Inadequate Niacin Intake Disrupts Growth and Retinol Homeostasis Resulting in Higher Liver and Lower Serum Retinol Concentrations in Male Rats.

BACKGROUND: Niacin-derived nicotinamide adenine dinucleotide is an essential cofactor for many dehydrogenase enzymes involved in vitamin A (VA) metabolism. Several countries with high prevalence of VA deficiency rely on maize, a poor source of available niacin, as a dietary staple. OBJECTIVES: This study evaluated the interaction of dietary niacin on VA homeostasis using male Sprague-Dawley rats, aged 21 d (baseline body weight 88.3 ± 6.6 g). METHODS: After 1 wk of acclimation, baseline samples were collected (n = 4). Remaining rats (n = 54) were split into 9 groups to receive low tryptophan, VA-deficient feed with 3 different amounts of niacin (0, 15, or 30 mg/kg) and 3 different oral VA doses (50, 350, or 3500 nmol/d) in a 3 × 3 design. After 4 wk, the study was terminated. Serum, livers, and small intestine were analyzed for retinoids using high-performance liquid chromatography. Niacin and metabolites were evaluated with nuclear magnetic resonance. Plasma pyridoxal-P (PLP) was measured with high-performance liquid chromatography. RESULTS: Niacin intake correlated with serum retinol concentrations (r = 0.853, P < 0.001). For rats receiving the highest VA dose, liver retinol concentrations were lower in the 30-mg/kg niacin group (5.39 ± 0.27 µmol/g) than those in the 0-mg/kg and 15-mg/kg groups (9.18 ± 0.62 and 8.75 ± 0.07 µmol/g, respectively; P ≤ 0.05 for both). This phenomenon also occurred in the lower VA doses (P ≤ 0.05 for all). Growth and tissue weight at endline were associated with niacin intake (P ≤ 0.001 for all). Plasma PLP correlated with estimated niacin intake (r = 0.814, P < 0.001). CONCLUSIONS: Optimal niacin intake is associated with lower liver VA and higher serum retinol and plasma PLP concentrations. The extent to which vitamin B intake affects VA homeostasis requires further investigation to determine if the effects are maintained in humans.

ssPINE: Probabilistic Algorithm for Automated Chemical Shift Assignment of Solid-State NMR Data from Complex Protein Systems.

The heightened dipolar interactions in solids render solid-state NMR (ssNMR) spectra more difficult to interpret than solution NMR spectra. On the other hand, ssNMR does not suffer from severe molecular weight limitations like solution NMR. In recent years, ssNMR has undergone rapid technological developments that have enabled structure-function studies of increasingly larger biomolecules, including membrane proteins. Current methodology includes stable isotope labeling schemes, non-uniform sampling with spectral reconstruction, faster magic angle spinning, and innovative pulse sequences that capture different types of interactions among spins. However, computational tools for the analysis of complex ssNMR data from membrane proteins and other challenging protein systems have lagged behind those for solution NMR. Before a structure can be determined, thousands of signals from individual types of multidimensional ssNMR spectra of samples, which may have differing isotopic composition, must be recognized, correlated, categorized, and eventually assigned to atoms in the chemical structure. To address these tedious steps, we have developed an automated algorithm for ssNMR spectra called "ssPINE". The ssPINE software accepts the sequence of the protein plus peak lists from a variety of ssNMR experiments as inputs and offers automated backbone and side-chain assignments. The alpha version of ssPINE, which we describe here, is freely available through a web submission form.

POKY software tools encapsulating assignment strategies for solution and solid-state protein NMR data.

NMR spectroscopy provides structural and functional information about biomolecules and their complexes. The complexity of these systems can make the NMR data difficult to interpret, particularly for newer users of NMR technology, who may have limited understanding of the tools available and how they are used. To alleviate this problem, we have created software based on standardized workflows for both solution and solid-state NMR spectroscopy of proteins. These tools assist with manual and automated peak picking and with chemical shift assignment and validation. They provide users with an optimized path through spectral analysis that can help them perform the necessary tasks more efficiently.

Coordination of Di-Acetylated Histone Ligands by the ATAD2 Bromodomain.

