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Recent advances in the field of in-cell NMR spectroscopy have made it possible to study proteins in the context of bacterial or mammalian cell extracts or even entire cells. As most mammalian cells are part of a multi-cellular complex, there is a need to develop novel NMR approaches enabling the study of proteins within the complexity of a 3D cellular environment. Here we investigate the use of the hanging drop method to grow spheroids which are homogenous in size and shape as a model system to study solid tumors using solid-state NMR (ssNMR) spectroscopy. We find that these spheroids are stable under magic-angle-spinning conditions and show a clear change in metabolic profile as compared to single cell preparations. Finally, we utilize dynamic nuclear polarization (DNP)-supported ssNMR measurements to show that low concentrations of labelled nanobodies targeting EGFR (7D12) can be detected inside the spheroids. These findings suggest that solid-state NMR can be used to directly examine proteins or other biomolecules in a 3D cellular microenvironment with potential applications in pharmacological research.

In recent years, the use of integrative, information-driven computational approaches for modeling the structure of biomolecules has been increasing in popularity. These are now recognized as a crucial complement to experimental structural biology techniques such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy and cryo-electron microscopy (cryo-EM). This trend can be credited to a few reasons such as the increased prominence of structures solved by cryo-EM, the improvements in proteomics approaches such as cross-linking mass spectrometry (XL-MS), the drive to study systems of higher complexity in their native state, and the maturation of many computational techniques combined with the widespread availability of information-driven integrative modeling platforms. In this review, we highlight recent works that exemplify how the use of integrative and/or information-driven approaches and platforms can produce highly accurate structural models. These examples include systems which present many challenges when studied with traditional structural biology techniques such as flexible and dynamic macromolecular assemblies and membrane-associated complexes. We also identify some key areas of interest for information-driven, integrative modeling and discuss how they relate to ongoing challenges in the fields of computational structural biology. These include the use of coarse-grained force fields for biomolecular simulations—allowing for simulations across longer (time-) and bigger (size-dimension) scales—the use of bioinformatics predictions to drive sampling and/or scoring in docking such as those derived from coevolution analysis and finally the study of membrane and membrane-associated protein complexes.

Contains fulltext : 220863.pdf (Publisher’s version ) (Open Access) Computational docking is a promising tool to model three-dimensional (3D) structures of protein–protein complexes, which provides fundamental insights of protein functions in the cellular life. Singling out near-native models from the huge pool of generated docking models (referred to as the scoring problem) remains as a major challenge in computational docking. We recently published iScore, a novel graph kernel based scoring function. iScore ranks docking models based on their interface graph similarities to the training interface graph set. iScore uses a support vector machine approach with random-walk graph kernels to classify and rank protein–protein interfaces. Here, we present the software for iScore. The software provides executable scripts that fully automate the computational workflow. In addition, the creation and analysis of the interface graph can be distributed across different processes using Message Passing interface (MPI) and can be offloaded to GPUs thanks to dedicated CUDA kernels.

The rising incidence of antibiotic-resistant lung infections has instigated a much-needed search for new therapeutic strategies. One proposed strategy is the use of exogenous surfactants to deliver antimicrobial peptides (AMPs), like CATH-2, to infected regions of the lung. CATH-2 can kill bacteria through a diverse range of antibacterial pathways and exogenous surfactant can improve pulmonary drug distribution. Unfortunately, mixing AMPs with commercially available exogenous surfactants has been shown to negatively impact their antimicrobial function. It was hypothesized that the phosphatidylglycerol component of surfactant was inhibiting AMP function and that an exogenous surfactant, with a reduced phosphatidylglycerol composition would increase peptide mediated killing at a distal site. To better understand how surfactant lipids interacted with CATH-2 and affected its function, isothermal titration calorimetry and solid-state nuclear magnetic resonance spectroscopy as well as bacterial killing curves against Pseudomonas aeruginosa were utilized. Additionally, the wet bridge transfer system was used to evaluate surfactant spreading and peptide transport. Phosphatidylglycerol was the only surfactant lipid to significantly inhibit CATH-2 function, showing a stronger electrostatic interaction with the peptide than other lipids. Although diluting the phosphatidylglycerol content in an existing surfactant, through the addition of other lipids, significantly improved peptide function and distal killing, it also reduced surfactant spreading. A synthetic phosphatidylglycerol-free surfactant however, was shown to further improve CATH-2 delivery and function at a remote site. Based on these in vitro experiments synthetic phosphatidylglycerol-free surfactants seem optimal for delivering AMPs to the lung.

