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Protomers

Protomers are the fundamental subunits that make up larger protein structures.
These individual polypeptide chains fold into unique three-dimensional shapes, which can then assemble into larger complexes.
Understanding protomers is crucial for studying protein function, structure, and interactions.
Protomers may exhibit different conformations or oligomeric states, contributing to the diverse roles proteins play in biological processes.
Researchers can leverage the latest AI-driven tools, such as PubCompare.ai, to streamline the identification and comparison of protomer-related protocols across scientific literature, preprints, and patents, enhancing the reproducibility and accuracy of their protien research.

Most cited protocols related to «Protomers»

3D Molecule Processing Pipeline

The 3D molecule processing
pipeline
is now disconnected from the 2D loading process, above. We now use
ChemAxon’s package and the command line tool CXCALC to calculate
protonation states and tautomers at or near physiologically relevant
pH^{69 (link)} in three pH tranches. These are physiological,
covering roughly pH 6.4 to 8.4, high, roughly pH 8.4 to 9.0, and low,
roughly pH 5.8 to 6.4. Each protomer is rendered into 3D using Jchem’s
molconvert (ChemAxon, Budapest, Hungary) and conformationally sampled
using Omega^{100 (link)} (OpenEye Scientific Software,
Santa Fe NM).¹⁰¹ Atomic charges and desolvation
penalties are calculated using AMSOL 7.1¹⁰² and our previously published protocol.^{103 (link)} Files are formatted for docking as flexibase files,^{70 (link),104 (link)} mol2,¹⁰⁵ sdf,¹⁰⁶ and pdbqt.¹⁰⁷

Sterling T, & Irwin J.J. (2015). ZINC 15 – Ligand Discovery for Everyone. Journal of Chemical Information and Modeling, 55(11), 2324-2337.

Publication 2015

Flexibase physiology Protomers

Docking Risperidone to DRD2 Homology Models

Risperidone was docked to the orthosteric binding site of the DRD2 homology models based on the DRD3 or DRD4 crystal structures, using DOCK3.7^{44 (link)}. DOCK3.7 places pre-generated flexible ligands into the binding site by superimposing atoms of each molecule on matching spheres, representing favorable positions for individual ligand atoms. Forty-five matching spheres were used here, based on the pose of the corresponding x-ray ligand (eticlopride/nemonapride) in the template structure. The resulting docked ligand poses were scored by summing the receptor-ligand electrostatics and van der Waals interaction energies, and corrected for context-dependent ligand desolvation. Receptor structures were protonated using Reduce^{48 (link)}. Partial charges from the united-atom AMBER⁴⁷ force field were used for all receptor atoms. Grids which evaluate the different energy terms of the DOCK scoring function were precalculated using AMBER⁴⁷ for the van der Waals term, QNIFFT^{49 (link),50} (an adaptation of DELPHI) for electrostatics, and ligand desolvation^{51 (link)}. Ligands were protonated with Marvin (version 15.11.23.0, ChemAxon, 2015; http://www.chemaxon.com), at pH 7.4. Each protomer was rendered into 3D using Corina⁵² (Molecular Networks GmbH) and conformationally sampled using Omega^{53 (link)} (OpenEye Scientific Software). Ligand charges and initial solvation energies were calculated using AMSOL^{54 ,55}.

Wang S., Che T., Levit A., Shoichet B.K., Wacker D, & Roth B.L. (2018). STRUCTURE OF THE D2 DOPAMINE RECEPTOR BOUND TO THE ATYPICAL ANTIPSYCHOTIC DRUG RISPERIDONE. Nature, 555(7695), 269-273.

Publication 2018

Acclimatization Binding Sites DRD2 protein, human DRD4 protein, human Electrostatics eticlopride Ligands nemonapride Protomers Risperidone Roentgen Rays Rumex

Optimized Compound Screening Library

For Cn, Hbp and Cdk2, we performed virtual screening using the ICCL. In this library we removed all the compounds with molecular weight over 750 g mol⁻¹ or consisting of less than 10 atoms. The molecules chelating exotic atoms (Au, Cu, Hg, I, Sn, …) or ions (Fe²⁺, Mg²⁺, …) were also filtered because they were not correctly handled by the docking programs. Finally 856 of the 15,163 compounds were removed and the virtual screening was performed on 14,307 unique compounds. The preparation of the chemical library for docking required different steps to generate accurate 3D molecular structures. When chirality was not specified in the chemical library, all the possible enantiomers were generated. In addition, the protonation states of the compounds were adjusted according to the pH of the medium surrounding the target. In our case, the physiological pH at 7.4 ± 1 was retained. All the protonation states with a probability of existence over 10 % at the given pH were generated as well as all the likely tautomers. The preparation of these compounds was achieved using LigPrep 2.8.0 (Schrödinger). After this preparation, the library amounted to 24,186 structures. Since some docking programs, such as GOLD, do not alter bond lengths and angles, thereby the ligands energy was minimized using the OPLS2005 force field to ensure proper bond distances and angles. We generated one conformer per molecule, the exploration of the ligand conformational space being managed by each of the four docking programs.
The DUD-E database includes the multi-mol2 files of active compounds and decoys for each target, which were prepared by the DUD-E team, with their enantiomers, protomers and tautomers [18 (link)]. We used them without any modification.

