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An information theoretic framework reveals a tunable allosteric network in group II chaperonins

Nature Structural & Molecular Biology volume 24, pages 726–733 (2017)Cite this article

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Abstract

ATP-dependent allosteric regulation of the ring-shaped group II chaperonins remains ill defined, in part because their complex oligomeric topology has limited the success of structural techniques in suggesting allosteric determinants. Further, their high sequence conservation has hindered the prediction of allosteric networks using mathematical covariation approaches. Here, we develop an information theoretic strategy that is robust to residue conservation and apply it to group II chaperonins. We identify a contiguous network of covarying residues that connects all nucleotide-binding pockets within each chaperonin ring. An interfacial residue between the networks of neighboring subunits controls positive cooperativity by communicating nucleotide occupancy within each ring. Strikingly, chaperonin allostery is tunable through single mutations at this position. Naturally occurring variants at this position that double the extent of positive cooperativity are less prevalent in nature. We propose that being less cooperative than attainable allows chaperonins to support robust folding over a wider range of metabolic conditions.

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Figure 1: Predicting coevolved residues in group II chaperonins.
Figure 2: Contiguous network connecting all nucleotide-binding sites of the group II chaperonin.
Figure 3: Allosteric signals controlling nucleotide cycling are communicated through Met47.
Figure 4: Chaperonin cycling is defined by nucleotide occupancy.
Figure 5: Influence of the extent of cooperativity on folding activity of a group II chaperonin.
Figure 6: Optimizing multisubunit enzyme cooperativity for robust proteostasis activity.

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Acknowledgements

We thank N. Douglas for advice and manuscript comments, R. Pfuetzner and A. Brunger for help with the SEC-MALS experiments, A. Colavin and K. Huang for discussions of the coupling algorithms and members of the Frydman lab for helpful discussions. Research was supported by the NIH awards GM074074 (JF), GM062868 (VP) and by award DE-SC0008504 from the Department of Energy. K.D. was a recipient of a Stanford Graduate Fellowship, and T.L. was supported by T32GM007276.

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Author notes

  1. Anthony Tomlinson

    Present address: Late Stage Pharmaceutical Development, Genentech, South San Francisco, California, USA

  2. Tom Lopez and Kevin Dalton: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biology, Stanford University, Stanford, California, USA

    Tom Lopez, Anthony Tomlinson & Judith Frydman

  2. Biophysics Program, Stanford University, Stanford, California, USA

    Kevin Dalton, Vijay Pande & Judith Frydman

  3. Department of Chemistry, Stanford University, Stanford, California, USA

    Vijay Pande

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Contributions

T.L. did all experiments, K.D. developed the algorithm and did analyses, A.T. assisted with light scattering measurements, V.P. directed algorithm development and J.F. directed all aspects of work. All authors contributed to the final manuscript.

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Correspondence to Judith Frydman.

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Integrated supplementary information

Supplementary Figure 1 Comparison of covariation measures

Using synthetic alignments, we investigate the entropy dependence of various covariation measures including normalized mutual information (NMI), average product corrected normalized mutual information (APC-NMI), and statistical coupling analysis version 5 (SCA5).

(A) The entropy dependence for a range of covariation scenarios. Covariation for the 500 sequence alignment with two residue types shown on the left was computed with four common measures. The entropy dependence of the upper right quadrant for the measure is plotted in the bottom row. Restricting analysis to this quadrant avoids including coupling between identical columns. 2-D histograms report the average value of the measure for a combination of marginal entropies. Z-scores (number of standard deviations from the mean value for each distribution) are computed from the raw measure and thus lower scores imply less covariation and higher values of APCNMI indicate more covariation between residue pairs. Negative z-scores correspond to values which are below the mean of the distribution encompassing all residue pairs for the alignment. The Results show that normalized and average product corrected normalized mutual information have a comparatively flat dependence on the entropy of the input residues and favor residue pairs with similar entropies.

(B) Noise sensitivity of measures was evaluated by calculating the measures on an alignment of two residue types over a range of entropies generated by randomized insertion of the second residue type. The values were converted into Z-scores before binning to aid in comparison. The results show that APC-NMI has a very flat dependence on marginal entropy and tends to downweight couples with high entropy.

