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Antibiotics that use novel mechanisms are needed to combat antimicrobial resistance1–3. Teixobactin4 represents a new class of antibiotics with a unique chemical scaffold and lack of detectable resistance. Teixobactin targets lipid II, a precursor of peptidoglycan5. Here we unravel the mechanism of teixobactin at the atomic level using a combination of solid-state NMR, microscopy, in vivo assays and molecular dynamics simulations. The unique enduracididine C-terminal headgroup of teixobactin specifically binds to the pyrophosphate-sugar moiety of lipid II, whereas the N terminus coordinates the pyrophosphate of another lipid II molecule. This configuration favours the formation of a β-sheet of teixobactins bound to the target, creating a supramolecular fibrillar structure. Specific binding to the conserved pyrophosphate-sugar moiety accounts for the lack of resistance to teixobactin4. The supramolecular structure compromises membrane integrity. Atomic force microscopy and molecular dynamics simulations show that the supramolecular structure displaces phospholipids, thinning the membrane. The long hydrophobic tails of lipid II concentrated within the supramolecular structure apparently contribute to membrane disruption. Teixobactin hijacks lipid II to help destroy the membrane. Known membrane-acting antibiotics also damage human cells, producing undesirable side effects. Teixobactin damages only membranes that contain lipid II, which is absent in eukaryotes, elegantly resolving the toxicity problem. The two-pronged action against cell wall synthesis and cytoplasmic membrane produces a highly effective compound targeting the bacterial cell envelope. Structural knowledge of the mechanism of teixobactin will enable the rational design of improved drug candidates.

Cellular dynamic nuclear polarization (DNP) has been an effective means of overcoming the intrinsic sensitivity limitations of solid-state nuclear magnetic resonance (ssNMR) spectroscopy, thus enabling atomic-level biomolecular characterization in native environments. Achieving DNP signal enhancement relies on doping biological preparations with biradical polarizing agents (PAs). Unfortunately, PA performance within cells is often limited by their sensitivity to the reductive nature of the cellular lumen. Herein, we report the synthesis and characterization of a highly bioresistant and hydrophilic PA (StaPol-1) comprising the trityl radical OX063 ligated to a gem-diethyl pyrroline nitroxide via a rigid piperazine linker. EPR experiments in the presence of reducing agents such as ascorbate and in HeLa cell lysates demonstrate the reduction resistance of StaPol-1. High DNP enhancements seen in small molecules, proteins and cell lysates at 18.8 T confirm that StaPol-1 is an excellent PA for DNP ssNMR investigations of biomolecular systems at high magnetic fields in reductive environments.

For almost five decades, solid-state NMR (ssNMR) has been used to study complex biomolecular systems. This article gives a view on how ssNMR methods and applications have evolved during this time period in a broader structural biology context. It also discusses possible directions for additional developments and the future role of ssNMR in a life science context and beyond.

Science advances 8(30), eabo0517 (2022). doi:10.1126/sciadv.abo0517, Nucleosome assembly requires the coordinated deposition of histone complexes H3-H4 and H2A-H2B to form a histone octamer on DNA. In the current paradigm, specific histone chaperones guide the deposition of first H3-H4 and then H2A-H2B. Here, we show that the acidic domain of DNA repair factor APLF (APLF$^{AD}$) can assemble the histone octamer in a single step and deposit it on DNA to form nucleosomes. The crystal structure of the APLF$^{AD}$-histone octamer complex shows that APLF$^{AD}$ tethers the histones in their nucleosomal conformation. Mutations of key aromatic anchor residues in APLF$^{AD}$ affect chaperone activity in vitro and in cells. Together, we propose that chaperoning of the histone octamer is a mechanism for histone chaperone function at sites where chromatin is temporarily disrupted., Published by Assoc., Washington, DC [u.a.]

The photochemical evolution of polycyclic aromatic hydrocarbons (PAHs), an abundant form of meteoritic organic carbon, is of great interest to early Earth and Mars origin-of-life studies and current organic molecule detection efforts on Mars. Fe-rich clay environments were abundant on early Earth and Mars, and may have played a role in prebiotic chemistry, catalyzing the breakdown of PAHs and freeing up carbon for subsequent chemical complexification. Current Mars is abundant in clay-rich environments, which are most promising for harboring organic molecules and have comprised the main studied features by the Curiosity rover in search of them. In this work we studied the photocatalytic effects of the Fe-rich clay nontronite on adsorbed PAHs. We tested the effect of ultraviolet radiation on pyrene, fluoranthene, perylene, triphenylene, and coronene adsorbed to nontronite using the spike technique, and in situ diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy in a Mars simulation chamber. We studied the infrared vibrational PAH bands with first order reaction kinetics and observed an extensive decrease of bands of pyrene, fluoranthene, and perylene, accompanied by the formation of PAH cations, while triphenylene and coronene remained preserved. We further analyzed our irradiated samples with nuclear magnetic resonance (NMR). Our study showed certain PAHs to be degraded via the (photo)Fenton mechanism, even under a dry, hypoxic atmosphere. Using solar spectra representative of early Earth, early Mars, and current Mars surface illumination up to 400 nm, the processes occurring in our set up are indicative of the UV-induced photochemistry taking place in Fe-rich clay environments on early Earth and Mars.

