensive, and Anti-Inflammatory Activities of Long-Term Ripened Cheddar Cheese Water-Soluble Extract.

During cheese ripening, the proteins in the cheese are broken down, and various functional peptides are produced. This research aimed to investigate the changes in peptides and their physiological activities during the long-term maturation of Cheddar cheese. Young (YC), medium (MC), and 3-year-aged extra-sharp (EC) Cheddar cheeses were extracted with water, and bioactive peptides were identified using ultra performance liquid chromatography-high resolution mass spectrometer. Peptides reported to have antioxidant, angiotensin-converting enzyme (ACE)-inhibitory, and anti-inflammatory effects were identified and evaluated in the extracts. MC and EC showed stronger antioxidant activity than YC. The 2,2'-azino-bis-(3-ethylbenzothiazoline-6-sulfonic) acid inhibition rates of MC and EC were similar, but EC exhibited a higher 2,2-diphenyl-1-picrylhydrazyl inhibition rate. The antihypertensive effect was found to increase owing to the appearance of peptides with ACE-inhibitory activity in MC and EC. Quantitative real time polymerase chain reaction and immunoblot were run to evaluate the anti-inflammatory effects. YC did not show anti-inflammatory activity, but MC and EC were shown to effectively inhibit inflammatory mRNA expression. The immunoblot results showed that EC did not inhibit IκBα phosphorylation, but had an inhibitory effect at the mRNA expression level. Overall, the peptides contained in aged Cheddar cheese were shown to have excellent antioxidant, anti-inflammatory, and antihypertensive activities, and long-term ripening appeared to have a positive effect on these activities. This is presumed to have affected not only the already identified peptides but also unknown peptides; therefore, it is expected that the discovery of bioactive peptides will be possible through additional research.

Stack-AVP: A Stacked Ensemble Predictor Based on Multi-view Information for Fast and Accurate Discovery of Antiviral Peptides

AVPs, or antiviral peptides, are short chains of amino acids capable of inhibiting viral replication, preventing viral entry, or disrupting viral membranes. They represent a promising area of research for developing new antiviral therapies due to their potential to target a broad spectrum of viruses, incorporating those resistant to traditional antiviral drugs. However, traditional experimental methods for identifying AVPs are often costly and labour-intensive. Thus far, multiple computational methods have been introduced for the in silico identification of AVPs, but these methods still have certain shortcomings. In this study, we propose a novel stacked ensemble learning framework, termed Stack-AVP, for fast and accurate AVP identification. In Stack-AVP, we investigated heterogeneous prediction models, which were trained with 12 commonly used machine learning algorithms coupled with a wide range of multiple feature encoding schemes. Subsequently, these prediction models were adopted to generate multi-view features providing class information and probability information. Finally, we applied our feature selection method to determine the best feature subset for the construction of the final stacked model. Comparative assessments on the independent test dataset revealed that Stack-AVP surpassed the performance of current state-of-the-art methods, with an accuracy of 0.930, MCC of 0.860, and AUC of 0.975. Furthermore, it was found that our multi-view features exhibited a crucial mechanism to improve the prediction performance of AVPs. To facilitate experimental scientists in performing high-throughput identification of AVPs, the prediction sever Stack-AVP is publicly accessible at https://pmlabqsar.pythonanywhere.com/Stack-AVP.

Proteomic analysis of psychedelic mushroom isolate and exploring potential antimicrobial peptides against bacteria

Psychedelic mushrooms belonging to basidiomycota have gained prominence in research due to the range of hallucinogenic compounds. To combat the challenge of antimicrobial resistance, exploring alternative antimicrobial peptides has become crucial. In this study, we present the proteomic analysis of psychedelic mushroom. Psilocybe cubensis was identified by molecular characterisation using the ITS1 and ITS4 regions. Subsequently, LC-MS/MS and gene ontology analyses were used to characterise the proteome of P. cubensis. The proteomic analysis unveiled several antimicrobial peptides. The results revealed favourable binding scores, suggesting the potential efficacy of these peptides against Staphylococcus aureus. Hence the inhibition of bacterial growth, supporting the antimicrobial properties of the identified peptides. In our findings, the individual peptides from P. cubensis against S. aureus suggest a promising avenue for the discovery of novel antimicrobial peptides.

Recombinant Production of The Cyclotide Kalata B1 by Conditional Split Inteins.

This study describes the design, production, and characterization of a novel conditional intein system for the recombinant production of cyclic peptides. The system is based on two key features: (1) a promiscuous extein recognition site allowing cyclization of virtually any peptide, and (2) a secondary split site within the intein itself enabling triggered splicing at will. Two intein precursors were recombinantly expressed, purified, and then self-assembled in vitro to cyclize the model peptide kalata B1 (kB1). Cyclized kB1 was successfully purified, refolded, and characterized by mass spectrometry and NMR, demonstrating correct disulfide bond formation and identical structure to synthetic kB1. Importantly, the intein-derived kB1 retained full biological activity as evidenced by insect cell toxicity assays. This work establishes a versatile and efficient approach for intein-mediated protein cyclization with potential applications in bioengineering and peptide discovery.

