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Many studies about classification and the functional annotation of intrinsically disordered proteins (IDPs) are based on either the occurrence of long disordered regions or the fraction of disordered residues in the sequence. Taking into account both criteria we separate the human proteome, taken as a case study, into three variants of proteins: i) ordered proteins (ORDPs), ii) structured proteins with intrinsically disordered regions (IDPRs), and iii) intrinsically disordered proteins (IDPs). The focus of this work is on the different functional roles of IDPs and IDPRs, which up until now have been generally considered as a whole. Previous studies assigned a large set of functional roles to the general category of IDPs. We show here that IDPs and IDPRs have non-overlapping functional spectra, play different roles in human diseases, and deserve to be treated as distinct categories of proteins. IDPs enrich only a few classes, functions, and processes: nucleic acid binding proteins, chromatin binding proteins, transcription factors, and developmental processes. In contrast, IDPRs are spread over several functional protein classes and GO annotations which they partly share with ORDPs. As regards to diseases, we observe that IDPs enrich only cancer-related proteins, at variance with previous results reporting that IDPs are widespread also in cardiovascular and neurodegenerative pathologies. Overall, the operational separation of IDPRs from IDPs is relevant towards correct estimates of the occurrence of intrinsically disordered proteins in genome-wide studies and in the understanding of the functional spectra associated to different flavors of protein disorder.

Complex I (NADH:ubiquinone oxidoreductase) uses the reducing potential of NADH to drive protons across the energy-transducing inner membrane and power oxidative phosphorylation in mammalian mitochondria. Recent cryo-EM analyses have produced near-complete models of all 45 subunits in the bovine, ovine and porcine complexes and have identified two states relevant to complex I in ischemia–reperfusion injury. Here, we describe the 3.3-Å structure of complex I from mouse heart mitochondria, a biomedically relevant model system, in the ‘active’ state. We reveal a nucleotide bound in subunit NDUFA10, a nucleoside kinase homolog, and define mechanistically critical elements in the mammalian enzyme. By comparisons with a 3.9-Å structure of the ‘deactive’ state and with known bacterial structures, we identify differences in helical geometry in the membrane domain that occur upon activation or that alter the positions of catalytically important charged residues. Our results demonstrate the capability of cryo-EM analyses to challenge and develop mechanistic models for mammalian complex I.

Potts models and variational autoencoders (VAEs) have recently gained popularity as generative protein sequence models (GPSMs) to explore fitness landscapes and predict mutation effects. Despite encouraging results, current model evaluation metrics leave unclear whether GPSMs faithfully reproduce the complex multi-residue mutational patterns observed in natural sequences due to epistasis. Here, we develop a set of sequence statistics to assess the “generative capacity” of three current GPSMs: the pairwise Potts Hamiltonian, the VAE, and the site-independent model. We show that the Potts model’s generative capacity is largest, as the higher-order mutational statistics generated by the model agree with those observed for natural sequences, while the VAE’s lies between the Potts and site-independent models. Importantly, our work provides a new framework for evaluating and interpreting GPSM accuracy which emphasizes the role of higher-order covariation and epistasis, with broader implications for probabilistic sequence models in general. Generative models have become increasingly popular in protein design, yet rigorous metrics that allow the comparison of these models are lacking. Here, the authors propose a set of such metrics and use them to compare three popular models.

Certain large DNA viruses, including those in the Marseilleviridae family, encode histones. Here we show that fused histone pairs Hβ-Hα and Hδ-Hγ from Marseillevirus are structurally analogous to the eukaryotic histone pairs H2B-H2A and H4-H3. These viral histones form ‘forced’ heterodimers, and a heterotetramer of four such heterodimers assembles DNA to form structures virtually identical to canonical eukaryotic nucleosomes. The cryo-EM structure of DNA-assembled histone pairs Hβ-Hα and Hδ-Hγ from Marseillevirus, a nucleocytoplasmic large DNA virus, reveals that these proteins form viral nucleosomes with highly conserved features when compared to canonical eukaryotic nucleosomes.

C -Glycosides, in which a sugar moiety is linked via a carbon-carbon (C-C) bond to a non-sugar moiety (aglycone), are found in our food and medicine. The C-C bond is cleaved by intestinal microbes and the resulting aglycones exert various bioactivities. Although the enzymes responsible for the reactions have been identified, their catalytic mechanisms and the generality of the reactions in nature remain to be explored. Here, we present the identification and structural basis for the activation of xenobiotic C -glycosides by heterocomplex C -deglycosylation enzymes from intestinal and soil bacteria. They are found to be metal-dependent enzymes exhibiting broad substrate specificity toward C -glycosides. X-ray crystallographic and cryo-electron microscopic analyses, as well as structure-based mutagenesis, reveal the structural details of these enzymes and the detailed catalytic mechanisms of their remarkable C-C bond cleavage reactions. Furthermore, bioinformatic and biochemical analyses suggest that the C -deglycosylation enzymes are widely distributed in the gut, soil, and marine bacteria. In C-glycosides the sugar moiety is linked through a carbon-carbon bond to the non-sugar moiety, which can be cleaved by intestinal microbes. Here, the authors use bioinformatics analysis to identify C-glycoside deglycosidase enzymes in intestinal and soil bacteria, biochemically characterise them and determine their structures and probe catalytic important residues in mutagenesis experiments.