Complexes between proteins are at the basis of almost every process in cells. Their study, from a structural perspective, has a pivotal role in understanding biological functions and, importantly, in drug development. X-ray crystallography represents the broadest source for the experimental structural characterization of protein-protein complexes. Correctly identifying the biologically relevant interface from the crystallographic ones is, however, not trivial and can be prone to errors. Over the past two decades, computational methodologies have been developed to study the differences of those interfaces and automatically classify them as biological or crystallographic. Overall, protein-protein interfaces show differences in terms of composition, energetics and evolutionary conservation between biological and crystallographic ones. Based on those observations, a number of computational methods have been developed for this classification problem, which can be grouped into three main categories: Energy-, empirical knowledge- and machine learning-based approaches. In this review, we give a comprehensive overview of the training datasets and methods so far implemented, providing useful links and a brief description of each method.

We report the performance of our newly introduced Ensemble Docking with Enhanced sampling of pocket Shape (EDES) protocol coupled to a template-based algorithm to generate near-native ligand conformations in the 2019 iteration of the Grand Challenge organized by the D3R consortium. Using either AutoDock4.2 or HADDOCK2.2 docking programs (each software in two variants of the protocol) our method generated native-like poses among the top 5 submitted for evaluation for most of the 20 targets with similar performances. The protein selected for GC4 was the human beta-site amyloid precursor protein cleaving enzyme 1 (BACE-1), a transmembrane aspartic-acid protease. We identified at least one pose whose heavy-atoms RMSD was less than 2.5 {\AA} from the native conformation for 16 (80%) and 17 (85%) of the twenty targets using AutoDock and HADDOCK, respectively. Dissecting the possible sources of errors revealed that: i) our EDES protocol (with minor modifications) was able to sample sub-{\aa}ngstrom conformations for all 20 protein targets, reproducing the correct conformation of the binding site within ~1 {\AA} RMSD; ii) as already shown by some of us in GC3, even in the presence of near-native protein structures, a proper selection of ligand conformers is crucial for the success of ensemble-docking calculations. Importantly, our approach performed best among the protocols exploiting only structural information of the apo protein to generate conformations of the receptor for ensemble-docking calculations.

Motivation Monoclonal antibodies are essential tools in the contemporary therapeutic armory. Understanding how these recognize their antigen is a fundamental step in their rational design and engineering. The rising amount of publicly available data is catalyzing the development of computational approaches able to offer valuable, faster and cheaper alternatives to classical experimental methodologies used for the study of antibody–antigen complexes. Results Here, we present proABC-2, an update of the original random-forest antibody paratope predictor, based on a convolutional neural network algorithm. We also demonstrate how the predictions can be fruitfully used to drive the docking in HADDOCK. Availability and implementation The proABC-2 server is freely available at: https://wenmr.science.uu.nl/proabc2/. Supplementary information Supplementary data are available at Bioinformatics online.

