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Peptide Library

Peptide Library: A collection of diverse peptide sequences, often used in research to explore the structural and functional properties of proteins.
These libraries are generated through combinatorial synthesis techniques and can be screened to identify peptides with desired characteristics, such as binding affinity, enzymatic activity, or therapeutic potential.
Peptide libraries play a crucial role in areas like drug discovery, epitope mapping, and the study of protein-protein interactions.
By providing a broad repertoire of peptide sequences, they enable researchers to systematically investigate the sequence-structure-function relationships of proteins and accelerate the development of novel peptide-based therapies and diagnostic tools.
The use of peptid libraries in biomedical reasearch has become an invaluable approach for advancing our understanding of complex biological systems.

Most cited protocols related to «Peptide Library»

The combinatorial libraries were synthesized as previously described [24 (link),41 (link)]. Peptides in each library are 13-mers with Alanine residues in positions 1, 2, 12 and 13. The central 9 residues in the peptides are equal mixtures of all 20 naturally occurring residues except for a single position per library which contains a fixed amino acid residue. A total of 180 libraries were used to cover all possible fixed residues at all positions in the 9-mer core. The IC50 values for an example peptide library (HLA-DPA1*0103-DPB1*0201) are shown in Additional file 1, Table S4.
The binding assay methods for MHC class II molecules in general [42 ,43 (link)] as well as HLA-DP [44 (link)] and HLA-DQ [45 (link)] molecules have been described in detail previously.
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Publication 2010
Alanine Amino Acids Biological Assay cDNA Library Histocompatibility Antigens Class II HLA-DPA1 HLA-DP Antigens Peptide Library Peptides
For the virome peptide library, we first downloaded all protein sequences in the UniProt database from viruses with human host and collapsed on 90% sequence identity (http://www.uniprot.org/uniref/?query=uniprot: (host:“Human+[9606]”)+identity:0.9). The clustering algorithm UniProt represents each group of protein sequences sharing at least 90% sequence similarity with a single representative sequence. Then, we created 56 aa peptide sequences tiling through all the proteins with 28 aa overlap. We reverse translated these peptide sequences into DNA codons optimized for expression in E. coli, making synonymous mutations when necessary to avoid restriction sites used in subsequent cloning steps (EcoRI and XhoI). Finally, we added the adapter sequence “aGGAATTCCGCTGCGT” to the 5′ end and “CAGGgaagagctcgaa” to the 3′ end to form the 200 nt oligonucleotide sequences.
For the scanning mutagenesis library, we first took the sequences of the peptides to be mutagenized. For each peptide, we made all single-mutants, and consecutive double- and triple-mutants sequences scanning through the whole peptide. Non-alanine amino acids were mutated to alanine and alanines were mutated to glycine. We reverse translated these peptide sequences into DNA codons, making synonymous mutations when necessary to avoid restriction sites used in subsequent cloning steps (EcoRI and XhoI). We also made synonymous mutations to ensure that the 50 nt at the 5′ end of peptide sequence is unique to allow unambiguous mapping of the sequencing results. Finally, we added the adapter sequence “aGGAATTCCGCTGCGT” to the 5′ end and “CAGGgaagagctcgaa” to the 3′ end to form the 200 nt oligonucleotide sequences.
The 200 nt oligonucleotide sequences were synthesized on a releasable DNA microarray. We PCR amplified the DNA using the primers T7-PFA (aatgatacggcggGAATTCCGCTGCGT) and T7-PRA (caagcagaagACTCGAGCTCTTCCCTG), digested the product with EcoRI and XhoI, and cloned the fragment into the EcoRI/SalI site of the T7FNS2 vector (8 (link)). The resulting library was packaged into T7 bacteriophage using the T7 Select Packaging Kit (EMD Millipore) and amplified using the manufacturer suggested protocol.
Publication 2015
Alanine Amino Acids Amino Acid Sequence Bacteriophage T7 Cloning Vectors Codon Deoxyribonuclease EcoRI DNA Chips DNA Library Escherichia coli Glycine Homo sapiens Mutagenesis Oligonucleotide Primers Oligonucleotides Peptide Library Peptides Proteins Silent Mutation Virome Virus
Proteome Discoverer version
1.4.288 (PD) was used for peptide identifications. All data sets were
searched with Mascot (version 2.2.1), SEQUEST (with probability score
calculation) as provided in PD, and MS Amanda. Advanced search settings
in PD were changed from default in order to store all PSMs in the
result file (all cutoff filters and thresholds were disabled).
Searches for the HeLa and the histone data sets were performed with
7 ppm precursor mass tolerance and 0.03 Da fragment ion mass tolerance
(0.5 for CID). Following Marx et al., we used 5 ppm precursor mass
tolerance and 0.02 Da fragment mass tolerance for the synthetic peptide
library. For HCD and CID, considered fragment ions were left at defaults
for Mascot and SEQUEST, and set to b and y ions for MS Amanda. ETD searches with Mascot and MS Amanda
were performed using c, y, z + 1, and z + 2 ions.
For the HeLa
data sets, oxidation(M) was set as variable modification,
carbamidomethyl(C) as fixed modification, and trypsin as enzyme
allowing up to two missed cleavages. The peptide library was searched
with oxidation(M) and phosphorylation(S,T,Y) as variable modifications
and up to four missed cleavage sites for trypsin.
Variable modification
settings for the histone data set were oxidation(M),
phosphorylation(S,T,Y), methyl(K,R), dimethyl(K,R), trimethyl(K),
and acetyl(K). Methylthio(C) was set as fixed modification,
GluC (C-terminal cleavage after D or E) as enzyme, and two as the
maximum number of missed cleavages.
Performance comparisons
were based on 1% FDR.33 (link),34 (link) We generated concatenated forward
and reverse (decoy) protein databases
with contaminants using MaxQuant Sequence Reverser (v1.0.13.13).14 (link) We searched the HeLa data sets against Swiss-Prot_human36 (link) (release 2013_10), merged the synthetic peptide
sequences with Swiss-Prot_human for the peptide library, and searched
the histone data against the complete Swiss-Prot (release 2013_10).
For FDR calculation, peptides shorter than 7 amino acids were discarded
and conservative FDR estimation was ensured by preferring the decoy
peptide to an equally scored peptide. Peptide grouping for unique
peptide level FDR estimation was solely based on the peptide sequence,
and the highest score was kept for each peptide group.
Publication 2014
Amino Acids Base Sequence Cytokinesis Enzymes glutamate carboxypeptidase II, human HeLa Cells Histones Homo sapiens Immune Tolerance Ions Peptide Library Peptides Phosphorylation Proteins Proteome Trypsin

