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Protein B

Protein B is a key biomolecule involved in various cellular processes.
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Most cited protocols related to «Protein B»

The procedures outlined above were evaluated using simulated as well as experimental data. First, a simulated density map, or phantom, was used to assess the accuracy and speed of the projection and back-projection operations. For this purpose, a set of atomic coordinates of the 70S ribosome (PDB-IDs 2J00 and 2J01) (Selmer et al., 2006 (link)) was converted to a density map of 128×128×128 voxels, with a voxel size of 2.8 Å, using the xmipp_convert_pdb2vol program (Sorzano et al., 2004 (link)). This map was projected in 5000 different orientations that were taken from a previously reported cryo-EM study on 70S ribosomes (Scheres et al., 2007a (link)). The resulting projections were then back-projected in their perfect orientations to generate a reconstructed density map, and the accuracy of this projection/reconstruction cycle was assessed by FSC-curves between this reconstruction and the original phantom.
Second, general refinement behaviour and computational costs of the MAP optimization approach were tested using an experimental cryo-EM data set of 5168 GroEL particles that is distributed as part of a workshop on the EMAN2 software package (Tang et al., 2007 (link)). Using standard procedures in XMIPP, see (Scheres, 2010 ) for details, all particles were normalized, 115 particles were discarded after initial sorting, and the remaining 5053 particles were windowed to images of 128 × 128 pixels, with a pixel size of 2.12 Å. Refinements with these data were performed in symmetry group D7; a soft spherical mask with a diameter of 205 Å was applied to the reconstructions at every iteration; and the starting model was obtained from a 50 Å low-pass filtered GroEL map from a previous study (Scheres, 2012 (link)). Reconstruction quality was assessed by FSC calculations between the reconstructed maps and a symmetrized GroEL crystal structure (PDB-ID 1XCK) (Bartolucci et al., 2005 (link)) that was also used to assess GroEL reconstructions in a previous study (Scheres, 2012 (link)). All estimated τ2 values in these refinements were multiplied by a constant T=4 . As explained in more detail in Scheres (2012) (link), values of T in the range of 2–4 typically yield better maps than those obtained with the original algorithm.
Additional tests to assess alignment accuracies were performed using simulated data that were designed to be similar to the experimental GroEL data. The symmetrized GroEL crystal structure was converted to a density map, to which a B-factor of 350 Å2 and an arbitrary scale factor were applied to yield a phantom with a similar power spectrum as the reconstruction obtained from the experimental data. This phantom was then projected into 5053 orientations, which comprised small random perturbations of the optimal orientations as determined for the experimental particles. For each simulated particle, identical CTF parameters were used as estimated for the experimental particles, and independent Gaussian noise was added in the Fourier domain using the same power spectra as estimated for the experimental data. FSC curves with the original phantom were used to assess the quality of reconstructions from these images, while the known orientations of all particles allowed the calculation of histograms of orientational error distributions.
Finally, to further illustrate its general applicability, RELION was applied to three additional cryo-EM data sets: 50,330 β-galactosidase particles that were described by Scheres et al. (2012) (link); 5403 hepatitis B capsids that were selected from re-scanned micrographs that were previously described by Boettcher et al. (1997) (link); and 3700 recoated rotavirus particle (RP7) that were described by Chen et al. (2009) (link). Crystal structures for these complexes are available: PDB-ID 3I3E for β-galactosidase (Dugdale et al., 2010 (link)); PDB-ID 1QGT for hepatitis B capsid (Wynne et al., 1999 (link)); and PDB-ID 1QHD for the rotavirus VP6 protein (Mathieu et al., 2001 (link)). FSC calculations of the reconstructed maps vs. these crystal structures were used to assess the quality of the refinement results.
All calculations described in this paper were performed on Dell M610 computing nodes of eight 2.4 GHz Xeon E5530 cores and 16 Gb of RAM each. Projection and back-projection operations with the phantom were performed using a single core, while all other calculations used the hybrid parallelization scheme to launch eight threads on each of seven nodes, i.e. using 56 cores in parallel.
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Publication 2012
The predicted structure is compared to the true structure from the PDB in terms of lDDT metric34 (link), as this metric reports the domain accuracy without requiring a domain segmentation of chain structures. The distances are either computed between all heavy atoms (lDDT) or only the Cα atoms to measure the backbone accuracy (lDDT-Cα). As lDDT-Cα only focuses on the Cα atoms, it does not include the penalty for structural violations and clashes. Domain accuracies in CASP are reported as GDT33 (link) and the TM-score27 (link) is used as a full chain global superposition metric.
