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Fingerprints, Peptide

Fingerprints, Peptide: A distinctive pattern of amino acid residues within a peptide sequence that can be used to identify and characterize the peptide.
These peptide fingerprints are often employed in proteomics research to enhance reproducibilty and accuracy when locating the best experimental protocols from literature, preprints, and patents.
Leveraging AI-driven comparisons, the PubCompare.ai platform optimizes peptide research by improving scientific outcomes through this cutting-edeg, intellgent tool.

Most cited protocols related to «Fingerprints, Peptide»

1
Clinical Applications of Western Blotting
Cited 24 times
Western blotting is a valuable tool to studies ranging from regulatory signaling processes to confirmatory serum diagnosis of HIV [68 (link)–70 (link)]. The evolution of western blot technique from identification of a specific protein in a complex mixture to the direct detection of protein in a single cell allows this technique to be an important analytical tool for clinical research. An advanced single cell western blotting technique was employed to study stem cell signaling and differentiation as well as drug response in tumor cells [69 (link)]. Through single cell western blotting it was possible to analyze cell-to-cell variations in approximately 2000 cells simultaneously within complex populations of cells [71 (link)]. With the integration of intact cell imaging, the technique allows the identification of protein expression changes of a single drug resistant tumor cell and its isoforms among heterogeneous population of tumor cells in human glioblastoma cells treated with chemotherapeutic daunomycin [69 (link)]. Identification of upregulated multidrug resistant protein, P-glycoprotein in living glioblastoma subpopulations was indicative of an active drug eflux pump as an underlying mechanism for drug resistance [69 (link),71 (link)]. With the application of 2-DE gel separation together with spotting of protein by peptide mass fingerprint, the analysis of clinically relevant Helicobacter pylori (H. pylori) in related gastric disease conditions (chronic gastritis, duodenal ulcer) was possible [72 (link)]. The database of H. pylori (low expressed and membrane proteins) was created through the application of one-dimensional or 2-DE/MALDI-mass spectrometry techniques [72 (link)]. In a similar manner, the Simple Western technique was employed for the analysis of 15-valent pneumococcal vaccine PCV15-CRM197 [73 (link)]. Due to its high sensitivity and automation, the Simple Western method may be extended to analyze serotypes of other polysaccharide protein conjugate vaccines [73 (link)].
Western blotting is commonly used for the clinical diagnosis of various parasitic and fungal diseases including echinococcosis [74 (link)], toxoplasmosis [75 (link)], and aspergillosis [76 (link)]. In a recent study, the assay was successfully used for the reliable serodiagnosis of Farmer’s lung disease (FLD), a pulmonary disorder caused by inhalation of antigenic particles [77 (link)]. Thus, this technique can be exploited for rapid routine diagnosis of FLD in clinics [77 (link)]. Similarly, for immunodiagnostic of tuberculosis meningitis which is a chronic disease of central nervous system different molecular and immunological methods were used for clinical diagnosis of the disease. However, each of these techniques has their own limitations [78 (link)]. To overcome diagnostic issues of lower sensitivity and specificity, the immunoreactivity to Mycobacterium tuberculosis antigens was performed by western blotting [78 (link)]. Furthermore, western blotting was performed for the early and sensitive diagnosis of congenital toxoplasmosis [79 (link)] and was employed for rapid and sensitive serological diagnosis of a serious infectious disease paracoccidioidomycosis (PCM) [80 (link)]. Using immunoblotting, a new subgroup of human lymphotropic retroviruses (HTLV), was detected in patients with the acquired immunodeficiency syndrome (AIDS) [81 (link)]. Antigens of HTLV-III, specifically detected by antibodies in serum from AIDS or pre-AIDS patients [81 (link)]. Western blotting has also been used as a test for variant Creutzfeldt-Jakob Disease [82 (link)], some forms of Lyme disease [83 (link)] and is sometimes used as a confirmatory test for Hepatitis B [84 ] and Herpes Type 2 [85 (link)] infections. Western blots have also been used to confirm feline immunodeficiency status in cats [86 (link)].
Recently, a commercial Aspergillus western blotting IgG kit was developed by LD Bio Diagnostics (France) to carry out immunoblotting for the clinical diagnosis of chronic aspergillosis. The commercial kit was found to be sensitive and can analyze hundreds of samples from patients with aspergillus disease [87 (link)]. Thus, the clinical applications of western blotting technique will continue to progress as further advancements are made to improve sensitivity and reproducibility of the western blot.
Mishra M., Tiwari S, & Gomes A.V. (2017). Protein purification and analysis: next generation Western blotting techniques. Expert review of proteomics, 14(11), 1037-1053.