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Publication 2014
Amino Acids cDNA Library Cells Clone Cells Cloning Vectors Codon Electroporation Homologous Recombination Mutagenesis Oligonucleotide Primers Peptide Library Saccharomyces cerevisiae Streptavidin Tetrameres
We identified MHC class I prediction tools through literature searches, and the IMGT link list at http://imgt.cines.fr/textes/IMGTbloc-notes/Immunoinformatics.html#tooMHCbp. Identical tools appearing on multiple websites—most often in combination with proteasomal cleavage/TAP transport predictions—were only included once. Several tools were not available at the time of the study (October/November 2005). One server containing multiple prediction tools (http://www.imtech.res.in/raghava/) could unfortunately not be included, as its terms of use limit the number of predictions to 20 a day.
Several tools allowed making predictions with different algorithms. In cases like this, we retrieved predictions for both, and treated them as separate tools: multipred provides predictions based on either an artificial neural network or a hidden Markov model, which we refer to as multipredann and multipredhmm. Similarly, netmhc provides neural network–based predictions (netmhc_ann) and matrix-based predictions (netmhc_matrix), and mappp provides predictions based on bimas (mapppB) and syfpeithi (mapppS) matrices.
For each tool, we mapped the MHC alleles for which predictions could be made to the four-digit HLA nomenclature (e.g., HLA-A*0201). If this mapping could not be done exactly, we left that allele–tool combination out of the evaluation. For example, HLA-A2 could refer to HLA-A*0201, A*0202, and A*0203, which do have a distinct binding specificity.
For each tool in the evaluation, we wrote a python script wrapper to automate prediction retrieval. The retrieved predictions were stored in a MySQL database. If a tool returns a nonnumeric score such as “–” to indicate nonbinding, an appropriate numeric value indicating nonbinding on the scale of the tool was stored instead.
The algorithms underlying each tool fall in the following categories: arbmatrix, bimas, hla_a2_smm, hlaligand, libscore, mapppB, mapppS, mhcpathway, mhcpred, netmhcmatrix, predbalbc, predep, rankpep, and syfpeithi are based on positional scoring matrices, while multipredann and netmhcann are based on ANNs, multipredhmm is based on a hidden Markov model, pepdist is based on a peptide–peptide distance function, and svmhc is based on a support vector machine. With two exceptions, the tools were generated based on data of peptides binding to or being eluted from individual MHC molecules. The first exception is libpred, which was generated using binding data of combinatorial peptide libraries to MHC molecules, and predep, where the 3-D structure of the MHC molecules was used to derive scoring matrices. References with more detailed description of each tool are indicated in the text.
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Publication 2006
Alleles Cytokinesis Fingers Genes, MHC Class I HLA-A2 Antigen Molecular Structure Multicatalytic Endopeptidase Complex Peptide Library Peptides Python

