Protein structures are key to understanding biomolecular mechanisms and diseases, yet their interpretation is hampered by limited knowledge of their biologically relevant quaternary structure (QS). A critical challenge in inferring QS information from crystallographic data is distinguishing biological interfaces from fortuitous crystal-packing contacts. Here, we tackled this problem by developing strategies for aligning and comparing QS states across both homologs and data repositories. QS conservation across homologs proved remarkably strong at predicting biological relevance and is implemented in two methods, QSalign and anti-QSalign, for annotating homo-oligomers and monomers, respectively. QS conservation across repositories is implemented in QSbio (http://www.QSbio.org), which approaches the accuracy of manual curation and allowed us to predict >100,000 QS states across the Protein Data Bank. Based on this high-quality data set, we analyzed pairs of structurally conserved interfaces, and this analysis revealed a striking plasticity whereby evolutionary distant interfaces maintain similar interaction geometries through widely divergent chemical properties.
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We thank H. Greenblatt for valued help with operating the computer cluster, and we thank O. Dym and S. Rogotner for providing the photo of a protein crystal used in Figure 1. We thank J. Sussman for feedback on the work and D. Fass for comments on the manuscript. This work was supported by a VATAT fellowship to S.D. by the Israel Science Foundation and the I-CORE Program of the Planning and Budgeting Committee (grant nos. 1775/12 and 2179/14), by the Marie Curie CIG Program to E.D.L. (project no. 711715), by the HFSP Career Development Award to E.D.L. (award no. CDA00077/2015), and by a research grant from A.-M. Boucher. E.D.L. is incumbent of the Recanati Career Development Chair of Cancer Research.
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Integrated supplementary information
Supplementary Figure 1 Measuring quaternary structure similarity using global versus local measures.
(a) Structural similarity of two protein complexes can be inferred from a global superposition, which yields a global score, as was done in this work. (b) Structural similarity can also be assessed at the level of pairwise interfaces1–14, but such information would have to be integrated to infer a global similarity measure when complexes contain multiple interfaces. For example, in the case of a tetramer with four interfaces, four similarity measures will be obtained and this number would increase further when comparing complexes with more subunits.
The names of the chains in a PDB file are arbitrary. For example, considering the two tetramers depicted, chains may be labeled clockwise in one PDB file but counter-clockwise in another. Thus, although two structures can be similar structurally, differences in chain order can yield a false negative result when structures are being compared. To circumvent this problem, we must infer chain-chain correspondences among the structures being compared. This was achieved using a seed superposition of the two structures, which is based on chains from the first QS maximizing the TM-score with the second QS. If the QSs are similar, this seed superposition naturally places structurally equivalent chains in proximity, which made their identification possible by analysis of the aligned coordinates. We then used this mapping to re-write the coordinate files in matching chain order, and recalculated a global superposition of the complete QSs using the re-ordered coordinates. The latter provided us with the final TM-score.
Each symmetry group is considered iteratively. Within each group, each QS is used to search for structural homologs. If a homologue is found, both QSs are annotated to be “correct.” Once all the QSs of a symmetry group have been processed, each QS is used again to search for proteins identical in sequence but having different QSs. If found, we considered such QSs to be likely non-biological and annotated them as such.
Supplementary Figure 5 Integrating pairwise interface information to infer biological relevance of quaternary structures.
QSbio needs to compare QSs from PDB with predictions from PISA, EPPIC, and QSalign/anti-QSalign. Comparing QSs between PDB and PISA is achieved with the full QS superimposition approach described above (Figure S2). However, to compare QSs between PDB and EPPIC, we must employ a different strategy because EPPIC provides pairwise interface information (as opposed to assembly information). We therefore mapped pairwise information from EPPIC onto QSs from PDB using the following approach. First, each QS from PDB was decomposed into pairs of chains, using all pairs burying >90 Å2. Each pair was subsequently matched to an interface group from EPPIC by structural superposition. Each interface group in EPPIC is classified as being either biological (green) or non-biological (magenta). In the case where all subunits of the QS could be linked by biological contacts, the QS was deemed to match EPPIC (example 1) and otherwise it was inferred as non-matching (example 2).
(a) We compared interfaces of structurally similar protein complexes. We examined whether interface properties of one complex were predictive of the same property in its homologues, given different levels of sequence identity between them. (b) We first compared the interaction propensity of interfaces. Higher values indicate interfaces with a high fraction of residues normally enriched at interfaces while lower values correspond to interfaces chemically close to solvent-exposed surfaces. (c) We then compared the hydrophobicity of interface pairs, defined as the ratio of non-polar residues to the total number of interface residues. (d) Finally, we compared evolutionary conservation of interface residues relative to surface residues. Values below 1 correspond to complexes where the interface is more conserved than the surface. The right-most plot summarizes the squared correlation coefficient (R2) for each property considered, calculated for pairs of proteins binned by shared sequence identity: < 30%, 30-45%, 45-60%, 60-75% and 75-90%. All properties show very low correlation values for pairs sharing less than 30% identity, showing that despite being structurally similar, interfaces can differ dramatically in their chemistry and evolutionary properties. One thousand random data points were sampled for each plot to ease visualization.
We annotated monomers based on the enrichment of monomeric homologs over oligomeric ones. This enrichment is used to derive probabilities by the formulae above. Proteins sharing at least 30% and at most 90% sequence identity and having an overlap of 60% or more were considered as homologs.
Supplementary Figures 1–7, Supplementary Tables 1–2 and Supplementary Note 1 (PDF 1050 kb)
Prediction details of PISA, EPPIC, QSalign/anti-QSalign and QSbio on the different datasets. (XLSX 118 kb)
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Dey, S., Ritchie, D. & Levy, E. PDB-wide identification of biological assemblies from conserved quaternary structure geometry. Nat Methods 15, 67–72 (2018). https://doi.org/10.1038/nmeth.4510
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