Regulation of DNA-templated processes such as gene transcription and DNA repair depend on the interaction of a wide range of proteins with the nucleosome, the fundamental building block of chromatin. Both solution and solid-state NMR spectroscopy have become an attractive approach to study the dynamics and interactions of nucleosomes, despite their high molecular weight of ∼200 kDa. For solid-state NMR (ssNMR) studies, dilute solutions of nucleosomes are converted to a dense phase by sedimentation or precipitation. Since nucleosomes are known to self-associate, these dense phases may induce extensive interactions between nucleosomes, which could interfere with protein-binding studies. Here, we characterized the packing of nucleosomes in the dense phase created by sedimentation using NMR and small-angle X-ray scattering (SAXS) experiments. We found that nucleosome sediments are gels with variable degrees of solidity, have nucleosome concentration close to that found in crystals, and are stable for weeks under high-speed magic angle spinning (MAS). Furthermore, SAXS data recorded on recovered sediments indicate that there is no pronounced long-range ordering of nucleosomes in the sediment. Finally, we show that the sedimentation approach can also be used to study low-affinity protein interactions with the nucleosome. Together, our results give new insights into the sample characteristics of nucleosome sediments for ssNMR studies and illustrate the broad applicability of sedimentation-based NMR studies.

An emerging class of therapeutic molecules are cyclic peptides with over 40 cyclic peptide drugs currently in clinical use. Their mode of action is, however, not fully understood, impeding rational drug design. Computational techniques could positively impact their design, but modeling them and their interactions remains challenging due to their cyclic nature and their flexibility. This study presents a step-by-step protocol for generating cyclic peptide conformations and docking them to their protein target using HADDOCK2.4. A dataset of 30 cyclic peptide-protein complexes was used to optimize both cyclization and docking protocols. It supports peptides cyclized via an N- and C-terminus peptide bond and/or a disulfide bond. An ensemble of cyclic peptide conformations is then used in HADDOCK to dock them onto their target protein using knowledge of the binding site on the protein side to drive the modeling. The presented protocol predicts at least one acceptable model according to the critical assessment of prediction of interaction criteria for each complex of the dataset when the top 10 HADDOCK-ranked single structures are considered (100% success rate top 10) both in the bound and unbound docking scenarios. Moreover, its performance in both bound and fully unbound docking is similar to the state-of-the-art software in the field, Autodock CrankPep. The presented cyclization and docking protocol should make HADDOCK a valuable tool for rational cyclic peptide-based drug design and high-throughput screening.

The work presented in this thesis describes a detailed investigation to understand the mechanism of lipid targeting antibiotics using an array of methods including ssNMR, advanced microscopy techniques, computational analysis, and biophysical and biochemical assays. Together, these methods enable a holistic description of the supramolecular mechanism of lipid-targeting antibiotics such as teixobactin or clovibactin. Chapter 1 introduces the current scenario antimicrobial resistance (AMR) and highlights the studies conducted on teixobactin to date. This chapter introduces the basics of ssNMR and other complimentary techniques. It also lays the foundation for the methodologies used in the following chapters to gain insights into the mechanism of lipid-targeting antibiotics, teixobactin and clovibactin. Chapter 2 is adapted from a study published in Nature Communications in 2020. Here we describe the mechanism of action of synthetic analogues of teixobactin, namely [R4L10] and [L10]. With the help of ssNMR studies, we could solve the structure of the complex formed between [R4L10]-teixobactin and its target, lipid II directly in membranes. Furthermore, we investigated the impact of the membrane composition on the mode of action of [R4L10]- teixobactin and demonstrate that the bacterial cell membrane affects the binding of the drug. Chapter 3 is adapted from a study published in Nature in 2022 where we describe a new dual mode of action for natural teixobactin. In this chapter, we determine the dual mechanism of teixobactin from an atomic to supramolecular scale using ssNMR, HS-AFM, and confocal microscopy. We present an atomically resolved ssNMR structure of the complex and elaborate on its evolution into fibrils and eventually micrometre-sized clusters which ultimately kill the bacteria. Based on our results we show that teixobactin ventures away from the classic 1-drug to 1-target ideology and uses a new type of antimicrobial action. Chapter 4 is adapted from a publication that is currently under review. This chapter emphasizes the discovery of a new antibiotic called clovibactin from the bacterial “dark matter.” We use our established methodology to study and elucidate the novel mechanism of clovibactin and showcase its multi-targeting action. Conclusively in Chapter 5, we showcase some recently acquired and unpublished data on the effect of anionic lipids on teixobactin’s mechanism of action and propose future studies which can be conducted to expand on the role of anionic membranes on the activity of lipid-targeting antibiotics. We also discuss how the efficient methodology developed can be useful in studying lipid-targeting antibiotics at different length scales under varying membrane compositions to provide a complete overview of their mechanism and help in designing better drugs to tackle the problem of AMR.

Solid-state NMR (ssNMR) spectroscopy facilitates the non-destructive characterization of structurally heterogeneous biomolecules in their native setting, for example, comprising proteins, lipids and polysaccharides. Here we demonstrate the utility of high and ultra-high field 1 H-detected fast MAS ssNMR spectroscopy, which exhibits increased sensitivity and spectral resolution, to further elucidate the atomic-level composition and structural arrangement of the cell wall of Schizophyllum commune, a mushroom-forming fungus from the Basidiomycota phylum. These advancements allowed us to reveal that Cu(II) ions and the antifungal peptide Cathelicidin-2 mainly bind to cell wall proteins at low concentrations while glucans are targeted at high metal ion concentrations. In addition, our data suggest the presence of polysaccharides containing N-acetyl galactosamine (GalNAc) and proteins, including the hydrophobin proteins SC3, shedding more light on the molecular make-up of cells wall as well as the positioning of the polypeptide layer. Obtaining such information may be of critical relevance for future research into fungi in material science and biomedical contexts.

