Biology:Structure validation

From HandWiki
Short description: Process of evaluating 3-dimensional atomic models of biomacromolecules
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.

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 ([1]) and Asia ([2]), 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,[33] follows the standard symbol nomenclature[34] 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

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

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

References

  1. ↑ Rupp 2009
  2. ↑ Cavanagh 2006
  3. ↑ "Single-crystal structure validation with the program PLATON". Journal of Applied Crystallography 36 (1): 7–13. 2003. doi:10.1107/S0021889802022112. Bibcode: 2003JApCr..36....7S. 
  4. ↑ "The Cambridge Structural Database: a quarter of a million crystal structures and rising". Acta Crystallographica Section B 58 (Pt 3 Pt 1): 380–8. June 2002. doi:10.1107/S0108768102003890. PMID 12037359. Bibcode: 2002AcCrB..58..380A. 
  5. ↑ "Crystallography Open Database - an open-access collection of crystal structures". Journal of Applied Crystallography 42 (Pt 4): 726–729. August 2009. doi:10.1107/s0021889809016690. PMID 22477773. Bibcode: 2009JApCr..42..726G. 
  6. ↑ "Free R value: a novel statistical quantity for assessing the accuracy of crystal structures". Nature 355 (6359): 472–5. January 1992. doi:10.1038/355472a0. PMID 18481394. Bibcode: 1992Natur.355..472B. 
  7. ↑ 7.0 7.1 "Accurate bond and angle parameters for X-ray protein structure refinement". Acta Crystallographica A 47 (4): 392–400. 1991. doi:10.1107/s0108767391001071. Bibcode: 1991AcCrA..47..392E. 
  8. ↑ "Tertiary templates for proteins. Use of packing criteria in the enumeration of allowed sequences for different structural classes". Journal of Molecular Biology 193 (4): 775–791. 1987. doi:10.1016/0022-2836(87)90358-5. PMID 2441069. 
  9. ↑ "PROCHECK: a program to check the stereochemical quality of protein structures". Journal of Applied Crystallography 26 (2): 283–291. 1993. doi:10.1107/s0021889892009944. Bibcode: 1993JApCr..26..283L. 
  10. ↑ "Errors in protein structures". Nature 381 (6580): 272. May 1996. doi:10.1038/381272a0. PMID 8692262. Bibcode: 1996Natur.381..272H. 
  11. ↑ "The Protein Data Bank: a computer-based archival file for macromolecular structures". Journal of Molecular Biology 112 (3): 535–42. May 1977. doi:10.1016/s0022-2836(77)80200-3. PMID 875032. 
  12. ↑ "The Protein Data Bank". Nucleic Acids Research 28 (1): 235–42. January 2000. doi:10.1093/nar/28.1.235. PMID 10592235. 
  13. ↑ "Announcing the worldwide Protein Data Bank". Nature Structural Biology 10 (12): 980. December 2003. doi:10.1038/nsb1203-980. PMID 14634627. 
  14. ↑ "Validation of protein crystal structures". Acta Crystallographica D 56 (Pt 3): 18–19. 2000. doi:10.1107/s0907444999016364. PMID 10713511. Bibcode: 2000AcCrD..56..249K. 
  15. ↑ "A new generation of crystallographic validation tools for the protein data bank". Structure 19 (10): 1395–412. October 2011. doi:10.1016/j.str.2011.08.006. PMID 22000512. 
  16. ↑ "Recommendations of the wwPDB NMR Validation Task Force". Structure 21 (9): 1563–70. September 2013. doi:10.1016/j.str.2013.07.021. PMID 24010715. 
  17. ↑ "Outcome of the first electron microscopy validation task force meeting". Structure 20 (2): 205–14. February 2012. doi:10.1016/j.str.2011.12.014. PMID 22325770. 
  18. ↑ "Geometric parameters in Nucleic Acids:Sugar and Phosphate Constituents". Journal of the American Chemical Society 118 (3): 519–529. 1996. doi:10.1021/ja9528846. 
  19. ↑ "Chirality errors in nucleic acid structures". Nature 387 (6634): 668. June 1997. doi:10.1038/42632. PMID 9192890. Bibcode: 1997Natur.387..668S. 
  20. ↑ "Smooth Backbone-Dependent Rotamer Library 2010". http://dunbrack.fccc.edu/lab/bbdep2010. 
  21. ↑ Dickerson, Richard E. (1989-02-01). "Definitions and Nomenclature of Nucleic Acid Structure Parameters". Journal of Biomolecular Structure and Dynamics 6 (4): 627–634. doi:10.1080/07391102.1989.10507726. ISSN 0739-1102. PMID 2619931. 
