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Automated identification of functional dynamic contact networks from X-ray crystallography

Nature Methods volume 10, pages 896–902 (2013)Cite this article

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Abstract

Protein function often depends on the exchange between conformational substates. Allosteric ligand binding or distal mutations can stabilize specific active-site conformations and consequently alter protein function. Observing alternative conformations at low levels of electron density, in addition to comparison of independently determined X-ray crystal structures, can provide mechanistic insights into conformational dynamics. Here we report a new algorithm, CONTACT, that identifies contact networks of conformationally heterogeneous residues directly from high-resolution X-ray crystallography data. Contact networks determined for Escherichia coli dihydrofolate reductase (ecDHFR) predict the observed long-range pattern of NMR chemical shift perturbations of an allosteric mutation. A comparison of contact networks in wild-type and mutant ecDHFR suggests that mutations that alter optimized contact networks of coordinated motions can impair catalytic function. CONTACT-guided mutagenesis can exploit the structure-dynamics-function relationship in protein engineering and design.

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Figure 1: Mechanisms for conformational exchange in cyclophilin A.
Figure 2: Characteristics of pathways and contact networks are sensitive to temperature.
Figure 3: All-atom contact networks in ecDHFR.
Figure 4: Contact-network analysis and an allosteric mutant (G121V) of ecDHFR.
Figure 5: Increased conformational heterogeneity at the active site of the crystal structure of the E:NADP+:FOL complex of the N23PP/S148A mutant.

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Acknowledgements

H.v.d.B. is supported by the US National Institute of General Medical Sciences Protein Structure Initiative (U54GM094586) at the Joint Center for Structural Genomics and SLAC National Accelerator Laboratory LDRD (Laboratory Directed Research and Development) grant SLAC-LDRD-0014-13-2; G.B. is supported as a Merck Fellow of the Damon Runyon Cancer Research Foundation (DRG-2136-12); K.Y. is supported by the General Wang Yaowu Stanford graduate fellowship; P.E.W. is supported by the US National Institutes of Health (GM75995); J.S.F. is supported by the US National Institutes of Health Early Independence Award (DP5OD009180). We acknowledge T. Alber, T. Kortemme, D. Keedy, D. Kern, D. Tawfik and R. Woldeyes for helpful discussions; N. Echols for advice on different treatments of hydrogen atoms in Phenix; J. Holton, J. Tanamachi and G. Meigs at Advanced Light Source Beamline 8.3.1 for support with X-ray data collection; B. Duggan and D. Boehr for chemical shift data on G121V ecDHFR complexes; and H. Axelrod and C. Trame for data collection and refinement of RT RBM39.

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Authors and Affiliations

  1. Joint Center for Structural Genomics, Stanford Synchrotron Radiation Lightsource, Stanford, California, USA

    Henry van den Bedem

  2. Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, California, USA

    Gira Bhabha

  3. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA

    Gira Bhabha & Peter E Wright

  4. Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California, USA

    Kun Yang

  5. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA

    James S Fraser

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  1. Henry van den Bedem
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Contributions

H.v.d.B., G.B., P.E.W. and J.S.F. designed and performed experiments, analyzed data and prepared the manuscript; G.B. and J.S.F. collected data; and H.v.d.B., K.Y. and J.S.F. developed analytical tools.

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Correspondence to Henry van den Bedem or James S Fraser.

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Supplementary Figures 1–7, Supplementary Tables 1–4 and Supplementary Note (PDF 6570 kb)

Supplementary Software

CONTACT (ZIP 481 kb)

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van den Bedem, H., Bhabha, G., Yang, K. et al. Automated identification of functional dynamic contact networks from X-ray crystallography. Nat Methods 10, 896–902 (2013). https://doi.org/10.1038/nmeth.2592

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