We also report accuracies using the r.m.s.d.95 (Cα r.m.s.d. at 95% coverage). We perform five iterations of (1) a least-squares alignment of the predicted structure and the PDB structure on the currently chosen Cα atoms (using all Cα atoms in the first iteration); (2) selecting the 95% of Cα atoms with the lowest alignment error. The r.m.s.d. of the atoms chosen for the final iterations is the r.m.s.d.95. This metric is more robust to apparent errors that can originate from crystal structure artefacts, although in some cases the removed 5% of residues will contain genuine modelling errors.
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Publication 2021
Vertebral Column
For evaluation on recent PDB sequences (Figs. 2a–d, 4a, 5a), we used a copy of the PDB downloaded 15 February 2021. Structures were filtered to those with a release date after 30 April 2018 (the date limit for inclusion in the training set for AlphaFold). Chains were further filtered to remove sequences that consisted of a single amino acid as well as sequences with an ambiguous chemical component at any residue position. Exact duplicates were removed, with the chain with the most resolved Cα atoms used as the representative sequence. Subsequently, structures with less than 16 resolved residues, with unknown residues or solved by NMR methods were removed. As the PDB contains many near-duplicate sequences, the chain with the highest resolution was selected from each cluster in the PDB 40% sequence clustering of the data. Furthermore, we removed all sequences for which fewer than 80 amino acids had the alpha carbon resolved and removed chains with more than 1,400 residues. The final dataset contained 10,795 protein sequences.
The procedure for filtering the recent PDB dataset based on prior template identity was as follows. Hmmsearch was run with default parameters against a copy of the PDB SEQRES fasta downloaded 15 February 2021. Template hits were accepted if the associated structure had a release date earlier than 30 April 2018. Each residue position in a query sequence was assigned the maximum identity of any template hit covering that position. Filtering then proceeded as described in the individual figure legends, based on a combination of maximum identity and sequence coverage.
The MSA depth analysis was based on computing the normalized number of effective sequences (Neff) for each position of a query sequence. Per-residue Neff values were obtained by counting the number of non-gap residues in the MSA for this position and weighting the sequences using the Neff scheme76 (link) with a threshold of 80% sequence identity measured on the region that is non-gap in either sequence.
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Publication 2021
Amino Acids Amino Acid Sequence Carbon Figs
We leveraged a variety of sources of internal and external validation data to calibrate filters and evaluate the quality of filtered variants (Supplementary Information Table 7). We adjusted the standard GATK variant site filtering37 (link) to increase the number of singleton variants that pass this filter, while maintaining a singleton transmission rate of 50.1%, very near the expected 50%, within sequenced trios. We then used the remaining passing variants to assess depth and genotype quality filters compared to >10,000 samples that had been directly genotyped using SNP arrays (Illumina HumanExome) and achieved 97–99% heterozygous concordance, consistent with known error rates for rare variants in chip-based genotyping38 (link). Relative to a “platinum standard” genome sequenced using five different technologies39 (link), we achieved sensitivity of 99.8% and false discovery rates (FDR) of 0.056% for single nucleotide variants (SNVs), and corresponding rates of 95.1% and 2.17% for insertions and deletions (indels). Lastly, we compared 13 representative Non-Finnish European exomes included in the call set with their corresponding 30x PCR-Free genome. The overall SNV and indel FDR was 0.14% and 4.71%, while for SNV singletons was 0.389%. The overall FDR by annotation classes missense, synonymous and protein truncating variants (including indels) were 0.076%, 0.055% and 0.471% respectively (Supplementary Information Table 5 and 6). Full details of quality assessments are described in the Supplementary Information Section 1.6.
