Advanced ensemble modelling of flexible macromolecules using X-ray solution scattering.

IUCrJ

European Molecular Biology Laboratory, Hamburg Outstation , c/o DESY, Notkestrasse 85, Hamburg, 22603, Germany.

Published: March 2015

AI Article Synopsis

  • Dynamic ensembles of macromolecules are crucial for understanding biological functions, especially for flexible molecules like intrinsically disordered proteins.
  • Small-angle X-ray scattering (SAXS) has been adapted to study these flexible systems, and the Ensemble Optimization Method (EOM) allows researchers to fit SAXS data using a selection of molecular conformations.
  • The latest advancements in EOM 2.0 enhance pool generation and selection processes, introduce metrics for characterizing molecular flexibility, and discuss both the benefits and limitations of this ensemble method in SAXS studies.

Article Abstract

Dynamic ensembles of macromolecules mediate essential processes in biology. Understanding the mechanisms driving the function and molecular interactions of 'unstructured' and flexible molecules requires alternative approaches to those traditionally employed in structural biology. Small-angle X-ray scattering (SAXS) is an established method for structural characterization of biological macromolecules in solution, and is directly applicable to the study of flexible systems such as intrinsically disordered proteins and multi-domain proteins with unstructured regions. The Ensemble Optimization Method (EOM) [Bernadó et al. (2007 ▶). J. Am. Chem. Soc. 129, 5656-5664] was the first approach introducing the concept of ensemble fitting of the SAXS data from flexible systems. In this approach, a large pool of macromolecules covering the available conformational space is generated and a sub-ensemble of conformers coexisting in solution is selected guided by the fit to the experimental SAXS data. This paper presents a series of new developments and advancements to the method, including significantly enhanced functionality and also quantitative metrics for the characterization of the results. Building on the original concept of ensemble optimization, the algorithms for pool generation have been redesigned to allow for the construction of partially or completely symmetric oligomeric models, and the selection procedure was improved to refine the size of the ensemble. Quantitative measures of the flexibility of the system studied, based on the characteristic integral parameters of the selected ensemble, are introduced. These improvements are implemented in the new EOM version 2.0, and the capabilities as well as inherent limitations of the ensemble approach in SAXS, and of EOM 2.0 in particular, are discussed.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4392415PMC
http://dx.doi.org/10.1107/S205225251500202XDOI Listing

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