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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.

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.

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.

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 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.

Diatoms are algae producing micro- and nano-structured cell walls mainly containing amorphous silica. The shape and patterning of these cell walls is species-specific. Herein, the biosilica of Cyclotella cryptica , a centric marine diatom with a massive organic matrix, is studied. Solid-state NMR spectroscopy is applied to gain deeper insight into the interactions at the organic–inorganic interface of the cell walls. The various organic compounds like polysaccharides as well as proteins and long-chain polyamines (LCPAs) are detected by observation of heteronuclei like 13 C and 15 N whereas the silica phase is studied using 29 Si NMR spectroscopy. The sensitivity of the NMR experiments is strongly enhanced by isotope-labeling of the diatoms during cultivation with 13 C, 15 N and 29 Si. The presence of two different chitin species in the biosilica is demonstrated. This observation is supported by a monosaccharide analysis of the silica-associated organic matrix where a high amount of glucosamine is found. Moreover, the Rotational Echo Double Resonance (REDOR) experiment provides distance information for heteronuclear spins. 13 C{ 29 Si} REDOR experiments reveal proximities between different organic compounds and the silica phase. The closest contacts between silica and organic compounds appear for different signals in the 13 C-chemical shift range of 40–60 ppm, the typical range for LCPAs.

Correction to: Communications Chemistry https://doi.org/10.1038/s42004-020-00417-7, published online 11 November 2020.

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.

Src kinase activity is controlled by various mechanisms involving a coordinated movement of kinase and regulatory domains. Notwithstanding the extensive knowledge related to the backbone dynamics, little is known about the more subtle side-chain dynamics within the regulatory domains and their role in the activation process. Here, we show through experimental methyl dynamic results and predicted changes in side-chain conformational couplings that the SH2 structure of Fyn contains a dynamic network capable of propagating binding information. We reveal that binding the phosphorylated tail of Fyn perturbs a residue cluster near the linker connecting the SH2 and SH3 domains of Fyn, which is known to be relevant in the regulation of the activity of Fyn. Biochemical perturbation experiments validate that those residues are essential for inhibition of Fyn, leading to a gain of function upon mutation. These findings reveal how side-chain dynamics may facilitate the allosteric regulation of the different members of the Src kinase family.

Elucidating at atomic level how proteins interact and are chemically modified in cells represents a leading frontier in structural biology. We have developed a tailored solid-state NMR spectroscopic approach that allows studying protein structure inside human cells at atomic level under high-sensitivity dynamic nuclear polarization (DNP) conditions. We demonstrate the method using ubiquitin (Ub), which is critically involved in cellular functioning. Our results pave the way for structural studies of larger proteins or protein complexes inside human cells, which have remained elusive to in-cell solution-state NMR spectroscopy due to molecular size limitations.

Predicting the 3D structure of protein interactions remains a challenge in the field of computational structural biology. This is in part due to difficulties in sampling the complex energy landscape of multiple interacting flexible polypeptide chains. Coarse-graining approaches, which reduce the number of degrees of freedom of the system, help address this limitation by smoothing the energy landscape, allowing an easier identification of the global energy minimum. They also accelerate the calculations, allowing to model larger assemblies. Here, we present the implementation of the MARTINI coarse-grained force field for proteins into HADDOCK, our integrative modelling platform. Docking and refinement are performed at the coarse-grained level and the resulting models are then converted back to atomistic resolution through a distance restraints-guided morphing procedure. Our protocol, tested on the largest complexes of the protein docking benchmark 5, shows an overall ~7-fold speed increase compared to standard all-atom calculations, while maintaining a similar accuracy and yielding substantially more near-native solutions. To showcase the potential of our method, we performed simultaneous 7 body docking to model the 1:6 KaiC-KaiB complex, integrating mutagenesis and hydrogen/deuterium exchange data from mass spectrometry with symmetry restraints, and validated the resulting models against a recently published cryo-EM structure.

International audience; Zeolite-based catalysts are globally employed in many industrial processes, such as in crude-oil refining and in the production of bulk chemicals. However, to be implemented in industrial reactors efficiently, zeolite powders are required to be shaped in catalyst bodies. Scale-up of zeolite catalysts into such forms comes with side effects to its overall physicochem-ical properties and to those of its constituting components. Although fundamental research into "technical" solid catalysts is scarce, binder effects have been reported to significantly impact their catalytic properties and lifetime. Given the large number of additional (in)organic components added in the formulation, it is somehow surprising to see that there is a distinct lack of research into the unintentional impact organic additives can have on the properties of the zeolite and the catalyst bodies in general. Here, we systematically prepared a series of alumina-bound zeolite ZSM-5-based catalyst bodies, with organic additives such as peptizing, plasticizing, and lubricating agents, to rationalize their impacts on the physicochemical properties of the shaped catalyst bodies. By utilizing a carefully selected arsenal of bulk and high-spatial resolution multiscale characterization techniques, as well as specifically sized bioinspired fluorescent nanoprobes to study pore accessibility, we clearly show that, although the organic additives achieve their primary function of a mechanically robust material, uncontrolled processes are taking place in parallel. We reveal that the extrusion process can lead to zeolite dealumination (from acid peptizing treatment, and localized steaming upon calcination); meso-and macropore structural rearrangement (via burning-out of organic plasticizing and lubricating agents upon calcination); and abating of known alumina binder effects (via scavenging of Al species via chelating lubricating agents), which significantly impact catalytic performance. Understanding the mechanisms behind such effects in industrial-grade catalyst formulations can lead to enhanced design of these important materials, which can improve process efficiency in a vast range of industrial catalytic reactions.