For evaluating the deepest evolutionary relationships among proteins, sequence similarity is too low for application of sequence-based homology search or phylogenetic methods. In such cases, comparison of protein structures, which are often better conserved than sequences, may provide an alternative means of uncovering deep evolutionary signal. Although major protein structure databases such as SCOP and CATH hierarchically group protein structures, they do not describe the specific evolutionary relationships within a hierarchical level. Structural phylogenies have the potential to fill this gap. However, it is difficult to assess evolutionary relationships derived from structural phylogenies without some means of assessing confidence in such trees. We therefore address two shortcomings in the application of structural data to deep phylogeny. First, we examine whether phylogenies derived from pairwise structural comparisons are sensitive to differences in protein length and shape. We find that structural phylogenetics is best employed where structures have very similar lengths, and that shape fluctuations generated during molecular dynamics simulations impact pairwise comparisons, but not so drastically as to eliminate evolutionary signal. Second, we address the absence of statistical support for structural phylogeny. We present a method for assessing confidence in a structural phylogeny using shape fluctuations generated via molecular dynamics or Monte Carlo simulations of proteins. Our approach will aid the evolutionary reconstruction of relationships across structurally defined protein superfamilies. With the Protein Data Bank now containing in excess of 158,000 entries (December 2019), we predict that structural phylogenetics will become a useful tool for ordering the protein universe.

Approaches are needed for therapy of the severe acute respiratory syndrome from SARS-CoV-2 coronavirus (COVID-19). Interfering with the interaction of viral antigens with the angiotensin converting enzyme 2 (ACE-2) receptor is a promising strategy by blocking the infection of the coronaviruses into human cells. We have implemented a novel protein engineering technology to produce a super-potent tetravalent form of ACE2, coupled to the human immunoglobulin γ1 Fc region, using a self-assembling, tetramerization domain from p53 protein. This high molecular weight Quad protein (ACE2-Fc-TD) retains binding to the SARS-CoV-2 receptor binding spike protein and can form a complex with the spike protein plus anti-viral antibodies. The ACE2-Fc-TD acts as a powerful decoy protein that out-performs soluble monomeric and dimeric ACE2 proteins and blocks both SARS-CoV-2 pseudovirus and SARS-CoV-2 virus infection with greatly enhanced efficacy. The ACE2 tetrameric protein complex promise to be important for development as decoy therapeutic proteins against COVID-19. In contrast to monoclonal antibodies, ACE2 decoy is unlikely to be affected by mutations in SARS-CoV-2 that are beginning to appear in variant forms. In addition, ACE2 multimeric proteins will be available as therapeutic proteins should new coronaviruses appear in the future because these are likely to interact with ACE2 receptor.

The small cyclic neuropeptide hormone oxytocin (OT) and its cognate receptor play a central role in the regulation of social behaviour and sexual reproduction. Here we report the single-particle cryo-electron microscopy structure of the active oxytocin receptor (OTR) in complex with its cognate ligand oxytocin. Our structure provides high-resolution insights into the OT binding mode, the OTR activation mechanism as well as the subtype specificity within the oxytocin/vasopressin receptor family. Here, Waltenspühl et al. report the cryo-EM structure of active human oxytocin receptor in complex with oxytocin and with a heterotrimeric G protein, providing insights into this hormone system critically involved in the regulation of social behaviour and reproduction.

Structural bioinformatics suffers from the lack of interfaces connecting biological structures and machine learning methods, making the application of modern neural network architectures impractical. This negatively affects the development of structure-based bioinformatics methods, causing a bottleneck in biological research. Here we present PyUUL ( https://pyuul.readthedocs.io/ ), a library to translate biological structures into 3D tensors, allowing an out-of-the-box application of state-of-the-art deep learning algorithms. The library converts biological macromolecules to data structures typical of computer vision, such as voxels and point clouds, for which extensive machine learning research has been performed. Moreover, PyUUL allows an out-of-the box GPU and sparse calculation. Finally, we demonstrate how PyUUL can be used by researchers to address some typical bioinformatics problems, such as structure recognition and docking. While artificial intelligence (AI) is quickly becoming ubiquitous, biology still suffers from the lack of interfaces connecting biological structures and modern AI methods. Here, the authors report PyUUL, a library to translate biological structures into 3D differentiable tensorial representations.

The Ff family of filamentous bacteriophages infect gram-negative bacteria, but do not cause lysis of their host cell. Instead, new virions are extruded via the phage-encoded pIV protein, which has homology with bacterial secretins. Here, we determine the structure of pIV from the f1 filamentous bacteriophage at 2.7 Å resolution by cryo-electron microscopy, the first near-atomic structure of a phage secretin. Fifteen f1 pIV subunits assemble to form a gated channel in the bacterial outer membrane, with associated soluble domains projecting into the periplasm. We model channel opening and propose a mechanism for phage egress. By single-cell microfluidics experiments, we demonstrate the potential for secretins such as pIV to be used as adjuvants to increase the uptake and efficacy of antibiotics in bacteria. Finally, we compare the f1 pIV structure to its homologues to reveal similarities and differences between phage and bacterial secretins. New virions of Ff bacteriophages are extruded from the host cell via the channel built from phage protein pIV, homologous to bacterial secretins. Here, the authors report the structure of this channel from the f1 filamentous bacteriophage and propose its use as an adjuvant to increase the uptake and efficacy of antibiotics.