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Validation of metal-binding sites in macromolecular structures with the CheckMyMetal web server

Nature Protocols volume 9, pages 156–170 (2014)Cite this article

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Abstract

Metals have vital roles in both the mechanism and architecture of biological macromolecules. Yet structures of metal-containing macromolecules in which metals are misidentified and/or suboptimally modeled are abundant in the Protein Data Bank (PDB). This shows the need for a diagnostic tool to identify and correct such modeling problems with metal-binding environments. The CheckMyMetal (CMM) web server (http://csgid.org/csgid/metal_sites/) is a sophisticated, user-friendly web-based method to evaluate metal-binding sites in macromolecular structures using parameters derived from 7,350 metal-binding sites observed in a benchmark data set of 2,304 high-resolution crystal structures. The protocol outlines how the CMM server can be used to detect geometric and other irregularities in the structures of metal-binding sites, as well as how it can alert researchers to potential errors in metal assignment. The protocol also gives practical guidelines for correcting problematic sites by modifying the metal-binding environment and/or redefining metal identity in the PDB file. Several examples where this has led to meaningful results are described in the ANTICIPATED RESULTS section. CMM was designed for a broad audience—biomedical researchers studying metal-containing proteins and nucleic acids—but it is equally well suited for structural biologists validating new structures during modeling or refinement. The CMM server takes the coordinates of a metal-containing macromolecule structure in the PDB format as input and responds within a few seconds for a typical protein structure with 2–5 metal sites and a few hundred amino acids.

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Figure 1: Quality of metal-binding sites grouped by year for structures in the PDB, as determined by CMM.
Figure 2
Figure 3: CheckMyMetal (CMM) server: screenshot of the various components of the results display interface.
Figure 4: Examples of problematic metal-binding sites in crystal structures, as detected by CMM.

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Acknowledgements

This work was supported by Federal funds from the National Institute of Allergy and Infectious Diseases, US National Institutes of Health, Department of Health and Human Services, under contract nos. HHSN272200700058C and HHSN272201200026C. We thank M. Grabowski, K.M. Langner and M. Domagalski for the CSGID website framework containing the CMM server; J. Hou, I.G. Shabalin, I.A. Shumilin, M. Demas and A.A. Knapik for server testing; W.F. Anderson for valuable discussion; and M.D. Zimmerman and H.C. Chapman for critically reading the manuscript.

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Authors and Affiliations

  1. Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA

    Heping Zheng, Mahendra D Chordia, David R Cooper, Maksymilian Chruszcz & Wladek Minor

  2. Center for Structural Genomics of Infectious Diseases (CSGID) Consortium, USA

    Heping Zheng, Mahendra D Chordia, David R Cooper, Maksymilian Chruszcz & Wladek Minor

  3. Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina, USA

    Maksymilian Chruszcz

  4. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Peter Müller

  5. Lehrstuhl für Strukturchemie, Universität Göttingen, Göttingen, Germany

    George M Sheldrick

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  1. Heping Zheng
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Contributions

H.Z. designed, implemented, tested and maintained the CMM server; H.Z. developed, implemented and optimized the NEIGHBORHOOD database; M.D.C. and D.R.C. helped design the geometry assignment algorithm and server interface; D.R.C. implemented the first version of the Jmol applet; P.M. and G.M.S. introduced the CBVS and VECSUM methods, which were slightly modified for this study; H.Z., M.D.C., D.R.C., M.C., P.M., G.M.S. and W.M. wrote and approved the manuscript; and W.M. supervised the project.

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Correspondence to Wladek Minor.

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Integrated supplementary information

Supplementary Figure 1 Valence distribution for the benchmark dataset.

Numbers of sample sizes and peak values for metal binding sites are indicated in parentheses. Valence values where the distribution heights were 50% or 10% of peak values were used as threshold values to define acceptable, borderline and outlier zones. Similar elements with same the expected valence are grouped (Supplementary Table 1). Poorly coordinated Na/K ions in the benchmark dataset resulted in a valence close to 0 and are considered as noise. These were removed prior to threshold estimation.

Supplementary Figure 2 Distribution of nVECSUM, gRMSD, vacancy and B-factor agreement for the benchmark dataset.

Metal binding sites with invalid parameter values are excluded from the statistics. Numbers of sample sizes and peak values for metal binding sites are indicated in parentheses. Values of parameters where the distributions had heights of 50% or 10% of the peak height are used as threshold values to define acceptable, borderline and outlier zones.

Supplementary Figure 3 One of the three modeled magnesiums (A901) in the catalytic center of the kinase domain of myosin heavy chain kinase A (PDB code: 3lkm).

Even though coordination by a phosphate of AMP often creates a favorable cation binding environment, this site has an improbable geometry (tetrahedral) for magnesium (which prefers octahedral geometry) (a). The ion is better interpreted as a water molecule (b). The other two modeled magnesiums (A902 and A903) in the same structure are re-refined as potassium ions. The re-refined site A902 is shown as an example in the main text in Fig. 4.

Supplementary information

Supplementary Figure 1

Valence distribution for the benchmark dataset. (PDF 566 kb)

Supplementary Figure 2

Distribution of nVECSUM, gRMSD, vacancy and B-factor agreement for the benchmark dataset. (PDF 455 kb)

Supplementary Figure 3

One of the three modeled magnesiums (A901) in the catalytic center of the kinase domain of myosin heavy chain kinase A (PDB code: 3lkm). (PDF 263 kb)

Supplementary Table 1

Comparison of CMM with other programs and services for metal binding site prediction or investigation. (PDF 221 kb)

Supplementary Table 2

Threshold values for CMM parameters. A parenthesis indicates that the nearest endpoint is excluded from the interval; a square bracket indicates that the endpoint is included in the interval. (PDF 306 kb)

Supplementary Table 3

Re-refinement statistics and geometry for the examples described in the main text. Clashscore, rotamer outliers, and the number of residues in the Ramachandran plot favored regions were calculated using MolProbity43. The Rfree set reported in the structure factor files available from the PDB were used for Rfree calculation. (PDF 296 kb)

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Zheng, H., Chordia, M., Cooper, D. et al. Validation of metal-binding sites in macromolecular structures with the CheckMyMetal web server. Nat Protoc 9, 156–170 (2014). https://doi.org/10.1038/nprot.2013.172

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