Motivation Gaining structural insights into the protein–protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning from protein–protein interfaces using convolutional neural network (CNN) approaches. However, CNN is not rotation invariant and data augmentation is required to desensitize the network to the input data orientation which dramatically impairs the computation performance. Representing protein–protein complexes as atomic- or residue-scale rotation invariant graphs instead enables using graph neural networks (GNN) approaches, bypassing those limitations. Results We have developed DeepRank-GNN, a framework that converts protein–protein interfaces from PDB 3D coordinates files into graphs that are further provided to a pre-defined or user-defined GNN architecture to learn problem-specific interaction patterns. DeepRank-GNN is designed to be highly modularizable, easily customized and is wrapped into a user-friendly python3 package. Here, we showcase DeepRank-GNN’s performance on two applications using a dedicated graph interaction neural network: (i) the scoring of docking poses and (ii) the discriminating of biological and crystal interfaces. In addition to the highly competitive performance obtained in those tasks as compared to state-of-the-art methods, we show a significant improvement in speed and storage requirement using DeepRank-GNN as compared to DeepRank. Availability and implementation DeepRank-GNN is freely available from https://github.com/DeepRank/DeepRank-GNN. Supplementary information Supplementary data are available at Bioinformatics online.

Voltage-gated potassium channels of the Kv7.x family are involved in a plethora of biological processes across many tissues in animals, and their misfunctioning could lead to several pathologies ranging from diseases caused by neuronal hyperexcitability, such as epilepsy, or traumatic injuries and painful diabetic neuropathy to autoimmune disorders. Among the members of this family, the Kv7.2 channel can form hetero-tetramers together with Kv7.3, forming the so-called M-channels, which are primary regulators of intrinsic electrical properties of neurons and of their responsiveness to synaptic inputs. Here, prompted by the similarity between the M-current and that in Kv7.2 alone, we perform a computational-based characterization of this channel in its different conformational states and in complex with the modulator retigabine. After validation of the structural models of the channel by comparison with experimental data, we investigate the effect of retigabine binding on the two extreme states of Kv7.2 (resting-closed and activated-open). Our results suggest that binding, so far structurally characterized only in the intermediate activated-closed state, is possible also in the other two functional states. Moreover, we show that some effects of this binding, such as increased flexibility of voltage sensing domains and propensity of the pore for open conformations, are virtually independent on the conformational state of the protein. Overall, our results provide new structural and dynamic insights into the functioning and the modulation of Kv7.2 and related channels.

Membrane proteins are known to exert many essential biological functions by forming complexes in cell membranes. An example refers to the β-barrel assembly machinery (BAM), a 200 kDa pentameric complex containing BAM proteins A–E that catalyzes the essential process of protein insertion into the outer membrane of gram-negative bacteria. While progress has been made in capturing three-dimensional structural snapshots of the BAM complex, the role of the lipoprotein BamC in the complex assembly in functional lipid bilayers has remained unclear. We have devised a component-selective preparation scheme to directly study BamC as part of the entire BAM complex in lipid bilayers. Combination with proton-detected solid-state NMR methods allowed us to probe the structure, dynamics, and supramolecular topology of full-length BamC embedded in the entire complex in lipid bilayers. Our approach may help decipher how individual proteins contribute to the dynamic formation and functioning of membrane protein complexes in membranes.

The modes of deactivation -and the extent to which their properties can be restored- of two catalyst bodies used in cascade for bio-oil synthesis have been studied. These catalysts include a solid acid granulate (namely ZrO2/desilicated zeolite ZSM-5/attapulgite clay) employed in ex-situ catalytic fast pyrolysis of biomass, and a base extrudate (K-exchanged zeolite USY/attapulgite clay) for the subsequent bio-oil upgrading. Post-mortem analyses of both catalyst bodies with Raman spectroscopy and confocal fluorescence microscopy revealed the presence of highly poly-aromatic coke distributed in an egg-shell manner. Deactivation due to coke adsorption onto acid sites affected the zeolite ZSM-5-based catalyst, while for the base catalyst it is structural integrity loss, resulting from KOH-mediated zeolite framework collapse, the main deactivating factor. A hydrothermal regeneration process reversed the detrimental effects of coke in the acid catalyst, largely recovering catalyst acidity (∼80%) and textural properties (∼90%), but worsened the structural damage suffered by the base catalyst.

Matrix effects in a fluid catalytic cracking (FCC) catalyst have been studied in terms of structure, accessibility, and acidity. An extensive characterization study into the structural and acidic properties of a FCC catalyst, its individual components (i.e., zeolite H‐Y, binder (boehmite/silica) and kaolin clay), and two model FCC catalyst samples containing only two components (i.e., zeolite‐binder and binder‐clay) was performed at relevant conditions. This allowed the drawing of conclusions about the role of each individual component, describing their mutual physicochemical interactions, establishing structure‐acidity relationships, and determining matrix effects in FCC catalyst materials. This has been made possible by using a wide variety of characterization techniques, including temperature‐programmed desorption of ammonia, infrared spectroscopy in combination with CO as probe molecule, transmission electron microscopy, X‐ray diffraction, Ar physisorption, and advanced nuclear magnetic resonance. By doing so it was, for example, revealed that a freshly prepared spray‐dried FCC catalyst appears as a physical mixture of its individual components, but under typical riser reactor conditions, the interaction between zeolite H‐Y and binder material is significant and mobile aluminum migrates and inserts from the binder into the defects of the zeolite framework, thereby creating additional Brønsted acid sites and restoring the framework structure., On the interplay between matrix and catalyst: A combination of advanced characterization techniques reveals that while a freshly prepared spray‐dried fluid catalytic cracking catalyst appears as a physical mixture of its individual components, significant matrix effects actually come into play under industrial riser reactor conditions, leading to changing Brønsted and Lewis acidity, pore accessibility as well as various structural changes.