CAMSAP/Patronins regulate microtubule minus-end dynamics. Their end specificity is mediated by their CKK domains, which we proposed recognise specific tubulin conformations found at minus ends. To critically test this idea, we compared the human CAMSAP1 CKK domain (HsCKK) with a CKK domain from Naegleria gruberi (NgCKK), which lacks minus-end specificity. Here we report near-atomic cryo-electron microscopy structures of HsCKK- and NgCKK-microtubule complexes, which show that these CKK domains share the same protein fold, bind at the intradimer interprotofilament tubulin junction, but exhibit different footprints on microtubules. NMR experiments show that both HsCKK and NgCKK are remarkably rigid. However, whereas NgCKK binding does not alter the microtubule architecture, HsCKK remodels its microtubule interaction site and changes the underlying polymer structure because the tubulin lattice conformation is not optimal for its binding. Thus, in contrast to many MAPs, the HsCKK domain can differentiate subtly specific tubulin conformations to enable microtubule minus-end recognition., CKK domain containing CAMSAP/Patronins recognise and regulate microtubule (MT) minus end dynamics. Here the authors compare cryo-EM structures of MT-bound human CKK and Naegleria gruberi CKK which lacks minus-end binding preference, finding NgCKK has a different interaction with, and inability to remodel, its MT binding site, shedding light on the CAMSAP/Patronin end binding mechanism.

Although industrialized, the mechanism for catalytic upgrading of bioethanol over solid‐acid catalysts (that is, the ethanol‐to‐hydrocarbons (ETH) reaction) has not yet been fully resolved. Moreover, mechanistic understanding of the ETH reaction relies heavily on its well‐known “sister‐reaction” the methanol‐to‐hydrocarbons (MTH) process. However, the MTH process possesses a C1‐entity reactant and cannot, therefore, shed any light on the homologation reaction sequence. The reaction and deactivation mechanism of the zeolite H‐ZSM‐5‐catalyzed ETH process was elucidated using a combination of complementary solid‐state NMR and operando UV/Vis diffuse reflectance spectroscopy, coupled with on‐line mass spectrometry. This approach establishes the existence of a homologation reaction sequence through analysis of the pattern of the identified reactive and deactivated species. Furthermore, and in contrast to the MTH process, the deficiency of any olefinic‐hydrocarbon pool species (that is, the olefin cycle) during the ETH process is also noted.

We analyzed the polar membrane lipids of 13 strains of halo(alkali)philic euryarchaea from hypersaline lakes. Nine belong to the class Halobacteria, representing two functional groups: aerobic polysaccharide utilizers and sulfur-respiring anaerobes. The other four strains represent halo(alkali)philic methanogens from the class Methanomicrobia and a recently discovered class Methanonatronarchaeia. A wide range of polar lipids were detected across the 13 strains including dialkyl glycerol diethers (archaeols), membrane-spanning glycerol tetraethers and diether-based cardiolipins. The archaeols contained a range of core lipid structures, including combinations of C20 and C25 isoprenoidal alkyl chains, unsaturations, and hydroxy moieties. Several diether lipids were novel, including: (a) a phosphatidylglycerolhexose (PG-Gly) headgroup, (b) a N,N,N-trimethyl aminopentanetetrol (APT)-like lipid with a methoxy group in place of a hydroxy group on the pentanetetrol, (c) a series of polar lipids with a headgroup with elemental composition of either C12H25NO13S or C12H25NO16S2, and (d) novel cardiolipins containing a putative phosphatidylglycerolphosphate glycerophosphate (PGPGP) polar moiety. We found that the lipid distribution of the 13 strains could be generally separated into two groups, the methanogens (group) and the Halobacteria (class) based on the presence of specific core lipids. Within the methanogens, adaption to a high or more moderate salt concentration resulted in different ratios of glycerol dialkyl glycerol tetraethers (GDGTs) to archaeol. The methanogen Methanosalsum natronophilum AME2T had the most complex diether lipid composition of any of the 13 strains, including hydroxy archaeol and macrocyclic archaeol which we surmise is an order-specific membrane adaption. The zwitterionic headgroups APT and APT-Me were detected only in the Methanomicrobiales member Methanocalculus alkaliphilus AMF2T which also contained the highest level of unsaturated lipids. Only alkaliphilic members of the Natrialbales order contained PGPGP cardiolipins and the PG-Gly headgroup. The four analyzed neutrophilic members of the Halobacteria were characterized by the presence of sulfur-containing headgroups and glycolipids. The presence of cardiolipins with one or more i-C25 alkyl chains, generally termed extended archaeol (EXT-AR), in one of the Methanonatronarchaeia strains was unexpected as only one other order of methanogenic archaea has been reported to produce EXT-AR. We examined this further by looking into the genomic potential of various archaea to produce EXT-AR. © 2019 Bale, Sorokin, Hopmans, Koenen, Rijpstra, Villanueva, Wienk and Sinninghe Damsté. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning-based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of interface Structure, Evolution and Energy-based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41 kcal mol-1 on a diverse training dataset consisting of 1102 mutations in 57 protein-protein complexes. It competes with existing state-of-the-art methods on two blind test datasets. Predictions for a new dataset of 540 mutations in 58 protein complexes from the recently published SKEMPI 2.0 database reveals that none of the current methods perform well (PCC