The ATPase Family, AAA domain-containing protein 2 (ATAD2) bromodomain (BRD) has a canonical bromodomain structure consisting of four α-helices. ATAD2 functions as a co-activator of the androgen and estrogen receptors as well as the MYC and E2F transcription factors. ATAD2 also functions during DNA replication, recognizing newly synthesized histones. In addition, ATAD2 is shown to be up-regulated in multiple forms of cancer including breast, lung, gastric, endometrial, renal, and prostate. Furthermore, up-regulation of ATAD2 is strongly correlated with poor prognosis in many types of cancer, making the ATAD2 bromodomain an innovative target for cancer therapeutics. In this study, we describe the recognition of histone acetyllysine modifications by the ATAD2 bromodomain. Residue-specific information on the complex formed between the histone tail and the ATAD2 bromodomain, obtained through nuclear magnetic resonance spectroscopy (NMR) and X-ray crystallography, illustrates key residues lining the binding pocket, which are involved in coordination of di-acetylated histone tails. Analytical ultracentrifugation, NMR relaxation data, and isothermal titration calorimetry further confirm the monomeric state of the functionally active ATAD2 bromodomain in complex with di-acetylated histone ligands. Overall, we describe histone tail recognition by ATAD2 BRD and illustrate that one acetyllysine group is primarily engaged by the conserved asparagine (N1064), the "RVF" shelf residues, and the flexible ZA loop. Coordination of a second acetyllysine group also occurs within the same binding pocket but is essentially governed by unique hydrophobic and electrostatic interactions making the di-acetyllysine histone coordination more specific than previously presumed.

The Protein Data Bank Archive.

Protein Data Bank is the single worldwide archive of experimentally determined macromolecular structure data. Established in 1971 as the first open access data resource in biology, the PDB archive is managed by the worldwide Protein Data Bank (wwPDB) consortium which has four partners-the RCSB Protein Data Bank (RCSB PDB; rcsb.org), the Protein Data Bank Japan (PDBj; pdbj.org), the Protein Data Bank in Europe (PDBe; pdbe.org), and BioMagResBank (BMRB; www.bmrb.wisc.edu ). The PDB archive currently includes ~175,000 entries. The wwPDB has established a number of task forces and working groups that bring together experts form the community who provide recommendations on improving data standards and data validation for improving data quality and integrity. The wwPDB members continue to develop the joint deposition, biocuration, and validation system (OneDep) to improve data quality and accommodate new data from emerging techniques such as 3DEM. Each PDB entry contains coordinate model and associated metadata for all experimentally determined atomic structures, experimental data for the traditional structure determination techniques (X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy), validation reports, and additional information on quaternary structures. The wwPDB partners are committed to following the FAIR (Findability, Accessibility, Interoperability, and Reproducibility) principles and have implemented a DOI resolution mechanism that provides access to all the relevant files for a given PDB entry. On average, >250 new entries are added to the archive every week and made available by each wwPDB partner via FTP area. The wwPDB partner sites also develop data access and analysis tools and make these available via their websites. wwPDB continues to work with experts in the community to establish a federation of archives for archiving structures determined using integrative/hybrid method where multiple experimental techniques are used.

iPick: Multiprocessing software for integrated NMR signal detection and validation.

Peak picking is a critical step in biomolecular NMR spectroscopy. The program, iPick, presented here provides a scripting tool and a graphical user interface (GUI), which allow the user to perform interactive and intuitive peak picking and validation. The click-and-run GUI requires no computer programming skills, while the scripting tool can be used by more advanced users to customize the application. If used with a multi-core CPU, the multiprocessing feature of iPick reduces the processing time significantly by invoking parallel computing. The GUI is a plugin, compatible with the popular NMRFAM-SPARKY software package and its newly released successor, the POKY software. Features implemented in iPick include automated noise level detection and threshold setting, cross-validation against multiple spectra, and a method for quantifying peak reliability. The iPick software is cross-platform, open-source, and freely available from https://github.com/pokynmr/ipick.

CHESPA/CHESCA-SPARKY: automated NMR data analysis plugins for SPARKY to map protein allostery.

