Structure validation

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Structure validation concept: model of a protein (each ball is an atom), and magnified region with electron density data and 3 bright flags for problems Structure validation concept.jpg
Structure validation concept: model of a protein (each ball is an atom), and magnified region with electron density data and 3 bright flags for problems

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 (see example in the image), come from structural biology experiments such as x-ray crystallography [1] or nuclear magnetic resonance (NMR). [2] 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.

Contents

Proteins and nucleic acids are the workhorses of biology, providing the necessary chemical reactions, structural organization, growth, mobility, reproduction, and environmental sensitivity. Essential to their biological functions are the detailed 3D structures of the molecules and the changes in those structures. To understand and control those functions, we need accurate knowledge about the models that represent those structures, including their many strong points and their occasional weaknesses.

End-users of macromolecular models include clinicians, teachers and students, as well as the structural biologists themselves, journal editors and referees, experimentalists studying the macromolecules by other techniques, and theoreticians and bioinformaticians studying more general properties of biological molecules. Their interests and requirements vary, but all benefit greatly from a global and local understanding of the reliability of the models.

Historical summary

Macromolecular crystallography was preceded by the older field of small-molecule x-ray crystallography (for structures with less than a few hundred atoms). Small-molecule diffraction data extends to much higher resolution than feasible for macromolecules, and has a very clean mathematical relationship between the data and the atomic model. The residual, or R-factor, measures the agreement between the experimental data and the values back-calculated from the atomic model. For a well-determined small-molecule structure the R-factor is nearly as small as the uncertainty in the experimental data (well under 5%). Therefore, that one test by itself provides most of the validation needed, but a number of additional consistency and methodology checks are done by automated software [3] as a requirement for small-molecule crystal structure papers submitted to the International Union of Crystallography (IUCr) journals such as Acta Crystallographica section B or C. Atomic coordinates of these small-molecule structures are archived and accessed through the Cambridge Structural Database (CSD) [4] or the Crystallography Open Database (COD). [5]

The first macromolecular validation software was developed around 1990, for proteins. It included Rfree cross-validation for model-to-data match, [6] bond length and angle parameters for covalent geometry, [7] and sidechain and backbone conformational criteria. [8] [9] [10] For macromolecular structures, the atomic models are deposited in the Protein Data Bank (PDB), still the single archive of this data. The PDB was established in the 1970s at Brookhaven National Laboratory, [11] moved in 2000 to the RCSB (Research Collaboration for Structural Biology) centered at Rutgers, [12] and expanded in 2003 to become the wwPDB (worldwide Protein Data Bank), [13] with access sites added in Europe () and Asia (), and with NMR data handled at the BioMagResBank (BMRB) in Wisconsin.

Validation rapidly became standard in the field, [14] with further developments described below. *Obviously needs expansion*

A large boost was given to the applicability of comprehensive validation for both x-ray and NMR as of February 1, 2008, when the worldwide Protein Data Bank (wwPDB) made mandatory the deposition of experimental data along with atomic coordinates. Since 2012 strong forms of validation have been in the process of being adopted for wwPDB deposition from recommendations of the wwPDB Validation Task Force committees for x-ray crystallography, [15] for NMR, [16] for SAXS (small-angle x-ray scattering), and for cryoEM (cryo-Electron Microscopy). [17]

Stages of validation

Validations can be broken into three stages: validating the raw data collected (data validation), the interpretation of the data into the atomic model (model-to-data validation), and finally validation on the model itself. While the first two steps are specific to the technique used, validating the arrangement of atoms in the final model is not.

Model validation

Geometry

[7] [18] [19]

Conformation (dihedrals): protein & RNA

The backbone and side-chain dihedral angles of protein and RNA have been shown to have specific combinations of angles which are allowed (or forbidden). For protein backbone dihedrals (φ, ψ), this has been addressed by the legendary Ramachandran Plot while for side-chain dihedrals (χ's), one should refer to the Dunbrack Backbone-dependent rotamer library. [20]

Though, mRNA structures are generally short-lived and single-stranded, there are an abundance of non-coding RNAs with different secondary and tertiary folding (tRNA, rRNA etc.) which contain a preponderance of the canonical Watson-Crick (WC) base-pairs, together with significant number of non-Watson Crick (NWC) base-pairs - for which such RNA also qualify for regular structural validation that apply for nucleic acid helices. The standard practice is to analyse the intra- (Transnational: Shift, Slide, Rise; Rotational: Tilt, Roll, Twist) and inter-base-pair geometrical parameters (Transnational: Shear, Stagger, Stretch, Rotational: Buckle, Propeller, Opening) - whether in-range or out-of-range with respect to their suggested values. [21] [22] These parameters describe the relative orientations of the two paired bases with respect to each other in two strands (intra) along with those of the two stacked base pairs (inter) with respect to each other, and, hence, together, they serve to validate nucleic acid structures in general. Since, RNA-helices are small in length (average: 10-20 bps), the use of electrostatic surface potential as a validation parameter [23] has been found to be beneficial, particularly for modelling purposes.