Graphical abstract, Highlights • Data processing and data management services for structural biology. • Enhancements to existing web services for structure solution and analysis. • New pipelines to link these services into more complex higher-level workflows. • New data management facilities. • Making the benefits of European e-Infrastructures more accessible to structural biologists., The West-Life project (https://about.west-life.eu/) is a Horizon 2020 project funded by the European Commission to provide data processing and data management services for the international community of structural biologists, and in particular to support integrative experimental approaches within the field of structural biology. It has developed enhancements to existing web services for structure solution and analysis, created new pipelines to link these services into more complex higher-level workflows, and added new data management facilities. Through this work it has striven to make the benefits of European e-Infrastructures more accessible to life-science researchers in general and structural biologists in particular.

Predicting the structure and thermodynamics of protein–protein interactions (PPIs) are key to a proper understanding and modulation of their function. Since experimental methods might not be able to catch up with the fast growth of genomic data, computational alternatives are therefore required. We present here a review dealing with various aspects of predicting binding affinity changes upon mutations (ΔΔG). We focus on predictors that consider three-dimensional structure information to estimate the impact of mutations on the binding affinity of a protein–protein complex, excluding the rigorous free energy perturbation methods. Training and evaluation, ΔΔG databases, data selection, and existing ΔΔG predictors are specially emphasized. We also establish the parallel with scoring functions used in docking since those share many similar PPI features with ΔΔG predictors. The field has seen a common evolution of ΔΔG predictors and scoring functions over time, transforming from purely energetic functions to statistical energy-based and further to machine learning-based functions. As machine learning has come to age, limitations in terms of quantity, quality and variety of the available data become the bottlenecks for the future development of these computational methods. This can be alleviated by building infrastructures for data generation, collection and sharing. Further developments can be catalyzed by conducting community-wide blind challenges for method assessment. This article is categorized under: Structure and Mechanism > Molecular Structures Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Interactions.

Motivation The use of experimental information has been demonstrated to increase the success rate of computational macromolecular docking. Many methods use information to post-filter the simulation output while others drive the simulation based on experimental restraints, which can become problematic for more complex scenarios such as multiple binding interfaces. Results We present a novel method for including interface information into protein docking simulations within the LightDock framework. Prior to the simulation, irrelevant regions from the receptor are excluded for sampling (filter of initial swarms) and initial ligand poses are pre-oriented based on ligand input information. We demonstrate the applicability of this approach on the new 55 cases of the Protein–Protein Docking Benchmark 5, using different amounts of information. Even with incomplete or incorrect information, a significant improvement in performance is obtained compared to blind ab initio docking. Availability and implementation The software is supported and freely available from https://github.com/brianjimenez/lightdock and analysis data from https://github.com/brianjimenez/lightdock_bm5. Supplementary information Supplementary data are available at Bioinformatics online.

Numerous proteins are involved in the multiple pathways of the DNA damage response network and play a key role to protect the genome from the wide variety of damages that can occur to DNA. An example of this is the structure-specific endonuclease ERCC1-XPF. This heterodimeric complex is in particular involved in nucleotide excision repair (NER), but also in double strand break repair and interstrand cross-link repair pathways. Here we review the function of ERCC1-XPF in various DNA repair pathways and discuss human disorders associated with ERCC1-XPF deficiency. We also overview our molecular and structural understanding of XPF-ERCC1.

