Related Research Articles
X-ray crystallography is the experimental science determining the atomic and molecular structure of a crystal, in which the crystalline structure causes a beam of incident X-rays to diffract into many specific directions. By measuring the angles and intensities of these diffracted beams, a crystallographer can produce a three-dimensional picture of the density of electrons within the crystal. From this electron density, the mean positions of the atoms in the crystal can be determined, as well as their chemical bonds, their crystallographic disorder, and various other information.
The Protein Data Bank (PDB) is a database for the three-dimensional structural data of large biological molecules, such as proteins and nucleic acids. The data, typically obtained by X-ray crystallography, NMR spectroscopy, or, increasingly, cryo-electron microscopy, and submitted by biologists and biochemists from around the world, are freely accessible on the Internet via the websites of its member organisations. The PDB is overseen by an organization called the Worldwide Protein Data Bank, wwPDB.
Structural alignment attempts to establish homology between two or more polymer structures based on their shape and three-dimensional conformation. This process is usually applied to protein tertiary structures but can also be used for large RNA molecules. In contrast to simple structural superposition, where at least some equivalent residues of the two structures are known, structural alignment requires no a priori knowledge of equivalent positions. Structural alignment is a valuable tool for the comparison of proteins with low sequence similarity, where evolutionary relationships between proteins cannot be easily detected by standard sequence alignment techniques. Structural alignment can therefore be used to imply evolutionary relationships between proteins that share very little common sequence. However, caution should be used in using the results as evidence for shared evolutionary ancestry because of the possible confounding effects of convergent evolution by which multiple unrelated amino acid sequences converge on a common tertiary structure.
Structural bioinformatics is the branch of bioinformatics that is related to the analysis and prediction of the three-dimensional structure of biological macromolecules such as proteins, RNA, and DNA. It deals with generalizations about macromolecular 3D structures such as comparisons of overall folds and local motifs, principles of molecular folding, evolution, binding interactions, and structure/function relationships, working both from experimentally solved structures and from computational models. The term structural has the same meaning as in structural biology, and structural bioinformatics can be seen as a part of computational structural biology. The main objective of structural bioinformatics is the creation of new methods of analysing and manipulating biological macromolecular data in order to solve problems in biology and generate new knowledge.
In physics, the phase problem is the problem of loss of information concerning the phase that can occur when making a physical measurement. The name comes from the field of X-ray crystallography, where the phase problem has to be solved for the determination of a structure from diffraction data. The phase problem is also met in the fields of imaging and signal processing. Various approaches of phase retrieval have been developed over the years.
Nuclear magnetic resonance spectroscopy, most commonly known as NMR spectroscopy or magnetic resonance spectroscopy (MRS), is a spectroscopic technique to observe local magnetic fields around atomic nuclei. The sample is placed in a magnetic field and the NMR signal is produced by excitation of the nuclei sample with radio waves into nuclear magnetic resonance, which is detected with sensitive radio receivers. The intramolecular magnetic field around an atom in a molecule changes the resonance frequency, thus giving access to details of the electronic structure of a molecule and its individual functional groups. As the fields are unique or highly characteristic to individual compounds, in modern organic chemistry practice, NMR spectroscopy is the definitive method to identify monomolecular organic compounds.
Nuclear magnetic resonance spectroscopy of proteins is a field of structural biology in which NMR spectroscopy is used to obtain information about the structure and dynamics of proteins, and also nucleic acids, and their complexes. The field was pioneered by Richard R. Ernst and Kurt Wüthrich at the ETH, and by Ad Bax, Marius Clore, Angela Gronenborn at the NIH, and Gerhard Wagner at Harvard University, among others. Structure determination by NMR spectroscopy usually consists of several phases, each using a separate set of highly specialized techniques. The sample is prepared, measurements are made, interpretive approaches are applied, and a structure is calculated and validated.
In bioinformatics, the root-mean-square deviation of atomic positions, or simply root-mean-square deviation (RMSD), is the measure of the average distance between the atoms of superimposed proteins. Note that RMSD calculation can be applied to other, non-protein molecules, such as small organic molecules. In the study of globular protein conformations, one customarily measures the similarity in three-dimensional structure by the RMSD of the Cα atomic coordinates after optimal rigid body superposition.
Frederic Middlebrook Richards, commonly referred to as Fred Richards, was an American biochemist and biophysicist known for solving the pioneering crystal structure of the ribonuclease S enzyme in 1967 and for defining the concept of solvent-accessible surface. He contributed many key experimental and theoretical results and developed new methods, garnering over 20,000 journal citations in several quite distinct research areas. In addition to the protein crystallography and biochemistry of ribonuclease S, these included solvent accessibility and internal packing of proteins, the first side-chain rotamer library, high-pressure crystallography, new types of chemical tags such as biotin/avidin, the nuclear magnetic resonance (NMR) chemical shift index, and structural and biophysical characterization of the effects of mutations.
Homology modeling, also known as comparative modeling of protein, refers to constructing an atomic-resolution model of the "target" protein from its amino acid sequence and an experimental three-dimensional structure of a related homologous protein. Homology modeling relies on the identification of one or more known protein structures likely to resemble the structure of the query sequence, and on the production of an alignment that maps residues in the query sequence to residues in the template sequence. It has been seen that protein structures are more conserved than protein sequences amongst homologues, but sequences falling below a 20% sequence identity can have very different structure.