Most recents protocols related to «Peptide Library»

The mouse whole proteome peptide microarray was designed based on the protein set downloaded from UniProt in December of 2018 for C57BL/6 mice (The UniProt, 2017 (link)). The library was generated in silico for synthesis on high-density peptide microarrays (Nimble Therapeutics, Madison WI). The library consisted of overlapping 16-mers representing the entire mouse proteome tiled at every second amino acid for reviewed proteins and every 4 amino acids for most unreviewed proteins. All redundant (non-unique) peptides were only printed once but later computationally mapped back to all UniProt IDs containing this peptide. The individual peptides in the library were randomly assigned to positions on the microarray to minimize the impact of spatial biases.
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Publication Preprint 2023
Amino Acids Anabolism cDNA Library GPER protein, human Mice, House Mice, Inbred C57BL Microarray Analysis Peptide Library Peptides Proteins Proteome SET protein, human Therapeutics

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Publication 2023
Acetylation Alkylation Cystine Cytokinesis Digestion Immune Tolerance Ions Lysine Methionine Mice, House Peptide Library Peptides Proteins Student Trypsin
Mice spleens were dissociated in gentleMACS C tubes (Miltenyi Biotec), filtered, treated with red blood cell lysing buffer (Sigma-Aldrich, no. R7757), and washed. Pellets were resuspended in 1 ml of CTL-Test medium [Cellular Technology Limited (CTL), no. CTLT 005] supplemented with 1% fresh glutamine and 1 mM P/S (Biological Industries, Israel), and single-cell suspensions were seeded into 96-well, high-protein-binding, polyvinylidene difluoride filter plates at 300,000 to 400,000 cells per well. Mice were tested individually in duplicates by stimulation with a 15-mer peptide library spanning the F1 protein (10 μg/ml) (GenScript), concanavalin A (2 μg/ml; Sigma-Aldrich, no. 0412) as positive control, or CTL medium as negative control (no antigen). Cells were incubated with antigens for 24 hours, and the frequency of interferon-γ (IFN-γ)–secreting cells was determined using a murine IFN-γ single-color enzymatic enzyme-linked immunosorbent spot (ELISpot) kit (CTL, no. MIFNG 1M/5) with strict adherence to the manufacturer’s instructions. Spot-forming units were counted using an automated ELISpot counter (CTL).
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Publication 2023
Antigens Biopharmaceuticals Buffers Cells Concanavalin A Enzymes Erythrocytes Glutamine Immunosorbents Interferon Type II Mus Pellets, Drug Peptide Library polyvinylidene fluoride Proteins
For each screening experiment, ∼400 mg of the macrocyclic peptide library was swollen in DCM and washed extensively with DMF, ddH2O and finally incubated overnight at 4°C in 10 mL of blocking buffer (30 mM sodium phosphate, pH 7.4, 150 mM NaCl, 0.05% Tween 20, 3% BSA and 0.1% gelatin). The solution was drained, and the resin was resuspended in 10 mL of blocking buffer containing 500 nM biotinylated Etf-1 for overnight at 4°C. The resin was washed with blocking buffer and resuspended in 10 mL of blocking buffer. M280 streptavidin-coated Dynabeads (Invitrogen; 20 μL) were added to the resin and the mixture was incubated on a rotary wheel for 1 h at 4°C. The magnetic beads were isolated from the bulk by using a TA Dynal MPC-1 magnetic particle concentrator (Invitrogen). The beads were transferred to a Bio-Spin column (0.8 mL; BioRad, Hercules, CA, USA) and incubated in blocking buffer containing 100 nM biotinylated Etf-1 for 4 h at 4°C. The solution was drained, and the resin was quickly washed with blocking buffer to remove any unbound protein. The resin was resuspended in 1 mL of blocking buffer and streptavidin-alkaline phosphatase (SA-AP) conjugate was added to the mixture (1 μg/mL final concentration). After 10 min at 4°C the solution was drained and the beads were quickly washed with 1 mL of blocking buffer (3×) and 1 mL of staining buffer (30 mM Tris, pH 8.5, 100 mM NaCl, 5 mM MgCl2, and 20 μM ZnCl2) (3×). The resin was resuspended in 1.5 mL of staining buffer in a Petri dish and 150 μL of a 5-bromo-4-chloro-3-indolyl-phosphate (BCIP) solution (5 mg/mL) was added. After 30 min, 100 μL of 1 M HCl was added to quench the reaction and the intensely turquoise positive beads were manually isolated under a dissecting microscope. Peptide sequences of hit beads were determined using partial Edman degradation-mass spectrometry (PED-MS) as previously described (25 (link), 37 (link)).