Cell division in bacteria is initiated by the polymerization of FtsZ at midcell in a ring-like structure called the Z-ring. ZapA and other proteins assist Z-ring formation and ZapA binds ZapB, which senses the presence of the nucleoids. The FtsZ&ndash, ZapA binding interface was analyzed by chemical cross-linking mass spectrometry (CXMS) under in vitro FtsZ-polymerizing conditions in the presence of GTP. Amino acids residue K42 from ZapA was cross-linked to amino acid residues K51 and K66 from FtsZ, close to the interphase between FtsZ molecules in protofilaments. Five different cross-links confirmed the tetrameric structure of ZapA. A number of FtsZ cross-links suggests that its C-terminal domain of 55 residues, thought to be largely disordered, has a limited freedom to move in space. Site-directed mutagenesis of ZapA reveals an interaction site in the globular head of the protein close to K42. Using the information on the cross-links and the mutants that lost the ability to interact with FtsZ, a model of the FtsZ protofilament&ndash, ZapA tetramer complex was obtained by information-driven docking with the HADDOCK2.2 webserver.

Correction to: Nature Structural and Molecular Biology https://doi-org.proxy.library.uu.nl/10.1038/nsmb.3483, published online 9 October 2017.

In the recent years, there have been no unique protein structure topologies deposited in the PDB, which may reflect the finiteness of unique protein structures that exist in nature. Moreover, traditional structural studies have neglected the cellular context of the biomolecules, as there were no techniques that enabled atomistic studies in a highly heterogeneous cellular environment. Developments in biomolecular NMR and cryo-EM techniques have fueled the conception and the growth of cellular structural biology as a field. While cryo-EM focusses on very large molecular machines and protein assemblies, NMR has tackled small and heterogenous proteins. The two techniques thus complement each other. An analogous complementarity also exists within NMR, where cellular studies using solution-state NMR has been limited to small and soluble proteins while solid-state NMR has enabled studies on rather large insoluble proteins. The motivation behind pursuing the research presented in this thesis is to design and implement solid-state NMR approaches which would enable the study of soluble and promiscuous proteins within the cellular milieu. Despite their soluble nature, promiscuous proteins have remained out of the reach for solution-state NMR as they interact with their binding partners leading to reduced tumbling in crowded cellular environments. We have developed and tested an innovative DNP-ssNMR approach which is compatible with both mammalian and bacterial cells.

Solid-state Nuclear Magnetic Resonance (ssNMR) spectroscopy has emerged as a powerful tool that allows the study of proteins and protein complexes in their native environment. Such information can be of great value especially in the field of drug development. Moreover, ssNMR enables the study of protein dynamics, drug – target interactions and heterogeneous systems. In this thesis, we have investigated the plectasin – lipid II complex, peptidic hydrogels for spinal cord tissue engineering, and modal gating behaviour in the K+ channel KcsA using state-of-the-art ssNMR experiments. Antimicrobial resistance is a growing threat to human health worldwide which urgently calls for novel antibiotics that act through unexploited pathways. A validated and promising target for antibiotic design is lipid II, an essential precursor in the biosynthesis of the peptidoglycan network. However, structural data on the drug – lipid II complex are scarce and unavailable in its physiologically relevant membrane environment. In Chapters 2 and 3, we exploit the plectasin – lipid II complex in membranes. We analysed the structure and dynamics of plectasin in the unbound and bound state in great detail using high resolution ssNMR. Comparing plectasin in the two states revealed strong structural changes in previously ignored structural regions of plectasin such as the αβ-loop. Moreover, using 15N,13C- labelled lipid II enabled us to experimentally determine the complex interface. Overall, we have found stark differences between the mode of action of plectasin in micelles and in membranes, which further underlines the need of physiologically relevant studies for drug design purposes. Material properties of tissue engineering scaffolds dominate the behaviour of stem cells and are inherent to the success of tissue engineering. In Chapter 4 we explore the feasibility of studying the mechanical properties of a series of peptidic hydrogels, designed for neural tissue regeneration for treatment of spinal cord injuries, using modern ssNMR at natural abundance. We could not only describe material properties such as the rigidity and assembly at high resolution, but we could also identify the scaffold conformational heterogeneity as novel and decisive design parameter for the functionality of scaffolds. Overall, we have demonstrated that ssNMR methods pave the way to obtain atomic level insights into mechanical properties of tissue engineering scaffolds. Modal gating shifts are a widespread regulatory phenomenon in many ion channels. However, the molecular underpinning of these shifts remains unknown. In Chapter 5 we explore the molecular origin of modal gating behaviour in the archetypical K+ channel KcsA in native-like membranes using extensive ssNMR dynamics studies. We characterized the conformation and dynamics of mutant channels that represent the different modal gating modes. By integrating our ssNMR data with MD simulations, we discovered that shifts in the selectivity filter dynamics, caused by fluctuations in the water and hydrogen bonding network, are the driving forces for modal gating behaviour in KcsA. Thereby, our results provide a foundation for an improved understanding of K+ channels in general.