Publication 2016
DNA Chips Europeans Exome Gene Deletion Genome Heterozygote Hypersensitivity INDEL Mutation Insertion Mutation Mutant Proteins Nucleotides Platinum Transmission, Communicable Disease TRIO protein, human
MDAnalysis, which was initially inspired by MDTools for Python (http://www.ks.uiuc.edu/Development/MDTools/Python/, J.C. Phillips, unpublished) and MMTK 8 , is implemented as a Python package. It consists of a core library, which is exposed via the Universe class in the top-level name space and the analysis sub-module, which contains an expanding selection of functionality that make use of the core library (Figure 1). A number of performance critical or low-level input/output (I/O) routines are written in C (either directly or using Cython), and hence installation requires a working C-compiler. It has been tested successfully on Linux and Mac OS X platforms. Reading of CHARMM DCD trajectory files utilizes open source code from catdcd (part of VMD7 (link)) and PDB files are read with the Bio.PDB package13 (link). For some analysis functions a fast linear algebra library such as LAPACK, ATLAS or the native vecLib framework on Mac OS X is needed. MDAnalysis depends on the NumPy package (http://numpy.scipy.org). The development process follows standard software engineering “best practice”14 by using a publicly accessible version-controlled source code repository with a bug tracker and a mailing list dedicated to the project. Importantly, individual code blocks are tested through an extensive unit test suite, ensuring that enhancements and bug fixes do not break old code or re-introduce bugs.
MDAnalysis is fully object-oriented and treats atoms, residues, segments and trajectories as objects. These objects are represented in Python as classes with appropriate functions (“methods”) and variables (“attributes”) defined on these objects. In the following we describe the overall architecture of the package and some of the most important classes to enable users to make best use of the library. A complete simulation system is represented by the Universe class (Figure 1). It is initialized from a topology file, which defines the atoms in the system, and a trajectory or coordinate file, which lists the position of each atom for a number of time frames or a single snapshot.
Universe contains the attribute atoms (an instance of an AtomGroup), which can be thought of as a list of all atoms that are represented as Atom instances. The Atom is the fundamental object in MDAnalysis. It contains data such as chemical type, partial charge, or its Cartesian coordinates. For convenience, atoms are grouped in residues (represented by a Residue class, which is a special AtomGroup); a list of residues can be referred to as a Segment (a ResidueGroup that inherits from AtomGroup). A segment can correspond to a chain in a PDB file or a whole multi-chain protein; similarly, a residue typically corresponds to a single amino acid in a peptide chain, water molecule, or lipid. Residues and segments are simply containers for Atom objects; an Atom records the Residue, Segment and Universe it belongs to. In this way it is possible to directly switch between structural hierarchies, depending on which level is the most convenient for a task at hand. Attributes of Atom instances can be read (and set) individually and thus provide fine grained control for specific analysis tasks. Python indexing and “slicing” of an AtomGroup returns an Atom or a list of Atoms; index operations on a Segment return Residue objects.
In many cases, the user is interested in a property of a group of atoms. Any AtomGroup has a number of methods predefined that provide properties of all atoms in the group (such as coordinates or masses) as NumPy arrays or aggregate properties of the whole group such as the center of mass, the total mass, or the principal axes (see Figure 2 and the documentation of the package, accessible from within Python via the help (Classname) command). Every AtomGroup (which includes Residue, ResidueGroup, Segment and SegmentGroup classes by inheritance) also has the attributes atoms, residues, and segments. They contain lists of those Atom, Residue, and Segment instances to which the atoms in the group belong; for instance, residues contains all residues of which the atoms are members so that one can quickly find all residues for which a certain atom-based selection criterion is true. Because such a consistent application programming interface (API) applies at all levels of the structural hierarchy, concise code can be written that processes segments, residues and arbitrary collections of atoms in the same manner. Additionally, managed attributes are automatically generated that simplify access to certain selections. For instance, any AtomGroup contains an attribute for each atom name that it contains (such as “CA” or “N”). Such an “instant selector” attribute provides a group of all atoms with the same name, e.g. all CA-atoms. Similarly, a Segment also contains instant selectors for residue names and residue numbers (the latter are prefixed with the letter “r” to make them valid Python identifiers). The Universe contains instant selectors for segment identifiers (prefixed with “s” if they start with a number).
A new AtomGroup can be generated from an instant selector or a selection via the selectAtoms() method. MDAnalysis contains a full selection language comparable to the one offered by CHARMM6 (link) or VMD7 (link). It includes selections by atom properties, connectivity, and geometry (such as distances). Selections can be grouped by Boolean operators to create arbitrarily complex expressions.
