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Protein Precursors

Protein Precursors are the preliminary forms of proteins that undergo various modifications to become the final, functional proteins.
These precursor molecules may undergo cleavage, folding, or other transformations to reach their mature state.
Understanding the structure and characteristics of protein precursors is crucial for researchers studying protein synthesis, cellular processes, and potential therapeutic interventions.
PubCompare.ai can help optimize your protein precursor research by locating the best protocols from literature, preprints, and patents, using intelligent comparisons to enhance reproducibility and accuarcy.
Streamline your research and find the right solutions with PubCompare.ai.

Most cited protocols related to «Protein Precursors»

Quantitative Mass Spectrometry Proteomics

Cited 25 times

Mass spectrometric data were collected on an Orbitrap Fusion Lumos mass spectrometer in-line with a Proxeon NanoLC-1200 UHPLC. The 100 μm capillary column was packed with 35 cm of Accucore 150 resin (2.6 μm, 150Å; ThermoFisher Scientific). Spectra were converted to mzXML using a modified version of ReAdW.exe. Database searching included all entries from the Saccharomyces Genome Database (SGD; August 21, 2017). This database was concatenated with one composed of all protein sequences in the reversed order. Searches were performed using a 50-ppm precursor ion tolerance for total protein level profiling. The product ion tolerance was set to 0.9 Da. These wide mass tolerance windows were chosen to maximize sensitivity in conjunction with SEQUEST searches and linear discriminant analysis (16 (link), 17 (link)). TMT tags on lysine residues and peptide N termini (+229.163 Da) and carbamidomethylation of cysteine residues (+57.021 Da) were set as static modifications, while oxidation of methionine residues (+15.995 Da) was set as a variable modification. For phosphorylation analysis, deamidation (+0.984) on asparagine and glutamine and phosphorylation (+79.966) on serine, threonine, and tyrosine were set as variable modifications. Peptide-spectrum matches (PSMs) were adjusted to a 1% false discovery rate (FDR) (18 (link), 19 (link)). PSM filtering was performed using a linear discriminant analysis, as described previously (17 (link)) and then assembled further to a final protein-level FDR of 1% (19 (link)). Phosphorylation site localization was determined using the AScore algorithm (13 (link)). AScore is a probability-based approach for high-throughput protein phosphorylation site localization. Specifically, a threshold of 13 corresponded to 95% confidence in site localization. Proteins were quantified by summing reporter ion counts across all matching PSMs, as described previously (20 (link)). Reporter ion intensities were adjusted to correct for the isotopic impurities of the different TMT reagents according to manufacturer specifications. The signal-to-noise (S/N) measurements of peptides assigned to each protein were summed and these values were normalized so that the sum of the signal for all proteins in each channel was equivalent, to account for equal protein loading. Lastly, each protein was scaled such that the summed signal-to-noise for that protein across all channels was greater than 100, thereby generating a relative abundance (RA) measurement. A detailed description of the methods in a step-by-step outline is available in the Supplementary Materials.

Navarrete-Perea J., Yu Q., Gygi S.P, & Paulo J.A. (2018). SL-TMT: A Streamlined Protocol for Quantitative (Phospho)proteome Profiling using TMT-SPS-MS3. Journal of proteome research, 17(6), 2226-2236.

Publication 2018

Amino Acid Sequence Asparagine Capillaries Cysteine Genome Glutamine Hypersensitivity Immune Tolerance Isotopes Lysine Mass Spectrometry Methionine Peptides Phosphorylation Protein Precursors Proteins Resins, Plant Saccharomyces Serine Signal Peptides Staphylococcal Protein A Threonine Tyrosine