Structural bioinformatics provides the scientific methods and tools to analyse, archive, validate, and present the biomolecular structure data generated by the structural biology community. It also provides an important link with the genomics community, as structural bioinformaticians also use the extensive sequence data to predict protein structures and their functional sites. A very broad and active community of structural bioinformaticians exists across Europe, and 3D-Bioinfo will establish formal platforms to address their needs and better integrate their activities and initiatives. Our mission will be to strengthen the ties with the structural biology research communities in Europe covering life sciences, as well as chemistry and physics and to bridge the gap between these researchers in order to fully realize the potential of structural bioinformatics. Our Community will also undertake dedicated educational, training and outreach efforts to facilitate this, bringing new insights and thus facilitating the development of much needed innovative applications e.g. for human health, drug and protein design. Our combined efforts will be of critical importance to keep the European research efforts competitive in this respect. Here we highlight the major European contributions to the field of structural bioinformatics, the most pressing challenges remaining and how Europe-wide interactions, enabled by ELIXIR and its platforms, will help in addressing these challenges and in coordinating structural bioinformatics resources across Europe. In particular, we present recent activities and future plans to consolidate an ELIXIR 3D-Bioinfo Community in structural bioinformatics and propose means to develop better links across the community. These include building new consortia, organising workshops to establish data standards and seeking community agreement on benchmark data sets and strategies. We also highlight existing and planned collaborations with other ELIXIR Communities and other European infrastructures, such as the structural biology community supported by Instruct-ERIC, with whom we have synergies and overlapping common interests.

The authors gratefully acknowledge financial support of NWO, the Smart Mix Program of the Netherlands Ministry of Economic Affairs and the Netherlands Ministry of Education, Culture and Science. The NWO Large grant 175.107.301.10 is also gratefully acknowledged. Kraft lignin, the main by-product of the pulping industry, is an abundant, yet highly underutilized renewable aromatic polymer. During kraft pulping, the lignin undergoes extensive structural modification, with many labile native bonds being replaced by new, more recalcitrant ones. Currently little is known about the nature of those bonds and linkages in kraft lignin, information that is essential for its efficient valorization to renewable fuels, materials or chemicals. Here, we provide detailed new insights into the structure of softwood kraft lignin, identifying and quantifying the major native as well as kraft pulping-derived units as a function of molecular weight. De novo synthetic kraft lignins, generated from (isotope labelled) dimeric and advanced polymeric models, provided key mechanistic understanding of kraft lignin formation, revealing different process dependent reaction pathways to be operating. The discovery of a novel kraft-derived lactone condensation product proved diagnostic for the identification of a previously unknown homovanillin based condensation pathway. The lactone marker is found in various different soft- and hardwood kraft lignins, suggesting the general pertinence of this new condensation mechanism for kraft pulping. These novel structural and mechanistic insights will aid the development of future biomass and lignin valorization technologies. Publisher PDF

Summary Distinguishing biologically relevant interfaces from crystallographic ones in biological complexes is fundamental in order to associate cellular functions to the correct macromolecular assemblies. Recently, we described a detailed study reporting the differences in the type of intermolecular residue–residue contacts between biological and crystallographic interfaces. Our findings allowed us to develop a fast predictor of biological interfaces reaching an accuracy of 0.92 and competitive to the current state of the art. Here we present its web-server implementation, PRODIGY-CRYSTAL, aimed at the classification of biological and crystallographic interfaces. PRODIGY-CRYSTAL has the advantage of being fast, accurate and simple. This, together with its user-friendly interface and user support forum, ensures its broad accessibility. Availability and implementation PRODIGY-CRYSTAL is freely available without registration requirements at https://haddock.science.uu.nl/services/PRODIGY-CRYSTAL.

Elucidating the reaction mechanism of a catalytic process is very challenging. Now, advanced solid-state nuclear magnetic resonance experiments demonstrate the importance of oxygenates to regulate the conversion of synthesis gas over an oxide–zeolite-based bifunctional catalyst material.