A simulation trajectory file consists of a sequence of coordinate frames. MDAnalysis provides a view or a “cursor” on the trajectory. A trajectory has a Timestep object associated with it (Figure 2). The Timestep contains the current frame number, the unitcell dimensions (if recorded in the trajectory file), and the coordinates of all atoms. Atom and AtomGroup coordinates always refer to the coordinates in the current time step; they update automatically. A trajectory Reader instance is responsible for reading the trajectory file and populating the Timestep whenever a new frame is read from the file. Reader classes can be used as Python iterators, they have a next() method to advance the trajectory cursor, and they typically also allow indexing to jump to a specific frame. A Writer class is used to write a Timestep to a file on disk. The design of the library is easily extensible; for instance, new trajectory readers and writers can be added through a common API by inheriting from the coordinates.base.Reader class and adding appropriate low-level code for the actual I/O.
The default units in MDAnalysis are the ångström (10−10 m) for length and the picosecond (10−12 s) for time. Data from trajectories are automatically converted to these units, and data written to trajectories is converted to the native format (although this behavior can be changed using internal flags). The unit cell representation differs between trajectory formats. MDAnalysis always makes the unit cell available in the dimensions attribute as a tuple (a,b,c,α,β,γ) for the lengths and angles of the parallelepiped forming the simulation box.
Publication 2011

Most recents protocols related to «Protein B»

Where: A = % Protein content in the sample before pepsin digestion & B = % Protein content in the sample after pepsin digestion 𝑃𝑟𝑜𝑡𝑒𝑖𝑛 𝐷𝑖𝑔𝑒𝑠𝑡𝑖𝑏𝑖𝑙𝑖𝑡𝑦 (%) = Digested protein 𝑇𝑜𝑡𝑎𝑙 𝑃𝑟𝑜𝑡𝑒𝑖𝑛 𝑖𝑛 𝑠𝑎𝑚𝑝𝑙𝑒 𝑥100
Publication 2024
The linear B cell epitopes of σB protein (TH11 strain, NCIB accession No. AFX68863.1) were predicted using Bepipred Linear Epitope Prediction 2.0 (http://tools.iedb.org/bcell/) (21 (link)). The multiple-epitope subunit vaccine was constructed using the B-cell epitopes through a GGGGG linker, and codon optimization was achieved using the Java Codon adaptation tool (http://www.jcat.de/) to improve its expression level in the BL21 (DE3) strain of E. coli (21 (link)). The optimized sequence was synthesized by Sangon Biotech (Shanghai, China) and cloned into the pSmart-I vector (Convenience Biology, Changzhou, China) downstream of the Sumo gene using the BamH I and Xho I restriction sites. The recombinant plasmid was used to transform the E. coli BL21 (DE3) cells; single colonies were selected and grown in LB medium with 50 μg/mL kanamycin at 37°C until an OD600 of 0.5–0.6 was reached. Protein expression was induced by adding isopropyl β-D-thiogalactopyranoside (IPTG) to a final concentration of 0.1 mM and incubated at 16°C overnight. Cells were collected, resuspended in buffer A [20 mM Tris, 250 mM NaCl, 5% (V/V) glycerine; pH ± 8.0], and ultrasonicated. The cell lysate was centrifuged at 13,000 g and 4°C for 30 min. The recombinant proteins in the supernatant were purified by nickel-nitrilotriacetate (Ni-NTA) affinity chromatography, as previously described (22 (link)). Briefly, the supernatants were loaded on the Ni-NTA agarose by gravity flow and washed with buffer B (20 mM Tris, 250 mM NaCl, 50 mM imidazole; pH 8.0). The protein of interest was eluted with buffer C (20 mM Tris, 250 mM NaCl, 250 mM imidazole; pH ± 8.0). The eluate was dialyzed in PBS buffer (pH 7.4) and analyzed via sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) and western blotting.
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Publication 2024
After cellular lysis, protein precipitation was performed by adding an equal volume of chloroform:isoamyl alcohol (24:1), and the samples were gently mixed to avoid shearing. Then, the samples were centrifuged at 9,464 g for 15 min at RT to separate the organic and aqueous phases. The aqueous phase was carefully collected and transferred into a fresh tube of 1.5 ml. To the aqueous phase, 650 µl of prechilled isopropanol solution was added, followed by gently mixing and inverting the tubes for 30 s, which leads to DNA precipitation by dehydrating the surrounding environment of DNA from the aqueous phase. The sample matrix was then stored at -20°C for overnight incubation. The solution mixture was centrifuged at 13,000 RPM for 20 min, and the supernatant was discarded. The precipitated DNA was washed twice with 70% chilled ethanol and centrifuged at 2744 g for 7 min. Finally, the pellet was dissolved in 200 µl Tris-EDTA (TE) buffer (pH 8.0). The samples were incubated overnight at 4°C to dissolve the pellet uniformly.