Publication 2017
Acquired Immunodeficiency Syndrome Antibodies Antigens Aspergillosis Aspergillus Biological Assay Biological Evolution Cells Central Nervous System Diseases Communicable Diseases Complex Mixtures CRM197 (non-toxic variant of diphtheria toxin) Daunorubicin Diagnosis Duodenal Ulcer Echinococcosis Farmers Felidae Fingerprints, Peptide Gastritis Glioblastoma Helicobacter pylori Hepatitis B HIV Antigens Homo sapiens Hypersensitivity Immunodiagnosis Immunologic Deficiency Syndromes Immunologic Techniques Infection Inhalation Lung Diseases Lyme Disease Mass Spectrometry Membrane Proteins Mycobacterium tuberculosis antigens Mycoses Neoplasms New Variant Creutzfeldt-Jakob Disease P-Glycoprotein Paracoccidioidomycosis Patients Pharmaceutical Preparations Pharmacotherapy Pneumococcal Vaccine Polysaccharides Population Group Protein Isoforms Proteins Resistance, Drug Retroviridae Serodiagnosis Serum Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Staphylococcal Protein A Stem, Plant Stem Cells Stomach Diseases T-Cell Leukemia Viruses, Human Toxoplasmosis Toxoplasmosis, Congenital Tuberculosis, Meningeal Vaccines, Conjugate Western Blot Western Blotting
2
PRIDE Converter 2: Enhanced Proteomics Data Submission
Cited 23 times
The original PRIDE Converter had some functional and practical limitations. Driving the work behind the development of the PRIDE Converter 2 tool suite was the desire to not only overcome the shortcomings of the original tool but also add functionality that had been repeatedly requested by our users.
As such, the PRIDE Converter 2 tool suite has added conversion support for a number of new data formats (see Table II) and other formats will likely follow over time. Moreover, support for existing formats has also been improved. For example, it is now possible to submit peptide mass fingerprint (PMF) data generated by Mascot (http://www.matrixscience.com). Also, the addition of quantitative data to PRIDE XML files has been greatly improved by integrating support for mzTab files.
The mzTab format is meant to be a light-weight, standard tab-delimited file for MS-based proteomics data, developed by the Proteomics Standards Initiative (PSI). Designed to be easy to parse, it contains only the minimal information required to evaluate the results of a proteomics experiment (http://mztab.googlecode.com). Users can generate skeleton mzTab files using the PRIDE mzTab Generator and then use the produced mzTab files as a basis to provide quantitative information as part of the conversion process in PRIDE Converter 2. Gel and spot-related information can also be added to the mzTab files, making the capture of gel-associated information much more straightforward (supplemental File S2, section 4). Users can now also provide their original search databases in FASTA format (supplemental File S1, section 3). This is essential to maintain data provenance for nonstandard protein databases and makes it easier to map the identified proteins across all protein databases, a process that is performed as a matter of course in the PRIDE database to maximize search capabilities (8 (link)).
Another user requirement fulfilled by the PRIDE Converter 2 tool suite is the ability to post-process the initially generated PRIDE XML files. For example, users can now use the PRIDE XML Filter tool to remove contaminants and empty spectra prior to submission. Finally, in the case of gel-based proteomics experiments in which each gel spot produces one MS experiment, the original PRIDE Converter tool would generate one PRIDE XML file per spot. This meant that a single project could cover several dozens, if not hundreds of PRIDE experiment accession numbers. The PRIDE XML Merger can now merge together an arbitrarily large number of PRIDE XML files into a single file, while keeping the links between identified peptides and their underlying spectra consistent. This means that users will be able to obtain a single PRIDE accession number to refer to their collated experimental data.
Côté R.G., Griss J., Dianes J.A., Wang R., Wright J.C., van den Toorn H.W., van Breukelen B., Heck A.J., Hulstaert N., Martens L., Reisinger F., Csordas A., Ovelleiro D., Perez-Rivevol Y., Barsnes H., Hermjakob H, & Vizcaíno J.A. (2012). The PRoteomics IDEntification (PRIDE) Converter 2 Framework: An Improved Suite of Tools to Facilitate Data Submission to the PRIDE Database and the ProteomeXchange Consortium. Molecular & Cellular Proteomics : MCP, 11(12), 1682-1689.
