Structure validation


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 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.
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. 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. 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 as a requirement for small-molecule crystal structure papers submitted to the International Union of Crystallography 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 or the Crystallography Open Database.
The first macromolecular validation software was developed around 1990, for proteins. It included Rfree cross-validation for model-to-data match, bond length and angle parameters for covalent geometry, and sidechain and backbone conformational criteria. For macromolecular structures, the atomic models are deposited in the Protein Data Bank, still the single archive of this data. The PDB was established in the 1970s at Brookhaven National Laboratory, moved in 2000 to the centered at Rutgers, and expanded in 2003 to become the , with access sites added in Europe and Asia, and with NMR data handled at the in Wisconsin.
Validation rapidly became standard in the field, 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 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 from recommendations of the wwPDB Validation Task Force committees for x-ray crystallography, for NMR, for SAXS, and for cryoEM.

Stages of validation

Validations can be broken into three stages: validating the raw data collected, the interpretation of the data into the atomic model, 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

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. For protein backbone dihedrals, this has been addressed by the legendary Ramachandran Plot while for side-chain dihedrals, one should refer to the .
Though, mRNA structures are generally short-lived and single-stranded, there are an abundance of non-coding RNAs with different secondary and tertiary folding which contain a preponderance of the canonical Watson-Crick base-pairs, together with significant number of non-Watson Crick 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- and inter-base-pair geometrical parameters - whether in-range or out-of-range with respect to their suggested values. These parameters describe the relative orientations of the two paired bases with respect to each other in two strands along with those of the two stacked base pairs with respect to each other, and, hence, together, they serve to validate nucleic acid structures in general. Since, RNA-helices are small in length, the use of electrostatic surface potential as a validation parameter has been found to be beneficial, particularly for modelling purposes.

Packing and Electrostatics: globular proteins

For globular proteins, interior atomic packing of side-chains has been shown to be pivotal in the structural stabilization of the protein-fold. On the other hand, the electrostatic harmony of the overall fold has also been shown to be essential for its stabilization. Packing anomalies include steric clashes, short contacts, holes and cavities while electrostatic disharmony refer to unbalanced partial charges in the protein core. While the clash-score of 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

The branched and cyclic nature of carbohydrates poses particular problems to structure validation tools. 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, sequences/structures should either match known structures, or be supported by complementary techniques such as Mass Spectrometry. Also, monosaccharides have clear conformational preferences, but errors introduced during model building and/or refinement can bring their atomic models out of their energy minima. Around 20% of the deposited carbohydrate structures are in unjustified energy minima.
A number of carbohydrate validation web services are available at , whereas the suite currently distributes , 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.
Privateer also generates scalable two-dimensional SVG diagrams according to the Essentials of Glycobiology standard symbol nomenclature containing all the validation information as tooltip annotations. This functionality is currently integrated into other CCP4 programs, such as the molecular graphics program CCP4mg 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. 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."

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 checks the chemical shifts list in BioMagResBank format for problems.
;PSVS: Protein Structure Validation Server at the NESG based on information retrieval statistics
;PROSESS: PROSESS 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

Dynamics: core vs loops, tails, and mobile domains

One of the critical needs for NMR structural ensemble validation is to distinguish well-determined regions 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 Cyro-EM

In SAXS

SAXS 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, a set of suggested standards for data inclusion in publications, and an initial proposal of statistically derived criteria for automated quality evaluation.

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 is CASP to evaluate predictions of 3D protein structure for newly solved crystallographic or NMR structures held in confidence until the end of the relevant competition. 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.

Link references