  22. ↑ Olson, Wilma K; Bansal, Manju; Burley, Stephen K; Dickerson, Richard E; Gerstein, Mark; Harvey, Stephen C; Heinemann, Udo; Lu, Xiang-Jun et al. (2001-10-12). "A standard reference frame for the description of nucleic acid base-pair geometry11Edited by P. E. Wright22This is a document of the Nomenclature Committee of IUBMB (NC-IUBMB)/IUPAC-IUBMB Joint Commission on Biochemical Nomenclature (JCBN), whose members are R. Cammack (chairman), A. Bairoch, H.M. Berman, S. Boyce, C.R. Cantor, K. Elliott, D. Horton, M. Kanehisa, A. Kotyk, G.P. Moss, N. Sharon and K.F. Tipton." (in en). Journal of Molecular Biology 313 (1): 229–237. doi:10.1006/jmbi.2001.4987. ISSN 0022-2836. PMID 11601858. 
  23. ↑ Bhattacharyya, Dhananjay; Halder, Sukanya; Basu, Sankar; Mukherjee, Debasish; Kumar, Prasun; Bansal, Manju (2017-01-19). "RNAHelix: computational modeling of nucleic acid structures with Watson–Crick and non-canonical base pairs". Journal of Computer-Aided Molecular Design 31 (2): 219–235. doi:10.1007/s10822-016-0007-0. ISSN 0920-654X. PMID 28102461. Bibcode: 2017JCAMD..31..219B. 
  24. ↑ "The optimal size of a globular protein domain: A simple sphere-packing model". Chemical Physics Letters 405 (1–3): 224–228. March 2005. doi:10.1016/j.cplett.2005.02.029. ISSN 0009-2614. Bibcode: 2005CPL...405..224S. 
  25. ↑ "Analysis of anisotropic side-chain packing in proteins and application to high-resolution structure prediction". Journal of Molecular Biology 342 (2): 651–64. September 2004. doi:10.1016/j.jmb.2004.07.038. PMID 15327962. 
  26. ↑ "Mapping the distribution of packing topologies within protein interiors shows predominant preference for specific packing motifs". BMC Bioinformatics 12 (1): 195. May 2011. doi:10.1186/1471-2105-12-195. PMID 21605466. 
  27. ↑ 27.0 27.1 "The jigsaw puzzle model: search for conformational specificity in protein interiors". Journal of Molecular Biology 333 (1): 211–26. October 2003. doi:10.1016/j.jmb.2003.08.013. PMID 14516754. 
  28. ↑ 28.0 28.1 "Self-complementarity within proteins: bridging the gap between binding and folding". Biophysical Journal 102 (11): 2605–14. June 2012. doi:10.1016/j.bpj.2012.04.029. PMID 22713576. Bibcode: 2012BpJ...102.2605B. 
  29. ↑ "MolProbity: all-atom structure validation for macromolecular crystallography". Acta Crystallographica Section D 66 (Pt 1): 12–21. January 2010. doi:10.1107/S0907444909042073. PMID 20057044. Bibcode: 2010AcCrD..66...12C. 
  30. ↑ "RosettaHoles: rapid assessment of protein core packing for structure prediction, refinement, design, and validation". Protein Science 18 (1): 229–39. January 2009. doi:10.1002/pro.8. PMID 19177366. 
  31. ↑ "Residue depth: a novel parameter for the analysis of protein structure and stability". Structure 7 (7): 723–32. July 1999. doi:10.1016/s0969-2126(99)80097-5. PMID 10425675. 
  32. ↑ "Applications of complementarity plot in error detection and structure validation of proteins". Indian Journal of Biochemistry & Biophysics 51 (3): 188–200. June 2014. PMID 25204080. 
  33. ↑ 33.0 33.1 33.2 "Privateer: software for the conformational validation of carbohydrate structures". Nature Structural & Molecular Biology 22 (11): 833–4. November 2015. doi:10.1038/nsmb.3115. PMID 26581513. http://eprints.whiterose.ac.uk/95794/1/Privateer_selfarchived.pdf. 
  34. ↑ 34.0 34.1 34.2 "Symbol Nomenclature for Graphical Representations of Glycans". Glycobiology 25 (12): 1323–4. December 2015. doi:10.1093/glycob/cwv091. PMID 26543186. 
  35. ↑ "Carbohydrate structure: the rocky road to automation". Current Opinion in Structural Biology. Carbohydrates • Sequences and topology 44: 39–47. June 2017. doi:10.1016/j.sbi.2016.11.011. PMID 27940408. http://eprints.whiterose.ac.uk/109296/1/COStBi_postprint.pdf. 
  36. ↑ "Building meaningful models of glycoproteins". Nature Structural & Molecular Biology 14 (5): 354; discussion 354–5. May 2007. doi:10.1038/nsmb0507-354a. PMID 17473875. 
  37. ↑ "Conformational analyses of the reaction coordinate of glycosidases". Accounts of Chemical Research 45 (2): 308–16. February 2012. doi:10.1021/ar2001765. PMID 21923088. 
  38. ↑ "Strategies for carbohydrate model building, refinement and validation". Acta Crystallographica Section D 73 (Pt 2): 171–186. February 2017. doi:10.1107/S2059798316016910. PMID 28177313. PMC 5297920. Bibcode: 2017AcCrD..73..171A. http://journals.iucr.org/d/issues/2017/02/00/ba5257/. 