Neuropeptide Precursor Identification in Asterias

Cited 15 times

To search for transcripts encoding putative neuropeptide or peptide hormone precursor proteins in A. rubens, the sequences of neuropeptide or peptide hormone precursors previously identified in the sea urchin S. purpuratus [5 (link),6 (link),11 (link),16 (link),17 (link),37 (link),38 (link)], the sea cucumber A. japonicus [10 (link)] and the starfish species Asterina pectinifera [39 (link)] were submitted individually as queries in tBLASTn searches of the contig database with the BLAST parameter e-value set to 1000. Contigs identified as encoding putative precursors were analysed after translation of their full-length DNA sequence into protein sequence using the ExPASy Translate tool (http://web.expasy.org/translate/). Proteins were assessed as potential precursors of secreted bioactive peptides by investigating: (i) the presence of a putative N-terminal signal peptide sequence, using the SignalP v. 3.0 online server [40 (link)], (ii) the presence of putative monobasic or dibasic cleavage sites N-terminal and C-terminal to the putative bioactive peptide(s), with reference to known consensus cleavage motifs [41 (link)–43 (link)], and (iii) the presence, in some cases, of a C-terminal glycine residue that is a potential substrate for amidation.

Semmens D.C., Mirabeau O., Moghul I., Pancholi M.R., Wurm Y, & Elphick M.R. (2016). Transcriptomic identification of starfish neuropeptide precursors yields new insights into neuropeptide evolution. Open Biology, 6(2), 150224.

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Publication 2016

Amino Acid Sequence Asterina pectinifera Cytokinesis DNA Sequence Glycine Neuropeptides Peptide Hormone Peptides polypeptide C Protein Precursors Proteins Ruthenium Ben Sea Cucumbers Sea Urchin Signal Peptides Starfish

Expression and Purification of Goat Cathelicidins

Cited 7 times

The recombinant plasmids for expression of the goat cathelicidins were constructed with the use of pET-based vector as described previously (Panteleev and Ovchinnikova, 2017 (link)). The target peptides were expressed in E. coli BL21 (DE3) as chimeric proteins that included 8 × His tag, the E. coli thioredoxin A with the M37L substitution (TrxL), methionine residue, and a mature cathelicidin. The ChMAP-28 amino acid sequence was translated from mRNA for the corresponding precursor protein (GenBank: AJ243126.1) as a 27-residue peptide without the C-terminal glycine, a common amidation signal in cathelicidins. The transformed E. coli BL21 (DE3) cells were grown up to OD₆₀₀ 1.0 at 37°C in lysogeny broth (LB) containing 20 mM glucose, 1 mM MgSO₄, and 0.1 mM CaCl₂, 100 μg/ml of ampicillin and then were induced with isopropyl β-D-1-thiogalactopyranoside (IPTG) at a final concentration of 0.3 mM. The cells were cultivated for 5 h at 30°C with intense agitation. Then the cells were pelleted by centrifugation and sonicated in immobilized metal affinity chromatography (IMAC) loading buffer containing 6 M guanidine hydrochloride. The clarified lysate was applied on a column packed with Ni Sepharose (GE Healthcare). The recombinant protein was eluted with the buffer containing 0.5 M imidazole. Then the eluate containing the fusion protein was acidified (up to pH 1.0) and cleaved by 100-fold molar excess of cyanogen bromide over methionine for 20 h at 25°C in the dark. The reaction products were lyophilized, dissolved in water, titrated to pH 5.0, and loaded on a semi-preparative Reprosil-pur C18-AQ column (10 mm × 250 mm, 5-μm particle size, Dr. Maisch GmbH). Reversed-phase high-performance liquid chromatography (RP-HPLC) was performed with a linear gradient of acetonitrile in water containing 0.1% trifluoroacetic acid. The peaks were monitored at 214 and 280 nm. The collected fractions were analyzed by MALDI-TOF mass-spectrometry using Reflex III instrument (Bruker Daltonics). The fractions containing the target peptides were lyophilized and dissolved in water. The synthetic melittin (>98% pure) was kindly provided by Dr. Sergey V. Sychev (M.M. Shemyakin and Yu. A. Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia). The recombinant tachyplesin-1 was obtained as described previously (Panteleev and Ovchinnikova, 2017 (link)). The peptides concentrations were estimated using UV absorbance.

