Peptide Database Research Articles

Making MS Omics Data ML-Ready: SpeCollate Protocols.

The increasing complexity and volume of mass spectrometry (MS) data have presented new challenges and opportunities for proteomics data analysis and interpretation. In this chapter, we provide a comprehensive guide to transforming MS data for machine learning (ML) training, inference, and applications. The chapter is organized into three parts. The first part describes the data analysis needed for MS-based experiments and a general introduction to our deep learning model SpeCollate-which we will use throughout the chapter for illustration. The second part of the chapter explores the transformation of MS data for inference, providing a step-by-step guide for users to deduce peptides from their MS data. This section aims to bridge the gap between data acquisition and practical applications by detailing the necessary steps for data preparation and interpretation. In the final part, we present a demonstrative example of SpeCollate, a deep learning-based peptide database search engine that overcomes the problems of simplistic simulation of theoretical spectra and heuristic scoring functions for peptide-spectrum matches by generating joint embeddings for spectra and peptides. SpeCollate is a user-friendly tool with an intuitive command-line interface to perform the search, showcasing the effectiveness of the techniques and methodologies discussed in the earlier sections and highlighting the potential of machine learning in the context of mass spectrometry data analysis. By offering a comprehensive overview of data transformation, inference, and ML model applications for mass spectrometry, this chapter aims to empower researchers and practitioners in leveraging the power of machine learning to unlock novel insights and drive innovation in the field of mass spectrometry-based omics.

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.

Prediction of the best signal peptide for periplasmic expression of melittin peptide in Escherichia coli

Prediction of the best signal peptide for periplasmic expression of melittin peptide in Escherichia coli

ПРИРОДНЫЙ И СИНТЕЗИРОВАННЫЙ ПЕПТИДЫ: СРАВНИТЕЛЬНАЯ АНТИМИКРОБНАЯ АКТИВНОСТЬ

A study of the comparative biological activity of a natural antimicrobial peptide isolated from trypsin hydrolysate of cow colostrum and its synthetic analogue was carried out. It was found that the isolated peptide consists of five amino acids and has a molecular weight of 407 Da. According to a search conducted on the Antimicrobial Peptide Database, the isolated peptide has 100 % similarity to the peptide AP01406 obtained from Eisenia foetida and has antiviral, antimicrobial and antitumor effects. The analysis of the chemical and physical properties of the peptide, screening of the results obtained by the Protein NCBI and Antimicrobial Peptide Database databases suggests that the isolated peptide has antimicrobial properties. A threephase synthesis of a natural analog peptide was carried out. It is proved that synthesized and natural peptides have antimicrobial and antifungal activity. The biological activity of the synthesized peptide is slightly higher than the natural one.

Virtual screening and structure optimization of xanthine oxidase inhibitory peptides from whole protein sequences of Pacific white shrimp via molecular docking.

Xanthine oxidase (XO) inhibitory peptides are safer than conventional pharmacological therapy in relieving hyperuricemia. However, traditional enzymatic hydrolysis, separation, and purification techniques for bio-active peptide preparation are time-consuming, inefficient, and labor-intensive. In this study, molecular docking and BLAST were used to virtually screen XO inhibitory peptides from whole protein sequences of Pacific white shrimp according to the bio-active peptides database, and the structure of peptides was optimized based on the structure-effective relationship. Seven new XO inhibitory peptides were virtual screened rapidly from Pacific white shrimp, and YNITGW (IC50=9.78±0.13mM) showed the strongest activity. The results of YNITGW optimization showed that the insertion of Trp residue in the middle position of peptides could effectively enhance the activity. This study revealed that screening and optimizing peptides by molecular docking were a novel and feasible method to obtain bio-active peptides.