Efficient screening and discovery of umami peptides in Douchi enhanced by molecular dynamics simulations

In this study, a partial least squares discriminant analysis (PLS-DA) discriminant model for umami peptides was constructed based on molecular dynamics simulation data, achieving a R2 value of 0.949 and a Q2 value of 0.558. Using this novel model and bioinformatics screening methods, five new umami peptides (EALEATAQ, SPPTEE, SEEG, KEE, and FEE, with umami taste thresholds of 0.139, 0.085, 0.096, 0.060, and 0.079 mg/mL, respectively) were identified in Douchi. Molecular docking revealed that the residues ASN150 of T1R1, as well as SER170, GLU301 and GLN389 of T1R3, might be key amino acid residues for the binding of umami peptides to T1R1/T1R3. Molecular dynamics simulations revealed significant differences in the root-mean-square fluctuation (RMSF) values between the two complex systems of umami peptides-T1R1/T1R3 and non-umami peptides-T1R1/T1R3. The newly constructed umami peptide discriminant model can improve the accuracy of umami peptide screening and enhance the efficiency of discovering new umami peptides.

Selection of Nucleotide-Encoded Mass Libraries of Macrocyclic Peptides for Inaccessible Drug Targets.

Technological advances and breakthrough developments in the pharmaceutical field are knocking at the door of the "undruggable" fortress with increasing insistence. Notably, the 21st century has seen the emergence of macrocyclic compounds, among which cyclic peptides are of particular interest. This new class of potential drug candidates occupies the vast chemical space between classic small-molecule drugs and larger protein-based therapeutics, such as antibodies. As research advances toward clinical targets that have long been considered inaccessible, macrocyclic peptides are well-suited to tackle these challenges in a post-rule of 5 pharmaceutical landscape. Facilitating their discovery is an arsenal of high-throughput screening methods that exploit massive randomized libraries of genetically encoded compounds. These techniques benefit from the incorporation of non-natural moieties, such as non- proteinogenic amino acids or stabilizing hydrocarbon staples. Exploiting these features for the strategic architectural design of macrocyclic peptides has the potential to tackle challenging targets such as protein-protein interactions, which have long resisted research efforts. This Review summarizes the basic principles and recent developments of the main high-throughput techniques for the discovery of macrocyclic peptides and focuses on their specific deployment for targeting undruggable space. A particular focus is placed on the development of new design guidelines and principles for the cyclization and structural stabilization of cyclic peptides and the resulting success stories achieved against well-known inaccessible drug targets.

Antiviral Peptide-Generative Pre-Trained Transformer (AVP-GPT): A Deep Learning-Powered Model for Antiviral Peptide Design with High-Throughput Discovery and Exceptional Potency.

Traditional antiviral peptide (AVP) discovery is a time-consuming and expensive process. This study introduces AVP-GPT, a novel deep learning method utilizing transformer-based language models and multimodal architectures specifically designed for AVP design. AVP-GPT demonstrated exceptional efficiency, generating 10,000 unique peptides and identifying potential AVPs within two days on a GPU system. Pre-trained on a respiratory syncytial virus (RSV) dataset, AVP-GPT successfully adapted to influenza A virus (INFVA) and other respiratory viruses. Compared to state-of-the-art models like LSTM and SVM, AVP-GPT achieved significantly lower perplexity (2.09 vs. 16.13) and higher AUC (0.90 vs. 0.82), indicating superior peptide sequence prediction and AVP classification. AVP-GPT generated a diverse set of peptides with excellent novelty and identified candidates with remarkably higher antiviral success rates than conventional design methods. Notably, AVP-GPT generated novel peptides against RSV and INFVA with exceptional potency, including four peptides exhibiting EC50 values around 0.02 uM-the strongest anti-RSV activity reported to date. These findings highlight AVP-GPT's potential to revolutionize AVP discovery and development, accelerating the creation of novel antiviral drugs. Future studies could explore the application of AVP-GPT to other viral targets and investigate alternative AVP design strategies.

Strategies for transportation of peptides across the skin for treatment of multiple diseases.

An established view in genetic engineering dictates an increase in the discovery of therapeutic peptides to enable the treatment of multiple diseases. The use of hypodermic needle for delivery of proteins and peptides occurs due to the hydrophilic nature, sensitivity toward proteolytic enzymes and high molecular weight. The non-invasive nature of the transdermal delivery technique offers multiple advantages over the invasive route to release drugs directly into the systemic circulation to enhance bioavailability, better patient compliance, reduced toxicity and local irritability. The transdermal route seems highly desirable from the pharmaco-therapeutic and patient compliance point of view, however, the lipophilic barrier of skin restricts the application. The use of several techniques like electrical methods (iontophoresis, sonophoresis etc.), chemical penetration enhancers (e.g. protease inhibitors, penetration enhancers, etc.) and nanocarriers (dendrimers, lipid nanocapsules, etc.) are utilized to improve the passage of drug molecules across the biomembranes. Additionally, such clinical interventions facilitate the physicochemical characteristics of peptides, to enable effective preservation, conveyance and release of therapeutic agents. Moreover, strategies ensure the attainment of the intended targets and enhance treatment outcomes for multiple diseases. This review article focuses on the techniques of peptide transportation across the skin to advance the delivery approaches and therapeutic efficiency.