Panteleev P.V., Bolosov I.A., Kalashnikov A.À., Kokryakov V.N., Shamova O.V., Emelianova A.A., Balandin S.V, & Ovchinnikova T.V. (2018). Combined Antibacterial Effects of Goat Cathelicidins With Different Mechanisms of Action. Frontiers in Microbiology, 9, 2983.

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Publication 2018

acetonitrile Amino Acid Sequence Ampicillin Buffers Cathelicidins Cells Centrifugation Chimera Chromatography, Affinity Chromatography, Reversed-Phase Liquid Cloning Vectors Cyanogen Bromide Escherichia coli Glucose Glycine Goat Hydrochloride, Guanidine imidazole Lysogeny Mass Spectrometry Melitten Metals Methionine Molar Peptides Plasmids polypeptide C Protein Precursors Proteins Recombinant Proteins Reflex Reprosil RNA, Messenger Sepharose Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Sulfate, Magnesium tachyplesin peptide, Tachypleus tridentatus Trifluoroacetic Acid TXN protein, human

Quantitative Mass Spectrometry Proteomics

Cited 6 times

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Publication 2016

6H,8H-3,4-dihydropyrimido(4,5-c)(1,2)oxazin-7-one Amino Acid Sequence Biological Processes Cellular Structures Cysteine Cytokinesis Genome Hypersensitivity Immune Tolerance Ions isolation Lysine Mass Spectrometry Methionine Peptides Protein Precursors Proteins Saccharomyces Saccharomyces cerevisiae SET protein, human Staphylococcal Protein A

Tissue Proteomics Workflow for LC-MS/MS

Cited 6 times

Extracts taken from tissue sections were pipetted directly into 3µL of the extraction solvent. Aliquots of 1–2µL, containing 25–50% of each tissue extract, were diluted 6-fold with 0.1% formic acid to reduce the organic composition in the solvent to 3% acetonitrile. For lens extract analysis, 1 µL (25%) of the tissue extract was used, and 2-µL aliquots (50%) of the kidney and brain extracts were analyzed by LC-MS/MS. The diluted protein extracts were loaded onto a reverse-phase capillary trap column using a helium-pressurized cell (pressure bomb). The trapping column (360µm × 150µm ID) was fritted with a filter end-fitting (IDEX Health & Science), and was packed with 5 cm of C₈ material (15 µm material, 300Å Grace Vydac or 5µm, 300 Å YMC Co.). Once the sample was loaded, an M-520 microfilter union (IDEX Health & Science) was used to connect the trap column to an analytical column (360µm × 100µm ID, 8 cm or 20 cm of C₈ stationary phase material), equipped with a laser-pulled emitter tip. Using an Eksigent NanoLC Ultra HPLC, peptides were gradient-eluted at a flow rate of 400 nL/min, and the mobile phase solvents consisted of 0.1% formic acid, 99.9% water (solvent A) and 0.1% formic acid, 99.9% acetonitrile (solvent B). The gradient consisted of 2–60 %B in 50 min, 60–90 %B in 10 min, 90 %B for 1 min, 90-2 %B in 1 min, and the column was equilibrated at 2 %B for 12 min. Upon gradient-elution, proteins were mass analyzed on a LTQ Velos mass spectrometer, equipped with a nanoelectrospray ionization source (Thermo Scientific). The instrument was operated using a data-dependent method or a combination method consisting of both data-dependent and targeted scan events. Dynamic exclusion was enabled allowing a repeat count of 4 within 15 s. For all LC-MS/MS analyses, full scan (m/z 360–2000) spectra were acquired as the initial scan event, and the seven or eight most abundant ions in each MS scan were selected for fragmentation in the LTQ. For electron transfer dissociation (ETD) of eluting protein species, an isolation width of 2.5 m/z and reaction times of 90–115 ms were used. The MSⁿ AGC target value in the ion trap was set to 1e⁴, with a maximum injection time of 100 ms. The ETD reagent ion AGC target was 3e⁵ or 5e⁵ with a maximum inject time of 100 ms. For a subset of LC-MS/MS analyses, a data-dependent ETD/CID toggle method was used, where sequential ETD and CID spectra were acquired of the same protein precursors in back-to-back scan events. ETD and CID MS/MS spectra were interpreted manually with assistance by the TagIdent tool (http://web.expasy.org/tagident/).