The Notch signaling system links cellular fate to that of its neighbors, driving proliferation, apoptosis, and cell differentiation in metazoans, whereas dysfunction leads to debilitating developmental disorders and cancers. Other than a five-by-five domain complex, it is unclear how the 40 extracellular domains of the Notch1 receptor collectively engage the 19 domains of its canonical ligand, Jagged1, to activate Notch1 signaling. Here, using cross-linking mass spectrometry (XL-MS), biophysical, and structural techniques on the full extracellular complex and targeted sites, we identify five distinct regions, two on Notch1 and three on Jagged1, that form an interaction network. The Notch1 membrane-proximal regulatory region individually binds to the established Notch1 epidermal growth factor (EGF) 8-EGF13 and Jagged1 C2-EGF3 activation sites as well as to two additional Jagged1 regions, EGF8-EGF11 and cysteine-rich domain. XL-MS and quantitative interaction experiments show that the three Notch1-binding sites on Jagged1 also engage intramolecularly. These interactions, together with Notch1 and Jagged1 ectodomain dimensions and flexibility, determined by small-angle X-ray scattering, support the formation of nonlinear architectures. Combined, the data suggest that critical Notch1 and Jagged1 regions are not distal but engage directly to control Notch1 signaling, thereby redefining the Notch1-Jagged1 activation mechanism and indicating routes for therapeutic applications.

The synthesis of many industrial bulk and fine chemicals frequently involves electrophilic aromatic substitution (SEAr) reactions. The most widely practiced example of the SEAr mechanism is the zeolite-catalysed ethylation of benzene, using ethylene as an alkylating agent. However, the current production route towards ethylbenzene is completely dependent on fossil resources, making the recent commercial successes in the zeolite-catalysed benzene ethylation process using bioethanol (instead of ethylene) very encouraging and noteworthy. Unfortunately, there is no information available on the reaction mechanism of this alternative synthesis route. Here, by employing a combination of advanced solid-state NMR spectroscopy and operando UV-Vis diffuse reflectance spectroscopy with on-line mass spectrometry, we have obtained detailed mechanistic insights into the bioethanol-mediated benzene ethylation process through the identification of active surface ethoxy species, surface-adsorbed zeolite–aromatic π-complexes, as well as the more controversial Wheland-type σ-complex. Moreover, we distinguish between rigid and mobile zeolite-trapped organic species, providing further evidence for distinctive host–guest chemistry during catalysis.

The nucleotide excision repair protein complex ERCC1-XPF is required for incision of DNA upstream of DNA damage. Functional studies have provided insights into the binding of ERCC1-XPF to various DNA substrates. However, because no structure for the ERCC1-XPF-DNA complex has been determined, the mechanism of substrate recognition remains elusive. Here we biochemically characterize the substrate preferences of the helix-hairpin-helix (HhH) domains of XPF and ERCC-XPF and show that the binding to single-stranded DNA (ssDNA)/dsDNA junctions is dependent on joint binding to the DNA binding domain of ERCC1 and XPF. We reveal that the homodimeric XPF is able to bind various ssDNA sequences but with a clear preference for guanine-containing substrates. NMR titration experiments and in vitro DNA binding assays also show that, within the heterodimeric ERCC1-XPF complex, XPF specifically recognizes ssDNA. On the other hand, the HhH domain of ERCC1 preferentially binds dsDNA through the hairpin region. The two separate non-overlapping DNA binding domains in the ERCC1-XPF heterodimer jointly bind to an ssDNA/dsDNA substrate and, thereby, at least partially dictate the incision position during damage removal. Based on structural models, NMR titrations, DNA-binding studies, site-directed mutagenesis, charge distribution, and sequence conservation, we propose that the HhH domain of ERCC1 binds to dsDNA upstream of the damage, and XPF binds to the non-damaged strand within a repair bubble.

Vγ9Vδ2 T cell activation plays an important role in antitumor and antimicrobial immune responses. However, there are conditions in which Vγ9Vδ2 T cell activation can be considered inappropriate for the host. Patients treated with aminobisphosphonates for hypercalcemia or metastatic bone disease often present with a debilitating acute phase response as a result of Vγ9Vδ2 T cell activation. To date, no agents are available that can clinically inhibit Vγ9Vδ2 T cell activation. In this study, we describe the identification of a single domain Ab fragment directed to the TCR of Vγ9Vδ2 T cells with neutralizing properties. This variable domain of an H chain–only Ab (VHH or nanobody) significantly inhibited both phosphoantigen-dependent and -independent activation of Vγ9Vδ2 T cells and, importantly, strongly reduced the production of inflammatory cytokines upon stimulation with aminobisphosphonate-treated cells. Additionally, in silico modeling suggests that the neutralizing VHH binds the same residues on the Vγ9Vδ2 TCR as the Vγ9Vδ2 T cell Ag-presenting transmembrane protein butyrophilin 3A1, providing information on critical residues involved in this interaction. The neutralizing Vγ9Vδ2 TCR VHH identified in this study might provide a novel approach to inhibit the unintentional Vγ9Vδ2 T cell activation as a consequence of aminobisphosphonate administration.