Publication 2012
Fingerprints, Peptide Light Peptides Skeleton Strains
3
Proteomics: Protein Identification by MALDI-TOF MS
Cited 6 times
Coomassie-stained gel spots were excised manually, washed, and digested according to previously described methods [49 (link)]. The mixture of tryptic peptides (0.5 μL) derived from each protein was spotted onto a MALDI target (384 anchorchip MTP 800 μm Anchorchip; Bruker Daltonik, Germany) together with 0.5 μL of matrix (10 mg α-cyano-4-hydroxycinnamic acid (CHCA) in 1 mL of 30% CH3CN and 0.1% aqueous CF3COOH) and left to dry (room temperature, RT) before MS analysis. Spectra were acquired on a MALDI-TOF MS (UltraFlexTrem, Bruker Daltonics, Germany) in the positive mode (target voltage 25 kV, pulsed ion extraction voltage 20 kV). The reflector voltage was set to 21 kV and the detector voltage to 17 kV. Peptide mass fingerprints (PMF) were calibrated against a standard mixture by assigning appropriate mono-isotopic masses to the peaks; that is, bradykinin (1–7), m/z 757.399; angiotensin I, m/z 1296.685; angiotensin II, m/z 1046.54; rennin-substrate, m/z 1758.93; ACTH clip (1–17), m/z 2093.086; and somatostatin, m/z 3147.471 (peptide calibration standard II, Bruker Daltonics, Germany). MS spectra were recorded automatically across the mass range m/z 700–3000 and spectra were typically the sum of 400 laser shots. The PMFs were processed using Flex AnalysisTM software (version 2.4, Bruker Daltonics, Germany) and the sophisticated numerical annotation procedure (SNAP) algorithms were used for peak detection (S/N, 3; maximum number of peaks, 100; quality factor threshold, 30). MS data were interpreted using BioTools v3.2 (Bruker Daltonics, Germany), together with the Mascot search algorithm (version 2.0.04 updated 09/05/2018; Matrix Science Ltd., UK). Mascot parameters were as follows: fixed cysteine modification with propionamide, variable modification due to methionine oxidation, one missed cleavage site (i.e., in the case of incomplete trypsin hydrolysis), and amass tolerance of 100 ppm. Identified proteins were accepted as correct if they showed a Mascot score greater than 56 and p < 0.05, sequence coverage of at least 20%, and a minimum of four matched peptides. Not all spots of interest could be identified because some proteins were of low abundance and did not yield sufficiently intense mass fingerprints, whereas others were mixtures of multiple proteins [48 (link)].
Masood A., Benabdelkamel H., Ekhzaimy A.A, & Alfadda A.A. (2020). Plasma-Based Proteomics Profiling of Patients with Hyperthyroidism after Antithyroid Treatment. Molecules, 25(12), 2831.
Publication 2020
Angiotensin I Angiotensin II Angiotensinogen Bradykinin Clip Coumaric Acids Cysteine Cytokinesis Exanthema Fingerprints, Peptide Hydrolysis Immune Tolerance Isotopes Methionine Peptides propionamide Proteins Somatostatin Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Trypsin Z1046
4
MALDI-TOF MS Protein Identification
Cited 6 times
Coomassie-stained gel spots corresponding to the same spots that showed statistically significant differential abundance in the 2D-DIGE gels were excised manually and were washed and digested according to previously described methods [53 (link)]. The mixture of tryptic peptides (0.5 µL) derived from each protein was spotted onto a MALDI target (384 Anchorchip MTP 800 µm Anchorchip; Bruker Daltonik, Bremen, Germany) together with 0.5 μL of matrix (10 mg α-cyano-4-hydroxycinnamic acid (CHCA) in 1 μL of 30% CH3CN and 0.1% aqueous CF3COOH) and then left to dry (room temperature, RT) before MS analysis. Spectra were acquired on a MALDI-TOF MS (UltraFlexTrem, Bruker Daltonics, Bremen, Germany) in the positive mode (target voltage 25 kV, pulsed ion extraction voltage 20 kV). The reflector voltage was set to 21 kV and the detector voltage to 17 kV. Peptide mass fingerprints (PMF) were calibrated against a standard (peptide calibration standard II, Bruker Daltonics). The PMFs were processed using Flex Analysis software (version 2.4, Bruker Daltonics). MS data were interpreted by using BioTools v3.2 (Bruker Daltonics), together with the Mascot search algorithm (version 2.0.04 updated 9 May 2015; Matrix Science Ltd., London, UK). Mascot parameters were as follows: fixed cysteine modification with propionamide, variable modification due to methionine oxidation, one missed cleavage site (i.e., in case of incomplete trypsin hydrolysis), and a mass tolerance of 100 ppm. Identified proteins were accepted as correct if they showed a Mascot score greater than 56 and p < 0.05. Not all the spots of interest could be identified because some proteins were of low abundance and did not yield sufficiently intense mass fingerprints; other spots were mixtures of multiple proteins.