  39. ↑ "Analysis and validation of carbohydrate three-dimensional structures". Acta Crystallographica Section D 65 (Pt 2): 156–68. February 2009. doi:10.1107/S0907444909001905. PMID 19171971. Bibcode: 2009AcCrD..65..156L. 
  40. ↑ "Data mining the PDB for glyco-related data". Glycomics. Methods in Molecular Biology. 534. 2009-01-01. pp. 293–310. doi:10.1007/978-1-59745-022-5_21. ISBN 978-1-58829-774-7. 
  41. ↑ "Carbohydrate 3D structure validation". Current Opinion in Structural Biology 44: 9–17. June 2017. doi:10.1016/j.sbi.2016.10.010. PMID 27816840. http://eprints.whiterose.ac.uk/109296/1/COStBi_postprint.pdf. 
  42. ↑ "Carbohydrate anomalies in the PDB". Nature Chemical Biology 11 (5): 303. May 2015. doi:10.1038/nchembio.1798. PMID 25885951. http://eprints.whiterose.ac.uk/95242/1/AgirreDaviesWIlsonCowtan_self_archived.pdf. 
  43. ↑ "pdb-care (PDB carbohydrate residue check): a program to support annotation of complex carbohydrate structures in PDB files". BMC Bioinformatics 5: 69. June 2004. doi:10.1186/1471-2105-5-69. PMID 15180909. 
  44. ↑ "Glycoblocks: a schematic three-dimensional representation for glycans and their interactions". Acta Crystallographica Section D 73 (Pt 2): 187–194. February 2017. doi:10.1107/S2059798316013553. PMID 28177314. Bibcode: 2017AcCrD..73..187M. 
  45. ↑ Rupp 2009, Chapter 13, Key Concepts
  46. ↑ "Assignment validation software suite for the evaluation and presentation of protein resonance assignment data". Journal of Biomolecular NMR 28 (4): 341–55. April 2004. doi:10.1023/B:JNMR.0000015420.44364.06. PMID 14872126. 
  47. ↑ "Protein NMR recall, precision, and F-measure scores (RPF scores): structure quality assessment measures based on information retrieval statistics". Journal of the American Chemical Society 127 (6): 1665–74. February 2005. doi:10.1021/ja047109h. PMID 15701001. 
  48. ↑ "CaBLAM Validation in Phenix". https://phenix-online.org/documentation/reference/cablam_validation.html. 
  49. ↑ Rohou, Alexis (February 2021). "Improving cryo-EM structure validation". Nature Methods 18 (2): 130–131. doi:10.1038/s41592-021-01062-1. PMID 33542515. 
  50. ↑ Yamashita, Keitaro; Palmer, Colin M.; Burnley, Tom; Murshudov, Garib N. (1 October 2021). "Cryo-EM single-particle structure refinement and map calculation using Servalcat". Acta Crystallographica Section D Structural Biology 77 (10): 1282–1291. doi:10.1107/S2059798321009475. PMID 34605431. Bibcode: 2021AcCrD..77.1282Y. 
  51. ↑ 51.0 51.1 Winn, Martyn (20 November 2020). "Cryo-EM validation tools in CCP-EM". www.ccpem.ac.uk/. https://www.ccpem.ac.uk/training/validation_symposium_2020/Winn_validation_in_CCP-EM_201120.pdf. 
  52. ↑ Falkner, B; Schröder, GF (28 May 2013). "Cross-validation in cryo-EM-based structural modeling.". Proceedings of the National Academy of Sciences of the United States of America 110 (22): 8930–5. doi:10.1073/pnas.1119041110. PMID 23674685. Bibcode: 2013PNAS..110.8930F. 
  53. ↑ Beckers, Maximilian; Mann, Daniel; Sachse, Carsten (March 2021). "Structural interpretation of cryo-EM image reconstructions". Progress in Biophysics and Molecular Biology 160: 26–36. doi:10.1016/j.pbiomolbio.2020.07.004. PMID 32735944. 
  54. ↑ "Cryo-EM Validation tools in Phenix" (in en). https://phenix-online.org/documentation/reference/validation_cryo_em.html. 
  55. ↑ "Report of the wwPDB Small-Angle Scattering Task Force: data requirements for biomolecular modeling and the PDB". Structure 21 (6): 875–81. June 2013. doi:10.1016/j.str.2013.04.020. PMID 23747111. 
  56. ↑ "Publication guidelines for structural modelling of small-angle scattering data from biomolecules in solution". Acta Crystallographica Section D 68 (Pt 6): 620–6. June 2012. doi:10.1107/S0907444912012073. PMID 22683784. Bibcode: 2012AcCrD..68..620J. 
  57. ↑ "The accurate assessment of small-angle X-ray scattering data". Acta Crystallographica Section D 71 (Pt 1): 45–56. January 2015. doi:10.1107/S1399004714010876. PMID 25615859. Bibcode: 2015AcCrD..71...45G. 
  58. ↑ "A large-scale experiment to assess protein structure prediction methods". Proteins 23 (3): ii-v. November 1995. doi:10.1002/prot.340230303. PMID 8710822. https://zenodo.org/record/1229334. 
  59. ↑ "LGA: A method for finding 3D similarities in protein structures". Nucleic Acids Research 31 (13): 3370–4. July 2003. doi:10.1093/nar/gkg571. PMID 12824330. 