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Publication 2023
Alkaline Phosphatase Buffers Dietary Fiber Gelatins Hyperostosis, Diffuse Idiopathic Skeletal Magnesium Chloride Mass Spectrometry Microscopy Peptide Library Peptides Phosphates Proteins Resins, Plant Sodium Chloride sodium phosphate Streptavidin Tromethamine Tween 20
The panel of 14 recombinant T-antigens used to characterize antibody reactivity has been previously described [18 (link), 29 (link)]. All T-antigens were expressed in Escherichia coli BL21 (DE3) cells, and N-terminal His6-tags were removed using recombinant tobacco etch virus (rTEV) prior to use in immunoassays.
The recombinant T18.1 dimer was constructed by inserting the coding sequence for tee18.1 into pProExHTA in tandem such that the vector encoded expression of two T18.1 antigens in which the C-terminal sortase motif of the first monomer is linked to the N-terminus of the following monomer, via a flexible (glycine)3 linker. The first five N-terminal residues from the second T18.1 monomer (ETAGV) were removed to allow the positioning of the sortase motif of the first monomer near the sortase motif peptide “binding” cleft in the second monomer. Expression and purification of the T18.1 dimer followed published protocols for T-antigen monomers [18 (link)]. Briefly, E. coli BL21 (DE3) cells transformed with the expression plasmid were induced with 0.3 mM isopropyl-β-D-1-thiogalactopyranoside (IPTG) and grown at 18°C for 16 h. Cell pellets were resuspended in lysis buffer (50 mM Tris-Cl [pH 8.0], 300 mM NaCl, 10 mM imidazole), lysed using a cell disruptor (Constant Cell Disruption Systems) and the protein enriched from the soluble phase using Ni2+-NTA affinity purification. The His6 affinity tag was cleaved with recombinant tobacco etch virus (rTEV) protease and rTEV-His6 protease, and uncleaved protein was removed by subtractive immobilized-metal affinity chromatography (IMAC). The unbound protein fraction containing recombinant T-antigen was further purified by size exclusion chromatography on a Superdex S200 10/300 column (GE Healthcare) in crystallization buffer (10 mM Tris-Cl [pH 8.0], 100 mM NaCl).
For generation of biotinylated recombinant T18.1, the tee18.1 sequence [18 (link)] was re-cloned into a pProExHTA vector modified to encode for an N-terminal His-tag and a C-terminal Avi-tag. E. coli BL21 (DE3) were co-transformed with pProExHTA-tee18.1-Avitag and pACYC184BirA, and expression media were supplemented with 20 uM D-Biotin for in cell biotinylation. The biotinylated T18.1 was purified using Ni2+-NTA affinity chromatography, the His6-tag removed using rTEV, and final purification was performed using size exclusion chromatography as described above [18 (link)]. Successful biotin labelling was confirmed by immunoaffinity pulldown with streptavidin-coupled M-280 Dynabeads (Thermofisher) following by SDS-PAGE.
The T18.1 peptide library was designed to cover the entirety of the mature T18.1 monomer. The N-terminal signal sequence was excluded, and the library ended at the conserved threonine residue of the Sortase C recognition site (QVPTG). The tiled peptide library comprised 2915-mer peptides overlapping by five residues (Supplementary Table S1). The peptides were synthesized (GenScript) and purified to >85%.
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Publication 2023
Antigens Biotin Biotinylation Buffers cDNA Library Cells Chromatography, Affinity Cloning Vectors Crystallization Escherichia coli Gel Chromatography Glycine his6 tag imidazole Immunoassay Immunoglobulins Isopropyl Thiogalactoside M 280 Metals Open Reading Frames Pellets, Drug Peptide Hydrolases Peptide Library Peptides Plasmids Proteins SDS-PAGE Signal Peptides Sodium Chloride sortase C Streptavidin TEV protease Threonine Tobacco etch virus Tromethamine Viral Tumor Antigens