Microtubules are important components of the eukaryotic cytoskeleton. Their structural organization is regulated by nucleotide binding and many microtubule-associated proteins (MAPs). While cryo-EM and X-ray crystallography have provided detailed views of interactions between MAPs with the microtubule lattice, little is known about how MAPs and their intrinsically disordered regions interact with the dynamic microtubule surface. NMR carries the potential to directly probe such interactions but so far has been precluded by the low tubulin yield. We present a protocol to produce [13C, 15N]-labeled, functional microtubules (MTs) from human cells for solid-state NMR studies. This approach allowed us to demonstrate that MAPs can differently modulate the fast time-scale dynamics of C-terminal tubulin tails, suggesting distinct interaction modes. Our results pave the way for in-depth NMR studies of protein dynamics involved in MT assembly and their interactions with other cellular components., Microtubule (MT) organization is regulated by many microtubule-associated proteins (MAPs) that contain intrinsically disordered regions. Here authors produce [13C, 15N] labeled, functional microtubules from human cells for solid-state NMR which allows studying MAP-MT interactions.

The nucleosome surface contains an area with negative electrostatic potential known as the acidic patch, which functions as a binding platform for various proteins to regulate chromatin biology. The dense clustering of acidic residues may impact their effective pKa and thus the electronegativity of the acidic patch, which in turn could influence nucleosome-protein interactions. We here set out to determine the pKa values of residues in and around the acidic patch in the free H2A-H2B dimer using NMR spectroscopy. We present a refined solution structure of the H2A-H2B dimer based on intermolecular distance restraints, displaying a well-defined histone-fold core. We show that the conserved histidines H2B H46 and H106 that line the acidic patch have pKa of 5.9 and 6.5, respectively, and that most acidic patch carboxyl groups have pKa values well below 5.0. For H2A D89 we find strong evidence for an elevated pKa of 5.3. Our data establish that the acidic patch is highly negatively charged at physiological pH, while protonation of H2B H106 and H2B H46 at slightly acidic pH will reduce electronegativity. These results will be valuable to understand the impact of pH changes on nucleosome-protein interactions in vitro, in silico or in vivo.

Distinct SARS-CoV-2 lineages, discovered through various genomic surveillance initiatives, have emerged during the pandemic following unprecedented reductions in worldwide human mobility. We here describe a SARS-CoV-2 lineage - designated B.1.620 - discovered in Lithuania and carrying many mutations and deletions in the spike protein shared with widespread variants of concern (VOCs), including E484K, S477N and deletions HV69Δ, Y144Δ, and LLA241/243Δ. As well as documenting the suite of mutations this lineage carries, we also describe its potential to be resistant to neutralising antibodies, accompanying travel histories for a subset of European cases, evidence of local B.1.620 transmission in Europe with a focus on Lithuania, and significance of its prevalence in Central Africa owing to recent genome sequencing efforts there. We make a case for its likely Central African origin using advanced phylogeographic inference methodologies incorporating recorded travel histories of infected travellers., Here, the authors describe the emergence and spread of a new potential SARS-CoV-2 variant of interest, B.1.620. They show that this lineage, first identified in Lithuania, has established local transmission in Europe on multiple occasions and likely emerged in Central Africa.

The synthesis, characterization and catalytic activity of a new class of diruthenium hydrido carbonyl complexes bound to the tBuPNNP expanded pincer ligand is described. Reacting tBuPNNP with two equiv of RuHCl(PPh3)3(CO) at 140 °C produces an insoluble air-stable complex, which was structurally characterized as [Ru2(tBuPNNP)H(μ-H)Cl(μ-Cl)(CO)2] (1) using solid-state NMR, IR and X-ray absorption spectroscopies and follow-up reactivity. A reaction with KOtBu results in deprotonation of a methylene linker to produce [Ru2(tBuPNNP*)H(μ-H)(μ-OtBu)(CO)2] (3) featuring a partially dearomatized naphthyridine core. This enables metal-ligand cooperative activation of H2 analogous to the mononuclear analogue, [Ru(tBuPNP*)H(CO)]. In contrast to the mononuclear system, the bimetallic analogue 3 catalyzes the E-selective semi-hydrogenation of alkynes at ambient temperature and atmospheric H2 pressure with good functional group tolerance. Monitoring the semi-hydrogenation of diphenylacetylene by 1H NMR spectroscopy shows the intermediacy of Z-stilbene, which is subsequently isomerized to the E-isomer. Initial findings into the mode of action of this system are provided, including the spectroscopic characterization of a polyhydride intermediate and the isolation of a deactivated species with a partially hydrogenated naphthyridine backbone.

Among all catalytic natural gas valorization processes, methane dehydroaromatization (MDA) still has a great potential to be utilized at an industrial level. Although the use of Mo/H-ZSM-5 as an MDA catalyst was first reported almost three decades ago, the process is yet to be industrialized, because of its inherent challenges. In order to improve the overall catalytic performance and lifetime, the co-feeding of water constitutes a promising option, because of its abundance and nontoxicity. Although water’s (limited) positive influence on catalyst lifetime has earlier been exhibited, the exact course of action (like mechanism or the water effect on active sites) is yet to be established. To bridge this knowledge gap, in this work, we have performed an in-depth investigation to elucidate the effects of water co-feeding over a well-dispersed Mo/H-ZSM-5 catalyst by using an array of advanced characterization techniques (nuclear magnetic resonance (NMR), electron paramagnetic resonance (EPR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), thermogravimetry-temperature-programmed oxidation/mass spectroscopy (TG-TPO/MS), scanning transmission electron microscopy (STEM), N2physisorption, Raman spectroscopy, inductively coupled plasma-optical emission spectroscopy (ICP-OES)). Our results demonstrate that the addition of water results in the occurrence of steam reforming (of both coke and methane) in parallel to MDA. Moreover, the presence of water affects the reducibility of Mo sites, as corroborated with computational analysis to examine the state and locality of Mo sites under various water levels and transformation of the catalyst structure during deactivation. We anticipate that our comprehensive study of the structure-function relationship on Mo/H-ZSM-5 under humid MDA conditions will be beneficial for the development of future methane valorization technologies.

