bioRxivBiophysics
Predicting X-ray Diffuse Scattering from Translation Libration Screw Structural Ensembles
Andrew Van Benschoten, Pavel Afonine, Thomas Terwilliger, Michael Wall, Colin Jackson, Nicholas Sauter, Paul Adams, Alexandre Urzhumtsev, James Fraser
Abstract
Identifying the intramolecular motions of proteins and nucleic acids is a major challenge in macromolecular X-ray crystallography. While Bragg diffraction describes the average positional distribution of crystalline atoms, many different models can fit this distribution equally well. Diffuse X-ray scattering can reduce this degeneracy by directly reporting on correlated atomic displacements. Although recent technological advances are increasing the potential to accurately measure diffuse scattering, computational modeling and validation tools are still needed to quantify the agreement between experimental data and different parameterizations of crystalline disorder. A new tool, phenix.diffuse, addresses this need by employing Guiniers equation to calculate diffuse scattering from Protein Data Bank (PDB)-formatted structural ensembles. As an example case, phenix.diffuse is applied to Translation-Libration-Screw (TLS) refinement, which models rigid body displacement for segments of the macromolecule. To enable calculation of diffuse scattering from TLS refined structures, phenix.tls_models builds multi-model PDB files that sample the underlying T, L and S tensors. In the glycerophosphodiesterase GpdQ, alternative TLS group partitioning and different motional correlations between groups yield markedly dissimilar diffuse scattering maps with distinct implications for molecular mechanism and allostery. These methods demonstrate how X-ray diffuse scattering can extend macromolecular structural refinement, validation, and analysis.\n\nSynopsisA method of simulating X-ray diffuse scattering from multi-model PDB files is presented. Despite similar agreement with Bragg data, different Translation-Libration-Screw refinement strategies produce unique diffuse intensity patterns.
- assay: structural biology
- research focus: protein structure prediction
- research focus: benchmarking
- research focus: software / tool
- approach: deep learning
- approach: statistical model
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