Packing and Electrostatics: globular proteins

For globular proteins, interior atomic packing (arising from short-range, local interactions) of side-chains [24] [25] [26] [27] has been shown to be pivotal in the structural stabilization of the protein-fold. On the other hand, the electrostatic harmony (non-local, long-range) of the overall fold [28] has also been shown to be essential for its stabilization. Packing anomalies include steric clashes, [29] short contacts, [27] holes [30] and cavities [31] while electrostatic disharmony [28] [32] refer to unbalanced partial charges in the protein core (particularly relevant for designed protein interiors). While the clash-score of Molprobity identifies steric clashes at a very high resolution, the Complementarity Plot combines packing anomalies with electrostatic imbalance of side-chains and signals for either or both.

Carbohydrates

A 2D diagram of an N-glycan linked to an antibody fragment in the structure with PDB accession code '4BYH '. This diagram, which has been generated with Privateer, follows the standard symbol nomenclature and includes, in its original svg format, annotations containing validation information, including ring conformation and detected monosaccharide types. (A)-ASN297.svg
A 2D diagram of an N-glycan linked to an antibody fragment in the structure with PDB accession code ' 4BYH ​'. This diagram, which has been generated with Privateer, follows the standard symbol nomenclature and includes, in its original svg format, annotations containing validation information, including ring conformation and detected monosaccharide types.

The branched and cyclic nature of carbohydrates poses particular problems to structure validation tools. [35] At higher resolutions, it is possible to determine the sequence/structure of oligo- and poly-saccharides, both as covalent modifications and as ligands. However, at lower resolutions (typically lower than 2.0Å), sequences/structures should either match known structures, or be supported by complementary techniques such as Mass Spectrometry. [36] Also, monosaccharides have clear conformational preferences (saturated rings are typically found in chair conformations), [37] but errors introduced during model building and/or refinement (wrong linkage chirality or distance, or wrong choice of model - see [38] for recommendations on carbohydrate model building and refinement and [39] [40] [41] for reviews on general errors in carbohydrate structures) can bring their atomic models out of the more likely low-energy state. Around 20% of the deposited carbohydrate structures are in a higher-energy conformation not justified by the structural data (measured using real-space correlation coefficient). [42]

A number of carbohydrate validation web services are available at glycosciences.de (including nomenclature checks and linkage checks by pdb-care, [43] and cross-validation with Mass Spectrometry data through the use of GlycanBuilder), whereas the CCP4 suite currently distributes Privateer, [33] which is a tool that is integrated into the model building and refinement process itself. Privateer is able to check stereo- and regio-chemistry, ring conformation and puckering, linkage torsions, and real-space correlation against positive omit density, generating aperiodic torsion restraints on ring bonds, which can be used by any refinement software in order to maintain the monosaccharide's minimal energy conformation. [33]

Privateer also generates scalable two-dimensional SVG diagrams according to the Essentials of Glycobiology [34] standard symbol nomenclature containing all the validation information as tooltip annotations (see figure). This functionality is currently integrated into other CCP4 programs, such as the molecular graphics program CCP4mg (through the Glycoblocks 3D representation, [44] which conforms to the standard symbol nomenclature [34] ) and the suite's graphical interface, CCP4i2.

Validation for crystallography

Overall considerations

Global vs local criteria

Many evaluation criteria apply globally to an entire experimental structure, most notably the resolution, the anisotropy or incompleteness of the data, and the residual or R-factor that measures overall model-to-data match (see below). Those help a user choose the most accurate among related Protein Data Bank entries to answer their questions. Other criteria apply to individual residues or local regions in the 3D structure, such as fit to the local electron density map or steric clashes between atoms. Those are especially valuable to the structural biologist for making improvements to the model, and to the user for evaluating the reliability of that model right around the place they care about - such as a site of enzyme activity or drug binding. Both types of measures are very useful, but although global criteria are easier to state or publish, local criteria make the greatest contribution to scientific accuracy and biological relevance. As expressed in the Rupp textbook, "Only local validation, including assessment of both geometry and electron density, can give an accurate picture of the reliability of the structure model or any hypothesis based on local features of the model." [45]