The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between values predicted by a model or an estimator and the values observed. The RMSD represents the square root of the second sample moment of the differences between predicted values and observed values or the quadratic mean of these differences. These deviations are called residuals when the calculations are performed over the data sample that was used for estimation and are called errors when computed out-of-sample. The RMSD serves to aggregate the magnitudes of the errors in predictions for various data points into a single measure of predictive power. RMSD is a measure of accuracy, to compare forecasting errors of different models for a particular dataset and not between datasets, as it is scale-dependent.
Molecular replacement is a method of solving the phase problem in X-ray crystallography. MR relies upon the existence of a previously solved protein structure which is similar to our unknown structure from which the diffraction data is derived. This could come from a homologous protein, or from the lower-resolution protein NMR structure of the same protein.
Nuclear magnetic resonance crystallography is a method which utilizes primarily NMR spectroscopy to determine the structure of solid materials on the atomic scale. Thus, solid-state NMR spectroscopy would be used primarily, possibly supplemented by quantum chemistry calculations, powder diffraction etc. If suitable crystals can be grown, any crystallographic method would generally be preferred to determine the crystal structure comprising in case of organic compounds the molecular structures and molecular packing. The main interest in NMR crystallography is in microcrystalline materials which are amenable to this method but not to X-ray, neutron and electron diffraction. This is largely because interactions of comparably short range are measured in NMR crystallography.
Macromolecular structure validation is the process of evaluating reliability for 3-dimensional atomic models of large biological molecules such as proteins and nucleic acids. These models, which provide 3D coordinates for each atom in the molecule, come from structural biology experiments such as x-ray crystallography or nuclear magnetic resonance (NMR). The validation has three aspects: 1) checking on the validity of the thousands to millions of measurements in the experiment; 2) checking how consistent the atomic model is with those experimental data; and 3) checking consistency of the model with known physical and chemical properties.
GeNMR method is the first fully automated template-based method of protein structure determination that utilizes both NMR chemical shifts and NOE -based distance restraints.
Protein Structure Evaluation Suite & Server (PROSESS) is a freely available web server for protein structure validation. It has been designed at the University of Alberta to assist with the process of evaluating and validating protein structures solved by NMR spectroscopy.
Protein chemical shift prediction is a branch of biomolecular nuclear magnetic resonance spectroscopy that aims to accurately calculate protein chemical shifts from protein coordinates. Protein chemical shift prediction was first attempted in the late 1960s using semi-empirical methods applied to protein structures solved by X-ray crystallography. Since that time protein chemical shift prediction has evolved to employ much more sophisticated approaches including quantum mechanics, machine learning and empirically derived chemical shift hypersurfaces. The most recently developed methods exhibit remarkable precision and accuracy.
Volume, Area, Dihedral Angle Reporter (VADAR) is a freely available protein structure validation web server that was developed as a collaboration between Dr. Brian Sykes and Dr. David Wishart at the University of Alberta. VADAR consists of over 15 different algorithms and programs for assessing and validating peptide and protein structures from their PDB coordinate data. VADAR is capable of determining secondary structure, identifying and classifying six different types of beta turns, determining and calculating the strength of C=O -- N-H hydrogen bonds, calculating residue-specific accessible surface areas (ASA), calculating residue volumes, determining backbone and side chain torsion angles, assessing local structure quality, evaluating global structure quality, and identifying residue "outliers". The results have been validated through extensive comparison to published data and careful visual inspection. VADAR produces both text and graphical output with most of the quantitative data presented in easily viewed tables. In particular, VADAR's output is presented in a vertical, tabular format with most of the sequence data, residue numbering and any other calculated property or feature presented from top to bottom, rather than from left to right.
SuperPose is a freely available web server designed to perform both pairwise and multiple protein structure superpositions. The “Structural superposition” term refers to the rotations and translations performed on one structure to make it match or align with another structure or structures. Structural superposition can be quantified either in terms of similarity or difference measures. The optimal superposition is the one in which the similarity measure is maximized or the difference measure is minimized. The “SuperPose” web server uses “RMSD” or Root-Mean-Square Deviation as a difference measure to find the optimal pairwise or multiple protein structure superposition. After an initial sequence and secondary structure alignment, SuperPose generates a Difference Distance (DD) matrix from the equivalent C-alpha atoms of two molecules. The sequence/structure alignment and DD matrix analysis information is then fed into a modified quaternion eigenvalue algorithm to rapidly perform the structural superposition and calculate the RMSD between aligned regions of two macromolecules.
References
- 1 2 3 4 5 Berjanskii, M; Zhou J; Liang Y; Li G; Wishart DS (July 2012). "Resolution-by-proxy: a simple measure for assessing and comparing the overall quality of NMR protein structures". J Biomol NMR. 53 (3): 167–80. doi:10.1007/s10858-012-9637-2. PMID 22678091. S2CID 43740468.
- ↑ Laskowski, RA; Rullmannn JA; MacArthur MW; Kaptein R; Thornton JM (1996). "AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR". J Biomol NMR. 8 (4): 477–486. doi:10.1007/bf00228148. PMID 9008363. S2CID 45664105.
- ↑ Chen VB, Arendall WB 3rd, Headd JJ, Keedy DA, Immormino RM, Kapral GJ, Murray LW, Richardson JS, Richardson DC (2010). "MolProbity: all-atom structure validation for macromolecular crystallography". Acta Crystallogr D. 66 (Pt 1): 12–21. doi:10.1107/S0907444909042073. PMC 2803126 . PMID 20057044.
- ↑ Sheffler, W; Baker D (2010). "RosettaHoles2: a volumetric packing measure for protein structure refinement and validation". Protein Sci. 19 (10): 1991–1995. doi:10.1002/pro.458. PMC 2998733 . PMID 20665689.