A novel endogenous antimicrobial peptide MP-4 derived from koumiss of Inner Mongolia by peptidomics, and effects on Staphylococcus aureus

Koumiss is known as a traditional milk-based beverage with high nutritional value and antimicrobial potential. In this study, we investigated the antibacterial effects of koumiss-derived antimicrobial peptides against Staphylococcus aureus. Then, we used peptidomics to analyze and identify 2381 peptides from mare's milk and koumiss, of which 53.25% were significantly different before and after fermentation, and these peptides were mainly derived from caseins. Among the identified peptides, five new antimicrobial peptides with high antimicrobial potential, as measured by the scores of predicted peptide sequences in the antimicrobial peptide database, and validation of their antimicrobial activity and stability. MP-4 was found to have antimicrobial activity, and it exerted significant and stable antibacterial effects. The antimicrobial peptide MP-4 could inhibit Staphylococcus aureus by changing its morphology and killing it via the induction of protein leakage. Therefore, it can be concluded that MP-4 is a novel antimicrobial peptide derived from koumiss that has good antibacterial properties against Staphylococcus aureus. Our study provides a theoretical basis for the application of koumiss-derived peptides.

Prediction and Validation of Proline-containing Tripeptides with Angiotensin I-converting Enzyme Inhibitory Activity Using Machine Learning Models

Background: Numerous inhibitory peptides against angiotensin I-converting enzyme, a target for hypertension treatment, have been found in previous studies. Recently, machine learning screening has been employed to predict unidentified inhibitory peptides using a database of known inhibitory peptides and descriptor data from docking simulations. Objective: The aim of this study is to focus on angiotensin I-converting enzyme inhibitory tripeptides containing proline, to predict novel inhibitory peptides using the machine learning algorithm PyCaret based on their IC50 and descriptors from docking simulations, and to validate the screening method by machine learning by comparing the results with in vitro inhibitory activity studies. Methods: IC50 of known inhibitory peptides were collected from an online database, and descriptor data were summarized by docking simulations. Candidate inhibitory peptides were predicted from these data using the PyCaret. Candidate tripeptides were synthesized by solid-phase synthesis and their inhibitory activity was measured in vitro. Results: Seven novel tripeptides were found from the peptides predicted to have high inhibitory activity by machine learning, and these peptides were synthesized and evaluated for inhibitory activity in vitro. As a result, the proline-containing tripeptide MPA showed high inhibitory activity, with an IC50 value of 8.6 µM. Conclusion: In this study, we identified a proline-containing tripeptide with high ACE inhibitory activity among the candidates predicted by machine learning. This finding indicates that the method of predicting by machine learning is promising for future inhibitory peptide screening efforts.

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

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

MFPPDB: a comprehensive multi-functional plant peptide database.

Plants produce a wide range of bioactive peptides as part of their innate defense mechanisms. With the explosive growth of plant-derived peptides, verifying the therapeutic function using traditional experimental methods are resources and time consuming. Therefore, it is necessary to predict the therapeutic function of plant-derived peptides more effectively and accurately with reduced waste of resources and thus expedite the development of plant peptides. We herein developed a repository of plant peptides predicted to have multiple therapeutic functions, named as MFPPDB (multi-functional plant peptide database). MFPPDB including 1,482,409 single or multiple functional plant origin therapeutic peptides derived from 121 fundamental plant species. The functional categories of these therapeutic peptides include 41 different features such as anti-bacterial, anti-fungal, anti-HIV, anti-viral, and anti-cancer. The detailed physicochemical information of these peptides was presented in functional search and physicochemical property search module, which can help users easily access the peptide information by the plant peptide species, ID, and functions, or by their peptide ID, isoelectric point, peptide sequence, and molecular weight through web-friendly interface. We further matched the predicted peptides to nine state-of-the-art curated functional peptide databases and found that at least 293,408 of the peptides possess functional potentials. Overall, MFPPDB integrated a massive number of plant peptides have single or multiple therapeutic functions, which will facilitate the comprehensive research in plant peptidomics. MFPPDB can be freely accessed through http://124.223.195.214:9188/mfppdb/index.