What can be seen in low vs high resolution macromolecular crystal structures Low vs high resolution hemoglobin detail.jpg
What can be seen in low vs high resolution macromolecular crystal structures

Relationship to resolution and B-factor

Data validation

Structure factors

Twinning

Model-to-data validation

Residuals and Rfree

Real-space correlation

Improvement by correcting diagnosed problems

In nuclear magnetic resonance

Data Validation: Chemical Shifts, NOEs, RDCs

AVS
Assignment validation suite (AVS) checks the chemical shifts list in BioMagResBank (BMRB) format for problems. [46]
PSVS
Protein Structure Validation Server at the NESG based on information retrieval statistics [47]
PROSESS
PROSESS (Protein Structure Evaluation Suite & Server) is a new web server that offers an assessment of protein structural models by NMR chemical shifts as well as NOEs, geometrical, and knowledge-based parameters.
LACS
Linear analysis of chemical shifts is used for absolute referencing of chemical shift data.

Model-to-data validation

TALOS+. Predicts protein backbone torsion angles from chemical shift data. Frequently used to generate further restraints applied to a structure model during refinement.

Model validation: as above

NMR structural ensemble for PDB file 2K5D, with well-defined structure for the beta strands (arrows) and undefined, presumably highly mobile regions for the orange loop and the blue N-terminus 2k5d NMR ensemble ribbons.jpg
NMR structural ensemble for PDB file 2K5D, with well-defined structure for the beta strands (arrows) and undefined, presumably highly mobile regions for the orange loop and the blue N-terminus

Dynamics: core vs loops, tails, and mobile domains

One of the critical needs for NMR structural ensemble validation is to distinguish well-determined regions (those that have experimental data) from regions that are highly mobile and/or have no observed data. There are several current or proposed methods for making this distinction such as Random Coil Index, but so far the NMR community has not standardized on one.

Software and websites

In cryo-EM

Cyro-EM presents special challenges to model-builders as the observed electron density is frequently insufficient to resolve individual atoms, leading to a higher likelihood of errors.

Geometry-based validation tools similar to those used in X-ray crystallography can be used to highlight implausible modeling choices and guide modeler toward more native-like structures. The CaBLAM method, which only uses Cα atoms, [48] is suitable for low-resolution structures from cyro-EM. [49]

A way to compute the difference density map has been formulated for cyro-EM. [50] [51] Cross-validation using a "free" map, comparable to the use of a free R-factor, is also available. [52] [53] Other methods for checking model-map fit include correlation coefficients, model-map FSC, [54] confidence maps, CryoEF (orientation bias check), and TEMPy SMOC. [51]

In SAXS

SAXS (small-angle x-ray scattering) is a rapidly growing area of structure determination, both as a source of approximate 3D structure for initial or difficult cases and as a component of hybrid-method structure determination when combined with NMR, EM, crystallographic, cross-linking, or computational information. There is great interest in the development of reliable validation standards for SAXS data interpretation and for quality of the resulting models, but there are as yet no established methods in general use. Three recent steps in this direction are the creation of a Small-Angle Scattering Validation Task Force committee by the worldwide Protein DataBank and its initial report, [55] a set of suggested standards for data inclusion in publications, [56] and an initial proposal of statistically derived criteria for automated quality evaluation. [57]

For computational biology

It is difficult to do meaningful validation of an individual, purely computational, macromolecular model in the absence of experimental data for that molecule, because the model with the best geometry and conformational score may not be the one closest to the right answer. Therefore, much of the emphasis in validation of computational modeling is in assessment of the methods. To avoid bias and wishful thinking, double-blind prediction competitions have been organized, the original example of which (held every 2 years since 1994) is CASP (Critical Assessment of Structure Prediction) to evaluate predictions of 3D protein structure for newly solved crystallographic or NMR structures held in confidence until the end of the relevant competition. [58] The major criterion for CASP evaluation is a weighted score called GDT-TS for the match of Calpha positions between the predicted and the experimental models. [59]

See also

Related Research Articles

<span class="mw-page-title-main">Structural biology</span> Study of molecular structures in biology

Structural biology, as defined by the Journal of Structural Biology, deals with structural analysis of living material at every level of organization.

