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The Biological Magnetic Resonance Data Bank (BioMagResBank or BMRB), founded in 1988, serves as the archive for data generated by nuclear magnetic resonance (NMR) spectroscopy of biological systems. NMR spectroscopy is unique among biophysical approaches in its ability to provide a broad range of atomic and higher-level information relevant to the structural, dynamic, and chemical properties of biological macromolecules, as well as report on metabolite and natural product concentrations in complex mixtures and their chemical structures. BMRB became a core member of the Worldwide Protein Data Bank (wwPDB) in 2007, and the BMRB archive is now a core archive of the wwPDB. Torin 2 ic50 Currently, about 10% of the structures deposited into the PDB archive are based on NMR spectroscopy. BMRB stores experimental and derived data from biomolecular NMR studies. Newer BMRB biopolymer depositions are divided about evenly between those associated with structure determinations (atomic coordinates and supporting information archived inevelop their own BMRB-based tools for data analysis, visualization, and manipulation of NMR-STAR formatted files. BMRB also provides users with direct access tools through the NMRbox platform.The VAST+ algorithm is an efficient, simple, and elegant solution to the problem of comparing the atomic structures of biological assemblies. Given two protein assemblies, it takes as input all the pairwise structural alignments of the component proteins. It then clusters the rotation matrices from the pairwise superpositions, with the clusters corresponding to subsets of the two assemblies that may be aligned and well superposed. It uses the Vector Alignment Search Tool (VAST) protein-protein comparison method for the input structural alignments, but other methods could be used, as well. From a chosen cluster, an "original" alignment for the assembly may be defined by simply combining the relevant input alignments. However, it is often useful to reduce/trim the original alignment, using a Monte Carlo refinement algorithm, which allows biologically relevant conformational differences to be more readily detected and observed. The method is easily extended to include RNA or DNA molecules. VAST+ results may be accessed via the URL https//www.ncbi.nlm.nih.gov/Structure , then entering a PDB accession or terms in the search box, and using the link [VAST+] in the upper right corner of the Structure Summary page.Macromolecular complexes play a key role in cellular function. Predicting the structure and dynamics of these complexes is one of the key challenges in structural biology. Docking applications have traditionally been used to predict pairwise interactions between proteins. However, few methods exist for modeling multi-protein assemblies. Here we present two methods, CombDock and DockStar, that can predict multi-protein assemblies starting from subunit structural models. CombDock can assemble subunits without any assumptions about the pairwise interactions between subunits, while DockStar relies on the interaction graph or, alternatively, a homology model or a cryo-electron microscopy (EM) density map of the entire complex. We demonstrate the two methods using RNA polymerase II with 12 subunits and TRiC/CCT chaperonin with 16 subunits.Recent improvements in cryo-electron microscopy (cryo-EM) in the past few years are now allowing to observe molecular complexes at atomic resolution. As a consequence, numerous structures derived from cryo-EM are now available in the Protein Data Bank. However, if for some complexes atomic resolution is reached, this is not true for all. This is also the case in cryo-electron tomography where the achievable resolution is still limited. Furthermore the resolution in a cryo-EM map is not a constant, with often outer regions being of lower resolution, possibly linked to conformational variability. Although those low- to medium-resolution EM maps (or regions thereof) cannot directly provide atomic structure of large molecular complexes, they provide valuable information to model the individual components and their assembly into them. Most approaches for this kind of modeling are performing rigid fitting of the individual components into the EM density map. While this would appear an obvious option, they ignore keted.Structural characterization of protein-protein interactions can provide essential details to understand biological functions at the molecular level and to facilitate their manipulation for biotechnological and biomedical purposes. Unfortunately, the 3D structure is available for only a small fraction of all possible protein-protein interactions, due to the technical limitations of high-resolution structural determination methods. In this context, low-resolution structural techniques, such as small-angle X-ray scattering (SAXS), can be combined with computational docking to provide structural models of protein-protein interactions at large scale. In this chapter, we describe the pyDockSAXS web server ( https//life.bsc.es/pid/pydocksaxs ), which uses pyDock docking and scoring to provide structural models that optimally satisfy the input SAXS data. This server, which is freely available to the scientific community, provides an automatic pipeline to model the structure of a protein-protein complex from SAXS data.Transmembrane proteins include membrane channels, pores, and receptors and, as such, comprise an important part of the proteome, yet our knowledge about them is much less complete than about soluble, globular proteins. An important aspect of transmembrane protein structure is their exact position within the lipid bilayer, a feature hard to investigate experimentally at the atomic level. Here we describe MemBlob, a novel approach utilizing difference electron density maps obtained by cryo-EM studies of transmembrane proteins. The idea behind is that the nonprotein part of such maps carries information on the exact localization of the membrane mimetics used in the experiment and can be used to extract the positional information of the protein within the membrane. MemBlob uses a structural model of the protein and an experimental electron density map to provide an estimation of the surface residues interacting with the membrane.