Revealing the Diversity of Sequences, Structures, and Targets of Peptides from South China Sea Macrodactyla doreensis Based on Transcriptomics.

The South China Sea is rich in sea anemone resources, and the protein and peptide components from sea anemone toxins comprise an important treasure trove for researchers to search for leading compounds. This study conducted a comprehensive transcriptomic analysis of the tentacles and column of Macrodactyla doreensis and explored the distribution and diversity of proteins and peptides in depth using bioinformatics, initially constructing a putative protein and peptide database. In this database, typical peptide families are identified through amino acid sequence analysis, and their 3D structures and potential biological activities are revealed through AlphaFold2 modeling and molecular docking. A total of 4239 transcripts were identified, of which the putative protein accounted for 81.53%. The highest content comprised immunoglobulin and a variety of proteases, mainly distributed in the column and related to biological functions. Importantly, the putative peptide accounted for 18.47%, containing ShK domain and Kunitz-type peptides, mainly distributed in the tentacles and related to offensive predatory behavior. Interestingly, 40 putative peptides belonging to eight typical peptide families were identified, and their structures and targets were predicted. This study reveals the diversity and complexity of Macrodactyla doreensis toxins and predicts their structure and targets based on amino acid sequences, providing a feasible approach for research regarding the discovery of peptides with potentially high activity.

David Garbers' Contributions to Chemotaxis Signaling in Sperm.

This review focuses on the contribution of the late David Garbers to chemotaxis of sperm, in particular from sea urchin. We will describe his discovery of chemotactic peptides and their cognate receptors, his discovery of a sperm-specific,unique Na+/H+ exchanger that represents a chimera between a solute carrier (SLC) and an ion channel. Finally, we will discuss his contributions to the understanding of cAMP signaling in sperm via soluble adenylyl cyclase (sAC) and its control by Ca2+ ions.

Machine Learning Accelerates the Discovery of Epitope-based Dual-bioactive Peptides Against Skin Infections

Skin injuries and infections are an inevitable part of daily human life, particularly with chronic wounds, becoming an increasing socioeconomic burden. In treating skin infections and promoting wound healing, bioactive peptides may hold significant potential, particularly those possessing antimicrobial and anti-inflammatory properties. However, obtaining these peptides solely through traditional wet laboratory experiments is costly and time-consuming, and peptides identified by current computer-assisted predictive models largely lack validation of their effects via wet laboratory experiments. Consequently, this study aimed to integrate computer-assisted methods and traditional wet laboratory experiments to identify anti-inflammatory and antimicrobial peptides. We developed a computer-assisted mining pipeline to screen potential peptides from the epitopes of the major histocompatibility complex class II (MHC-II). The peptide AIMP1 was identified, with the ability to physically damage Escherichia coli by increasing bacterial cell membrane permeability and with the ability to inhibit inflammation by binding to endotoxin-lipopolysaccharide. Additionally, in an LPS-induced inflammation animal model, AIMP1 slightly increased levels of proinflammatory cytokines (TNF-α, IL-1β, and IL-6), and in a skin wound infection animal model, AIMP1 effectively accelerated healing, reduced levels of these pro-inflammatory cytokines, and showed no acute hepatotoxicity or nephrotoxicity. In conclusion, this study not only developed a computer-assisted mining pipeline for identifying anti-inflammatory and antimicrobial peptides but also successfully pinpointed the peptide AIMP1, demonstrating its therapeutic potential for skin injury treatment.

Establishment of a workflow for high-throughput identification of anti-inflammatory peptides from sea cucumbers

Developing an effective workflow for screening anti-inflammatory peptides is crucial for discovering novel food-derived anti-inflammatory peptides and optimizing the screening and identification process of bioactive peptides. Virtual screening identified three major yolk proteins as target precursor proteins for anti-inflammatory peptides in sea cucumbers. A portfolio of 170 peptides was identified from hydrolysates after 9 h of alcalase treatment by combining antioxidant activity determination and peptidomics analysis. Among these, 12 high-confidence anti-inflammatory peptides were identified through virtual screening. Three of these peptides were shown to effectively inhibit the production of NO and the release of pro-inflammatory cytokines in RAW264.7 cells. Molecular docking demonstrated that these three peptides exerted their anti-inflammatory effects primarily by binding to the active sites of cyclooxygenase-2 and inducible nitric oxide synthase through hydrophobic interactions. This study provided a reference workflow for screening anti-inflammatory peptides, facilitating the discovery of novel anti-inflammatory peptides and the high-value utilization of sea cucumber cooking liquid.