Evolving SAXS versatility: solution X-ray scattering for macromolecular architecture, functional landscapes, and integrative structural biology

Abstract

Small-angle X-ray scattering (SAXS) has emerged as an enabling integrative technique for comprehensive analyses of macromolecular structures and interactions in solution. Over the past two decades, SAXS has become a mainstay of the structural biologist’s toolbox, supplying multiplexed measurements of molecular shape and dynamics that unveil biological function. Here, we discuss evolving SAXS theory, methods, and applications that extend the field of small-angle scattering beyond simple shape characterization. SAXS, coupled with size-exclusion chromatography (SEC-SAXS) and time-resolved (TR-SAXS) methods, is now providing high-resolution insight into macromolecular flexibility and ensembles, delineating biophysical landscapes, and facilitating high-throughput library screening to assess macromolecular properties and to create opportunities for drug discovery. Looking forward, we consider SAXS in the integrative era of hybrid structural biology methods, its potential for illuminating cellular supramolecular and mesoscale structures, and its capacity to complement high-throughput bioinformatics sequencing data. As advances in the field continue, we look forward to proliferating uses of SAXS based upon its abilities to robustly produce mechanistic insights for biology and medicine.

Small angle X‐ray scattering‐assisted protein structure prediction in CASP13 and emergence of solution structure differences

“…A primary rationale for using SAXS data as experimental input for structure prediction is that collecting SAXS data is high‐throughput (HT) and straightforward . In SAXS, no labeling or crystallization is required.…”

“…A primary rationale for using SAXS data as experimental input for structure prediction is that collecting SAXS data is high-throughput (HT) and straightforward. [2][3][4][5][6] In SAXS, no labeling or crystallization is required. Data collection for basic research is provided for free by all biological SAXS beamlines, with one at every U.S. synchrotron.…”

Pulsed Dipolar EPR Spectroscopy and Metal Ions: Methodology and Biological Applications

“…Since most of the obtained PELDOR time traces were orientation selective, the central question of these studies was how to account for orientation selectivity in the data analysis. In the early studies, [55,58,59] PELDOR time traces were converted into dipolar spectra via Fourier transformation and the obtained spectra were interpreted as showing the θ = 90°singularity of the Pake doublet, which enabled calculation of r according to Equation (7). Although this approach is quick and straightforward, it has several disadvantages: 1) it is difficult to read of the frequencies of the singularities in the case of broad distance distributions, 2) only the mean inter-spin distance but not its distribution can be determined, and 3) orientation selectivity may lead to a distribution P(θ), in which the most probable value does not correspond to θ = 90°.…”

“…As most of the biomolecular samples are solutions, it is important to note that the rotation of spin-carrying biomolecules with a frequency exceeding D dd averages the angular term in Eqs. (7), (8) and (10) to zero. For this reason, the measurement of non-zero dipolar coupling is possible only when the biomolecules are immobilized.…”

Nanoscale Structure Determination of Murray Valley Encephalitis and Powassan Virus Non-coding RNAs

“…Small-angle X-ray scattering allows solution structure studies of biomolecules and their complexes, albeit at low-resolution. Recent developments with instrumentation in terms of an HPLC unit connected inline with SAXS detection allows for data collection of monodispersed sample devoid of aggregated and degraded products [51,52]. As outlined in the Materials and Methods section, we collected selected HPLC-SAXS data for all four RNAs, followed by the selection of data from a monodispersed peak, buffer-subtraction, and merging of selected datasets.…”