The ATSAS software suite encompasses a number of programs for the processing, visualization, analysis and modelling of small-angle scattering data, with a focus on the data measured from biological macromolecules. Here, new developments in the ATSAS 3.0 package are described. They include IMSIM, for simulating isotropic 2D scattering patterns; IMOP, to perform operations on 2D images and masks; DATRESAMPLE, a method for variance estimation of structural invariants through parametric resampling; DATFT, which computes the pair distance distribution function by a direct Fourier transform of the scattering data; PDDFFIT, to compute the scattering data from a pair distance distribution function, allowing comparison with the experimental data; a new module in DATMW for Bayesian consensus-based concentration-independent molecular weight estimation; DATMIF, an ab initio shape analysis method that optimizes the search model directly against the scattering data; DAMEMB, an application to set up the initial search volume for multiphase modelling of membrane proteins; ELLLIP, to perform quasi-atomistic modelling of liposomes with elliptical shapes; NMATOR, which models conformational changes in nucleic acid structures through normal mode analysis in torsion angle space; DAMMIX, which reconstructs the shape of an unknown intermediate in an evolving system; and LIPMIX and BILMIX, for modelling multilamellar and asymmetric lipid vesicles, respectively. In addition, technical updates were deployed to facilitate maintainability of the package, which include porting the PRIMUS graphical interface to Qt5, updating SASpy – a PyMOL plugin to run a subset of ATSAS tools – to be both Python 2 and 3 compatible, and adding utilities to facilitate mmCIF compatibility in future ATSAS releases. All these features are implemented in ATSAS 3.0, freely available for academic users at https://www.embl-hamburg.de/biosaxs/software.html.

Molecular mass (MM) is one of the key structural parameters obtained by small-angle X-ray scattering (SAXS) of proteins in solution and is used to assess the sample quality, oligomeric composition and to guide subsequent structural modelling. Concentration-dependent assessment of MM relies on a number of extra quantities (partial specific volume, calibrated intensity, accurate solute concentration) and often yields limited accuracy. Concentration-independent methods forgo these requirements being based on the relationship between structural parameters, scattering invariants and particle volume obtained directly from the data. Using a comparative analysis on 165,982 unique scattering profiles calculated from high-resolution protein structures, the performance of multiple concentration-independent MM determination methods was assessed. A Bayesian inference approach was developed affording an accuracy above that of the individual methods, and reports MM estimates together with a credibility interval. This Bayesian approach can be used in combination with concentration-dependent MM methods to further validate the MM of proteins in solution, or as a reliable stand-alone tool in instances where an accurate concentration estimate is not available.

We describe the updated size-exclusion chromatography small angle X-ray scattering (SEC-SAXS) set-up used at the P12 bioSAXS beam line of the European Molecular Biology Laboratory (EMBL) at the PETRAIII synchrotron, DESY Hamburg (Germany). The addition of size exclusion chromatography (SEC) directly on-line to the SAXS capillary has become a well-established approach to reduce the effects of the sample heterogeneity on the SAXS measurements. The additional use of multi-angle laser light scattering (MALLS), UV absorption spectroscopy, refractive index (RI), and quasi-elastic light scattering (QELS) in parallel to the SAXS measurements enables independent molecular weight validation and hydrodynamic radius estimates. This allows one to address sample monodispersity as well as conformational heterogeneity. The benefits of the current SEC-SAXS set-up are demonstrated on a set of selected standard proteins. The processed SEC-SAXS data and models are provided in the Small Angle Scattering Biological Data Bank (SASBDB) and are labeled as “bench-marked” datasets that include the unsubtracted data frames spanning the respective SEC elution profiles and corresponding MALLS-UV-RI-QELS data. These entries provide method developers with datasets suitable for testing purposes, in addition to an educational resource for SAS data analysis and modeling.