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More about "Peptide Library"

Peptide libraries are a powerful research tool used to explore the structural and functional properties of proteins.
These diverse collections of peptide sequences are generated through combinatorial synthesis techniques, enabling researchers to systematically investigate sequence-structure-function relationships and accelerate the development of novel peptide-based therapies and diagnostic tools.
Peptide libraries play a crucial role in areas like drug discovery, epitope mapping, and the study of protein-protein interactions.
By providing a broad repertoire of peptide sequences, they enable researchers to screen for peptides with desired characteristics such as binding affinity, enzymatic activity, or therapeutic potential.
The use of peptide libraries in biomedical research has become an invaluable approach for advancing our understanding of complex biological systems.
Researchers may utilize various related techniques and tools to support their peptide library research, such as DMSO (dimethyl sulfoxide) for sample preparation, AutoSpot-Robot ASS 222 for automated peptide synthesis, Mascot for peptide identification, and Quantity One software for data analysis.
Additionally, Bovine serum albumin (BSA) is often used as a blocking agent to reduce non-specific binding in peptide library screenings, while WesternBright ECL (enhanced chemiluminescence) can be employed for sensitive detection of peptide-protein interactions.
JANUS liquid handling stations can automate various liquid handling tasks involved in peptide library workflows, and the Human 20S proteasome is a common target for peptide-based inhibitor discovery.
Furthermore, Antibody to human CD107a, a marker of degranulation, can be used to assess the functional properties of peptides that modulate immune cell responses.
Ultimately, the versatility and power of peptide libraries have made them an indispensable tool in the arsenal of biomedical researchers, enabling groundbreaking discoveries and advancing our understanding of the complex world of proteins.
By leveraging the latest technologies and techniques, researchers can maximize the impact of their peptide library research and drive progress in fields such as drug development, diagnostics, and personalized medicine.