Screening Of Peptides Research Articles

A Multiplexed Cell-Free Assay to Screen for Antimicrobial Peptides in Double Emulsion Droplets.

The global surge in bacterial resistance against traditional antibiotics triggered intensive research for novel compounds, with antimicrobial peptides (AMPs) identified as a promising candidate. Automated methods to systematically generate and screen AMPs according to their membrane preference, however, are still lacking. We introduce a novel microfluidic system for the simultaneous cell‐free production and screening of AMPs for their membrane specificity. On our device, AMPs are cell‐free produced within water‐in‐oil‐in‐water double emulsion droplets, generated at high frequency. Within each droplet, the peptides can interact with different classes of co‐encapsulated liposomes, generating a membrane‐specific fluorescent signal. The double emulsions can be incubated and observed in a hydrodynamic trapping array or analyzed via flow cytometry. Our approach provides a valuable tool for the discovery and development of membrane‐active antimicrobials.

Directed mutational scanning reveals a balance between acidic and hydrophobic residues in strong human activation domains.

SUMMARYAcidic activation domains are intrinsically disordered regions of the transcription factors that bind coactivators. The intrinsic disorder and low evolutionary conservation of activation domains have made it difficult to identify the sequence features that control activity. To address this problem, we designed thousands of variants in seven acidic activation domains and measured their activities with a high-throughput assay in human cell culture. We found that strong activation domain activity requires a balance between the number of acidic residues and aromatic and leucine residues. These findings motivated a predictor of acidic activation domains that scans the human proteome for clusters of aromatic and leucine residues embedded in regions of high acidity. This predictor identifies known activation domains and accurately predicts previously unidentified ones. Our results support a flexible acidic exposure model of activation domains in which the acidic residues solubilize hydrophobic motifs so that they can interact with coactivators. A record of this paper’s transparent peer review process is included in the supplemental information.

Structural characterization of tyrosine kinase substrate binding and array-based functional peptide screening of Src kinase

Protein kinases are a highly targeted class of enzymes for cancer therapeutics. All current FDA approved drugs bind within the ATP site and many demonstrate toxicity due to off-target effects. A new avenue is to design drugs that compete with substrate interactions. This requires the knowledge of how kinases interact with their substrates. Current structure information on kinase-substrate interactions is particularly deficient due to the transient nature of tyrosine kinase-substrate complexes. To overcome this deficiency, we are using NMR approaches and the native solution state to characterize structural patterns for substrate binding of Src tyrosine kinase, a well-known cancer-related drug target.

An exploration of angiotensin-converting enzyme (ACE) inhibitory peptides derived from gastrointestinal protease hydrolysate of milk using a modified bioassay-guided fractionation approach coupled with in silico analysis

An improved bioassay-guided fractionation was performed to effectively screen angiotensin-I converting enzyme inhibitory (ACEI) peptides from milk protein hydrolysate. The aqueous normal phase liquid chromatography, namely hydrophilic interaction liquid chromatography (HILIC), was used as a format of solid-phase extraction (SPE) short column for the first fractionation, then the HILIC-SPE fraction with the best ACEI activity (IC50 = 61.75 ± 5.74 µg/mL; IC50 = half-maximal inhibitory concentration) was obtained when eluted by 95% acetonitrile + 0.1% formic acid (fraction F1). The best HILIC-SPE fraction was further fractionated using reversed-phase (RP)-SPE short column. The best RP-SPE fraction was obtained when eluted by 20% acetonitrile + 0.1% formic acid (fraction P3) with an ACEI activity of IC50 36.22 ± 1.18 µg/mL. After the 2-step fractionation, the IC50 value of fraction P3 significantly decreased by 8.92-fold when compared with the crude hydrolysate. Several peptides were identified from fraction P3 using liquid chromatography-tandem mass spectrometry. The in silico analysis of these identified sequences based on the BIOPEP database predicted that HLPLPLL (HL-7) was the most active peptide against angiotensin-converting enzyme (ACE). The HL-7 derived from β-casein showed a potent ACEI activity (IC50 value is 16.87 ± 0.3 µM). The contents of HL-7 in the gastrointestinal protease hydrolysate and RP-SPE fraction originated from 1 mg of milk proteins were quantified using a multiple reaction monitoring mode upon liquid chromatography-tandem mass spectrometry analysis to give 19.86 ± 1.14 pg and 14,545.8 ± 572.9 pg, respectively. Besides, the kinetic study indicated that HL-7 was a competitive inhibitor and the result was rationalized using the docking simulation. The study demonstrated an efficient screening of ACEI peptides from commercially available milk powders using a simple SPE process instead of a sophisticated instrument such as HPLC. Moreover, the potent ACEI peptide HL-7 uncovered by this method could be a natural ACE inhibitor.