Alfadda A.A., Benabdelkamel H., Masood A., Jammah A.A, & Ekhzaimy A.A. (2018). Differences in the Plasma Proteome of Patients with Hypothyroidism before and after Thyroid Hormone Replacement: A Proteomic Analysis. International Journal of Molecular Sciences, 19(1), 88.
Publication 2018
Coumaric Acids Cysteine Cytokinesis Exanthema Fingerprints, Peptide Gels Hydrolysis Immune Tolerance Methionine Peptides propionamide Proteins Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Trypsin Two-Dimensional Difference Gel Electrophoresis
5
Mass Spectrometry Identification of Proteins
Cited 4 times
Selected spots (1-mm) were excised from the gels and submitted to trypsin proteolysis as described [17] (link). In brief, gel spots were incubated at 37°C for 30 min in 50 mM NH4HCO3, dehydrated twice for 5 min each in 100-µl acetonitrile and dried, and then, in-gel proteins were digested at 37°C for 6 h with 10 µl of trypsin solution (1% trypsin in 25 mM ammonium bicarbonate). After digestion, 1 µl of peptide mixture was directly spotted onto a MALDI-TOF-MS/MS target plate with 1 µl of alpha-cyano-4-hydroxycinnamic acid matrix solution (5 mg/ml in 50% acetonitrile). Peptides were analyzed by using a MALDI-TOF/TOF ABI 4800 Proteomics Analyzer (Applied Biosystems). The Applied Biosystems software package included the 4000 Series Explorer (v. 3.6 RC1) with Oracle Database Schema Version (v. 3.19.0) and Data Version (3.80.0) to acquire and analyze MS and MS/MS spectral data. The instrument was operated in a positive ion reflectron mode with the focus mass set at 1700 Da (mass range: 850–3000 Da). For MS data, 1000–2000 laser shots were acquired and averaged from each protein spot. Automatic external calibration was performed by using a peptide mixture with the reference masses 904.468, 1296.685, 1570.677, and 2465.199. MALDI MS/MS was performed on several (5–10) abundant ions from each protein spot. A 1-kV positive ion MS/MS method was used to acquire data under post-source decay (PSD) conditions. The instrument precursor selection window was +/− 3 Da. Automatic external calibration was performed by using reference fragment masses 175.120, 480.257, 684.347, 1056.475, and 1441.635 (from precursor mass 1570.700).
Applied Biosystems GPS Explorer™ (v. 3.6) software was used in conjunction with MASCOT to search the respective protein database by using both MS and MS/MS spectral data for protein identification. Protein match probabilities were determined by using expectation values and/or MASCOT protein scores. The MS peak filtering included the following parameters: a mass range of 800 Da to 3000 Da, minimum S/N filter = 10, mass exclusion list tolerance = 0.5 Da and mass exclusion list for trypsin and keratin-containing compounds included masses 842.51, 870.45, 1045.56, 1179.60, 1277.71, 1475.79, and 2211.1. The MS/MS peak filtering included the following parameters: minimum S/N filter = 10, maximum missed cleavages = 1, fixed modification of carbamidomethyl (C), variable modifications due to oxidation (M), precursor tolerance = 0.2 Da, MS/MS fragment tolerance = 0.3 Da, mass = monoisotopic, and peptide charges = +1. The significance of a protein match, based on the peptide mass fingerprint (PMF) in the MS and the MS/MS data from several precursor ions, is presented as expectation values (p<0.001).
Dhiman M., Zago M.P., Nunez S., Amoroso A., Rementeria H., Dousset P., Burgos F.N, & Garg N.J. (2012). Cardiac-Oxidized Antigens Are Targets of Immune Recognition by Antibodies and Potential Molecular Determinants in Chagas Disease Pathogenesis. PLoS ONE, 7(1), e28449.