The aim of this study is to develop polymeric chemisorbents with a high density of ninhydrin groups, able to covalently bind urea under physiological conditions and thus potentially suitable for use in a wearable artificial kidney. Macroporous beads are prepared by suspension polymerization of 5-vinyl-1-indanone (vinylindanone) using a 90:10 (v/v) mixture of toluene and nitrobenzene as a porogen. The indanone groups are subsequently oxidized in a one-step procedure into ninhydrin groups. Their urea absorption kinetics are evaluated under both static and dynamic conditions at 37 °C in simulated dialysate (urea in phosphate buffered saline). Under static conditions and at a 1:1 molar ratio of ninhydrin: urea the sorbent beads remove ≈0.6–0.7 mmol g−1 and under dynamic conditions and at a 2:1 molar excess of ninhydrin ≈0.6 mmol urea g−1 sorbent in 8 h at 37 °C, which is a step toward a wearable artificial kidney.

Chapter 1 provided a detailed and comprehensive review on the types of data than can be used by Integrative Modelling software like HADDOCK, ROSETTA and IMP, with a particular emphasis on the experimental techniques which can be used to map interfaces, derive distance restraints or shape-based approaches. Another focal point of the chapter is how recent advancements have affected the field of membrane protein modelling. Chapters 2 and 3 also relate to membrane protein modelling with the former describing a recently available benchmark comprised of ready-to-dock membrane protein complexes and the baseline performance of HADDOCK for the entries of the benchmark, and the latter, ongoing work regarding development of a protocol for HADDOCK for the docking of transmembrane protein complexes. The remaining of the thesis focused on small molecule modelling with Chapters 4-6 detailing three separate protocols for the docking of small molecules and protein receptors, with every protocol and chapter reflecting methodological improvements over the previous one. In Chapter 4, I described the participation of HADDOCK in the 2016 iteration of the Grand Challenge, the blind docking experiment organised by the D3R consortium. While our performance in the pose prediction component was not impressive, we could identify the main factor limiting HADDOCK’s performance, namely the selection of appropriate templates for the receptor and came up with an improved way of selecting receptors. Chapter 5 described additional improvements in our protocol related to the way the compound conformers are selected prior to docking which led to our participation in the 2017 iteration of the Grand Challenge being evaluated as one of the best. Chapter 6 detailed the development of a new protocol for protein-small molecule docking, by combining the lessons and conclusions from Chapters 4-5 and formalising their approaches in a method that relies on HADDOCK’s main strength, its ability to incorporate information to guide the simulation. This new, shape-restrained docking protocol outperformed all our previous efforts while at the same time not relying on any external software. A common denominator between the membrane protein work and the small ligand docking discussed in this thesis is the use of shape information. Indeed, chapters 3 and 6 both describe applications of shape information represented as beads to drive the modelling process. In Chapter 3 one or more layers of beads are used to implicitly represent the membrane and in Chapter 6 ligand docking is restrained to a shape based on the structure of a homologous compound. Despite the commonalities between the two protocols, the outcome of the docking is very different between the two, with the small molecule protocol achieving high-quality results and improving upon our previous efforts in this area, whereas the membrane one achieves results which are only marginally better than defining centre-of-mass restraints between the transmembrane segments of the partners for the docking. A main limiting factor in the case of membrane protein complexes seems to be the size of the complex, which defines the number of restraints defined between shape and molecules and negatively impacts performance.

Chromatin provides the required structural compaction of DNA to fit in the nucleus and plays crucial roles in controlling cell fate and protecting genome integrity. Chromatin function ultimately depends on the structural and dynamical properties of the nucleosome, its fundamental repeating unit. The nucleosome acts as a gate keeper by regulating the binding of proteins that carry out DNA-templated processes like transcription, replication and repair. The studies described in this thesis aimed to contribute to our basic understanding of chromatin structure and function, involving chromatin remodeling and pioneer factor binding in particular. We specifically focused on the contribution of the nucleosomal DNA in these processes. In Chapter 2, we used computational modeling to study newly discovered archaeal histones that were only identified at the sequence level. We analyzed their propensity to form a continuous histone-DNA complex, or hypernucleosome, analogous to what was recently reported for archaeal histones HMfA and HMfB. As eukaryotes are classified as being part of the archaeal evolutionary branch, this complex might represent the evolutionary ancestor of chromatin organization into nucleosomes in eukaryotes. In Chapter 3, we introduced the method of ramified rolling circle amplification (rRCA) for the efficient large-scale production of a genomic nucleosomal DNA sequence from the human LIN28B locus. We showed that rRCA can be used to produce milligram amounts of nucleosomal DNA in an isothermal, one-pot reaction overnight that is easily scalable. Importantly, rRCA offers flexibility in choice of sequence. This may facilitate future studies on native, genomic nucleosomes, which might have structural and dynamic properties different from nucleosomes reconstituted on the common artificial strong-positioning sequences. Chromatin remodelers are ATP-dependent molecular motors that are essential for proper control of gene expression and protection of genome integrity. In Chapter 4, we applied Fluorescence Anisotropy (FA) and Nuclear Magnetic Resonance (NMR) to identify and characterize conformational changes of the ISWI remodeler upon interaction with its activating nucleosomal epitopes. To prevent spurious consumption of ATP, these lobes are locked in an inactive conformation in the free state of the remodeler, and this auto-inhibition is only relieved by interaction with the substrate, in particular the nucleosomal DNA and histone H4 tail. We showed that the ISWI ATPase domain is only able to bind the H4 tail in presence of dsDNA and that this ISWI-DNA- H4 complex requires presence of ATP-mimic ADP-BeFx for stability. We proposed that the first step of autoinhibition release is the interaction of ISWI with dsDNA. As the nucleosomal DNA plays a crucial role in nucleosome interactions with chromatin remodelers, pioneer transcription factors and other proteins, in Chapter 5, we explored the possibility of directly observing the nucleosomal DNA in NMR studies. We developed a method for segmental one-strand isotope-labeling of six thymine residues in the nucleosomal DNA-601 sequence, which creates the opportunity of using MeTROSY NMR experiments to improve signal to noise ratios in large complexes like the nucleosome.