Chikungunya Virus Vaccine Development: Through Computational Proteome Exploration for Finding of HLA and cTAP Binding Novel Epitopes as Vaccine Candidates.

Chikungunya virus is a major arbovirus of great public health concern in the whole world, but no vaccine is yet available, still advance therapeutic treatment and effective vaccines are in progress. The present multistep screening and structural binding analysis with CHIKV proteome exploration can be crucial in the development phase of CHIKV epitope based vaccine. The approach employed in two phases (i) Sequence based screening of peptides through propred and IEDB Server (ii) Structure based study through autodocking and NAMD VMD simulation analysis. Among all 29 extracted peptides, only two peptides 2LLANTTFPC10 of protein E3 and 98VNSVAIPLL106 of protein nsP3 were observed most prominent over all consider parameters such as peptide conserve nature, supertype population coverage, TAP binding, docking and simulation study. During docking interaction study, the best peptide and allele docked complexes such as 2LLANTTFPC10–B*0702 allele and 98VNSVAIPLL106–A*0301 allele exhibited best binding energy of − 3.13 kcal/mol and − 3.19 kcal/mol, respectively, with stable bonding patterns and their motion during NAMD simulation which confirm conserve peptide and allele stable interaction. The current study also exhibited the good docking interaction of both peptides 2LLANTTFPC10 and 98VNSVAIPLL106 with c TAP1 protein (1jj7 -PDB ID) cavity which confirm as a channel passageway to peptide transport through the cytoplasm to lumen of ER during antigen processing and presentation. Overall, this multistep screening and crosscheck structural binding analysis with an exploration of the complete proteome of CHIKV can be a novel step in the development of CHIKV epitope based vaccine as well as diagnostic development with aspect of time, cost and side effects.

Genome-wide high-throughput signal peptide screening via plasmid pUC256E improves protease secretion in Lactiplantibacillus plantarum and Pediococcus acidilactici

BackgroundProteases catalyze the hydrolysis of peptide bonds of proteins, thereby improving dietary protein digestibility, nutrient availability, as well as flavor and texture of fermented food and feed products. The lactobacilli Lactiplantibacillus plantarum (formerly Lactobacillus plantarum) and Pediococcus acidilactici are widely used in food and feed fermentations due to their broad metabolic capabilities and safe use. However, extracellular protease activity in these two species is low. Here, we optimized protease expression and secretion in L. plantarum and P. acidilactici via a genetic engineering strategy.ResultsTo this end, we first developed a versatile and stable plasmid, pUC256E, which can propagate in both L. plantarum and P. acidilactici. We then confirmed expression and secretion of protease PepG1 as a functional enzyme in both strains with the aid of the previously described L. plantarum-derived signal peptide LP_0373. To further increase secretion of PepG1, we carried out a genome-wide experimental screening of signal peptide functionality. A total of 155 predicted signal peptides originating from L. plantarum and 110 predicted signal peptides from P. acidilactici were expressed and screened for extracellular proteolytic activity in the two different strains, respectively. We identified 12 L. plantarum signal peptides and eight P. acidilactici signal peptides that resulted in improved yield of secreted PepG1. No significant correlation was found between signal peptide sequence properties and its performance with PepG1.ConclusionThe vector developed here provides a powerful tool for rapid experimental screening of signal peptides in both L. plantarum and P. acidilactici. Moreover, the set of novel signal peptides identified was widely distributed across strains of the same species and even across some closely related species. This indicates their potential applicability also for the secretion of other proteins of interest in other L. plantarum or P. acidilactici host strains. Our findings demonstrate that screening a library of homologous signal peptides is an attractive strategy to identify the optimal signal peptide for the target protein, resulting in improved protein export.