Publication 2024
For protein–protein crosslinking and mass spectrometric experiments, spliceosomes were prepared as described above for cryo-EM but with the following modifications: after MS2 affinity selection, eluted spliceosomal complexes were crosslinked with 350 μM BS3 for 35 min at 18 °C in a total volume of 1.6 ml, and subsequently subjected to glycerol gradient centrifugation at 17,000 rpm for 18 h at 4 °C in a TST41.14 rotor (Thermo Fischer Scientific) using a linear 10–30% (v/v) glycerol gradient. The three peak gradient fractions containing dimeric B complexes were pooled and pelleted by ultracentrifugation in a S100-AT4 rotor (Thermo Fisher Scientific). The pelleted, crosslinked dimeric B complexes (~7.5 pmol) were dissolved in 50 mM ammonium bicarbonate buffer, containing 4 M urea, reduced with dithiothreitol, alkylated with iodoacetamide and, after diluting the urea to 1 M, in-solution digested with trypsin. Peptides were reverse-phase extracted using Sep-Pak Vac tC18 1cc cartridges (Waters) and fractionated by gel filtration on a Superdex Peptide PC3.2/30 column (GE Healthcare). Next, 50 μl fractions corresponding to an elution volume of 1.2–1.8 ml were analyzed in triplicate on a Thermo Scientific Orbitrap Fusion Lumos Tribrid mass spectrometer coupled to an Ultimate 3000 uHPLC (Thermo Scientific). The protein composition of the spliceosomal complexes was determined by a search with MASCOT 2.3.02 against a UniProt human reference proteome. Based on the MASCOT results, a restricted database was compiled and used for protein–protein crosslink identification by performing a search with pLink 2.3.9 (http://pfind.org/software/pLink/) according to the developer’s recommendations (Chen et al, 2019 (link)). For simplicity, the crosslink score is represented as a negative value of the common logarithm of the original pLink score (i.e., Score = –log10(pLink Score). For model building, a maximum distance of 30 Å between the Cα atoms of the crosslinked lysines was allowed.
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Publication 2024
Fixed HUVECs at metaphase stained for DNA satellite sequences (-satellite and cenpb) and the CENP-B protein were used to generate comprehensive heatmaps. 3D overlays were generated by overlaying individual cells along the centrosome, or x-axis, in Adobe Photoshop and the composite image was exported to ImageJ software to create a heatmap (ImageJ Interactive 3D Surface Plot plugin).
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Publication Preprint 2024

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More about "Protein B"

Protein B is a critical biomolecule involved in numerous cellular processes, making it a key target for biological research.
This versatile protein plays a vital role in a wide range of functions, including cell signaling, metabolism, and gene regulation.
Optimizing research on Protein B can be streamlined with the PubCompare.ai platform, which leverages advanced AI-powered features to enhance reproducibility and accuracy.
With PubCompare.ai, researchers can easily locate relevant protocols from the literature, preprints, and patents, and utilize AI-driven comparisons to identify the best protocols and products for their Protein B studies.
To further enhance your Protein B research, consider incorporating other essential laboratory tools and reagents.
For example, FBS (Fetal Bovine Serum) can provide essential nutrients and growth factors for cell cultures, while TRIzol reagent can be used for efficient RNA extraction.
Lipofectamine 2000 is a commonly used transfection reagent for introducing genetic material into cells, and PVDF membranes are essential for Western blotting techniques.
Additionally, the use of cell culture media like DMEM (Dulbecco's Modified Eagle Medium) and antibiotics such as Penicillin/Streptomycin can help maintain healthy cell environments.
The RNeasy Mini Kit is a reliable tool for purifying high-quality RNA, and Prism 8 is a powerful data analysis software that can aid in interpreting your Protein B research results.
The Pierce BCA Protein Assay Kit is a convenient method for determining protein concentrations.
By leveraging these tools and techniques, alongside the streamlined workflow offered by PubCompare.ai, researchers can optimize their Protein B studies, ensuring accurate and reproducible results that advance our understanding of this crucial biomolecule and its implications in various cellular processes.