3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources.

While scientists can often infer the biological function of proteins from their 3-dimensional quaternary structures, the gap between the number of known protein sequences and their experimentally determined structures keeps increasing. A potential solution to this problem is presented by ever more sophisticated computational protein modeling approaches. While often powerful on their own, most methods have strengths and weaknesses. Therefore, it benefits researchers to examine models from various model providers and perform comparative analysis to identify what models can best address their specific use cases. To make data from a large array of model providers more easily accessible to the broader scientific community, we established 3D-Beacons, a collaborative initiative to create a federated network with unified data access mechanisms. The 3D-Beacons Network allows researchers to collate coordinate files and metadata for experimentally determined and theoretical protein models from state-of-the-art and specialist model providers and also from the Protein Data Bank.

ATSAS 3.0: expanded functionality and new tools for small-angle scattering data analysis.

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.

SASBDB: Towards an automatically curated and validated repository for biological scattering data.

Small-angle scattering (SAS) of X-rays and neutrons is a fundamental tool to study the nanostructural properties, and in particular, biological macromolecules in solution. In structural biology, SAS recently transformed from a specialization into a general technique leading to a dramatic increase in the number of publications reporting structural models. The growing amount of data recorded and published has led to an urgent need for a global SAS repository that includes both primary data and models. In response to this, a small-angle scattering biological data bank (SASBDB) was designed in 2014 and is available for public access at www.sasbdb.org. SASBDB is a comprehensive, free and searchable repository of SAS experimental data and models deposited together with the relevant experimental conditions, sample details and instrument characteristics. SASBDB is rapidly growing, and presently has over 1,000 entries containing more than 1,600 models. We describe here the overall organization and procedures of SASBDB paying most attention to user-relevant information during submission. Perspectives of further developments, in particular, with OneDep system of the Protein Data Bank, and also widening of SASBDB including new types of data/models are discussed.