Structural biology aims at characterizing the structural and dynamic properties of biological macromolecules at atomic details. Gaining insight into three dimensional structures of biomolecules and their interactions is critical for understanding the vast majority of cellular processes, with direct applications in health and food sciences. Since 2010, the WeNMR project (www.wenmr.eu) has implemented numerous web-based services to facilitate the use of advanced computational tools by researchers in the field, using the high throughput computing infrastructure provided by EGI. These services have been further developed in subsequent initiatives under H2020 projects and are now operating as Thematic Services in the European Open Science Cloud (EOSC) portal (www.eosc-portal.eu), sending >12 millions of jobs and using around 4000 CPU-years per year. Here we review 10 years of successful e-infrastructure solutions serving a large worldwide community of over 23,000 users to date, providing them with user-friendly, web-based solutions that run complex workflows in structural biology. The current set of active WeNMR portals are described, together with the complex backend machinery that allows distributed computing resources to be harvested efficiently., 14 pages, 3 tables, 2 figures

Lipo-tridecapeptides, a class of bacterial non-ribosomally produced peptides, show strong antimicrobial activity against Gram-positive pathogens, including antibiotic-resistant Staphylococcus aureus and Enterococcus spp. However, many of these lipo-tridecapeptides have shown high hemolytic activity and cytotoxicity, which has limited their potential to be developed into antibiotics. Recently, we reported a novel antimicrobial lipo-tridecapeptide, brevibacillin 2V, which showed no hemolytic activity against human red blood cells at a high concentration of 128 mg/L, opposite to other brevibacillins and lipo-tridecapeptides. In addition, brevibacillin 2V showed much lower cytotoxicity than the other members of the brevibacillin family. In this study, we set out to elucidate the antimicrobial mode of action of brevibacillin 2V. The results show that brevibacillin 2V acts as bactericidal antimicrobial agent against S. aureus (MRSA). Further studies show that brevibacillin 2V exerts its bactericidal activity by binding to the bacterial cell wall synthesis precursor Lipid II and permeabilizing the bacterial membrane. Combined solid-state NMR, circular dichroism, and isothermal titration calorimetry assays indicate that brevibacillin 2V binds to the GlcNAc-MurNAc moiety and/or the pentapeptide of Lipid II. This study provides an insight into the antimicrobial mode of action of brevibacillin 2V. As brevibacillin 2V is a novel and promising antibiotic candidate with low hemolytic activity and cytotoxicity, the here-elucidated mode of action will help further studies to develop it as an alternative antimicrobial agent.

Understanding protein–protein interactions (PPIs) is fundamental to describe and to characterize the formation of biomolecular assemblies, and to establish the energetic principles underlying biological networks. One key aspect of these interfaces is the existence and prevalence of hot-spots (HS) residues that, upon mutation to alanine, negatively impact the formation of such protein–protein complexes. HS have been widely considered in research, both in case studies and in a few large-scale predictive approaches. This review aims to present the current knowledge on PPIs, providing a detailed understanding of the microspecifications of the residues involved in those interactions and the characteristics of those defined as HS through a thorough assessment of related field-specific methodologies. We explore recent accurate artificial intelligence-based techniques, which are progressively replacing well-established classical energy-based methodologies. This article is categorized under: Data Science > Databases and Expert Systems Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Interactions.

Specific interactions with phospholipids are often critical for the function of proteins or drugs, but studying these interactions at high resolution remains difficult, especially in complex membranes that mimic biological conditions. In principle, molecular interactions with phospholipids could be directly probed by solid-state NMR (ssNMR). However, due to the challenge to detect specific lipids in mixed liposomes and limited spectral sensitivity, ssNMR studies of specific lipids in complex membranes are scarce. Here, by using purified biological 13C,15N-labeled phospholipids, we show that we can selectively detect traces of specific lipids in complex membranes. In combination with 1H-detected ssNMR, we show that our approach provides unprecedented high-resolution insights into the mechanisms of drugs that target specific lipids. This broadly applicable approach opens new opportunities for the molecular characterization of specific lipid interactions with proteins or drugs in complex fluid membranes.

Bacterial non-ribosomally produced peptides (NRPs) form a rich source of antibiotics, including more than 20 of these antibiotics that are used in the clinic, such as penicillin G, colistin, vancomycin, and chloramphenicol. Here we report the identification, purification, and characterization of a novel NRP, i.e., brevibacillin 2V (lipo-tridecapeptide), from Brevibacillus laterosporus DSM 25. Brevibacillin 2V has a strong antimicrobial activity against Gram-positive bacterial pathogens (minimum inhibitory concentration = 2 mg/L), including difficult-to-treat antibiotic-resistant Enterococcus faecium, Enterococcus faecalis, and Staphylococcus aureus. Notably, brevibacillin 2V has a much lower hemolytic activity (HC50 > 128 mg/L) and cytotoxicity (CC50 = 45.49 ± 0.24 mg/L) to eukaryotic cells than previously reported NRPs of the lipo-tridecapeptide family, including other brevibacillins, which makes it a promising candidate for antibiotic development. In addition, our results demonstrate that brevibacillins display a synergistic action with established antibiotics against Gram-negative bacterial pathogens. Probably due to the presence of non-canonical amino acids and D-amino acids, brevibacillin 2V showed good stability in human plasma. Thus, we identified and characterized a novel and promising antimicrobial candidate (brevibacillin 2V) with low hemolytic activity and cytotoxicity, which can be used either on its own or as a template for further total synthesis and modification.