Publication 2012
acetonitrile alpha-cyano-4-hydroxycinnamic acid ammonium bicarbonate Cytokeratin Cytokinesis Digestion Exanthema Fingerprints, Peptide Immune Tolerance Ions Peptides Proteins Proteolysis PRSS1 protein, human Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Staphylococcal Protein A Tandem Mass Spectrometry Trypsin

Most recents protocols related to «Fingerprints, Peptide»

1
Dengue Virus NS1 Protein Inhibitor Screening
Drugbank database (Wishart et al. 2018 (link)) was first downloaded in SDF format (Standard Delay Format). Virtual screening has been done for Dengue serotype 2 NS1 protein only. Downloaded compounds were prepared for screening using the Ligprep module in Schrödinger (Madhavi Sastry et al. 2013 (link)). OPLS3e (Harder et al. 2016 (link)) force field and pH 7.4 was used for ligand preparation. Specified chirality was retained, and one structure per ligand was specified as an output. Protein was prepared for docking using Protein-preparation wizard (Madhavi Sastry et al. 2013 (link)) in Maestro Schrödinger (Schrödinger Release 2022–3: Maestro, Schrödinger, LLC, New York, NY, 2021.) Docking is performed on a site having conserved solvent-exposed residues with zero mutational capacity and functional relevance. A receptor grid around the docking region was specified and generated using a receptor-grid generation module in Glide (Halgren et al. 2004 (link)). Residues from predicted sites were specified, and rotatable bonds across the site (if any) were enabled during grid generation. Protein–ligand docking was performed using a glide docking module in the Schrödinger suite. We followed 3-step docking as mentioned in suit: (a) HTVS to narrow the list of potential ligands. According to their docking score top, 10% ligands were taken further for (b) SP mode, and finally, the compound obtained from the SP step with a docking score less than 5 kcal/mol was screened by (c). Extra Precision mode (XP) (Friesner et al. 2006 (link)). MM-GBSA (Molecular Mechanics energies combined with Generalized Born and Surface Area continuum solvation) score was then calculated for the top 20% of compounds obtained from the XP step. After MM-GBSA calculations, the protein–ligand interaction fingerprint was generated using the SIFT module from Schrödinger. The screened hits were clustered based on the fingerprint and Tanimoto coefficient (Halgren 2007 (link); Wang et al. 2015 (link)). Potential compounds were selected according to their interaction with critical residues, mode of action, MM-GBSA score, XP score, and cluster size. Top six compounds were identified as potential inhibitor candidates, and were taken forward for the MD simulation run. To streamline the virtual screening pipeline, we have used Desmond, a utility from the Schrödinger suite, to perform the simulation runs. The pipeline for MD simulation was as followed in our previous study (Sharma et al. 2020 (link)).
Sharma A., Krishna S, & Sowdhamini R. (2023). Bioinformatics Analysis of Mutations Sheds Light on the Evolution of Dengue NS1 Protein With Implications in the Identification of Potential Functional and Druggable Sites. Molecular Biology and Evolution, 40(3), msad033.
Publication 2023
Childbirth compound 20 Dengue Fever Fingerprints, Peptide Ligands Mechanics Mutation Proteins Solvents
2
MALDI-TOF/TOF MS Identification of Microbial Strains
MALDI–TOF/TOF MS was performed at the Synchrotron Light Research Institute, Thailand, using Autoflex® maX (Bruker Daltonics, Bremen, Germany). Four to five pure single colonies (5–10 mg) of each microbial strain on NA medium were transferred to 1.5 mL microcentrifuge tubes and then mixed with 300 μL of HPLC–grade water. Next, 900 μL of ethanol was added, and the mixture was vortexed for 1 min. The sample solution was centrifuged at 13,500 ×g for 2 min two times. The cells were dried in a laminar air flow cabinet for 2 min, and then 5 µL of 70% formic acid was added. The sample was vortexed for 1 min. Afterwards, 5 µL of acetonitrile was added, and the solution was centrifuged at 13,000 ×g for 2 min. At this point, the clear solution was transferred to a fresh tube. Next, 1 μL of each sample was dropped on the MALDI–TOF/TOF MS target plate and left to dry; subsequently, 1 µL of HCCA matrix was dropped and left to dry. The target plates containing the samples were analyzed using MALDI–TOF/TOF MS (Autoflex® maX, Bruker Daltonics, Karlsruhe, Germany). The spectra were recorded in the positive linear mode at the laser frequency of 60 Hz and in the mass range of m/z 2,000–10,000 Dalton. Calibration was performed using the Escherichia coli strain DH5α, which presents ribosomal protein mass with RNA and myoglobin at peaks of 5096.8, 5381.4, 6255.4, 7274.5, 10,300.1, 13,683.2, and 16,952.3 m/z. Two–thousand shots of the mass spectrum profile data were collected from each sample. The mass spectrum profiles were processed with Flexcontrol v.3.4 (Karlsruhe, Germany). The protein mass fingerprints were analyzed at a molecular weight tolerance of 300 ppm. The resulting data were imported to MALDI BioTyper v.4.0 (Karlsruhe, Germany) to compare the sample mass fingerprints to the database (Elshafie et al., 2015 (link)).
Thepbandit W., Srisuwan A., Siriwong S., Nawong S, & Athinuwat D. (2023). Bacillus vallismortis TU-Orga21 blocks rice blast through both direct effect and stimulation of plant defense. Frontiers in Plant Science, 14, 1103487.