<span class="mw-page-title-main">X-ray crystallography</span> Technique used for determining crystal structures and identifying mineral compounds

X-ray crystallography is the experimental science of determining the atomic and molecular structure of a crystal, in which the crystalline structure causes a beam of incident X-rays to diffract in specific directions. By measuring the angles and intensities of the X-ray diffraction, a crystallographer can produce a three-dimensional picture of the density of electrons within the crystal and the positions of the atoms, as well as their chemical bonds, crystallographic disorder, and 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, which is overseen by the Worldwide Protein Data Bank (wwPDB). These structural data are obtained and deposited by biologists and biochemists worldwide through the use of experimental methodologies such as X-ray crystallography, NMR spectroscopy, and, increasingly, cryo-electron microscopy. All submitted data are reviewed by expert biocurators and, once approved, are made freely available on the Internet under the CC0 Public Domain Dedication. Global access to the data is provided by the websites of the wwPDB member organisations.

<span class="mw-page-title-main">Structural bioinformatics</span> Bioinformatics subfield

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.

<span class="mw-page-title-main">Ramachandran plot</span> Visual representation of allowable protein conformations

In biochemistry, a Ramachandran plot, originally developed in 1963 by G. N. Ramachandran, C. Ramakrishnan, and V. Sasisekharan, is a way to visualize energetically allowed regions for backbone dihedral angles ψ against φ of amino acid residues in protein structure. The figure on the left illustrates the definition of the φ and ψ backbone dihedral angles. The ω angle at the peptide bond is normally 180°, since the partial-double-bond character keeps the peptide bond planar. The figure in the top right shows the allowed φ,ψ backbone conformational regions from the Ramachandran et al. 1963 and 1968 hard-sphere calculations: full radius in solid outline, reduced radius in dashed, and relaxed tau (N-Cα-C) angle in dotted lines. Because dihedral angle values are circular and 0° is the same as 360°, the edges of the Ramachandran plot "wrap" right-to-left and bottom-to-top. For instance, the small strip of allowed values along the lower-left edge of the plot are a continuation of the large, extended-chain region at upper left.

Electron crystallography is a subset of methods in electron diffraction focusing just upon detailed determination of the positions of atoms in solids using a transmission electron microscope (TEM). It can involve the use of high-resolution transmission electron microscopy images, electron diffraction patterns including convergent-beam electron diffraction or combinations of these. It has been successful in determining some bulk structures, and also surface structures. Two related methods are low-energy electron diffraction which has solved the structure of many surfaces, and reflection high-energy electron diffraction which is used to monitor surfaces often during growth.

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<span class="mw-page-title-main">Crystallographic Information File</span> File format used to store crystalographic data

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In X-ray crystallography, a difference density map or Fo–Fc map shows the spatial distribution of the difference between the measured electron density of the crystal and the electron density explained by the current model.

<span class="mw-page-title-main">Jane S. Richardson</span> American biophysicist

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Acta Crystallographica is a series of peer-reviewed scientific journals, with articles centred on crystallography, published by the International Union of Crystallography (IUCr). Originally established in 1948 as a single journal called Acta Crystallographica, there are now six independent Acta Crystallographica titles:

<span class="mw-page-title-main">Helen M. Berman</span> American chemist

Helen Miriam Berman is a Board of Governors Professor of Chemistry and Chemical Biology at Rutgers University and a former director of the RCSB Protein Data Bank. A structural biologist, her work includes structural analysis of protein-nucleic acid complexes, and the role of water in molecular interactions. She is also the founder and director of the Nucleic Acid Database, and led the Protein Structure Initiative Structural Genomics Knowledgebase.

<span class="mw-page-title-main">Single particle analysis</span> Method of analyzing transmission electron microscopy imagery

Single particle analysis is a group of related computerized image processing techniques used to analyze images from transmission electron microscopy (TEM). These methods were developed to improve and extend the information obtainable from TEM images of particulate samples, typically proteins or other large biological entities such as viruses. Individual images of stained or unstained particles are very noisy, and so hard to interpret. Combining several digitized images of similar particles together gives an image with stronger and more easily interpretable features. An extension of this technique uses single particle methods to build up a three-dimensional reconstruction of the particle. Using cryo-electron microscopy it has become possible to generate reconstructions with sub-nanometer resolution and near-atomic resolution first in the case of highly symmetric viruses, and now in smaller, asymmetric proteins as well. Single particle analysis can also be performed by inductively coupled plasma mass spectrometry (ICP-MS).