Membrane proteins (MPs) are key players in a variety of different cellular processes and constitute the target of around 60% of all Food and Drug Administration–approved drugs. Despite their importance, there is still a massive lack of relevant structural, biochemical and mechanistic information mainly due to their localization within the lipid bilayer. To help fulfil this gap, we developed the MEmbrane protein dimer Novel Structure Analyser database (MENSAdb). This interactive web application summarizes the evolutionary and physicochemical properties of dimeric MPs to expand the available knowledge on the fundamental principles underlying their formation. Currently, MENSAdb contains features of 167 unique MPs (63% homo- and 37% heterodimers) and brings insights into the conservation of residues, accessible solvent area descriptors, average B-factors, intermolecular contacts at 2.5 Å and 4.0 Å distance cut-offs, hydrophobic contacts, hydrogen bonds, salt bridges, π–π stacking, T-stacking and cation–π interactions. The regular update and organization of all these data into a unique platform will allow a broad community of researchers to collect and analyse a large number of features efficiently, thus facilitating their use in the development of prediction models associated with MPs. Database URL: http://www.moreiralab.com/resources/mensadb.

De novo macrocyclic peptides, derived using selection technologies such as phage and mRNA display, present unique and unexpected solutions to challenging biological problems. This is due in part to their unusual folds, which are able to present side chains in ways not available to canonical structures such as α-helices and β-sheets. Despite much recent interest in these molecules, their folding and binding behavior remains poorly characterized. In this work, we present cocrystallization, docking, and solution NMR structures of three de novo macrocyclic peptides that all bind as competitive inhibitors with single-digit nanomolar K i to the active site of human pancreatic α-amylase. We show that a short stably folded motif in one of these is nucleated by internal hydrophobic interactions in an otherwise dynamic conformation in solution. Comparison of the solution structures with a target-bound structure from docking indicates that stabilization of the bound conformation is provided through interactions with the target protein after binding. These three structures also reveal a surprising functional convergence to present a motif of a single arginine sandwiched between two aromatic residues in the interactions of the peptide with the key catalytic residues of the enzyme, despite little to no other structural homology. Our results suggest that intramolecular hydrophobic interactions are important for priming binding of small macrocyclic peptides to their target and that high rigidity is not necessary for high affinity.

Spontaneous activity shifts at constant experimental conditions represent a widespread regulatory mechanism in ion channels. The molecular origins of these modal gating shifts are poorly understood. In the K+ channel KcsA, a multitude of fast activity shifts that emulate the native modal gating behaviour can be triggered by point-mutations in the hydrogen bonding network that controls the selectivity filter. Using solid-state NMR and molecular dynamics simulations in a variety of KcsA mutants, here we show that modal gating shifts in K+ channels are associated with important changes in the channel dynamics that strongly perturb the selectivity filter equilibrium conformation. Furthermore, our study reveals a drastically different motional and conformational selectivity filter landscape in a mutant that mimics voltage-gated K+ channels, which provides a foundation for an improved understanding of eukaryotic K+ channels. Altogether, our results provide a high-resolution perspective on some of the complex functional behaviour of K+ channels.

A wide range of cellular processes requires the formation of multimeric protein complexes. The rise of cryo-electron microscopy (cryo-EM) has enabled the structural characterization of these protein assemblies. The density maps produced can, however, still suffer from limited resolution, impeding the process of resolving structures at atomic resolution. In order to solve this issue, monomers can be fitted into low- to medium-resolution maps. Unfortunately, the models produced frequently contain atomic clashes at the protein-protein interfaces (PPIs), as intermolecular interactions are typically not considered during monomer fitting. Here, we present a refinement approach based on HADDOCK2.4 to remove intermolecular clashes and optimize PPIs. A dataset of 14 cryo-EM complexes was used to test eight protocols. The best-performing protocol, consisting of a semi-flexible simulated annealing refinement with centroid restraints on the monomers, was able to decrease intermolecular atomic clashes by 98% without significantly deteriorating the quality of the cryo-EM density fit.

Proteins play crucial roles in every cellular process by interacting with each other, with nucleic acids, metabolites, and other molecules. The resulting assemblies can be very large and intricate and pose challenges to experimental methods. In the current era of integrative modeling, it is often only by a combination of various experimental techniques and computations that 3D models of those molecular machines can be obtained. Among the various computational approaches available, molecular docking is often the method of choice when it comes to predicting 3D structures of complexes. Docking can generate particularly accurate models when taking into account the available information on the complex of interest. We review here the use of experimental and bioinformatics data in protein-protein docking, describing recent software developments and highlighting applications for the modeling of antibody-antigen complexes and membrane protein complexes, and the use of evolutionary and shape information., 17 pages, 1 figure, 1 table