Schey K.L., Anderson D.M, & Rose K.L. (2013). Spatially-Directed Protein Identification from Tissue Sections by Top-Down LC-MS/MS with Electron Transfer Dissociation. Analytical chemistry, 85(14), 6767-6774.

Publication 2013

acetonitrile Brain Capillaries Cells Electron Transport formic acid Helium High-Performance Liquid Chromatographies isolation Kidney Lens, Crystalline Peptides Pressure Protein Precursors Proteins Radionuclide Imaging Solvents Tandem Mass Spectrometry Tissues Z-360

Most recents protocols related to «Protein Precursors»

Increased TLR3 Expression in Vero Cells Administered HSV-1 TLR3 ROVV

Not available on PMC !

Example 2

Table 1 below summarizes data obtained from Vero cells that were administered an HSV-1 TLR3 ROVV and TLR3 protein levels were compared against control Vero cells that did not receive the HSV-1 TLR3 ROVV (control). Specifically, about 2.5×10⁶Vero confluent cells were administered about 3×10⁸HSV-1 TLR3 ROVV. About 48 hours later, expression levels of human TLR3 mRNA were determined by qRT-PCR, and converted to expressed TLR3 protein levels.

TABLE 1

TLR3 Protein Levels

HSV-1 TLR3

ControlROVV recipient

Molecule(ag)(ag)P-value

TLR34450.0052

n.b. ag = attogram 1 × 10⁻¹⁸grams

As shown in Table 1, cells that received the HSV-1 TLR3 ROVV that included SEQ ID No. 1 and that encoded for increased production of a TLR3 precursor protein demonstrated statistically significant higher amounts of TLR3 than the control cells that did not receive the HSV-1 TLR3 ROVV. Without being bound by any particular theory, the cells that were administered the HSV-1 TLR3 ROVV had higher levels of TLR3 than cells that did not receive the HSV-1 TLR3 ROVV.

US11873505B2. Compositions and methods for regulating production of a precursor protein (2024-01-16). Wyvern Pharmaceuticals Inc. [CA]. Inventors: Bradley G. Thompson [CA].

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Patent 2024

Cells Homo sapiens Human Herpesvirus 1 Protein Precursors Proteins RNA, Messenger Vero Cells

Expressing Precursor Proteins Using ROVV

Not available on PMC !

Example 1

Expression cassettes for expressing a precursor protein were synthesized. Each cassette contained a CMV promotor, followed by the sequence for the precursor protein. The synthesized precursor protein expression cassettes were cloned into the ROVV using the methods described by Law el al. (A New Approach to Assessing HSV-1 Recomination during Intracellular Spread. Viruses 2018, 10, 220), the disclosure of which is incorporated herein by reference.

US11873505B2. Compositions and methods for regulating production of a precursor protein (2024-01-16). Wyvern Pharmaceuticals Inc. [CA]. Inventors: Bradley G. Thompson [CA].