For realization of a wearable artificial kidney based on regeneration of a small volume of dialysate, efficient urea removal from dialysate is a major challenge. Here a potentially suitable polymeric sorbent based on phenylglyoxaldehyde (PGA), able to covalently bind urea under physiological conditions, is described. Sorbent beads containing PGA groups were obtained by suspension polymerization of either styrene or vinylphenylethan-1-one (VPE), followed by modification of the aromatic groups of poly(styrene) and poly(VPE) into PGA. It was found that PGA-functionalized sorbent beads had maximum urea binding capacities of 1.4–2.2 mmol/g and removed ∼0.6 mmol urea/g in 8 h at 37 °C under static conditions from urea-enriched phosphate-buffered saline, conditions representative of dialysate regeneration. This means that the daily urea production of a dialysis patient can be removed with a few hundred grams of this sorbent which, is an important step forward in the development of a wearable artificial kidney.

Liquid–liquid phase separation is increasingly recognized as a process involved in cellular organization. Thus far, a detailed structural characterization of this intrinsically heterogeneous process has been challenging. Here we combine solid- and solution-state NMR spectroscopy to obtain atomic-level insights into the assembly and maturation of cytoplasmic processing bodies that contain mRNA as well as enzymes involved in mRNA degradation. In detail, we have studied the enhancer of decapping 3 (Edc3) protein that is a central hub for processing body formation in yeast. Our results reveal that Edc3 domains exhibit diverse levels of structural organization and dynamics after liquid–liquid phase separation. In addition, we find that interactions between the different Edc3 domains and between Edc3 and RNA in solution are largely preserved in the condensed protein state, allowing processing bodies to rapidly form and dissociate upon small alterations in the cellular environment., Processing bodies are membrane less organelles that contain enzymes involved in mRNA turnover, among them enhancer of decapping 3 (Edc3). Here the authors use solid- and solution-state NMR spectroscopy to characterize the structural organization and dynamics of Edc3 and find that its interactions with RNA and between the different Edc3 domains are largely preserved in the phase-separated state.

Modeling biomolecular assemblies is an important field in computational structural biology. The inherent complexity of their energy landscape and the computational cost associated with modeling large and complex assemblies are major drawbacks for integrative modeling approaches. The so-called coarse-graining approaches, which reduce the degrees of freedom of the system by grouping several atoms into larger “pseudo-atoms,” have been shown to alleviate some of those limitations, facilitating the identification of the global energy minima assumed to correspond to the native state of the complex, while making the calculations more efficient. Here, we describe and assess the implementation of the MARTINI force field for DNA into HADDOCK, our integrative modeling platform. We combine it with our previous implementation for protein-protein coarse-grained docking, enabling coarse-grained modeling of protein-nucleic acid complexes. The system is modeled using MARTINI topologies and interaction parameters during the rigid body docking and semi-flexible refinement stages of HADDOCK, and the resulting models are then converted back to atomistic resolution by an atom-to-bead distance restraints-guided protocol. We first demonstrate the performance of this protocol using 44 complexes from the protein-DNA docking benchmark, which shows an overall ~6-fold speed increase and maintains similar accuracy as compared to standard atomistic calculations. As a proof of concept, we then model the interaction between the PRC1 and the nucleosome (a former CAPRI target in round 31), using the same information available at the time the target was offered, and compare all-atom and coarse-grained models.