Photosynthetic light-harvesting complexes of higher plants, moss and green algae can undergo dynamic conformational transitions, which have been correlated to their ability to adapt to fluctuations in the light environment. Herein, we demonstrate the application of solid-state NMR spectroscopy on native, heterogeneous thylakoid membranes of Chlamydomonas reinhardtii (Cr) and on Cr Light-Harvesting Complex II (LHCII) in thylakoid lipid bilayers to detect LHCII conformational dynamics in its native membrane environment. We show that membrane-reconstituted LHCII contains selective sites that undergo fast, large-amplitude motions, including the phytol tails of two chlorophylls. Protein plasticity is also observed in the N-terminal stromal loop and in protein fragments facing the lumen, involving sites that stabilize the xanthophyll-cycle carotenoid violaxanthin and the two luteins. The results report on the intrinsic flexibility of LHCII pigment-protein complexes in a membrane environment, revealing putative sites for conformational switching. In thylakoid membranes, fast dynamics of protein and pigment sites is significantly reduced, which suggests that in their native organelle membranes, LHCII complexes are locked in specific conformational states.STATEMENT OF SIGNIFICANCEPhotosynthetic Light-Harvesting Complexes undergo dynamic conformational transitions that regulate the capacity of the light-harvesting antenna. We demonstrate the application of solid-state (ss)NMR spectroscopy to investigate the structural dynamics of LHCII, the most abundant LHC complex of plants and algae, in native membranes. Selective dynamic protein and pigment residues are identified that are putative sites for a conformational switch.

Catalyst deactivation due to coking is a major challenge in the catalytic fast pyrolysis (CFP) of biomass. Here, a multitechnique investigation of a technical Al2O3-bound ZSM-5-based extrudate catalyst, used for the CFP of pine wood and cellulose (at a reactor temperature of 500 °C), provided insight into the effects of extrusion, the catalytic pyrolysis process, and catalyst regeneration on the catalyst structure. As a result of a reduction in acidity and surface area due to the coking catalyst, the activity dropped drastically with increasing time-on-stream (TOS), as evidenced by a decrease in aromatics yield. Strikingly, confocal fluorescence microscopy at the single-particle level revealed that vapor components derived from whole biomass or just the cellulose component coke differently. While pine-wood-derived species mainly blocked the external area of the catalyst particle, larger carbon deposits were formed inside the catalyst's micropores with cellulose-derived species. Pyridine FT-IR and solid-state NMR spectroscopy demonstrated irreversible changes after regeneration, likely due to partial dealumination. Taken together with

Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance. We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression. We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive with, or outperforms, state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology., The authors present DeepRank, a deep learning framework for the data mining of large sets of 3D protein-protein interfaces (PPI). They use DeepRank to address two challenges in structural biology: distinguishing biological versus crystallographic PPIs in crystal structures, and secondly the ranking of docking models.

Membrane proteins are among the most challenging systems to study with experimental structural biology techniques. The increased number of deposited structures of membrane proteins has opened the route to modeling their complexes by methods such as docking. Here, we present an integrative computational protocol for the modeling of membrane-associated protein assemblies. The information encoded by the membrane is represented by artificial beads, which allow targeting of the docking toward the binding-competent regions. It combines efficient, artificial intelligence-based rigid-body docking by LightDock with a flexible final refinement with HADDOCK to remove potential clashes at the interface. We demonstrate the performance of this protocol on eighteen membrane-associated complexes, whose interface lies between the membrane and either the cytosolic or periplasmic regions. In addition, we provide a comparison to another state-of-the-art docking software, ZDOCK. This protocol should shed light on the still dark fraction of the interactome consisting of membrane proteins., Most approaches for modeling the membrane protein complexes are not capable of incorporating the topological information provided by the membrane. Here authors present an integrative computational protocol for the modeling of membrane-associated protein assemblies, specifically complexes consisting of a membrane-embedded protein and a soluble partner.

Cell walls are essential in the interaction of fungi with the (a)biotic environment and are also key to hyphal morphogenesis and mechanical strength. Here, we used solid-state NMR (ssNMR) spectroscopy combined with HPLC and GC–MS to study the structural organization of the cell wall of a representative of the Basidiomycota, one of the two main phyla of fungi. Based on the data we propose a refined model for the cell wall of a basidiomycete. In this model, the rigid core is built from α- and β-(1,3)-glucan, β-(1,3)-(1,6)-glucan, highly branched and single stranded β-(1,4)-chitin as well as polymeric fucose. The mobile fraction of the cell wall is composed of β-(1,3)-glucan, β-(1,3)-(1,6)-glucan, β-(1,6)-glucan, α-linked reducing and non-reducing ends and polymeric mannose. Together, these findings provide novel insights into the structural organization of the cell wall of the model basidiomycete S. commune that was previously based on destructive chemical and enzymatic analysis.

We report the supramolecular assembly of artificial metalloenzymes (ArMs), based on the Lactococcal multidrug resistance regulator (LmrR) and an exogeneous copper(II)–phenanthroline complex, in the cytoplasm of E. coli cells. A combination of catalysis, cell‐fractionation, and inhibitor experiments, supplemented with in‐cell solid‐state NMR spectroscopy, confirmed the in‐cell assembly. The ArM‐containing whole cells were active in the catalysis of the enantioselective Friedel–Crafts alkylation of indoles and the Diels–Alder reaction of azachalcone with cyclopentadiene. Directed evolution resulted in two different improved mutants for both reactions, LmrR_A92E_M8D and LmrR_A92E_V15A, respectively. The whole‐cell ArM system required no engineering of the microbial host, the protein scaffold, or the cofactor to achieve ArM assembly and catalysis. We consider this a key step towards integrating abiological catalysis with biosynthesis to generate a hybrid metabolism., Artificial metalloenzymes were created by supramolecular assembly in the cytoplasm of E. coli cells. The cells were then applied to the enantioselective biocatalysis of a Friedel–Crafts alkylation of indoles (see picture) and a Diels–Alder reaction. Directed evolution of the artificial metalloenzymes inside the cells provided improved variants for both whole‐cell biocatalytic reactions.