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Patent 2024

Human Herpesvirus 1 Protein Precursors Protoplasm Virus

Comparative Analysis of Tc1-like Transposases

The reference genomes of O. polymorpha HU-11/CBS4732 (GenBank: CP073033-40), NCYC495 (GenBank: NW_017264698–704), DL-1 (GenBank: CP080316-22), C. elegans (RefSeq: NC_003279–84), M. piriformis (GenBank: OW971867-72) and S. cerevisiae (Assembly: GCF_000146045.2) were downloaded from the NCBI GenBank or RefSeq database for the analysis in local servers. The reference genome of C. elegans was analyzed using the UCSC genome browser and tools. Another genome of O. polymorpha DL-1 (RefSeq: NC_027860–66) was used to confirm the results obtained using the DL-1 genome (GenBank: CP080316-22). The nt sequences of Tc1-OP1, Tc1-MP1, Tc1-CE1, and IS630-AB1 are located in the genomes of O. polymorpha DL-1 (CP080317: 544314–549,972), M. piriformis (OW971871: 2795447–2,793,760), C. elegans (NC_003283: 17805799–17,807,409) and A. baumannii CAM180-1 (CP044356: 173741–174,626). InterProScan (Jones et al., 2014 (link)) v5.56–89.0 was used to predict protein domains against InterPro consortium member databases with default parameter setting. The repeats in all the analyzed genomes were detected using the software RepeatMasker v4.1.2 with the RepBase v20181026 and Dfam v3.6 databases. The software BLAST v2.12.0 was used to search for homologs in a local NCBI NR database with default parameter setting. Signal peptides of protein precursors were detected using SignalP v5.0. The secondary structures of putative proteins were predicted using PSIPRED (Buchan and Jones, 2019 (link)) v4.0. The 3D structures of putative proteins were predicted using trRosetta (Yang et al., 2020 (link)), trRosettaX, AlphaFold v2.2.0 and RoseTTAFold v1.1.0. The top 5 models of the Tc1-OP1 protein predicted by AlphaFold had low per-residue confidence scores (pLDDTs) around 50 which indicated that most of the protein regions may be unstructured, likewise, those predicted by RoseTTAFold also had low quality scores about 0.3. We had to use trRosetta to improve the prediction of the Tc1-OP1 protein for a better performance. The top 1 model (Supplementary File S2) of the Tc1-OP1 protein (139–1,155 aa) predicted by trRosettaX had the best template modeling (TM) score of 0.334, although it was much lower than the TM scores of Tc1-MP1 and Tc1-CE1 transposases that are 0.62 and 0.688, respectively. DaliLite v5 and 3D-BLAST vbeta102 were used for protein structural alignment and structure database search. The analysis and plotting of protein structures were performed using PyMOL v2.5.26; The neighbor joining (NJ) analyses were performed using MEGA (Kumar et al., 1994 (link)) v7.0.26; The maximum likelihood (ML) and Bayesian inference (BI) analyses were performed using PhyloSuite (Zhang et al., 2020 (link)) v1.2.2. Statistics and plotting were conducted using the software R v2.15.3 with the Bioconductor packages (Gao et al., 2014 ). All other data processing were carried out using Perl scripts.

Chang J., Duan G., Li W., Yau T.O., Liu C., Cui J., Xue H., Bu W., Hu Y, & Gao S. (2023). The first discovery of Tc1 transposons in yeast. Frontiers in Microbiology, 14, 1141495.

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Publication 2023

Caenorhabditis elegans Genome Protein Domain Protein Precursors Proteins Saccharomyces cerevisiae Signal Peptides Tc1 transposase

Comprehensive MS-based Protein Quantification

All raw MS files were processed using MaxQuant (version 1.6.1.0) (19 (link)). MS/MS spectra were searched against the Human UniprotKB protein sequence database (December 2014, with 88,657 entries of 20,459 human genes) using the Andromeda search engine (20 (link)). Primary searches were performed using 6 ppm precursor ion tolerance for total protein-level analysis. MS/MS ion tolerance was set at 20 ppm. Cysteine carbamidomethylation was used as a fixed modification. Protein N-acetylation and methionine oxidation are considered variable modifications. Enzyme specificity was set to full tryptic digestion. Peptides with a minimum length of six amino acids and up to two missed cleavages were considered. The required false discovery rate (FDR) was set to 1% at peptide, protein, and modification levels. To maximize the number of quantification events across samples, we enabled the “Match between Runs” options on the MaxQuant platform. The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (21 (link)) partner repository with the dataset identifier PXD034646. Annotated MS/MS spectra can be accessed through MS-Viewer (22 (link)) (https://msviewer.ucsf.edu/cgi-bin/mssearch.cgi?report_title=MS-Viewer&search_key=bzgazjrsgb&search_name=msviewer) with the following search keys: bzgazjrsgb.

