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Bladder cancer remains one of the most common forms of cancer and yet there are limited small molecule targeted therapies. Here, we present a computational platform to identify new potential targets for bladder cancer therapy. Our method initially exploited a set of known driver genes for bladder cancer combined with predicted bladder cancer genes from mutationally enriched protein domain families. We enriched this initial set of genes using protein network data to identify a comprehensive set of 323 putative bladder cancer targets. Pathway and cancer hallmarks analyses highlighted putative mechanisms in agreement with those previously reported for this cancer and revealed protein network modules highly enriched in potential drivers likely to be good targets for targeted therapies. 21 of our potential drug targets are targeted by FDA approved drugs for other diseases — some of them are known drivers or are already being targeted for bladder cancer (FGFR3, ERBB3, HDAC3, EGFR). A further 4 potential drug targets were identified by inheriting drug mappings across our in-house CATH domain functional families (FunFams). Our FunFam data also allowed us to identify drug targets in families that are less prone to side effects i.e., where structurally similar protein domain relatives are less dispersed across the human protein network. We provide information on our novel potential cancer driver genes, together with information on pathways, network modules and hallmarks associated with the predicted and known bladder cancer drivers and we highlight those drivers we predict to be likely drug targets.

Peripheral membrane proteins (PMPs) include a wide variety of proteins that have in common to bind transiently to the chemically complex interfacial region of membranes through their interfacial binding site (IBS). In contrast to protein-protein or protein-DNA/RNA interfaces, peripheral protein-membrane interfaces are poorly characterized.We collected a dataset of PMP domains representative of the variety of PMP functions: membrane-targeting domains (Annexin, C1, C2, discoidin C2, PH, PX), enzymes (PLA, PLC/D) and lipid-transfer proteins (START). The dataset contains 1328 experimental structures and 1194 AphaFold models. We mapped the amino acid composition and structural patterns of the IBS of each protein in this dataset, and evaluated which were more likely to be found at the IBS compared to the rest of the domains’ accessible surface.In agreement with earlier work we find that about two thirds of the PMPs in the dataset have protruding hydrophobes (Leu, Ile, Phe, Tyr, Trp and Met) at their IBS. The three aromatic amino acids Trp, Tyr and Phe are a hallmark of PMPs IBS regardless of whether they protrude on loops or not. This is also the case for lysines but not arginines suggesting that, unlike for Arg-rich membrane-active peptides, the less membrane-disruptive lysine is preferred in PMPs. Another striking observation was the over-representation of glycines at the IBS of PMPs compared to the rest of their surface, possibly procuring IBS loops a much-needed flexibility to insert in-between membrane lipids. The analysis of the 9 superfamilies revealed amino acid distribution patterns in agreement with their known functions and membrane-binding mechanisms. Besides revealing novel amino acids patterns at protein-membrane interfaces, our work contributes a new PMP dataset and an analysis pipeline that can be further built upon for future studies of PMPs properties, or for developing PMPs prediction tools using for example, machine learning approaches.

Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologs. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologs in the Protein Databank. We noticed that the model quality was higher and the Root mean square deviation (RMSD) lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein–protein interfaces and conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, whether they destabilized the protein structure based on ddG calculations or whether they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms, a larger percentage of disease-associated missense mutations were buried, closer to predicted functional sites, predicted as destabilizing and pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.

SARS-CoV-2, the agent of the COVID-19 pandemic, can infect a wide range of mammals. Since its spread in humans, secondary host jumps of SARS-CoV-2 from humans to a variety of domestic and wild populations of mammals have been documented. The evolution of SARS-CoV-2 in different host species is of fundamental interest while also providing indication of how SARS-CoV-2 may have adapted to human hosts soon after the initial host jump, a time window for which there are no genome sequences available. Moreover, the study of SARS-CoV-2 circulating in animals is critical to assess the risk that the transmission of animal-adapted viral lineages back into humans (i.e., spillback) may pose. Here, we compared the genomic landscapes of SARS-CoV-2 isolated from animal species relative to that in humans, profiling the mutational biases indicative of potentially different selective pressures in animals. We focused on viral genomes collected in infected mink (Neovison vison) and white-tailed deer (Odocoileus virginianus) for which reports of multiple independent spillover events and subsequent animal-to-animal transmission are available. We identified six candidate mutations for animal-specific adaptation in mink (NSP9_G37E, Spike_F486L, Spike_N501T, Spike_Y453F, ORF3a_T229I, ORF3a_L219V), and one in deer (NSP3a_L1035F), though these mutations appear to confer minimal advantage for circulation in humans. Additionally, circulation of SARS-CoV-2 in mink and deer has not caused considerable changes to the evolutionary trajectory of SARS-CoV-2 thus far. Finally, our results suggest that minimal adaptation was required for human-to-animal spillover and subsequent onward transmission in mink and deer, highlighting the ‘generalist’ nature of SARS-CoV-2 as a pathogen of mammalian hosts.