Publication 2023
acetonitrile Cells Escherichia coli Ethanol Fingerprints, Peptide formic acid High-Performance Liquid Chromatographies Immune Tolerance Light Mass Spectrometry Myoglobin Proto-Oncogene Mas Ribosomal RNA Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Strains
3
Thermal Titration Molecular Dynamics for Protein-Ligand Binding
Cited 1 time
As thoroughly described in the work of Pavan et al. [25 (link)], Thermal Titration Molecular Dynamics (TTMD) is an alternative enhanced sampling MD approach originally developed for the qualitative estimation of protein-ligand unbinding kinetics. The sampling method consists of a series of short classic MD simulations (defined as TTMD-steps), performed at progressively increasing temperature values. The progress of the simulation is monitored by evaluating the conservation of the starting binding mode for a certain protein-ligand complex (a docking pose, in this case) through an interaction fingerprint-based scoring function [26 (link)], originally implemented in open-source Autogrow4 [69 (link)] program, a semi-automatized workflow for de novo drug design. The protocol described afterward is implemented as a Python 3.10 code and relies on the NumPy, MDAnalysis [70 (link),71 (link)], Open Drug Discovery Toolkit [72 (link)], and Scikit-learn external libraries. The code to run TTMD simulations is available free of charge, under a permissive MIT license, at github.com/molecularmodelingsection/TTMD (accessed on 25 January 2023).
The user must define the “temperature ramp”, i.e., the temperature values that are sampled throughout the simulation and the time spent simulating at each temperature. Different from the original version of the code, in the current one, the length of each simulation window is not fixed, so different temperatures can be sampled for a different amount of time. In the present work, for consistency with the previous paper, the same temperature ramp was used: the starting temperature was set at 300 K, the end temperature was set at 450 K, the temperature increment between each TTMD-step was set at 10 K, while the length of each TTMD-step was 10 ns. The extension of the temperature ramp must be carefully chosen based on prior knowledge of the target, especially concerning the conservation of the native protein fold throughout the simulation (which, in this case, was carried out by monitoring the backbone RMSD).
As anticipated, the progress of the simulation is evaluated by a scoring function based on protein-ligand interaction fingerprints. This scoring protocol, named IFPCS, exploits the Scikit-learn Python module to calculate the cosine similarity between two vectors, i.e., the protein-ligand interaction fingerprints of a reference and a query, where the reference is the protein-ligand complex extracted from the last frame of the second equilibration stage, and the query is each protein-ligand complex obtained from each frame of a certain TTMD-step. Specifically, each interaction fingerprint is an integer vector of r × 8 length, where r represents the number of protein residues and 8 represents the possible type of protein-ligand interaction that can be encoded in the vector (hydrophobic contacts, aromatic face-to-face, aromatic edge-to-face, hydrogen bonds with the protein acting as a donor, hydrogen bonds with the protein acting as an acceptor, salt bridge with the protein acting as the positively charged member, salt bridge with the protein acting as the positively negative member, and an ionic bond with a metal ion, respectively). The cosine similarity value is then multiplied by −1 to comply with most scoring functions, where negative values indicate high affinity towards the target and low, near-zero, values indicate lower affinity.
The values of IFPCS can range from −1, highlighting a total convergence between the reference (the native binding mode) and the query (the binding pose sampled at a certain time during the simulation), to zero, indicating that each interaction feature of the reference has been lost during the simulation.
After the conclusion of each TTMD-step, the average IFPCS calculated across each frame of the step is calculated: if this value is null, implying that the native binding mode has not been sampled for the whole duration of the step, the TTMD simulation is terminated; otherwise, the simulation proceeds with the next TTMD-step.
Menin S., Pavan M., Salmaso V., Sturlese M, & Moro S. (2023). Thermal Titration Molecular Dynamics (TTMD): Not Your Usual Post-Docking Refinement. International Journal of Molecular Sciences, 24(4), 3596.