Single-stranded DNA-binding proteins (SSBs) are required for all known DNA metabolic events such as DNA replication, recombination and repair. While a wealth of structural and functional data is available on the essential human SSB, hSSB1 (NABP2/OBFC2B), the close homolog hSSB2 (NABP1/OBFC2A) remains relatively uncharacterized. Both SSBs possess a well-structured OB (oligonucleotide/oligosaccharide-binding) domain that is able to recognize single-stranded DNA (ssDNA) followed by a flexible carboxyl-tail implicated in the interaction with other proteins. Despite the high sequence similarity of the OB domain, several recent studies have revealed distinct functional differences between hSSB1 and hSSB2. In this study, we show that hSSB2 is able to recognize cyclobutane pyrimidine dimers (CPD) that form in cellular DNA as a consequence of UV damage. Using a combination of biolayer interferometry and NMR, we determine the molecular details of the binding of the OB domain of hSSB2 to CPD-containing ssDNA, confirming the role of four key aromatic residues in hSSB2 (W59, Y78, W82, and Y89) that are also conserved in hSSB1. Our structural data thus demonstrate that ssDNA recognition by the OB fold of hSSB2 is highly similar to hSSB1, indicating that one SSB may be able to replace the other in any initial ssDNA binding event. However, any subsequent recruitment of other repair proteins most likely depends on the divergent carboxyl-tail and as such is likely to be different between hSSB1 and hSSB2.

The function of chromatin ultimately depends on the many chromatin-associated proteins and protein complexes that regulate all DNA-templated processes such as transcription, repair and replication. As the molecular docking platform for these proteins, the nucleosome is the essential gatekeeper to the genome. As such, the nucleosome-binding activity of a myriad of proteins is essential for a healthy cell. Here, we review the molecular basis of nucleosome-protein interactions and classify the different binding modes available. The structural data needed for such studies not only come from traditional sources such as X-Ray crystallography but also increasingly from other sources. In particular, we highlight how partial interaction data, derived from for example NMR or mutagenesis, are used in data-driven docking to drive the modeling of the complex into an atomistic structure. This approach has opened up detailed insights for several nucleosome-protein complexes that were intractable or recalcitrant to traditional methods. These structures guide the formation of new hypotheses and advance our understanding of chromatin function at the molecular level.

The regulation of chromatin biology ultimately depends on the manipulation of its smallest subunit, the nucleosome. The proteins that bind and operate on the nucleosome do so, while their substrate is part of a polymer embedded in the dense nuclear environment. Their molecular interactions must in some way be tuned to deal with this complexity. Due to the rapid increase in the number of high-resolution structures of nucleosome-protein complexes and the increasing understanding of the cellular chromatin structure, it is starting to become clearer how chromatin factors operate in this complex environment. In this review, we analyze the current literature on the interplay between nucleosome-protein interactions and higher-order chromatin structure. We examine in what way nucleosomes-protein interactions can affect and can be affected by chromatin organization at the oligonucleosomal level. In addition, we review the characteristics of nucleosome-protein interactions that can cause phase separation of chromatin. Throughout, we hope to illustrate the exciting challenges in characterizing nucleosome-protein interactions beyond the nucleosome.