MOTIVATION: Correlated Nuclear Magnetic Resonance (NMR) chemical shift changes identified through the CHEmical Shift Projection Analysis (CHESPA) and CHEmical Shift Covariance Analysis (CHESCA) reveal pathways of allosteric transitions in biological macromolecules. To address the need for an automated platform that implements CHESPA and CHESCA and integrates them with other NMR analysis software packages, we introduce here integrated plugins for NMRFAM-SPARKY that implement the seamless detection and visualization of allosteric networks. AVAILABILITY AND IMPLEMENTATION: CHESCA-SPARKY and CHESPA-SPARKY are available in the latest version of NMRFAM-SPARKY from the National Magnetic Resonance Facility at Madison (http://pine.nmrfam.wisc.edu/download_packages.html), the NMRbox Project (https://nmrbox.org) and to subscribers to the SBGrid (https://sbgrid.org). The assigned spectra involved in this study and tutorial videos using this dataset are available at https://sites.google.com/view/chescachespa-sparky. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Online.

Structural Insights into the Recognition of Mono- and Diacetylated Histones by the ATAD2B Bromodomain.

Bromodomains exhibit preferences for specific patterns of post-translational modifications on core and variant histone proteins. We examined the ligand specificity of the ATAD2B bromodomain and compared it to its closely related paralogue in ATAD2. We show that the ATAD2B bromodomain recognizes mono- and diacetyllysine modifications on histones H4 and H2A. A structure-function approach was used to identify key residues in the acetyllysine-binding pocket that dictate the molecular recognition process, and we examined the binding of an ATAD2 bromodomain inhibitor by ATAD2B. Our analysis demonstrated that critical contacts required for bromodomain inhibitor coordination are conserved between the ATAD2/B bromodomains, with many residues playing a dual role in acetyllysine recognition. We further characterized an alternative splice variant of ATAD2B that results in a loss of function. Our results outline the structural and functional features of the ATAD2B bromodomain and identify a novel mechanism regulating the interaction of the ATAD2B protein with chromatin.

Uncovering a membrane-distal conformation of KRAS available to recruit RAF to the plasma membrane.

The small GTPase KRAS is localized at the plasma membrane where it functions as a molecular switch, coupling extracellular growth factor stimulation to intracellular signaling networks. In this process, KRAS recruits effectors, such as RAF kinase, to the plasma membrane where they are activated by a series of complex molecular steps. Defining the membrane-bound state of KRAS is fundamental to understanding the activation of RAF kinase and in evaluating novel therapeutic opportunities for the inhibition of oncogenic KRAS-mediated signaling. We combined multiple biophysical measurements and computational methodologies to generate a consensus model for authentically processed, membrane-anchored KRAS. In contrast to the two membrane-proximal conformations previously reported, we identify a third significantly populated state using a combination of neutron reflectivity, fast photochemical oxidation of proteins (FPOP), and NMR. In this highly populated state, which we refer to as "membrane-distal" and estimate to comprise ∼90% of the ensemble, the G-domain does not directly contact the membrane but is tethered via its C-terminal hypervariable region and carboxymethylated farnesyl moiety, as shown by FPOP. Subsequent interaction of the RAF1 RAS binding domain with KRAS does not significantly change G-domain configurations on the membrane but affects their relative populations. Overall, our results are consistent with a directional fly-casting mechanism for KRAS, in which the membrane-distal state of the G-domain can effectively recruit RAF kinase from the cytoplasm for activation at the membrane.

Probabilistic identification of saccharide moieties in biomolecules and their protein complexes.

The chemical composition of saccharide complexes underlies their biomedical activities as biomarkers for cardiometabolic disease, various types of cancer, and other conditions. However, because these molecules may undergo major structural modifications, distinguishing between compounds of saccharide and non-saccharide origin becomes a challenging computational problem that hinders the aggregation of information about their bioactive moieties. We have developed an algorithm and software package called "Cheminformatics Tool for Probabilistic Identification of Carbohydrates" (CTPIC) that analyzes the covalent structure of a compound to yield a probabilistic measure for distinguishing saccharides and saccharide-derivatives from non-saccharides. CTPIC analysis of the RCSB Ligand Expo (database of small molecules found to bind proteins in the Protein Data Bank) led to a substantial increase in the number of ligands characterized as saccharides. CTPIC analysis of Protein Data Bank identified 7.7% of the proteins as saccharide-binding. CTPIC is freely available as a webservice at (http://ctpic.nmrfam.wisc.edu).