Publication 2023
Cloning Vectors Face Fingerprints, Peptide Hydrogen Bonds Kinetics Ligands Metals Molecular Dynamics Proteins Python Reading Frames Sodium Chloride Tissue Donors Titrimetry Vertebral Column
4
SDS-PAGE Protein Identification
Concentrated virus samples were loaded onto 4–15% gradient gels (Bio-Rad) and run in Bio-Rad PROTEAN III equipment using Laemmli SDS-PAGE (sodium dodecyl sulfate polyacrylamide gel electrophoresis) [29 (link)] buffers. Coomassie G250 stained protein bands were developed as described previously [30 (link)]. Gel pieces of approximately 3 mm3 were destained with a 50 mM ammonium bicarbonate/40% acetonitrile solution, dehydrated with 100 μL acetonitrile, and rehydrated with 5 μL of digestion solution containing 20 mM ammonium bicarbonate and 15 ng/μL of sequencing-grade trypsin (Promega). Digestion was carried out at 37 °C for 5 h. Peptides were extracted with 10 μL of a 0.5% trifluoroacetic acid solution. To obtain the peptide mass fingerprint, 1 μL of the extract was mixed with 0.5 μL of a 2,5-dihydroxybenzoic acid-saturated solution in a 30% acetonitrile and 0.5% trifluoroacetic acid solution on a stainless steel MALDI sample target plate. Mass spectra were recorded on an UltrafleXtreme MALDI-TOF-TOF mass spectrometer (Bruker Daltonics, DE, Billerica, MA, USA) equipped with an Nd laser. The MH+ molecular ions were detected in reflecto-mode. The accuracy of the monoisotopic mass peak measurement was 50 ppm. Fragment ion spectra were obtained in lift mode. The accuracy of the fragment ion mass peak measurements was within 1 Da. Protein identification was carried out through MS + MS/MS ion searches using Mascot software (Matrix Science; http://www.matrixscience.com/ (accessed on 1 July 2022; 15 September 2022)) through the NCBI and home protein databases.
Kordyukova L.V., Moiseenko A.V., Serebryakova M.V., Shuklina M.A., Sergeeva M.V., Lioznov D.A, & Shanko A.V. (2023). Structural and Immunoreactivity Properties of the SARS-CoV-2 Spike Protein upon the Development of an Inactivated Vaccine. Viruses, 15(2), 480.
Publication 2023
2,3-dihydroxybenzoic acid acetonitrile ammonium bicarbonate Buffers Digestion Fingerprints, Peptide Mass Spectrometry Peptides Promega Proteins SDS-PAGE Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Stainless Steel Tandem Mass Spectrometry Trifluoroacetic Acid Trypsin Virus
5
Profiling Autoantibody Patterns in Wet AMD
Blood samples were centrifuged at 1000× g for ten minutes and the supernatant was stored at −80 °C for subsequent analysis. Magnetic protein G beads (Dynal, Oslo, Norway) were incubated with the patient’s sera. After several washings, the patient’s antibodies were covalently bound to the beads using ethanolamine. The bead-antibody complexes were then incubated with homogenized retinal antigens. The antigens bound to the patient’s autoantibodies were be eluted, concentrated and analysed by SELDI time-of-flight (TOF) MS ProteinChips with two different chromatographic surfaces (CM10 cation exchange and H50 reversed phase). The samples were measured with a SELDI-TOF MS ProteinChip system (Bio-Rad, Hercules, CA, USA) on a PBS-IIc ProteinChip Reader. Raw data was transferred to CiphergenExpress 2.1 database software (Bio-Rad, Hercules, CA, USA) for workup and analysis. An in-house developed Proteomics Software Project (PSP) statistically evaluated the spectra using different statistical approaches to guarantee a high specificity and sensitivity of antibody patterns for the observed study groups. The PSP additionally searched for highly significant biomarkers directing a Statistical based analysis using above mentioned algorithms. The identification of biomarkers was performed by MALDI-TOF/TOF MS analysis (Bruker, Billerica, MA, USA). We aimed to generate at least eight highly specific biomarkers (significance level α = 0.05 and power (1 − ß) = 90%) for “wet” AMD.
Statistical calculations of sample sizes were based on experiences from previous studies (e.g., [28 (link)]): the calculated number of cases is sufficient to detect an effect on the serum antibody profiles, given a significance level α = 0.05 and power (1 − ß) = 90%. The statistical analysis demonstrated that the antibody composition against retinal antigens within sera change over time. A comparison to the control group showed if the modifications are beneficial, i.e., the serum compositions become more similar to the serum of healthy subjects or not. A subsequent biomarker identification using MALDI-TOF/TOF MS (Bruker, Billerica, MA, USA). revealed valuable hints on the systemic effects. After electrophoretic separation, proteins were be typically digested, crystallized on matrix and analysed on a MALDI target. The obtained peptide mass fingerprint data were exported into BioTools (Version 3.1, Bruker, MA, USA) and used for an internal Mascot database search (Matrix Science, London, England; Uniprot release 07, 2012), leading to protein identifications.
Korb C.A., Beck S., Wolters D., Lorenz K., Pfeiffer N, & Grus F.H. (2023). Serum Autoantibodies in Patients with Dry and Wet Age-Related Macular Degeneration. Journal of Clinical Medicine, 12(4), 1590.
