Many computational algorithms devoted to the interpretation and modeling of small angle scattering (SAS) data have been developed over the last several decades. In addition to the commonly used ASCII text files containing fits to data, real space transforms, modeling parameters, etc., modeling algorithms often generate coordinate files containing 3D coordinates of atomic or coarse-grained models to describe the object. Due to their versatility and community acceptance, coordinate files have become popular for representing models from a variety of different algorithms including bead modeling, rigid body modeling, ensemble modeling, flexible fitting, molecular dynamics, etc. and have found wide spread adoption in the SAS community. As novel algorithms are developed, new representations of particles are often required that may not be compatible with conventional coordinate models. Here I will describe the program DENSS¹ which generates low-resolution 3D density maps from 1D solution scattering data using a novel ab initio reconstruction algorithm. The primary output of DENSS is an MRC file, commonly used in the electron microscopy community (and similar to the CCP4 format used in crystallography), which represents objects on a 3D grid of voxels where each voxel has a value corresponding to the density at that location. DENSS offers advantages over conventional algorithms that are implicit to its use of density to represent particles. Accurate and unbiased interpretation of a density map requires understanding how visualization programs graphically represent the 3D grid of values and how the low-resolution nature of the reconstruction affects this visualization. This includes tasks such as selecting appropriate contour thresholds and how to accurately and unbiasedly display such low-resolution density maps in publication figures and archives. Community engagement in this area will help to generate a set of standards for accurately publishing low-resolution density maps to avoid overinterpretation, as has previously been done for validation of conventional SAS models².

Proteins generally populate multiple structural states in solution. Transitions between these states are important for function, such as allosteric signaling and enzyme catalysis. Structures solved by X-ray crystallography provide valuable, but static, atomic resolution structural information. In contrast, cross-linking mass spectrometry (XLMS) and small angle X-ray scattering (SAXS) datasets contain information about conformational and compositional states of the system. The challenge lies in the data interpretation since the cross-links in the data often comes from multiple structural states. We have developed a novel computational method that simultaneously uncovers the set of structural states that are consistent with a given dataset (XLMS or SAXS). The input is a single atomic structure, a list of flexible residues, and an experimental dataset. The method finds multi-state models (models that specify two or more co-existing structural states) that are consistent with the data. The method was applied on multiple SAXS and XLMS datasets, including large multi-domain proteins and proteins with long disordered fragments. The applicability of the method extends to other datasets, such as 2D class averages from Electron Microscopy, and residual dipolar couplings.