A Novel Insight into Screening for Antioxidant Peptides from Hazelnut Protein: Based on the Properties of Amino Acid Residues.

This study used the properties of amino acid residues to screen antioxidant peptides from hazelnut protein. It was confirmed that the type and position of amino acid residues, grand average of hydropathy, and molecular weight of a peptide could be comprehensively applied to obtain desirable antioxidants after analyzing the information of synthesized dipeptides and BIOPEP database. As a result, six peptides, FSEY, QIESW, SEGFEW, IDLGTTY, GEGFFEM, and NLNQCQRYM were identified from hazelnut protein hydrolysates with higher antioxidant capacity than reduced Glutathione (GSH) against linoleic acid oxidation. The peptides having Tyr residue at C-terminal were found to prohibit the oxidation of linoleic acid better than others. Among them, peptide FSEY inhibited the rancidity of hazelnut oil very well in an oil-in-water emulsion. Additionally, quantum chemical parameters proved Tyr-residue to act as the active site of FSEY are responsible for its antioxidation. This is the first presentation of a novel approach to excavating desired antioxidant peptides against lipid oxidation from hazelnut protein via the properties of amino acid residues.

Isolation and screening of umami peptides from preserved egg yolk by nano-HPLC-MS/MS and molecular docking

The material basis leading to the rich umami flavor of preserved egg yolk is poorly understood. This study used nano-high-performance liquid chromatography − tandem mass spectrometry (nano-HPLC-MS/MS) to isolate, identify, and screen umami peptides from preserved egg yolk. Five novel umami peptides—AGFMPLP, APYSGY, PPMF, SLSSLMK, and VAMNPVDHPH—were identified. Molecular docking showed that Phe527 on the taste receptor T1R1/T1R3 (T1R1, taste receptor type 1 member 1; T1R3, taste receptor type 1 member 3) was the key interaction site. Hydrogen bonding, electrostatic interactions, and hydrophobic interactions were the main binding forces between T1R1/T1R3 and umami peptides. These results contribute to understanding the umami peptides in preserved egg yolk and the interaction mechanism between umami peptides and umami receptors.

In silico screening and characterization of novel natural peptides as spike protein inhibitors of novel coronavirus (severe acute respiratory syndrome coronavirus 2)

BACKGROUND: The present work is a computational approach to discover the novel peptides that can interact and inhibit the action of spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). MATERIALS AND METHODS: A total of 193 numbers probable naturally occurring antiviral peptides were retrieved from the antimicrobial database. The three-dimensional structure of all the peptides was predicted by the Chimera tool followed by energy minimization. Similarly, the spike protein of SARS-CoV-2 chain A (PDB ID 6VBY) structure was obtained from the Protein Data Bank (PDB) and used as the receptor. RESULTS: Protein–protein docking was performed for all the peptides followed by some screening criteria that resulted in three numbers of potential peptides such as CAP11 binds to a receptor-binding domain (RBD), mytilin B to S1/S2 cleavage regions, and mBD-1 as N-terminal-binding domain of spike protein. Further screening and evaluation of solubility and the toxic properties of the peptides it was obtained that the peptide molecules CAP11 and mytilin B are nontoxic. Further, the RBD-binding nature of CAP11 peptide was evaluated comparatively by taking the human ACE2 protein and RBD region of the wild-type SARS-CoV-2, triple mutant, South African mutant (B.1.351), by using molecular docking followed by interface analysis. It was predicted that the CAP11 peptide was able to bind perfectly with the RBD domain of both wild type and triple mutant one but not to the South African mutant.