Development of a Database of Peptides with Potential for Pharmacological Intervention in Human Pathogen Molecular Targets.

Development of a Database of Peptides with Potential for Pharmacological Intervention in Human Pathogen Molecular Targets.

The antimicrobial peptide database is 20 years old: Recent developments and future directions.

In 2023, the Antimicrobial Peptide Database (currently available at https://aps.unmc.edu) is 20-years-old. The timeline for the APD expansion in peptide entries, classification methods, search functions, post-translational modifications, binding targets, and mechanisms of action of antimicrobial peptides (AMPs) has been summarized in our previous Protein Science paper. This article highlights new database additions and findings. To facilitate antimicrobial development to combat drug-resistant pathogens, the APD has been re-annotating the data for antibacterial activity (active, inactive, and uncertain), toxicity (hemolytic and nonhemolytic AMPs), and salt tolerance (salt sensitive and insensitive). Comparison of the respective desired and undesired AMP groups produces new knowledge for peptide design. Our unification of AMPs from the six life kingdoms into "natural AMPs" enabled the first comparison with globular or transmembrane proteins. Due to the dominance of amphipathic helical and disulfide-linked peptides, cysteine, glycine, and lysine in natural AMPs are much more abundant than those in globular proteins. To include peptides predicted by machine learning, a new "predicted" group has been created. Remarkably, the averaged amino acid composition of predicted peptides is located between the lower bound of natural AMPs and the upper bound of synthetic peptides. Synthetic peptides in the current APD, with the highest cationic and hydrophobic amino acid percentages, are mostly designed with varying degrees of optimization. Hence, natural AMPs accumulated in the APD over 20 years have laid the foundation for machine learning prediction. We discuss future directions for peptide discovery. It is anticipated that the APD will continue to play a role in research and education.

Fingerprinting of Proteases, Protease Inhibitors and Indigenous Peptides in Human Milk.

The presence of proteases and their resulting level of activity on human milk (HM) proteins may aid in the generation of indigenous peptides as part of a pre-digestion process, of which some have potential bioactivity for the infant. The present study investigated the relative abundance of indigenous peptides and their cleavage products in relation to the abundance of observed proteases and protease inhibitors. The proteomes and peptidomes in twelve HM samples, representing six donors at lactation months 1 and 3, were profiled. In the proteome, 39 proteases and 29 protease inhibitors were identified in 2/3 of the samples. Cathepsin D was found to be present in higher abundance in the proteome compared with plasmin, while peptides originating from plasmin cleavage were more abundant than peptides from cathepsin D cleavage. As both proteases are present as a system of pro- and active- forms, their activation indexes were calculated. Plasmin was more active in lactation month 3 than month 1, which correlated with the total relative abundance of the cleavage product ascribed to plasmin. By searching the identified indigenous peptides in the milk bioactive peptide database, 283 peptides were ascribed to 10 groups of bioactivities. Antimicrobial peptides were significantly more abundant in month 1 than month 3; this group comprised 103 peptides, originating from the β-CN C-terminal region.

Bioactive peptides against angiotensin‐converting enzyme I: An in silico study

AbstractBioactive peptides are currently emerging as nonpharmacological alternatives for treating noncommunicable diseases, including hypertension. Pharmacological treatments for hypertension typically involve angiotensin I converting enzyme inhibitors such as Captopril and enalapril. This study aimed to evaluate the activity of bioactive peptides derived from food sources against this enzyme and show its possible mechanism of inhibitory action. A comprehensive search was conducted in peptide databases to identify peptides with antihypertensive properties. Subsequently, docking studies, simulations, and predictive tests assessing ADME parameters, intestinal stability, and allergenicity were performed using bioinformatics tools. The docking and simulation results demonstrated that PEP2 exhibited superior ACE1 inhibitory potential compared to other peptides, even though it had lower human intestinal absorption. In conclusion, this study suggests that these peptides hold promising potential as nutraceuticals for hypertension treatment.