The cytotoxicity of gallium maltolate in glioblastoma cells is enhanced by metformin through combined action on mitochondrial complex 1.

New drugs are needed for glioblastoma, an aggressive brain tumor with a dismal prognosis. We recently reported that gallium maltolate (GaM) retards the growth of glioblastoma in a rat orthotopic brain tumor model by inhibiting mitochondrial function and iron-dependent ribonucleotide reductase (RR). However, GaM's mechanism of action at the mitochondrial level is not known. Given the interaction between gallium and iron metabolism, we hypothesized that gallium might target iron-sulfur (Fe-S) cluster-containing mitochondrial proteins. Using Extracellular Flux Analyzer technology, we confirmed that after a 24-h incubation, GaM 50 µmol/L inhibited glioblastoma cell growth by <10% but inhibited cellular oxygen consumption rate by 44% and abrogated mitochondrial reserve capacity. GaM blocked mitochondrial complex I activity and produced a 2.9-fold increase in cellular ROS. NMR spectroscopy revealed that gallium binds to IscU, the bacterial scaffold protein for Fe-S cluster assembly and stabilizes its folded state. Gallium inhibited the rate of in vitro cluster assembly catalyzed by bacterial cysteine desulfurase in a reaction mixture containing IscU, Fe (II), DTT, and L-cysteine. Metformin, a complex I inhibitor, enhanced GaM's inhibition of complex I, further increased cellular ROS levels, and synergistically enhanced GaM's cytotoxicity in glioblastoma cells in 2-D and 3-D cultures. Metformin did not affect GaM action on cellular iron uptake or transferrin receptor1 expression nor did it enhance the cytotoxicity of the RR inhibitor Didox. Our results show that GaM inhibits complex I by disrupting iron-sulfur cluster assembly and that its cytotoxicity can be synergistically enhanced by metformin through combined action on complex I.

Fragment screening targeting Ebola virus nucleoprotein C-terminal domain identifies lead candidates.

The Ebola Virus is a causative agent of viral hemorrhagic fever outbreaks and a potential global health risk. The outbreak in West Africa (2013-2016) led to 11,000+ deaths and 30,000+ Ebola infected individuals. The current outbreak in the Democratic Republic of Congo (DRC) with 3000+ confirmed cases and 2000+ deaths attributed to Ebola virus infections provides a reminder that innovative countermeasures are still needed. Ebola virus encodes 7 open reading frames (ORFs). Of these, the nucleocapsid protein (eNP) encoded by the first ORF plays many significant roles, including a role in viral RNA synthesis. Here we describe efforts to target the C-terminal domain of eNP (eNP-CTD) that contains highly conserved residues 641-739 as a pan-Ebola antiviral target. Interactions of eNP-CTD with Ebola Viral Protein 30 (eVP30) and Viral Protein 40 (eVP40) have been shown to be crucial for viral RNA synthesis, virion formation, and virion transport. We used nuclear magnetic response (NMR)-based methods to screened the eNP-CTD against a fragment library. Perturbations of 1D 1H NMR spectra identified of 48 of the 439 compounds screened as potential eNP CTD interactors. Subsequent analysis of these compounds to measure chemical shift perturbations in 2D 1H,15N NMR spectra of 15N-labeled protein identified six with low millimolar affinities. All six perturbed an area consisting mainly of residues at or near the extreme C-terminus that we named "Site 1" while three other sites were perturbed by other compounds. Our findings here demonstrate the potential utility of eNP as a target, several fragment hits, and provide an experimental pipeline to validate viral-viral interactions as potential panfiloviral inhibitor targets.

ISCU interacts with NFU1, and ISCU[4Fe-4S] transfers its Fe-S cluster to NFU1 leading to the production of holo-NFU1.