Chaput L., Martinez-Sanz J., Quiniou E., Rigolet P., Saettel N, & Mouawad L. (2016). vSDC: a method to improve early recognition in virtual screening when limited experimental resources are available. Journal of Cheminformatics, 8, 1.

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Publication 2016

CDK2 protein, human cDNA Library Gold Ions Ligands physiology Protomers Rumex

Crystallization and Structure Determination of σ1 Receptor

To obtain phases, crystals of σ₁ receptor bound to PD144418 were grown using a hanging-drop LCP methodology adapted from a previous report^{32 (link)}. In brief, this entailed dispensing cubic phase drops onto a plastic cover film (Art Robbins Instruments) and overlaying with precipitant solution as described above. This film was then inverted over a matched plate with identical crystallization solutions to the precipitant surrounding the lipid drop. The resulting crystals could be soaked and resealed, unlike conventional glass sandwich lipidic cubic phase plates. Crystals prepared in this way were soaked with tantalum bromide clusters for approximately 12 hours by adding crushed granules of tantalum clusters to the edge of the well. The crystals were harvested and data collected as described above, but at a wavelength of 1.2548 Å.
Initial phases were obtained in SHARP³³ using single isomorphous replacement and anomalous scattering (SIRAS). Three transmembrane α-helices were identifiable in the initial map, suggesting three molecules in the asymmetric unit with an unusual solvent content of ~70%. Experimental phases were iteratively combined with model-derived phase to improve the electron density map through solvent flattening in SHARP. Model building was performed in Coot³⁴, and refinement was performed in phenix.refine³⁵. All three chains are highly similar in structure, with all-atom pairwise RMSD of cytosolic domains ranging from 0.22 Å to 0.26 Å, while the orientation of the transmembrane helix relative to the soluble domain varies among protomers.
Assignment of sequence register was straightforward and unambiguous due to the relatively high resolution, almost completely ordered structure, and high frequency bulky amino acid side chains (σ₁ receptor is roughly 5% tryptophan). As a control for register assignment, the structure was built and register assigned in two independent ways. First it was manually built and register assigned by inspection of electron density. In parallel, sequence register was independently assigned automatically with phenix.autobuild, and results were confirmed to be identical throughout the entire polypeptide chain of each protomer. Representative composite omit map density is shown in Extended Data Figure 2. Ligands were manually placed into F_o−F_c difference maps (Extended Data Figure 7). In the case of PD144418 the electron density was clear, and ligand position and pose were unambiguous. For 4-IBP, the pose was unambiguous due to the high F_o−F_c peak resulting from the ligand iodine atom. Following refinement, structure quality was assessed using MolProbity³⁶, and figures were prepared in PyMOL³⁷ and UCSF Chimera^{38 (link)}. All crystallographic data processing, refinement, and analysis software was compiled and supported by the SBGrid Consortium³⁹.

Schmidt H.R., Zheng S., Gurpinar E., Koehl A., Manglik A, & Kruse A.C. (2016). Crystal structure of the human σ1 receptor. Nature, 532(7600), 527-530.

Publication 2016

Amino Acids Crystallization Crystallography Cuboid Bone Cytoplasmic Granules Cytosol Dietary Fiber Electrons Helix (Snails) Iodine Ligands Lipids Microtubule-Associated Proteins OPRS1 protein, human PD 144418 Polypeptides Protomers Solvents Tantalum tantalum bromide Tryptophan