Screening and bioavailability evaluation of anti-oxidative selenium-containing peptides from soybeans based on specific structures.

Our previous study has evaluated the antioxidant capacity and identified the sequences of soybean selenium-containing peptides. Herein, pharmacophore screening, gastrointestinal simulation and in vivo pharmacokinetics were performed to predict the potentials of selenium-containing peptides in terms of antioxidant activity, safety and bioavailability. A pharmacophore model with 6 structure features was constructed for virtual screening to determine the potential activities of 85 selenium sequences from soybean peptides. Strong reversing effects (p < 0.05) of the targeted sequences were observed in tumor necrosis factor-α (TNF-α)-induced inflammatory cytokines and adhesion factors burst in EA·hy926/Caco-2 co-culture cell models. Ser-Phe-Gln-SeMet (SFQSeM), a promising peptide selected from both virtual screening and cell models, was proved to be stable in the gastrointestinal tract and could be transported across the Caco-2 monolayer via the paracellular pathway. Additionally, SFQSeM showed a long residence time (89.42 ± 1.34 min) and half-life (81.60 ± 11.88 min) after consumption, and it induced lower liver alanine/aspartate transaminase (ALT/AST) and serum nitric oxide (NO) levels compared to Na2SeO3 and SeMet (p < 0.05). The potency of SFQSeM against oxidative stress as well as its oral bioavailability and low risk highlight its potential utility as an effective Se nutritional supplement.

Screening and identification of a novel antidiabetic peptide from collagen hydrolysates of Chinese giant salamander skin: network pharmacology, inhibition kinetics and protection of IR-HepG2 cells.

In this study, a novel peptide GPPGPA was screened from the collagen hydrolysates of Chinese giant salamander (Andrias davidianus) skin, and its anti-diabetes mechanism was predicted by network pharmacology, and an inhibitory effect on α-glycosidase and protective effect on IR (insulin resistance) and oxidative stress of IR-HepG2 cells were detected. Through network pharmacology screening, GPPGPA was found to have good drug-like properties, and 103 targets of GPPGPA overlap with T2DM targets. These targets were mainly enriched in the PI3K-Akt signaling pathway associated with T2DM, the AGE-RAGE signaling pathway in diabetic complications, the TNF signaling pathway, insulin resistance and so on. The core targets were identified as AKT1, MAPK8, MAPK10 and JUN by constructing a "peptide-target-pathway" network. The molecular docking results showed that GPPGPA was well bound to the core targets. These results suggested that GPPGPA had the potential to reduce T2DM. Further in vitro experiments showed that GPPGPA as a competitive inhibitor could effectively inhibit the activity of α-glucosidase. The results of the IR-HepG2 cell model experiments showed that GPPGPA was not toxic to HepG2 cells, and could reduce IR of HepG2 cells induced by high-glucose and high-insulin, improve glucose consumption, increase the activity of superoxide dismutase (SOD), and reduce the content of malondialdehyde (MDA). The above results suggested that GPPGPA could improve T2DM by reducing insulin resistance through a multi-target and multi-pathway mechanism. GPPGPA could be developed and utilized as a novel hyperglycemic inhibitor in functional food.

Screening of Anti-Atrophic Peptides by Using Photo-Cleavable Peptide Array and 96-Well Scale Contractile Human Skeletal Muscle Atrophy Models

Screening of Anti-Atrophic Peptides by Using Photo-Cleavable Peptide Array and 96-Well Scale Contractile Human Skeletal Muscle Atrophy Models

Screening of Xanthine Oxidase Inhibitory Peptides by Ligand Fishing Combined with Molecular Docking Technology: High-Value Utilization of By-Products from Scophthalmus maximus

Screening of Xanthine Oxidase Inhibitory Peptides by Ligand Fishing Combined with Molecular Docking Technology: High-Value Utilization of By-Products from Scophthalmus maximus

Screening for cysteine-stabilized scaffolds for developing proteolytic-resistant AMPs.