NFU1 is a late-acting factor in the biogenesis of human mitochondrial iron-sulfur proteins. Mutations in NFU1 are associated with genetic diseases such as multiple mitochondrial dysfunctions syndrome 1 (MMDS1) that involve defects in mitochondrial [4Fe-4S] proteins. We present results from NMR spectroscopy, small angle X-ray scattering, size exclusion chromatography, and isothermal titration calorimetry showing that the structured conformer of human ISCU binds human NFU1. The dissociation constant determined by ITC is Kd = 1.1 ± 0.2 µM. NMR and SAXS studies led to a structural model for the complex in which the cluster binding region of ISCU interacts with two α-helices in the C-terminal domain of NFU1. In vitro experiments demonstrate that ISCU[4Fe-4S] transfers its Fe-S cluster to apo-NFU1, in the absence of a chaperone, leading to the assembly of holo-NFU1. By contrast, the cluster of ISCU[2Fe-2S] remains bound to ISCU in the presence of apo-NFU1.

BioMagResBank (BMRB) as a Resource for Structural Biology.

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. 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 in the PDB) and those reporting experimental information on molecular dynamics, conformational transitions, ligand binding, assigned chemical shifts, or other results from NMR spectroscopy. BMRB also provides resources for NMR studies of metabolites and other small molecules that are often macromolecular ligands and/or nonstandard residues. This chapter is directed to the structural biology community rather than the metabolomics and natural products community. Our goal is to describe various BMRB services offered to structural biology researchers and how they can be accessed and utilized. These services can be classified into four main groups: (1) data deposition, (2) data retrieval, (3) data analysis, and (4) services for NMR spectroscopists and software developers. The chapter also describes the NMR-STAR data format used by BMRB and the tools provided to facilitate its use. For programmers, BMRB offers an application programming interface (API) and libraries in the Python and R languages that enable users to develop 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.

Metabolic Changes in Synaptosomes in an Animal Model of Schizophrenia Revealed by 1H and 1H,13C NMR Spectroscopy.

Synaptosomes are isolated nerve terminals that contain synaptic components, including neurotransmitters, metabolites, adhesion/fusion proteins, and nerve terminal receptors. The essential role of synaptosomes in neurotransmission has stimulated keen interest in understanding both their proteomic and metabolic composition. Mass spectrometric (MS) quantification of synaptosomes has illuminated their proteomic composition, but the determination of the metabolic composition by MS has been met with limited success. In this study, we report a proof-of-concept application of one- and two-dimensional nuclear magnetic resonance (NMR) spectroscopy for analyzing the metabolic composition of synaptosomes. We utilize this approach to compare the metabolic composition synaptosomes from a wild-type rat with that from a newly generated genetic rat model (Disc1 svΔ2), which qualitatively recapitulates clinically observed early DISC1 truncations associated with schizophrenia. This study demonstrates the feasibility of using NMR spectroscopy to identify and quantify metabolites within synaptosomal fractions.

PISA-SPARKY: an interactive SPARKY plugin to analyze oriented solid-state NMR spectra of helical membrane proteins.

MOTIVATION: Two-dimensional [15N-1H] separated local field solid-state nuclear magnetic resonance (NMR) experiments of membrane proteins aligned in lipid bilayers provide tilt and rotation angles for α-helical segments using Polar Index Slant Angle (PISA)-wheel models. No integrated software has been made available for data analysis and visualization. RESULTS: We have developed the PISA-SPARKY plugin to seamlessly integrate PISA-wheel modeling into the NMRFAM-SPARKY platform. The plugin performs basic simulations, exhaustive fitting against experimental spectra, error analysis and dipolar and chemical shift wave plotting. The plugin also supports PyMOL integration and handling of parameters that describe variable alignment and dynamic scaling encountered with magnetically aligned media, ensuring optimal fitting and generation of restraints for structure calculation. AVAILABILITY AND IMPLEMENTATION: PISA-SPARKY is freely available in the latest version of NMRFAM-SPARKY from the National Magnetic Resonance Facility at Madison (http://pine.nmrfam.wisc.edu/download_packages.html), the NMRbox Project (https://nmrbox.org) and to subscribers of the SBGrid (https://sbgrid.org). The pisa.py script is available and documented on GitHub (https://github.com/weberdak/pisa.py) along with a tutorial video and sample data. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Templated Collagen "Double Helices" Maintain Their Structure.