The way a cell controls which part of its long DNA to access and read, which part not, and the way it maintains the integrity of the genome, are crucial for the proper functioning of an organism. These processes depend to a large extent on the packing of the DNA in nucleosomes that, through their presence or absence and types of epigenetic modifications they carry, allow genome maintenance and regulation. Conjointly, malfunctions in these mechanisms are detrimental to the cell and have been frequently correlated with cancer. A structural understanding of how these mechanisms operate on nucleosomes to ensure the proper functioning of a cell is thus crucial. In this work, we attempted to extend the toolbox of Nuclear Magnetic Resonance (NMR)-based approaches for the study of the nucleosome, with the long-term aim of helping with the biological understanding of chromatin function and epigenetics. In the first chapter are provided an introduction of key aspects of nucleosome structure and function and an introduction of the basic principles of NMR in layman terms. In Chapter 2, the remarkable 1D spectrum of a small molecule, designed to mimic a trimethyl lysine for methyl-TROSY NMR studies, is described in detail. The protons in its symmetrical and isotope-labelled trimethylammonium group are chemically equivalent, yet magnetically inequivalent due to the presence of 3JCH couplings. This resulted in small 4JHH couplings that perturbed the spectral pattern in a complex yet surprisingly simple way due to the large 1JCH coupling maintaining a weak coupling system. Subspectral analysis allowed us to intuitively describe and build up the deceptively complicated spectrum. Additionally, we showed that the system can be returned to magnetic equivalence by selective homonuclear decoupling. In Chapters 3 to 5, we developed, characterized and used a new approach for the study of the nucleosome making use of the well-established techniques of sedimentation coupled to 1H-detected solid-state NMR. High quality spectra were obtained for histones H2A and H3, which were subsequently assigned. Their secondary structure within the nucleosome was de-novo determined and was in good agreement with previously reported crystal structures, confirming the sample integrity. Co-sedimentation and further characterization of a proof-of-concept known peptide allowed the obtention of clear perturbation of H2A signals, allowing the determination of the binding site and subsequent data-driven docking, in high agreement with the previously reported crystal structure. The new approach was then applied to the study of Dot1p, the yeast H3K79 methyltransferase. Through a divide-and-conquer approach and the combined use of biochemical and NMR methods, we found that the lysine-rich sequence directly N-terminal of the Dot1p core has a high affinity towards DNA, suggesting a nucleosome-anchoring role, in close vicinity from H3K79, for this domain. Additionally, the catalytic core of Dot1p has very low affinity for ubiquitin, in agreement with previous in-vivo assays. We then characterized the nucleosome sediment through SAXS and NMR, showing that our preparation is devoid of long-range ordering of nucleosomes. We finally attempted to co-sediment a low affinity, H3 tail-binding protein with the nucleosome, challenging the formation of a rigid, saturated protein nucleosome complex. Using solution- and solid-state NMR, we showed that the PHD2 domain of CHD4 counterintuitively combines low affinity interaction and a slow exchange interaction, while the bound state remained invisible. In Chapter 6, the results from the thesis are further discussed and put in perspective along with suggestions for future works and prospects

Understanding how biomolecules work provides the basis to explain the cellular mechanisms that drive life. Structural biology addresses this challenge by describing the 3D structures of biomolecules using a range of structural techniques, such as Nuclear Magnetic Resonance. This thesis exploits new methods of solid state Nuclear Magnetic Resonance to study, at the atomic level, the structures of membrane proteins in their native environment. This is important because it provides a method to validate the biological relevance of findings obtained with other techniques, and to probe unexpected phenomena that only occur at the cell. The results revealed biologically important features that can only appreciated when proteins are measured within their physiologically relevant environment, as it is shown with the potassium channel KcsA and the antibiotic Nisin. Therefore, the approach presented here can provide critical information for the development of new and better drugs, for example.

At any time in the nucleus of a eukaryotic cell, chromatin, the protein-DNA supercomplex that harbors the genome, undergoes various processes to meet the needs of the cell during its life cycle. These processes serve to regulate the replication, transcription, or repair of the DNA and are as such dependent on the dynamics of the nucleosome, the fundamental repeating unit of chromatin. A nucleosome is assembled from a protein core of histone proteins around which DNA is wrapped. However, for nuclear processes in which the DNA serves as template for proteins, DNA has to be (partially) unwrapped in a process called nucleosome disassembly. Nucleosome assembly and disassembly are dependent on a family of proteins called histone chaperones. These chaperones bind and manage the histone proteins for all chromatin processes. One of these processes is repair of DNA double-strand breaks (DSBs), a common type of DNA damage in eukaryotic cells. The major pathway for DSB repair in human cells is non-homologous end joining (NHEJ). The NHEJ DNA repair factor Aprataxin and Polynucleotide kinase Like Factor (APLF) was shown to harbor histone chaperone function in its C-terminal acidic domain (APLFAD). The topic of this dissertation is the characterization of the histone chaperone function of APLFAD. A multidisciplinary approach is used to better understand how this histone chaperone functions, from histone binding to nucleosome assembly. The aim is to provide a better understanding of NHEJ DNA repair and the mechanism of nucleosome assembly.

The dynamic instability of microtubules (MT) is an essential feature in eukaryotic cells and related to basic cellular processes such as intracellular transport, cell division, migration, or polarization. Microtubule-associated proteins (MAPs) are critically involved in such MT dynamics by binding to MTs and reshaping the molecular and structural organization. Although the structure and dynamics of MTs, as well as the MT interactomic network have been intensively studied over the past decades, a detailed understanding on how different MAPs interact with MTs and how they jointly contribute to MT functioning in the cell remains largely elusive. For this reason, atomic level knowledge about the structural organization of MT-MAPs complexes is indispensable for understanding the mechanistic details of the MT interactome and its dynamic features. In this thesis, my goal was to study the binding modes of specific MAPs to MTs, and on the other hand investigate structural and dynamical alternations of MTs that occur upon MAP binding. The studies presented in this thesis relied on the advancements in solid-state NMR (ssNMR) spectroscopy to study large protein complexes, including the use of 1H-detected ssNMR and dynamic nuclear polarization (DNP).