Apoptin NLS2 homodimerization strategy for improved antibacterial activity and bio-stability.

The emergence of antibiotic resistance prompts exploration of viable antimicrobial peptides (AMPs) designs. The present study explores the antimicrobial prospects of Apoptin nuclear localization sequence (NLS2)-derived peptide ANLP (PRPRTAKRRIRL). Further, we examined the utility of the NLS dimerization strategy for improvement in antimicrobial activity and sustained bio-stability of AMPs. Initially, the antimicrobial potential of ANLP using antimicrobial peptide databases was analyzed. Then, ANLP along with its two homodimer variants namely ANLP-K1 and ANLP-K2 were synthesized and evaluated for antimicrobial activity against Escherichia coli and Salmonella. Among three AMPs, ANLP-K2 showed efficient antibacterial activity with 12µM minimum inhibitory concentration (MIC). Slow degradation of ANLP-K1 (26.48%) and ANLP-K2 (13.21%) compared with linear ANLP (52.33%) at 480min in serum stability assay indicates improved bio-stability of dimeric peptides. The AMPs presented no cytotoxicity in Vero cells. Dye penetration assays confirmed the membrane interacting nature of AMPs. The zeta potential analysis reveals effective charge neutralization of both lipopolysaccharide (LPS) and bacterial cells by dimeric AMPs. The dimeric AMPs on scanning electron microscopy studies showed multiple pore formations on the bacterial surface. Collectively, proposed Lysine scaffold dimerization of Apoptin NLS2 strategy resulted in enhancing antibacterial activity, bio-stability, and could be effective in neutralizing the off-target effect of LPS. In conclusion, these results suggest that nuclear localization sequence with a modified dimeric approach could represent a rich source of template for designing future antimicrobial peptides.

Expanding the Landscape of Amyloid Sequences with CARs-DB: A Database of Polar Amyloidogenic Peptides from Disordered Proteins.

Several databases collecting amyloidogenic regions have been released to provide information on protein sequences able to form amyloid fibrils. However, most of these resources are built with data from experiments that detect highly hydrophobic stretches located within transiently exposed protein segments. We recently demonstrated that cryptic amyloidogenic regions (CARs) of polar nature have the potential to form amyloid fibrils in vitro. Given the underrepresentation of these types of sequences in current amyloid databases, we developed CARs-DB, the first repository that collects thousands of predicted CARs from intrinsically disordered regions. This protocol chapter describes how to use CARs-DB to search for sequences of interest that might be connected to disease or functional protein-protein interactions. In addition, we provide study cases to illustrate the database's features to users. The CARs-DB is readily accessible at http://carsdb.ppmclab.com/ .

Characterization and Classification In Silico of Peptides with Dual Activity (Antimicrobial and Wound Healing).

The growing challenge of chronic wounds and antibiotic resistance has spotlighted the potential of dual-function peptides (antimicrobial and wound healing) as novel therapeutic strategies. The investigation aimed to characterize and correlate in silico the physicochemical attributes of these peptides with their biological activity. We sourced a dataset of 207 such peptides from various peptide databases, followed by a detailed analysis of their physicochemical properties using bioinformatic tools. Utilizing statistical tools like clustering, correlation, and principal component analysis (PCA), patterns and relationships were discerned among these properties. Furthermore, we analyzed the peptides' functional domains for insights into their potential mechanisms of action. Our findings spotlight peptides in Cluster 2 as efficacious in wound healing, whereas Cluster 1 peptides exhibited pronounced antimicrobial potential. In our study, we identified specific amino acid patterns and peptide families associated with their biological activities, such as the cecropin antimicrobial domain. Additionally, we found the presence of polar amino acids like arginine, cysteine, and lysine, as well as apolar amino acids like glycine, isoleucine, and leucine. These characteristics are crucial for interactions with bacterial membranes and receptors involved in migration, proliferation, angiogenesis, and immunomodulation. While this study provides a groundwork for therapeutic development, translating these findings into practical applications necessitates additional experimental and clinical research.