Publication 2023
Antibodies Antigens Autoantibodies Biological Markers BLOOD Chromatography Electrophoresis Ethanolamine Fingerprints, Peptide G-substrate Healthy Volunteers Hypersensitivity Immunoglobulins Patients ProteinChip Proteins Retina Serum Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization

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Mascot software by Matrix Science
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C18 ZipTips are a type of pipette tip used in sample preparation for mass spectrometry and other analytical techniques. They are coated with a C18 reversed-phase material, which can selectively retain and concentrate analytes of interest from complex biological samples.
4
Mascot search engine by Matrix Science
Sourced in United Kingdom, United States, Germany
The Mascot search engine is a software tool designed for the identification of proteins from mass spectrometry data. It provides a comprehensive solution for the analysis and interpretation of proteomic data.
5
Trypsin by Promega
Sourced in United States, Germany, United Kingdom, China, Japan, France, Switzerland, Sweden, Italy, Netherlands, Spain, Canada, Brazil, Australia, Macao
Trypsin is a serine protease enzyme that is commonly used in cell culture and molecular biology applications. It functions by cleaving peptide bonds at the carboxyl side of arginine and lysine residues, which facilitates the dissociation of adherent cells from cell culture surfaces and the digestion of proteins.
6
Ultraflex 3 by Bruker
Sourced in Germany, United States
The Ultraflex III is a high-performance MALDI-TOF/TOF mass spectrometer designed for advanced proteomics and biomolecular analysis. It provides high mass resolution, sensitivity, and accurate mass measurement capabilities.
7
Maldi tof ms by Bruker
Sourced in Germany, United States, France, United Kingdom, Japan, Italy, Switzerland, Canada, Poland
MALDI-TOF MS is a type of mass spectrometry instrument that uses Matrix-Assisted Laser Desorption/Ionization (MALDI) as the ionization technique and Time-of-Flight (TOF) as the mass analyzer. It is designed to analyze and identify a wide range of compounds, including proteins, peptides, lipids, and small molecules.
8
Ultraflextreme maldi tof tof mass spectrometer by Bruker
Sourced in Germany, United States
The UltrafleXtreme MALDI-TOF/TOF mass spectrometer is a high-performance analytical instrument designed for advanced proteomics, metabolomics, and molecular imaging applications. It features a matrix-assisted laser desorption/ionization (MALDI) ion source coupled with a tandem time-of-flight (TOF/TOF) mass analyzer, providing high mass accuracy and resolution for the identification and characterization of a wide range of molecules.
9
Sequencing grade trypsin by Promega
Sourced in United States, Germany, United Kingdom, Italy, France, Spain, Switzerland
Sequencing grade trypsin is a proteolytic enzyme used to cleave peptide bonds in protein samples, primarily for use in protein sequencing applications. It is purified to ensure high-quality, consistent performance for analytical processes.
10
Mascot server by Matrix Science
Sourced in United Kingdom, United States, Germany, Japan
The Mascot Server is a software application that provides protein identification and characterization services. It is designed to analyze mass spectrometry data and match it against protein sequence databases to identify the proteins present in a sample. The Mascot Server operates as a centralized system, allowing multiple users to access and utilize the software's capabilities simultaneously.

More about "Fingerprints, Peptide"

Peptide fingerprinting is a powerful technique that leverages the distinctive patterns of amino acid residues within a peptide sequence to identify and characterize peptides.
This approach is widely used in proteomics research, as it enhances the reproducibility and accuracy of experimental protocols.
The PubCompare.ai platform optimizes peptide research by utilizing AI-driven comparisons to help researchers locate the best protocols from literature, preprints, and patents.
Peptide fingerprinting is often employed in conjunction with mass spectrometry techniques, such as MALDI-TOF MS and MALDI-TOF/TOF mass spectrometry.
The Mascot software and Mascot search engine are commonly used tools for analyzing and identifying peptides based on their unique fingerprints.
Trypsin, a serine protease, is frequently used to digest proteins into peptides, which are then analyzed using Mascot and other bioinformatics tools.
The Ultraflex III and UltrafleXtreme MALDI-TOF/TOF mass spectrometers are commonly used for high-resolution peptide analysis and sequencing.
Sequencing grade trypsin is often employed to ensure consistent and reliable peptide digestion.
The Mascot server provides a centralized platform for peptide identification and analysis, further enhancing the efficiency and accuracy of proteomics research.
By leveraging the power of peptide fingerprinting and the cutting-edge tools available, researchers can optimize their peptide research, improve scientific outcomes, and drive advancements in the field of proteomics.
The PubCompare.ai platform offers a comprehensive solution for locating the best protocols and enhancing the reproducibility and accuracy of peptide research.