Antimicrobial peptides (AMP) are present in all organisms and can present several activities and potential applications in human and animal health. Screening these molecules scaffolds represents a key point for discovering and developing novel biotechnological products, including antimicrobial, antiviral and anticancer drugs candidates and insecticidal molecules with potential applications in agriculture. Therefore, considering the amount of biological data currently deposited on public databases, computational approaches have been commonly used to predicted and identify novel cysteine-rich peptides scaffolds with known or unknown biological properties. Here, we describe a step-by-step in silico screening for cysteine-rich peptides employing molecular modeling (with a core focus on comparative modeling) and atomistic molecular dynamics simulations. Moreover, we also present the concept of additional tools aiming at the computer-aided screening of new Cs-AMPs based drug candidates. After the computational screening and peptide chemical synthesis, we also provide the reader with a step-by-step in vitro activity evaluation of these candidates, including antibacterial, antifungal, and antiviral assays.

Screening of Peptide Selectively Recognizing Prostate Specific Antigen and its Application in Detecting Total Prostate-Specific Antigen

Screening of Peptide Selectively Recognizing Prostate Specific Antigen and its Application in Detecting Total Prostate-Specific Antigen

Rapid Identification of MHCII-Binding Peptides Through Microsphere-Assisted Peptide Screening (MAPS).

CD4+ T cells play a vital role in the immune response, and their function requires T cell receptor (TCR) recognition of peptide epitopes presented in complex with MHC class II (MHCII)molecules. Consequently, rapidly identifying peptides that bind MHCII is critical to understanding and treating infectious disease, cancer, autoimmunity, allergy, and transplant rejection. Computational methods provide a fast, ultrahigh-throughput approach to predict MHCII-binding peptides but lack the accuracy of experimental methods. In contrast, experimental methods offer accurate, quantitative results at the expense of speed. To address the gap between these two approaches, we developed a high-throughput, semiquantitative experimental screening strategy termed microsphere-assisted peptide screening (MAPS). Here, we use the Zika virus envelope protein as an example to demonstrate the rapid identification of MHCII-binding peptides from a single pathogenic protein using MAPS. This process involves several key steps including peptide library design, peptide exchange into MHCII, peptide-MHCII loading onto microspheres, flow cytometry screening, and data analysis to identify peptides that bind to one or more MHCII alleles.

Large-Scale Screening of Antifungal Peptides Based on Quantitative Structure-Activity Relationship.

Antifungal peptides are effective, biocompatible, and biodegradable, and thus, they are promising to be the next generation of drugs for treating infections caused by fungi. The identification processes of highly active peptides, however, are still time-consuming and labor-intensive. Quantitative structure-activity relationships (QSARs) have dramatically facilitated the discovery of many bioactive drug molecules without a priori knowledge. In this study, we have established an effective QSAR protocol for screening antifungal peptides. The screening protocol integrates an accurate antifungal peptide classification model and four activity prediction models against specified target fungi. A demonstrative application was performed on more than three million candidate peptides, and three outstanding peptides were identified. The whole screening took only a few days, which was much faster than our previous experimental screening works. In conclusion, the protocol is useful and effective for reducing repetitive laboratory efforts in antifungal peptide discovery. The prediction server (antifungal Web server) is freely available at www.chemoinfolab.com/antifungal.

UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning.

Umami ingredients have been identified as important factors in food seasoning and production. Traditional experimental methods for characterizing peptides exhibiting umami sensory properties (umami peptides) are time-consuming, laborious, and costly. As a result, it is preferable to develop computational tools for the large-scale identification of available sequences in order to identify novel peptides with umami sensory properties. Although a computational tool has been developed for this purpose, its predictive performance is still insufficient. In this study, we use a feature representation learning approach to create a novel machine-learning meta-predictor called UMPred-FRL for improved umami peptide identification. We combined six well-known machine learning algorithms (extremely randomized trees, k-nearest neighbor, logistic regression, partial least squares, random forest, and support vector machine) with seven different feature encodings (amino acid composition, amphiphilic pseudo-amino acid composition, dipeptide composition, composition-transition-distribution, and pseudo-amino acid composition) to develop the final meta-predictor. Extensive experimental results demonstrated that UMPred-FRL was effective and achieved more accurate performance on the benchmark dataset compared to its baseline models, and consistently outperformed the existing method on the independent test dataset. Finally, to aid in the high-throughput identification of umami peptides, the UMPred-FRL web server was established and made freely available online. It is expected that UMPred-FRL will be a powerful tool for the cost-effective large-scale screening of candidate peptides with potential umami sensory properties.