The self-assembly of collagen-mimetic peptides (CMPs) that form sticky-ended triple helices has allowed the production of surprisingly stable artificial collagen fibers and hydrogels. Assembly through sticky ends requires the recognition of a single strand by a templated strand dimer. Although CMPs and their triple helices have been studied extensively, the structure of a strand dimer is unknown. Here, we evaluate the physical characteristics of such dimers, using disulfide-templated (PPG)10 dimers as a model. Such "linked-dimers" retain their collagen-like structure even in the absence of a third strand, but only when their strands are capable of adopting a triple-helical fold. The intrinsic collagen-like structure of templated CMP pairs helps to explain the success of sticky-ended CMP association and changes the conception of new synthetic collagen designs.

The BRPF1 bromodomain is a molecular reader of di-acetyllysine.

Bromodomain-containing proteins are often part of chromatin-modifying complexes, and their activity can lead to altered expression of genes that drive cancer, inflammation and neurological disorders in humans. Bromodomain-PHD finger protein 1 (BRPF1) is part of the MOZ (monocytic leukemic zinc-finger protein) HAT (histone acetyltransferase) complex, which is associated with chromosomal translocations known to contribute to the development of acute myeloid leukemia (AML). BRPF1 contains a unique combination of chromatin reader domains including two plant homeodomain (PHD) fingers separated by a zinc knuckle (PZP domain), a bromodomain, and a proline-tryptophan-tryptophan-proline (PWWP) domain. BRPF1 is known to recruit the MOZ HAT complex to chromatin by recognizing acetylated lysine residues on the N-terminal histone tail region through its bromodomain. However, histone proteins can contain several acetylation modifications on their N-terminus, and it is unknown how additional marks influence bromodomain recruitment to chromatin. Here, we identify the BRPF1 bromodomain as a selective reader of di-acetyllysine modifications on histone H4. We used ITC assays to characterize the binding of di-acetylated histone ligands to the BRPF1 bromodomain and found that the domain binds preferentially to histone peptides H4K5acK8ac and H4K5acK12ac. Analytical ultracentrifugation (AUC) experiments revealed that the monomeric state of the BRPF1 bromodomain coordinates di-acetylated histone ligands. NMR chemical shift perturbation studies, along with binding and mutational analyses, revealed non-canonical regions of the bromodomain-binding pocket that are important for histone tail recognition. Together, our findings provide critical information on how the combinatorial action of post-translational modifications can modulate BRPF1 bromodomain binding and specificity.

Solution structure of human myeloid-derived growth factor suggests a conserved function in the endoplasmic reticulum.

Human myeloid-derived growth factor (hMYDGF) is a 142-residue protein with a C-terminal endoplasmic reticulum (ER) retention sequence (ERS). Extracellular MYDGF mediates cardiac repair in mice after anoxic injury. Although homologs of hMYDGF are found in eukaryotes as distant as protozoans, its structure and function are unknown. Here we present the NMR solution structure of hMYDGF, which consists of a short α-helix and ten ß-strands distributed in three ß-sheets. Conserved residues map to the unstructured ERS, loops on the face opposite the ERS, and the surface of a cavity underneath the conserved loops. The only protein or portion of a protein known to have a similar fold is the base domain of VNN1. We suggest, in analogy to the tethering of the VNN1 nitrilase domain to the plasma membrane via its base domain, that MYDGF complexed to the KDEL receptor binds cargo via its conserved residues for transport to the ER.

Federating Structural Models and Data: Outcomes from A Workshop on Archiving Integrative Structures.

Structures of biomolecular systems are increasingly computed by integrative modeling. In this approach, a structural model is constructed by combining information from multiple sources, including varied experimental methods and prior models. In 2019, a Workshop was held as a Biophysical Society Satellite Meeting to assess progress and discuss further requirements for archiving integrative structures. The primary goal of the Workshop was to build consensus for addressing the challenges involved in creating common data standards, building methods for federated data exchange, and developing mechanisms for validating integrative structures. The summary of the Workshop and the recommendations that emerged are presented here.