Supplementary Figure 2 Covariation of the archaeal chaperonin alignment

(A) Alignment of 573 archaeal chaperonin peptide sequences from the CpnDB.

(B) Approximate maximum likelihood tree of the archaeal chaperonin sequences. Generated using FasTree on codon aligned nucleotide sequences of the same 573 taxa illustrated in the peptide sequence (A). Render from FigTree.

(C) Comparison of average product corrected normalized mutual information (APC-NMI, bottom) with normalized mutual information (NMI, top) computed for the alignment in panel A. Measures are presented as z-scores to ease comparison. Higher scores are more significant.

(D) Comparison of average product corrected normalized mutual information (APC-NMI, bottom) with normalized average product corrected mutual information (MIp, top) computed for the alignment in panel A. Measures are presented as z-scores to ease comparison. Higher scores are more significant.

Supplementary Figure 3 Spectral Decomposition of APC-NMI

(A)The eigenvalues of the APC-NMI matrix computed from the alignment of archaeal chaperonins.

(B) Comparison of two most significant eigenvectors of APC-NMI. Each dot is colored by the Shannon entropy of the corresponding residue from the protein MSA (Figure S2A).

(C) Comparison of the second and third most significant eigenvectors of APC-NMI. Each dot is colored by the Shannon entropy of the corresponding residue from the protein MSA (Figure S2A).

Supplementary Figure 4 Summary of subnetworks

(A) (Left) Surface rendering all residues participating in couples below a bootstrap p-value threshold of 0.005 shown on the structure of the closed MmCpn (PDBID: 3RUW). Residues are colored by the first component of the APC-NMI matrix. (Right) Residues viewed on a single ring looking at the base of the equatorial domain towards the chamber lid along the eight-fold symmetry axis.

(B) All subnetworks from Figure 1F have been given identifiers for observation in the pymol session included in supplemental data set 1 and the renderings in panel C.

(C) Small subnetworks that correlate strongly with the first component of the APC-NMI matrix shown on single Cpn subunits (Upper) and as enhanced views (Lower).

Supplementary Figure 5 Binding site architecture for Met-47

(A) Surface rendering of binding site filled by Met47 (left) and alone (right).

(B) Hypothetical structures of TRiC-like mutants, M47I (left) and M47L (right). Rotamers were limited to only those that were attainable at the observed backbone angles.

(C) Mutants predicted to interfere with the steric architecture of the binding site. (left) Mutation to alanine would empty the binding site. (right) Deleting the residue responsible for creating the constricted binding site, Ile-512, would similarly create a largely empty pocket.

Supplementary Figure 6 HPLC-SEC-MALLS of chaperonin mutants

(A) Size exclusion chromatography of Cpn. Mutants were run on 500Å gel filtration column, as in Figure 2B.

(B) Calculated molecular masses and observed polydispersities by SEC-MALLS/QELS shown with the calculated uncertainty based on Debye plot fit.

Supplementary Figure 7 Nucleotide cycling of WT and M47L

(A) Nucleotide hydrolysis measured by an enzyme coupled assay at 1 mM ATP, from Figure 3C. ADP generated calculated by monitoring NADH oxidation at 340 nm. Single representative shown.

(B) Hill equation parameters for the first allosteric transition of nucleotide cycling with the standard deviation of three trials.

Supplementary Figure 8 Nucleotide hydrolyzed by the chaperonin

Amount of hydrolyzed nucleotide calculated as the difference between α- and γ-labeled 32P-ATP recoveries from Figure 4B i-iii.

Supplementary Figure 9 Suppression of Substrate Aggregation

Substrate aggregation monitored after dilution from denaturant seen in Figure 5B.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9 and Supplementary Note 1 (PDF 1824 kb)

Supplementary Data set 1

Identified covarying networks from APC-NMI analysis of archaeal group II chaperonins. All identified subnetworks from Figure 1F and S4B shown on Cpn from M. maripaludis. PDBID: 3RUW. (ZIP 6390 kb)

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Lopez, T., Dalton, K., Tomlinson, A. et al. An information theoretic framework reveals a tunable allosteric network in group II chaperonins. Nat Struct Mol Biol 24, 726–733 (2017). https://doi.org/10.1038/nsmb.3440

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