Skip to Main content Skip to Navigation
Journal articles

PDB-wide identification of biological assemblies from conserved quaternary structure geometry

Sucharita Dey 1 David Ritchie 2 Emmanuel Levy 1
2 CAPSID - Computational Algorithms for Protein Structures and Interactions
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : Protein structures are key to understanding bio-molecular mechanisms and diseases, yet their interpretation is hampered by limited knowledge of their biologically relevant quaternary structures (QSs). A critical challenge in obtaining QSs from crystallographic data is to distinguish biological interfaces from crystal packing contacts. We tackled this challenge with two strategies for aligning and comparing QS states, both across homologs (QSalign), and across data repositories (QSbio). QS conservation across homologs was a remarkably strong predictor of biological relevance and allowed annotating of >80,000 biological QS states. QS conservation across methods enabled us to create a meta-predictor, QSbio, from which we inferred confidence estimates for >110,000 assemblies in the Protein Data Bank, which approach the accuracy of manual curation. Based on the dataset obtained, we analyzed interaction interfaces among pairs of structurally conserved QSs. This revealed a striking plasticity of interfaces, which can maintain a similar interaction geometry through widely different chemical properties.
Document type :
Journal articles
Complete list of metadatas

Cited literature [58 references]  Display  Hide  Download

https://hal.inria.fr/hal-01652359
Contributor : David Ritchie <>
Submitted on : Monday, December 11, 2017 - 10:15:14 PM
Last modification on : Wednesday, April 17, 2019 - 2:30:04 PM

File

Dey_etal_10_oct_2017.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Sucharita Dey, David Ritchie, Emmanuel Levy. PDB-wide identification of biological assemblies from conserved quaternary structure geometry. Nature Methods, Nature Publishing Group, 2018, 15, pp.67-72. ⟨10.1038/nmeth.4510⟩. ⟨hal-01652359⟩

Share

Metrics

Record views

340

Files downloads

384