<span class="mw-page-title-main">Macromolecular assembly</span>

The term macromolecular assembly (MA) refers to massive chemical structures such as viruses and non-biologic nanoparticles, cellular organelles and membranes and ribosomes, etc. that are complex mixtures of polypeptide, polynucleotide, polysaccharide or other polymeric macromolecules. They are generally of more than one of these types, and the mixtures are defined spatially, and with regard to their underlying chemical composition and structure. Macromolecules are found in living and nonliving things, and are composed of many hundreds or thousands of atoms held together by covalent bonds; they are often characterized by repeating units. Assemblies of these can likewise be biologic or non-biologic, though the MA term is more commonly applied in biology, and the term supramolecular assembly is more often applied in non-biologic contexts. MAs of macromolecules are held in their defined forms by non-covalent intermolecular interactions, and can be in either non-repeating structures, or in repeating linear, circular, spiral, or other patterns. The process by which MAs are formed has been termed molecular self-assembly, a term especially applied in non-biologic contexts. A wide variety of physical/biophysical, chemical/biochemical, and computational methods exist for the study of MA; given the scale of MAs, efforts to elaborate their composition and structure and discern mechanisms underlying their functions are at the forefront of modern structure science.

Resolution by Proxy (ResProx) is a method for assessing the equivalent X-ray resolution of NMR-derived protein structures. ResProx calculates resolution from coordinate data rather than from electron density or other experimental inputs. This makes it possible to calculate the resolution of a structure regardless of how it was solved. ResProx was originally designed to serve as a simple, single-number evaluation that allows straightforward comparison between the quality/resolution of X-ray structures and the quality of a given NMR structure. However, it can also be used to assess the reliability of an experimentally reported X-ray structure resolution, to evaluate protein structures solved by unconventional or hybrid means and to identify fraudulent structures deposited in the PDB. ResProx incorporates more than 25 different structural features to determine a single resolution-like value. ResProx values are reported in Angstroms. Tests on thousands of X-ray structures show that ResProx values match very closely to resolution values reported by X-ray crystallographers. Resolution-by-proxy values can be calculated for newly determined protein structures using a freely accessible ResProx web server. This server accepts protein coordinate data and generates a resolution estimate for that input structure.

<span class="mw-page-title-main">Randy Read</span> Canadian-British scientist (1957–)

Randy John Read is a Wellcome Trust Principal Research Fellow and professor of protein crystallography at the University of Cambridge.

<span class="mw-page-title-main">Cryogenic electron microscopy</span> Form of transmission electron microscopy (TEM)

Cryogenic electron microscopy (cryo-EM) is a cryomicroscopy technique applied on samples cooled to cryogenic temperatures. For biological specimens, the structure is preserved by embedding in an environment of vitreous ice. An aqueous sample solution is applied to a grid-mesh and plunge-frozen in liquid ethane or a mixture of liquid ethane and propane. While development of the technique began in the 1970s, recent advances in detector technology and software algorithms have allowed for the determination of biomolecular structures at near-atomic resolution. This has attracted wide attention to the approach as an alternative to X-ray crystallography or NMR spectroscopy for macromolecular structure determination without the need for crystallization.

Microcrystal electron diffraction, or MicroED, is a CryoEM method that was developed by the Gonen laboratory in late 2013 at the Janelia Research Campus of the Howard Hughes Medical Institute. MicroED is a form of electron crystallography where thin 3D crystals are used for structure determination by electron diffraction. Prior to this demonstration, macromolecular (protein) electron crystallography was mainly used on 2D crystals, for example. The method is one of several modern versions of approaches to determine atomic structures using electron diffraction first demonstrated for the positions of hydrogen atoms in NH4Cl crystals by W. E. Laschkarew and I. D. Usykin in 1933, which has since been used for surfaces, via precession electron diffraction, with much of the early work described in the work of Boris Vainshtein and Douglas L. Dorset.

This is a timeline of crystallography.

<span class="mw-page-title-main">Wladek Minor</span> Polish-American structural biologist

Władysław Minor also known as Wladek Minor is a Polish-American biophysicist, a specialist in structural biology and protein crystallography. He is a Harrison Distinguished Professor of Molecular Physiology and Biological Physics at the University of Virginia. Minor is a co-author of HKL2000/HKL3000 – crystallographic data processing and structure solution software used to process data and solve structures of macromolecules, as well as small molecules. He is a co-founder of HKL Research, a company that distributes the software. He is also a co-author of a public repository of diffraction images (proteindiffraction.org) for some of the protein structures available in the Protein Data Bank and other software tools for structural biology.

References

  1. ↑ Rupp 2009
  2. ↑ Cavanagh 2006
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  1. ↑ Kleywegt GJ, Harris MR, Zou JY, Taylor TC, Wählby A, Jones TA (December 2004). "The Uppsala Electron-Density Server". Acta Crystallographica Section D. 60 (Pt 12 Pt 1): 2240–9. Bibcode:2004AcCrD..60.2240K. doi: 10.1107/s0907444904013253 . PMID   15572777.
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