Conformational Sampling and Docking Preparation

The 3D molecule processing pipeline is now disconnected from the 2D loading process, above. We now use ChemAxon’s package and the command line tool CXCALC to calculate protonation states and tautomers at or near physiologically relevant pH^{69 (link)} in three pH tranches. These are physiological, covering roughly pH 6.4 to 8.4, high, roughly pH 8.4 to 9.0, and low, roughly pH 5.8 to 6.4. Each protomer is rendered into 3D using Jchem’s molconvert (ChemAxon, Budapest, Hungary) and conformationally sampled using Omega^{100 (link)} (OpenEye Scientific Software, Santa Fe NM).¹⁰¹ Atomic charges and desolvation penalties are calculated using AMSOL 7.1¹⁰² and our previously published protocol.^{103 (link)} Files are formatted for docking as flexibase files,^{70 (link), 104 (link)} mol2,¹⁰⁵ sdf,¹⁰⁶ and pdbqt.¹⁰⁷

Sterling T, & Irwin J.J. (2015). ZINC 15 – Ligand Discovery for Everyone. Journal of Chemical Information and Modeling, 55(11), 2324-2337.

Publication 2015

Flexibase physiology Protomers

Most recents protocols related to «Protomers»

Structural Analysis of SARS-CoV-2 Spike Protein

The instructor provided to the students a brief introduction
to the most important features of the structure of SARS-CoV-2. The
four major structural proteins are displayed: the envelope (E), membrane
(M), nucleocapsid (N), and spike (S) proteins (Figure 1).^{7 (link)}It is highlighted that spike protein (approximately 180–200
kDa) is the surface glycoprotein anchored to the viral membrane that
plays an essential role when the infection process of SARS-CoV-2 takes
place. This protein is a trimer of three identical protomers (Figure 2). Each protomer
contains three segments: a short intracellular tail (IC), a transmembrane
anchor (TM), and a large ectodomain that extends outward from the
virus which is coated with sugar chains to hide the virus from the
immune system^{8 (link)} and comprises S1 and S2
subunits.
Next, the students are invited to study the ectodomain by analyzing
the requested structural features that they must observe manipulating
PyMOL.
Although hundreds of structures of this spike protein
are already
available in the Protein Data Bank, the one with the code 7DWY(9 (link)) has been selected and must be loaded in a PyMOL session.
They are encouraged to distinguish the four different levels of the
protein structures: primary, secondary, tertiary, and quaternary,
changing the representation of the molecule from lines or wireframe
to cartoon.
They must learn how to select individual residues
or different
chains, how to change their colors, how to generate objects, how to
show and hide different parts of the protein, how to measure distances
and angles for bonds, and how to generate surfaces.
They have
to realize that the spike protein is a complex of three
identical chains. A schematic illustration of the spike protein (Figure 3) is given to the
students, and they must recognize every single domain in the ectodomain,
extracting them as different objects and coloring them in the suggested
color.
The S1 subunit has an N-terminal
domain (NTD) and a receptor-binding
domain (RBD) located in the C-terminal domain, which is implied in
recognition and binding to the host cell receptor. S2 consists of
the fusion peptide (FP), two heptad repeats 1 (HR1 and HR2) which
operate the fusion of viral and host membranes, a transmembrane domain
(TM), and a cytoplasmic tail (CT).
When different species of
coronavirus are compared, the S2 subunit
is highly conserved, but the sequence of the S1 subunit varies greatly.
S1 and S2 are connected to the S1/S2 cleavage site in which specific
proteases act. The cleavage transforms the spike protein into a fusion
competent form that suffers several conformational changes and allows
it to anchor to the host membrane leading to the membrane fusion.^{10 (link)}

, & Maya C. (2023). Using PyMOL to Understand Why COVID-19 Vaccines Save Lives. Journal of Chemical Education, 100(3), 1351-1356.

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Publication 2023

Carbohydrates Cells COVID 19 Cytokinesis Cytoplasm Membrane Fusion Membrane Glycoproteins M protein, multiple myeloma Nucleocapsid Peptides Proteins Protein Subunits Protomers Protoplasm SARS-CoV-2 Student Tail Tissue, Membrane Virus

Modeling Polydisperse Oligomeric Protein Mixtures

The experimental SAXS profiles of SPOP report on the average of a polydisperse mixture of oligomeric species in solution. The concentration of each oligomer should follow the isodesmic model where the concentration of the smallest subunit, the BTB-BTB dimer, is given by:

c_{1} = \frac{2 c_{t o t} K_{A} + 1 - \sqrt{4 c_{t o t} K_{A} + 1}}{2 c_{t o t} K_{A}^{2}}

The concentration c_i of any larger oligomer with

i

subunits can be calculated given c₁ and the concentration of oligomer

i

–1, c_i-1:

c_{i} = K_{A} c_{i - 1} c_{1}

K_{A}

is the isodesmic association constant and

c_{t o t}

is the total concentration of protomers. Here we assume that the SPOP BTB-BTB dimer is always fully formed (Marzahn et al., 2016 (link)) and

c_{t o t}

in Equation 1 is thus half of the total protein concentration reported for the SAXS experiments, which refers to the SPOP monomer concentration. Given the concentration c_i of each oligomer

i

from the isodesmic model, we can calculate the volume fraction

ϕ_{i}

of the oligomer:

ϕ_{i} = \frac{i c_{i}}{\sum_{i}^{N} i c_{i}}

The average SAXS intensities from the mixture of oligomers

{⟨ I ⟩}_{mix}

are then given by:

⟨ I ⟩_{mix} = \sum_{i}^{N} ⟨ I ⟩_{i, ensemble} ϕ_{i}

where

{⟨ I ⟩}_{i, ensemble}

is the conformationally averaged SAXS intensity of oligomer

i

. Note that the magnitude of the SAXS intensities calculated with Pepsi-SAXS were set to be proportional to the number of subunits in the oligomer, so given Equations 3 and 4 the total contribution of each oligomer to the averaged SAXS intensity is proportional to

i^{2} c_{i}

Thomasen F.E., Cuneo M.J., Mittag T, & Lindorff-Larsen K. (2023). Conformational and oligomeric states of SPOP from small-angle X-ray scattering and molecular dynamics simulations. eLife, 12, e84147.

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Publication 2023

Proteins Protein Subunits Protomers

Conservation Analysis of TLR10 Orthologs

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Publication 2023

Amino Acids Methionine Muscle Tissue Mutation Protomers Threonine

Structural Modeling and Validation

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Publication 2023

Mutation Protomers Tail

Structural Elucidation of β2-Microglobulin Amyloid Fibrils

Despite having a similar superficial appearance, the published wt-β₂m structure of fibrils grown at pH 2 (EMDB-0014; PDB-6gk3^{32 (link)}) did not fit well into the maps of either β₂m-ΔN6 and β₂m-D76N from neutral pH fibrillations. Therefore, the highest resolution map, ΔN6-2PFa at 3.0 Å, was used to de novo build the peptide model for one layer of the fibril core using Coot^{67 (link)}. One protomer was built first and then used to guide building of the second protomer as their folds were very similar between residues Cys25-Cys80. Both Ramachandran and rotamer outliers were monitored and minimised during building in Coot. The final built layer was then repeated and rigid body fit to generate a model for 3 layers of the fibril core, which was then used for real space refinement against the deposited map using Phenix v1.17.1^{68 (link)}. NCS restraints were applied to prevent divergence of repeating chains in the layers.
A similar approach was then used for modelling the ΔN6-2PFb, D76N-2PFa and V27M-4PFa structures, but in each case starting with the ΔN6-2PFa structure as an initial template. The final real space refined models for each structure were assessed using MolProbity^{69 (link)}. The final model statistics for all structures solved at high resolution are summarised in Table 1.Table 1

Cryo-EM data collection, refinement and validation statistics for the β₂m DN6 dataset

	β₂m-∆N6 2PF_A (EMDB-15222) (PDB 8a7o)	β₂m-∆N6 2PF_B (EMDB-15223) (PDB 8a7p)	β₂m-D76N 2PF_A (EMDB-15225) (PDB 8a7t)]	β₂m-V27M 4PF (EMDB-15224) (PDB 8a7q)
Data collection and processing
Magnification	96,000		96,000	130,000
Voltage (kV)	300		300	300
Detector	Falcon4		Falcon4	Falcon4-Selectris
Pixel size (Å)	0.83		0.83	0.94
Electron exposure (e^–/Å²)	43		43	43
Exposure rate (e^–/pixel/s)	4.4		5.2	5.9
Nominal defocus range (μm)	−1.3 to −2.5		−0.8 to −3.0	−1.3 to −2.5
Movies collected	4095		3849	4079
Initial particle images (no.)	612,949		1,117,062	229,692
Final particle images (no.)	133,576	10,594	78,097	4732
Symmetry imposed	C1	C1	C1	C2
Map resolution (Å) FSC threshold	3.0 0.143	3.4 0.143	3.0 0.143	2.8 0.143
Map resolution range (Å)	2.9–5.5	3.3–6.8	3.0–6.6	2.8–4.8
Helical parameters
Helical twist (°)	359.18	359.40	359.01	359.27
Helical rise (Å)	4.85	4.86	4.80	4.85
Crossover (nm)	106	145	87	120
Refinement
Initial model used (PDB code)	–	8a7o	8a7o	8a7o
Map sharpening B factor (Å²)	−72	−37	−30	−38
Model to map correlation	0.88	0.78	0.85	0.79
Model composition
Non-hydrogen atoms	4356	4314	3696	6480
Protein residues total	528	522	444	768
Protein residues modelled	6–93(A,B)	6–92(A,B)	6–90(A), 21–83(B)	21–84(A,B,C,D)
Chains per helical layer	2	2	2	4
Helical layers modelled	3	3	3	3
B factors (Å²)
Protein	53	81	86	41
R.m.s. deviations
Bond lengths (Å)	0.002	0.003	0.003	0.004
Bond angles (°)	0.540	0.535	0.454	0.667
Validation
MolProbity score	1.5	1.9	1.4	1.6
Clashscore	6.7	6.4	5.0	7.8
Poor rotamers (%)	0.0	1.9	0.0	0.0
Ramachandran plot
Favored (%)	97.1	95.3	97.2	96.8
Allowed (%)	2.9	4.7	2.8	3.2
Disallowed (%)	0.0	0.0	0.0	0.0