Genome3D consortium is a collaborative project involving protein structure prediction and annotation resources developed by six world-leading structural bioinformatics groups, based in the United Kingdom (namely Blundell, Murzin, Gough, Sternberg, Orengo, and Jones). The main objective of Genome3D serves as a common portal to provide both predicted models and annotations of proteins in model organisms, using several resources developed by these labs such as CATH-Gene3D, DOMSERF, pDomTHREADER, PHYRE, SUPERFAMILY, FUGUE/TOCATTA, and VIVACE. These resources primarily use SCOP- and/or CATH-based protein domain assignments. Another objective of Genome3D is to compare structural classifications of protein domains in CATH and SCOP databases and to provide a consensus mapping of CATH and SCOP protein superfamilies. CATH/SCOP mapping analyses led to the identification of total of 1429 consensus superfamilies.Currently, Genome3D provides structural annotations for ten model organisms, including Homo sapiens, Arabidopsis thaliana, Mus musculus, Escherichia coli, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Plasmodium falciparum, Staphylococcus aureus, and Schizosaccharomyces pombe. Thus, Genome3D serves as a common gateway to each structure prediction/annotation resource and allows users to perform comparative assessment of the predictions. It, thus, assists researchers to broaden their perspective on structure/function predictions of their query protein of interest in selected model organisms.

Protein domains mediate drug-protein interactions and this principle can guide the design of multi-target drugs i.e. polypharmacology. In this study, we associate multi-target drugs with CATH functional families through the overrepresentation of targets of those drugs in CATH functional families. Thus, we identify CATH functional families that are currently enriched in drugs (druggable CATH functional families) and we use the network properties of these druggable protein families to analyse their association with drug side effects. Analysis of selected druggable CATH functional families, enriched in drug targets, show that relatives exhibit highly conserved drug binding sites. Furthermore, relatives within druggable CATH functional families occupy central positions in a human protein functional network, cluster together forming network neighbourhoods and are less likely to be within proteins associated with drug side effects. Our results demonstrate that CATH functional families can be used to identify drug-target interactions, opening a new research direction in target identification.

Gene3D (http://gene3d.biochem.ucl.ac.uk) is a database of globular domain annotations for millions of available protein sequences. Gene3D has previously featured in the Database issue of NAR and here we report a significant update to the Gene3D database. The current release, Gene3D v16, has significantly expanded its domain coverage over the previous version and now contains over 95 million domain assignments. We also report a new method for dealing with complex domain architectures that exist in Gene3D, arising from discontinuous domains. Amongst other updates, we have added visualization tools for exploring domain annotations in the context of other sequence features and in gene families. We also provide web-pages to visualize other domain families that co-occur with a given query domain family.

This article presents a historical review of the protein structure classification database CATH. Together with the SCOP database, CATH remains comprehensive and reasonably up-to-date with the now more than 100,000 protein structures in the PDB. We review the expansion of the CATH and SCOP resources to capture predicted domain structures in the genome sequence data and to provide information on the likely functions of proteins mediated by their constituent domains. The establishment of comprehensive function annotation resources has also meant that domain families can be functionally annotated allowing insights into functional divergence and evolution within protein families., Highlights • We present a historical review of the protein structure database CATH. • We review the expansion of the CATH and SCOP resources with sequence data and functional annotations. • How functional annotation resources allow insights into functional divergence and evolution within protein families.

Summary Study participants with HIV-associated sensory polyneuropathy (HIV-SN) had higher plasma triglyceride concentrations, depression, anxiety, catastrophizing scores, and prevalence of insomnia than HIV participants without HIV-SN., HIV-associated sensory neuropathy (HIV-SN) is a frequent complication of HIV infection and a major source of morbidity. A cross-sectional deep profiling study examining HIV-SN was conducted in people living with HIV in a high resource setting using a battery of measures which included the following: parameters of pain and sensory symptoms (7 day pain diary, Neuropathic Pain Symptom Inventory [NPSI] and Brief Pain Inventory [BPI]), sensory innervation (structured neurological examination, quantitative sensory testing [QST] and intraepidermal nerve fibre density [IENFD]), psychological state (Pain Anxiety Symptoms Scale-20 [PASS-20], Depression Anxiety and Positive Outlook Scale [DAPOS], and Pain Catastrophizing Scale [PCS], insomnia (Insomnia Severity Index [ISI]), and quality of life (Short Form (36) Health Survey [SF-36]). The diagnostic utility of the Brief Peripheral Neuropathy Screen (BPNS), Utah Early Neuropathy Scale (UENS), and Toronto Clinical Scoring System (TCSS) were evaluated. Thirty-six healthy volunteers and 66 HIV infected participants were recruited. A novel triumvirate case definition for HIV-SN was used that required 2 out of 3 of the following: 2 or more abnormal QST findings, reduced IENFD, and signs of a peripheral neuropathy on a structured neurological examination. Of those with HIV, 42% fulfilled the case definition for HIV-SN (n = 28), of whom 75% (n = 21) reported pain. The most frequent QST abnormalities in HIV-SN were loss of function in mechanical and vibration detection. Structured clinical examination was superior to QST or IENFD in HIV-SN diagnosis. HIV-SN participants had higher plasma triglyceride, concentrations depression, anxiety and catastrophizing scores, and prevalence of insomnia than HIV participants without HIV-SN.