Transcriptomic analysis of Andrias davidianus meat and experimental validation for exploring its bioactive components as functional foods

Andrias davidianus (Chinese giant salamander, CGS) is the largest and oldest extant amphibian species in the world and is a source of prospective functional food in China. However, the progress of functional peptides mining was slow due to lack of reference genome and protein sequence data. In this study, we illustrated full-length transcriptome sequencing to interpret the proteome of CGS meat and obtain 10703 coding DNA sequences. By functional annotation and amino acid composition analysis, we have discovered various genes related to signal transduction, and 16 genes related to longevity. We have also found vast variety of functional peptides through protein coding sequence (CDS) analysis by comparing the data obtained with the functional peptide database. Val-Pro-Ile predicted by the CDS analysis was released from the CGS meat through enzymatic hydrolysis, suggesting that our approach is reliable. This study suggested that transcriptomic analysis can be used as a reference to guide polypeptide mining in CGS meat, thereby providing a powerful mining strategy for the bioresources with unknown genomic and proteomic sequences.

Ovalbumin’s potential as a wound-healing medicament in tooth extraction socket by induction of cell proliferation through the ERK2 pathway in silico

Background: The trend of studies on dental medicaments is increasing rapidly. Antibacterial or anti-inflammatory activity is most frequently studied. Ovalbumin is one of the proteins whose benefits have been studied, but these benefits are still limited because of ovalbumin’s potential for proliferative bioactivity. Purpose: The aim of this study is to examine ovalbumin’s potential as a woundhealing medicament through molecular docking analysis on a protein related to the extracellular signal-regulated kinases/mitogenactivated protein kinase (ERK/MAPK) signaling pathway. Methods: Ovalbumin was hydrolyzed through BIOPEP-UWM (The BIOPEPUWM™ database of bioactive peptides). Protein target and interaction were predicted using Similarity Ensemble Approach target prediction webserver, SuperPred webserver, STRING webserver, and Cytoscape version 3.9.1. Selected fragments were docked using Autodock Vina in PyRx 0.8 with Tukey’s multiple comparison test and Biovia Discovery Studio version 19.1.0.18287 for visualization. Results: This study found that ovalbumin has the potential to positively regulate cell proliferation, angiogenesis, and fibroblast growth factor production. Six of the 131 fragments of ovalbumin could interact with 73 proteins, and the 20 proteins with the highest probability and score of betweenness centrality showed potential for bioactivity. Five fragments and povidone-iodine interacted inside the Adenosine triphosphate (ATP) phosphorylation site of ERK2, whereas fragment 1 (F1) and glycerin interacted outside the site. F1 could decrease the binding energy required for adenosine 5′-[,-methylene]triphosphate or an ATP-analogue chemical compound to interact with ERK2 compared to the control, with a score that was not significant. Conclusion: Ovalbumin has the potential to induce cell proliferation by affecting ERK2-ligand interactions.

Production and Characterization of ACE Inhibitory and Anti-Diabetic Peptides from Buffalo and Camel Milk Fermented with Lactobacillus and Yeast: A Comparative Analysis with In Vitro, In Silico, and Molecular Interaction Study.