Human coronaviruses OC43 and HKU1 are respiratory pathogen of zoonotic origin that have gained worldwide distribution. OC43 apparently emerged from a bovine coronavirus (BCoV) spill-over. All three viruses attach to 9-O-acetylated sialoglycans via spike protein S with hemagglutinin-esterase HE acting as a receptor-destroying enzyme. In BCoV, an HE lectin domain promotes esterase activity towards clustered substrates. OC43 and HKU1, however, lost HE lectin function as an adaptation to humans. Replaying OC43 evolution, we knocked-out BCoV HE lectin function and performed forced evolution-population dynamics analysis. Loss of HE receptor-binding selected for second-site mutations in S, decreasing S binding affinity by orders of magnitude. Irreversible HE mutations selected for cooperativity in virus swarms with low-affinity S minority variants sustaining propagation of high-affinity majority phenotypes. Salvageable HE mutations induced successive second-site substitutions in both S and HE. Apparently, S and HE are functionally interdependent and co-evolve to optimize the balance between attachment and release. This mechanism of glycan-based receptor usage, entailing a concerted, fine-tuned activity of two envelope protein species, is unique among CoVs, but reminiscent of that of influenza A viruses (IAVs). Apparently, general principles fundamental to virion-sialoglycan interactions prompted convergent evolution of two important groups of human and animal pathogens.

Small-molecule docking remains one of the most valuable computational techniques for the structure prediction of protein-small-molecule complexes. It allows us to study the interactions between compounds and the protein receptors they target at atomic detail in a timely and efficient manner. Here, we present a new protocol in HADDOCK (High Ambiguity Driven DOCKing), our integrative modeling platform, which incorporates homology information for both receptor and compounds. It makes use of HADDOCK's unique ability to integrate information in the simulation to drive it toward conformations, which agree with the provided data. The focal point is the use of shape restraints derived from homologous compounds bound to the target receptors. We have developed two protocols: in the first, the shape is composed of dummy atom beads based on the position of the heavy atoms of the homologous template compound, whereas in the second, the shape is additionally annotated with pharmacophore data for some or all beads. For both protocols, ambiguous distance restraints are subsequently defined between those beads and the heavy atoms of the ligand to be docked. We have benchmarked the performance of these protocols with a fully unbound version of the widely used DUD-E (Database of Useful Decoys-Enhanced) dataset. In this unbound docking scenario, our template/shape-based docking protocol reaches an overall success rate of 81% when a reliable template can be identified (which was the case for 99 out of 102 complexes in the DUD-E dataset), which is close to the best results reported for bound docking on the DUD-E dataset.

Molecular docking excels at creating a plethora of potential models of protein-protein complexes. To correctly distinguish the favorable, native-like models from the remaining ones remains, however, a challenge. We assessed here if a protocol based on molecular dynamics (MD) simulations would allow distinguishing native from non-native models to complement scoring functions used in docking. To this end, the first models for 25 protein-protein complexes were generated using HADDOCK. Next, MD simulations complemented with machine learning were used to discriminate between native and non-native complexes based on a combination of metrics reporting on the stability of the initial models. Native models showed higher stability in almost all measured properties, including the key ones used for scoring in the Critical Assessment of PRedicted Interaction (CAPRI) competition, namely the positional root mean square deviations and fraction of native contacts from the initial docked model. A random forest classifier was trained, reaching a 0.85 accuracy in correctly distinguishing native from non-native complexes. Reasonably modest simulation lengths of the order of 50-100 ns are sufficient to reach this accuracy, which makes this approach applicable in practice.

Herein, we investigate a novel set of polarizing agents—mixed‐valence compounds—by theoretical and experimental methods and demonstrate their performance in high‐field dynamic nuclear polarization (DNP) NMR experiments in the solid state. Mixed‐valence compounds constitute a group of molecules in which molecular mobility persists even in solids. Consequently, such polarizing agents can be used to perform Overhauser‐DNP experiments in the solid state, with favorable conditions for dynamic nuclear polarization formation at ultra‐high magnetic fields., Mixed‐valence compounds were investigated as novel polarizing agents for DNP NMR. These compounds constitute a group of molecules in which molecular mobility persists even in solids. Consequently, they can be used to perform Overhauser‐DNP NMR experiments in solid state, and they specifically show favorable properties for applications at ultra‐high magnetic fields (DNP=dynamic nuclear polarization, NMR=nuclear magnetic resonance).