Screening for Anti-Diabetic Peptides from Moringa oleifera leaves

Moringa oleifera commonly called as Drumstick tree is a multipurpose tree and it is widely present in the places like India, Asia, Africa, etc. Its leaves are emetic and their juices are used for medicinal purposes. Diabetes mellitus is a clinical syndrome with insufficient insulin secretion and abnormal glucose tolerance. It is a group of diseases which results in too much sugar in the blood. Leaves from Moringa oleifera were collected, analysed for their anti- diabetic activity. First the proteins are extracted using protein extraction methods, estimated using Lowry’s method, and then anti- diabetic activity was checked using Glucose uptake assay by yeast cells, where the plant extract (small proteins) was able to uptake glucose. SDS-PAGE was carried out to check the protein’s size and molecular weight. The characterization of Moringa oleifera conducted in this study shows that the small peptides from the leaves of this plant can contribute significantly in the daily recommended intake of it, since it serves as a rich source of all minerals, vitamins etc. This study has confirmed that the small peptides from the leaves of M.oleifera can fight against diabetics and the result indicated that Murungai (Moringa) leaves are suitable source of green leaf to reduce the diabetic complications in diabetic patients.

Abstract 10516: Identification of a PCSK9-LDLR Disruptor Macrocycle with in vivo Function

Elevated LDL-C is a major risk factor for atherosclerotic cardiovascular disease, the leading cause of death worldwide. Proprotein convertase subtilisin/kexin type 9 (PCSK9) has pronounced effects on LDL-C levels via its modulation of hepatic LDL receptors (LDLR), the main pathway for cholesterol removal from the circulation. The epidermal growth factor precursor homology domain A (EGF-A) of the LDLR serves as a primary contact with PCSK9 via a flat interface, presenting a challenge for identifying small molecule PCSK9-LDLR disruptors. We employed an affinity-based screen of 10 13 in vitro -translated macrocyclic peptides to identify high affinity PCSK9 ligands that utilize a novel, induced-fit pocket and partially disrupt the PCSK9-LDLR interaction (Hit 1 - PCSK9-LDLR FRET, IC 50 2 nM, A max 41%). Structure-based design led to 13 PCSK9i , a molecule with enhanced in vitro function (PCSK9-LDLR FRET, IC 50 2 nM, A max 78%) and pharmacokinetic properties suitable for in vivo evaluation. To determine if 13 PCSK9i ’s functional activity in vitro would translate in vivo , C57BL/6 mice were dosed with vehicle or 13 PCSK9i via subcutaneous injection twice-daily for 3 days, and blood and liver were collected for the measurement of plasma total cholesterol (TC) and hepatic LDLR density. A PCSK9 antibody was included as a positive control. As illustrated in the figure, 13 PCSK9i increased hepatic LDLR density in a dose-dependent manner. Despite being 1/100 th its size, 13 PCSK9i (MW = 1.65 kDa) increased hepatic LDLR density on par with a PCSK9 antibody (MW = 144 kDa). Plasma TC levels were significantly reduced versus vehicle in all 13 PCSK9i dose groups, with similar reductions noted in the 13 PCSK9i 30 mg/kg and PCSK9 antibody groups (44% and 48%, respectively). In summary, 13 PCSK9i breaks new ground with its previously undescribed allosteric mechanism and, in turn, as the smallest molecule identified to date with in vivo PCSK9-LDLR disruptor function.