The investigation aimed at assessing a comparative study on the production and characterization of ACE inhibitory, anti-diabetic, and anti-inflammatory activities, along with the production of ACE inhibitory and anti-diabetic peptides through the fermentation of buffalo and camel milk by Limosilactobacillus fermentum (KGL4) and Saccharomyces cerevisiae (WBS2A). The angiotensin-converting enzyme (ACE) inhibitory and anti-diabetic properties were evaluated at particular time intervals (12, 24, 36, and 48 h) at 37 °C, and we discovered maximum activity at 37 °C after 48 h of incubation. The maximum ACE inhibitory, lipase inhibitory activities, alpha-glucosidase inhibitory, and alpha-amylase inhibitory activities were found in the fermented camel milk (77.96 ± 2.61, 73.85 ± 1.19, 85.37 ± 2.15, and 70.86 ± 1.02), as compared to the fermented buffalo milk (FBM) (75.25 ± 1.72, 61.79 ± 2.14, 80.09 ± 0.51, and 67.29 ± 1.75). Proteolytic activity was measured with different inoculation rates (1.5%, 2.0%, and 2.5%) and incubation times (12, 24, 36, and 48 h) to optimize the growth conditions. Maximum proteolysis was found at a 2.5% inoculation rate and at a 48 h incubation period in both fermented buffalo (9.14 ± 0.06) and camel milk (9.10 ± 0.17). SDS-PAGE and 2D gel electrophoresis were conducted for protein purification. The camel and buffalo milk that had not been fermented revealed protein bands ranging from 10 to 100 kDa and 10 to 75 kDa, respectively, whereas all the fermented samples showed bands ranging from 10 to 75 kDa. There were no visible protein bands in the permeates on SDS-PAGE. When fermented buffalo and camel milk were electrophoresed in 2D gel, 15 and 20 protein spots were detected, respectively. The protein spots in the 2D gel electrophoresis ranged in size from 20 to 75 kDa. To distinguish between different peptide fractions, water-soluble extract (WSE) fractions of ultrafiltration (3 and 10 kDa retentate and permeate) of fermented camel and buffalo milk were employed in RP-HPLC (reversed-phase high-performance liquid chromatography). The impact of fermented buffalo and camel milk on inflammation induced by LPS (lipopolysaccharide) was also investigated in the RAW 264.7 cell line. Novel peptide sequences with ACE inhibitory and anti-diabetic properties were also analyzed on the anti-hypertensive database (AHTDB) and bioactive peptide (BIOPEP) database. We found the sequences SCQAQPTTMTR, EMPFPK, TTMPLW, HPHPHLSFMAIPPK, FFNDKIAK, ALPMHIR, IPAVFK, LDQWLCEK, and AVPYPQR from the fermented buffalo milk and the sequences TDVMPQWW, EKTFLLYSCPHR, SSHPYLEQLY, IDSGLYLGSNYITAIR, and FDEFLSQSCAPGSDPR from the fermented camel milk.

Characterization and Engineering Studies of a New Endolysin from the Propionibacterium acnes Bacteriophage PAC1 for the Development of a Broad-Spectrum Artilysin with Altered Specificity.

The emergence of multidrug-resistant (MDR) bacteria has risen rapidly, leading to a great threat to global public health. A promising solution to this problem is the exploitation of phage endolysins. In the present study, a putative N-acetylmuramoyl-L-alanine type-2 amidase (NALAA-2, EC 3.5.1.28) from Propionibacterium bacteriophage PAC1 was characterized. The enzyme (PaAmi1) was cloned into a T7 expression vector and expressed in E. coli BL21 cells. Kinetics analysis using turbidity reduction assays allowed the determination of the optimal conditions for lytic activity against a range of Gram-positive and negative human pathogens. The peptidoglycan degradation activity of PaAmi1 was confirmed using isolated peptidoglycan from P. acnes. The antibacterial activity of PaAmi1 was investigated using live P. acnes cells growing on agar plates. Two engineered variants of PaAmi1 were designed by fusion to its N-terminus two short antimicrobial peptides (AMPs). One AMP was selected by searching the genomes of Propionibacterium bacteriophages using bioinformatics tools, whereas the other AMP sequence was selected from the antimicrobial peptide databases. Both engineered variants exhibited improved lytic activity towards P. acnes and the enterococci species Enterococcus faecalis and Enterococcus faecium. The results of the present study suggest that PaAmi1 is a new antimicrobial agent and provide proof of concept that bacteriophage genomes are a rich source of AMP sequences that can be further exploited for designing novel or improved endolysins.