Kim S.I., Hwangbo S., Dan K., Kim H.S., Chung H.H., Kim J.W., Park N.H., Song Y.S., Han D, & Lee M. (2023). Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma. Molecular & Cellular Proteomics : MCP, 22(3), 100502.

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Publication 2023

Acetylation Amino Acids Cysteine Cytokinesis Digestion Enzymes Homo sapiens Immune Tolerance Methionine nucleoprotein, Measles virus Peptides Protein Precursors Proteins Tandem Mass Spectrometry Trypsin

Identification of Insect Neuropeptide Sequences

The cDNA and translated protein of each neuropeptide were selected by searching for the sequences of closely related orthopteran species against the in-house database of G. bimaculatus transcripts constructed in previous reports [12 (link)]. Among the 43 neuropeptides that were reported in insects, 35 neuropeptides were provisionally determined as the reference neuropeptide sequences, which are the mature, precursor, or cDNA sequences (Table S1). In particular, the sequences of ACP [7 (link)], Ast B [12 (link)], corazonin [6 (link)], elevenin [6 (link)], myosuppressin [13 (link)], and PDF [14 (link)] were used as seed sequences as described in previously published studies. These selected proteins, except for sequences published in previous studies, were submitted with BLASTP [24 (link)] on the NCBI website (https://blast.ncbi.nlm.nih.gov/Blast.cgi (accessed on 16 July 2022)) with the clustered nr database to confirm homology with the sequences of closely related orthopteran species. These precursor proteins and their amino acid sequences deduced from cDNA were confirmed to belong to that they belong to appropriate protein clusters (Table S2).

Mochizuki T., Sakamoto M., Tanizawa Y., Seike H., Zhu Z., Zhou Y.J., Fukumura K., Nagata S, & Nakamura Y. (2023). Best Practices for Comprehensive Annotation of Neuropeptides of Gryllus bimaculatus. Insects, 14(2), 121.

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Publication 2023

Amino Acid Sequence corazonin protein, insect DNA, Complementary Homologous Sequences Insecta Neuropeptides Protein Precursors Proteins

Top products related to «Protein Precursors»

Rabbit reticulocyte lysate by Promega

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Rabbit reticulocyte lysate is a cell-free protein synthesis system derived from rabbit reticulocytes, which are immature red blood cells. The lysate contains the necessary components for in vitro translation of mRNA, enabling the synthesis of proteins from exogenously added templates.

Tnt coupled reticulocyte lysate system by Promega

Sourced in United States

The TNT Coupled Reticulocyte Lysate System is a cell-free protein expression system that enables the in vitro synthesis of proteins from DNA templates. It uses a rabbit reticulocyte lysate to provide the necessary cellular components for transcription and translation of the target protein.

Cobas 8000 by Roche

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Kta fplc system by GE Healthcare

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The ÄKTA FPLC system is a versatile liquid chromatography instrument designed for fast protein liquid chromatography (FPLC) applications. It is capable of performing automated purification and analysis of proteins, peptides, and other biomolecules. The system features precise flow and pressure control, advanced software for method development and data analysis, and is compatible with a wide range of chromatography columns and accessories.

35s methionine by PerkinElmer

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[35S]-methionine is a radioactive isotope of the amino acid methionine. It is commonly used as a labeling agent in various biochemical and molecular biology applications.

Sig 39320 by BioLegend

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Tnt quick coupled transcription translation kit by Promega

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Amicon 10 000 mwco concentrator by Merck Group

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The Amicon 10,000 MWCO concentrator is a laboratory device used to concentrate macromolecules, such as proteins, peptides, and nucleic acids, by removing excess water and small molecules. The device utilizes a semi-permeable membrane with a molecular weight cut-off (MWCO) of 10,000 Daltons, which allows the passage of smaller molecules while retaining the larger target molecules.