Wilkinson M., Gallardo R.U., Martinez R.M., Guthertz N., So M., Aubrey L.D., Radford S.E, & Ranson N.A. (2023). Disease-relevant β2-microglobulin variants share a common amyloid fold. Nature Communications, 14, 1190.

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Publication 2023

Complement Factor B Helix (Snails) Human Body Hydrogen Microtubule-Associated Proteins Muscle Rigidity Peptides Proteins Protomers

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What are Protomers?

How can Protomers be used in research?

Protomers have a wide range of applications in research, from studying protein structure and function to investigating biological processes. By understanding the unique three-dimensional shapes and assembly patterns of protomers, researchers can gain insights into how proteins interact with one another and carry out their roles within the cell. This knowledge is essential for developing targeted therapies, designing novel enzymes, and unraveling the complexities of cellular signaling pathways.

What are some common challenges in working with Protomers?

One of the main challenges in working with protomers is the diversity of conformations and oligomeric states they can exhibit. Protomers can adopt different shapes and assemble into various complexes, which can make it difficult to study their behavior and function. Additionally, the dynamic nature of protomers can complicate experimental design and data interpretation. Researchers must carefully consider factors such as buffer conditions, temperature, and the presence of other biomolecules when investigating protomer-related phenomena.

How can PubCompare.ai help with Protomer research?

PubComapre.ai is a powerful tool that can streamline the identification and comparison of protomer-related protocols across scientific literature, preprints, and patents. The platform's AI-driven analysis can help researchers quickly identify the most effective protocols for their specific research goals, enabling them to enhance the reproducibility and accuracy of their protein studies. By leveraging the cutting-edge capabilities of PubCompare.ai, researchers can save time, reduce experimental waste, and focus on the most impactful aspects of their protomer-based investigations.

What are some common applications of Protomers?

Protomers have a wide range of applications in various fields of research and development. In the pharmaceutical industry, understanding protomer structures and interactions is crucial for drug discovery and design. Protomers can also be used in the development of novel enzymes, biocatalysts, and biomaterials. In the field of structural biology, the study of protomer conformations and assemblies provides valuable insights into the mechanisms of cellular processes, such as signal transduction, membrane transport, and metabolic pathways. Additionally, protomer research has implications in the study of protein misfolding diseases, where the aberrant behavior of protomers can lead to the formation of harmful aggregates.

More about "Protomers"

Protomers are the fundamental building blocks that make up larger protein structures.
These individual polypeptide chains fold into unique three-dimensional shapes, which can then assemble into larger protein complexes.
Understanding the structure and behavior of protomers is crucial for studying protein function, interactions, and biological processes.
Researchers can leverage advanced tools like PubCompare.ai, an AI-driven platform, to streamline the identification and comparison of protomer-related protocols across scientific literature, preprints, and patents.
This enhances the reproducibility and accuracy of their protein research.
Key subtopics and related terms include polypeptide chains, protein folding, oligomeric states, conformations, and protein assemblies.
Relevant equipment and techniques may include the Vitrobot Mark IV for cryo-electron microscopy, PELCO easiGlow for glow discharge treatment, Whatman No. 1 filter paper, data analysis software, Anti-human IgG Fc capture (AHC) biosensors, Octet K2 system for biolayer interferometry, and Superose 6 Increase 10/300 GL for size exclusion chromatography.
F-octylmaltoside solution may also be utilized for protein solubilization and stabilization.
By understanding the insights and applications of protomers, researchers can unlock the potential of their protein-based studies and advance our knowledge of biological systems.
PubCompare.ai offers a powerful tool to streamline this process and enhance the reproducibility and accuracy of proteomic research.