External links

Link references

  1. ↑ "The Uppsala Electron-Density Server". Acta Crystallographica Section D 60 (Pt 12 Pt 1): 2240–9. December 2004. doi:10.1107/s0907444904013253. PMID 15572777. Bibcode: 2004AcCrD..60.2240K. 
  2. ↑ "Features and development of Coot". Acta Crystallographica Section D 66 (Pt 4): 486–501. April 2010. doi:10.1107/s0907444910007493. PMID 20383002. Bibcode: 2010AcCrD..66..486E. 
  3. ↑ "PDB_REDO: constructive validation, more than just looking for errors". Acta Crystallographica Section D 68 (Pt 4): 484–96. April 2012. doi:10.1107/s0907444911054515. PMID 22505269. Bibcode: 2012AcCrD..68..484J. 
  4. ↑ "Protein NMR recall, precision, and F-measure scores (RPF scores): structure quality assessment measures based on information retrieval statistics". Journal of the American Chemical Society 127 (6): 1665–74. February 2005. doi:10.1021/ja047109h. PMID 15701001. 
  5. ↑ "AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR". Journal of Biomolecular NMR 8 (4): 477–86. December 1996. doi:10.1007/bf00228148. PMID 9008363. 
  6. ↑ Liebschner, D; Afonine, PV; Moriarty, NW; Poon, BK; Chen, VB; Adams, PD (1 January 2021). "CERES: a cryo-EM re-refinement system for continuous improvement of deposited models.". Acta Crystallographica. Section D, Structural Biology 77 (Pt 1): 48–61. doi:10.1107/S2059798320015879. PMID 33404525. Bibcode: 2021AcCrD..77...48L. 

Further reading

  • Cavanagh, John; Fairbrother, Wayne J.; Palmer, Arthur G. III; Skelton, Nicholas J. (2006). Protein NMR Spectroscopy: Principles and Practice (2nd ed.). Academic Press. ISBN 978-0-12-164491-8. 
  • Rupp, Bernhard (2009). Biomolecular Crystallography: Principles, Practice, and Application to Structural Biology. Garland Science. ISBN 978-0815340812. 



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