Crosslinking mass spectrometry (Crosslinking MS) has substantially matured as a method over the last two decades through parallel development in multiple labs, demonstrating its applicability for protein structure determination, conformation analysis and mapping protein interactions in complex mixtures. Crosslinking MS has become a much-appreciated and routinely applied tool especially in structural biology. Therefore, it is timely that the community commits to the development of methodological and reporting standards. This white paper builds on an open process comprising a number of events at community conferences since 2015 and identifies aspects of Crosslinking MS for which guidelines should be developed as part of a Crosslinking MS standards initiative., Comment: 26 pages, 1 figure

Targeting of proteins in the histone modification machinery has emerged as a promising new direction to fight disease. The search for compounds that inhibit proteins that readout histone modification has led to several new epigenetic drugs, mostly for proteins involved in recognition of acetylated lysines. However, this approach proved to be a challenging task for methyllysine readers, which typically feature shallow binding pockets. Moreover, reader proteins of trimethyllysine K36 on the histone H3 (H3K36me3) not only bind the methyllysine but also the nucleosomal DNA. Here, we sought to find peptide-based binders of H3K36me3 reader PSIP1, which relies on DNA interactions to tightly bind H3K36me3 modified nucleosomes. We designed several peptides that mimic the nucleosomal context of H3K36me3 recognition by including negatively charged Glu-rich regions. Using a detailed NMR analysis, we find that addition of negative charges boosts binding affinity up to 50-fold while decreasing binding to the trimethyllysine binding pocket. Since screening and selection of compounds for reader domains is typically based solely on affinity measurements due to their lack of enzymatic activity, our case highlights the need to carefully control for the binding mode, in particular for the challenging case of H3K36me3 readers.

Signaling trough p53 is a major cellular stress response mechanism and increases upon nutrient stresses such as starvation. Here, we show in a human hepatoma cell line that starvation leads to robust nuclear p53 stabilization. Using BioID, we determine the cytoplasmic p53 interaction network within the immediate-early starvation response and show that p53 is dissociated from several metabolic enzymes and the kinase PAK2 for which direct binding with the p53 DNA-binding domain was confirmed with NMR studies. Furthermore, proteomics after p53 immunoprecipitation (RIME) uncovered the nuclear interactome under prolonged starvation, where we confirmed the novel p53 interactors SORBS1 (insulin receptor signaling) and UGP2 (glycogen synthesis). Finally, transcriptomics after p53 re-expression revealed a distinct starvation-specific transcriptome response and suggested previously unknown nutrient-dependent p53 target genes. Together, our complementary approaches delineate several nodes of the p53 signaling cascade upon starvation, shedding new light on the mechanisms of p53 as nutrient stress sensor. Given the central role of p53 in cancer biology and the beneficial effects of fasting in cancer treatment, the identified interaction partners and networks could pinpoint novel pharmacologic targets to fine-tune p53 activity.

Dynamic nuclear polarization (DNP) is a powerful method to enhance nuclear magnetic resonance (NMR) signal intensities, enabling unprecedented applications in life and material science. An ultimate goal is to expand the use of DNP-enhanced solid-state NMR to ultrahigh magnetic fields where optimal spectral resolution and sensitivity are integrated. Trityl-nitroxide (TN) biradicals have attracted significant interest in high-field DNP, but their application to complex (bio)molecules has so far been limited. Here we report a novel postmodification strategy for synthesis of hydrophilic TN biradicals in order to improve their use in biomolecular applications. Initially, three TN biradicals (referred to as NATriPols 1-3) with amino-acid linkers were synthesized. EPR studies showed that the α-position of the amino-acid linkers is an ideal modification site for these biradicals since their electron-electron magnetic interactions are marginally affected by the substituents at this position. On the basis of this finding, we synthesized NATriPol-4 with pyridine disulfide appended at the α-position. Postmodification of NATriPol-4 via thiol-click chemistry resulted in various TN biradicals including hydrophilic NATriPol-5 in a quantitative manner. Interestingly, DNP enhancements at 18.8 T of NATriPols for 13C,15N-proline in a glycerol/water matrix are inversely correlated with their hydrophobicity. Importantly, applications of hydrophilic NATriPol-5 and NATriPol-3 to biomolecules including a globular soluble protein and a membrane targeting peptide reveal significantly improved performance compared to TEMTriPol-1 and AMUPol. Our work provides an efficient approach for one-step synthesis of new polarizing agents with tunable physicochemical properties, thus expediting optimization of new biradicals for biomolecular applications at ultrahigh magnetic fields.

Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.

We report on the unexpected finding that click modification of iduronyl azides results in a conformational flip of the pyranose ring, which led to the development of a new strategy for the design of superior enzyme substrates for the diagnostic assaying of iduronate-2-sulfatase (I2S), a lysosomal enzyme related to Hunter syndrome. Synthetic substrates are essential in testing newborns for metabolic disorders to enable early initiation of therapy. Our click-flipped iduronyl triazole showed a remarkably better performance with I2S than commonly used O-iduronates. We found that both O- and triazole-linked substrates are accepted by the enzyme, irrespective of their different conformations, but only the O-linked product inhibits the activity of I2S. Thus, in the long reaction times required for clinical assays, the triazole substrate substantially outperforms the O-iduronate. Applying our click-flipped substrate to assay I2S in dried blood spots sampled from affected patients and random newborns significantly increased the confidence in discriminating between these groups, clearly indicating the potential of the click-flip strategy to control the biomolecular function of carbohydrates., Click-triggered flip of the conformation of a sulfated iduronyl azide afforded a superior enzyme substrate to screen for Hunter syndrome.

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.

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.

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.