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Complete protease inhibitor mixture by Roche

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Complete protease inhibitor mixture is a laboratory product that functions to inhibit the activity of proteases, which are enzymes that break down proteins. It is designed to be used in various research applications where the preservation of protein structure and function is critical.

What are protein precursors and why are they important?

Protein precursors are the preliminary forms of proteins that undergo various modifications to become the final, functional proteins. These precursor molecules may undergo cleavage, folding, or other transformations to reach their mature state. Understanding the structure and characteristics of protein precursors is crucial for researchers studying protein synthesis, cellular processes, and potential therapeutic interventions.

How can PubCompare.ai help with protein precursor research?

PubCompare.ai allows you to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. The platform can help researchers identify the most effective protocols related to Protein Precursors for their specific research goals. The AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuarcy.

What are some common challenges in working with protein precursors?

One of the main challenges in working with protein precursors is ensuring the proper folding and processing of these molecules to reach their mature, functional state. Researchers must often optimize conditions like pH, temperature, and enzymatic activity to facilitate the correct transformations. Additionally, identifying and isolating specific precursor forms can be difficult, requring specialized techniques and analytical methods.

Are there different types or variations of protein precursors?

Yes, there are various types and variations of protein precursors. These can include signal peptides, pro-proteins, and zymogens, among others. Signal peptides are short amino acid sequences that target proteins for secretion or membrane localization. Pro-proteins are inactive precursor forms that require cleavage to become the mature, active protein. Zymogens are enzymatic precursors that need to be activated, often through proteolytic cleavage, to become the functional enzyme.

What are some key applications of protein precursor research?

Understanding protein precursors is crucial for a range of applications in biology and medicine. Studying precursor processing can provide insights into cellular protein trafficking and quality control mechanisms. Identifying and characterizing precursor forms can also aid in the development of targeted therapeutics, as well as the production of recombinant proteins for industrial and research purposes. Additionally, monitoring protein precursor levels and modifications can serve as biomarkers for certain disease states.

More about "Protein Precursors"

Protein precursors are the initial forms of proteins that undergo various modifications to become the final, functional proteins.
These precursor molecules may be subjected to cleavage, folding, or other transformations to reach their mature state.
Understanding the structure and characteristics of protein precursors is crucial for researchers studying protein synthesis, cellular processes, and potential therapeutic interventions.
PubCompare.ai can help optimize your protein precursor research by locating the best protocols from literature, preprints, and patents, using intelligent comparisons to enhance reproducibility and accuracy.
Streamline your research and find the right solutions with PubCompare.ai.
Protein precursors, also known as proproteins or preproteins, are the preliminary forms of proteins that undergo a series of modifications to become the final, functional proteins.
These precursor molecules may undergo cleavage, folding, or other transformations to reach their mature state.
Understanding the structure and characteristics of protein precursors is crucial for researchers studying protein synthesis, cellular processes, and potential therapeutic interventions.
PubCompare.ai, an AI-driven platform, can help researchers optimize their protein precursor research by locating the best protocols from literature, preprints, and patents.
The platform uses intelligent comparisons to enhance reproducibility and accuracy, allowing researchers to streamline their research and find the right solutions.
Researchers studying protein precursors may utilize tools and techniques such as Rabbit reticulocyte lysate, TNT Coupled Reticulocyte Lysate System, Cobas 8000, ÄKTA FPLC system, [35S]-methionine, SIG-39320, TNT Quick Coupled Transcription/Translation kit, Amicon 10,000 MWCO concentrator, and A8717 Complete protease inhibitor mixture.
These resources can aid in the analysis, purification, and modification of protein precursors, as well as the study of their synthesis and processing pathways.
By leveraging the insights and capabilities of PubCompare.ai, researchers can streamline their protein precursor research, enhance reproducibility, and ultimately accelerate the advancement of knowledge in this crucial field of study.