UniProt: the universal protein knowledgebase in 2021

DGLinker: flexible knowledge-graph prediction of disease-gene associations

As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration.

catRAPID omics v2.0: going deeper and wider in the prediction of protein-RNA interactions

Prediction of protein-RNA interactions is important to understand post-transcriptional events taking place in the cell. Here we introduce catRAPID omics v2.0, an update of our web server dedicated to the computation of protein-RNA interaction propensities at the transcriptome- and RNA-binding proteome-level in 8 model organisms. The server accepts multiple input protein or RNA sequences and computes their catRAPID interaction scores on updated precompiled libraries. Additionally, it is now possible to predict the interactions between a custom protein set and a custom RNA set. Considerable effort has been put into the generation of a new database of RNA-binding motifs that are searched within the predicted RNA targets of proteins. In this update, the sequence fragmentation scheme of the catRAPID fragment module has been included, which allows the server to handle long linear RNAs and to analyse circular RNAs. For the top-scoring protein-RNA pairs, the web server shows the predicted binding sites in both protein and RNA sequences and reports whether the predicted interactions are conserved in orthologous protein-RNA pairs. The catRAPID omics v2.0 web server is a powerful tool for the characterization and classification of RNA-protein interactions and is freely available at http://service.tartaglialab.com/page/catrapid_omics2_group along with documentation and tutorial.

An in-silico analysis reveals 7,7'-bializarin as a promising DNA gyrase B inhibitor on Gram-positive and Gram-negative bacteria

Bacterial diseases are considered by the World Health Organization to be one of the greatest threats to public health worldwide, mainly due to the increasingly frequent resistance to traditional antibiotics. Estimates from the World Bank indicate that the annual global economic impacts of antibiotic resistance will reach US$1.0-3.4 trillion by 2030. With this, the demand for studies aiming at the discovery of new antibiotics or molecules that may play a synergistic role within the spectrum of drug-resistant bacteria is of fundamental importance. In this in silico study, ligands generated from anthraquinones with established antibacterial activity were evaluated as potential inhibitors of the DNA gyrase subunit B of two species of Gram-positive and two Gram-negative bacteria. The main result of molecular docking-based virtual screening reveals several anthraquinones with remarkable binding energies, of which 7,7'-bializarin (ZINC000004783172) exhibited the highest value for all DNA gyrases subunit B studied and formed stable complexes, as evidenced by molecular dynamics simulations. Collectively, the results presented here reveal the potential of this molecule to bind tightly to the active site of DNA gyrases subunit B of Escherichia coli, Salmonella enterica (subtype typhi), Enterococcus faecalis, and Staphylococcus aureus, and therefore represents a promising candidate for further in vitro testing aimed at evaluating its antibacterial effect.

Bayesian optimization with evolutionary and structure-based regularization for directed protein evolution

Background

Directed evolution (DE) is a technique for protein engineering that involves iterative rounds of mutagenesis and screening to search for sequences that optimize a given property, such as binding affinity to a specified target. Unfortunately, the underlying optimization problem is under-determined, and so mutations introduced to improve the specified property may come at the expense of unmeasured, but nevertheless important properties (ex. solubility, thermostability, etc). We address this issue by formulating DE as a regularized Bayesian optimization problem where the regularization term reflects evolutionary or structure-based constraints.

Results

We applied our approach to DE to three representative proteins, GB1, BRCA1, and SARS-CoV-2 Spike, and evaluated both evolutionary and structure-based regularization terms. The results of these experiments demonstrate that: (i) structure-based regularization usually leads to better designs (and never hurts), compared to the unregularized setting; (ii) evolutionary-based regularization tends to be least effective; and (iii) regularization leads to better designs because it effectively focuses the search in certain areas of sequence space, making better use of the experimental budget. Additionally, like previous work in Machine learning assisted DE, we find that our approach significantly reduces the experimental burden of DE, relative to model-free methods.

Conclusion

Introducing regularization into a Bayesian ML-assisted DE framework alters the exploratory patterns of the underlying optimization routine, and can shift variant selections towards those with a range of targeted and desirable properties. In particular, we find that structure-based regularization often improves variant selection compared to unregularized approaches, and never hurts.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13015-021-00195-4.

PPI-MASS: An Interactive Web Server to Identify Protein-Protein Interactions From Mass Spectrometry-Based Proteomics Data

Mass spectrometry-based proteomics methods are widely used to identify and quantify protein complexes involved in diverse biological processes. Specifically, tandem mass spectrometry methods represent an accurate and sensitive strategy for identifying protein-protein interactions. However, most of these approaches provide only lists of peptide fragments associated with a target protein, without performing further analyses to discriminate physical or functional protein-protein interactions. Here, we present the PPI-MASS web server, which provides an interactive analytics platform to identify protein-protein interactions with pharmacological potential by filtering a large protein set according to different biological features. Starting from a list of proteins detected by MS-based methods, PPI-MASS integrates an automatized pipeline to obtain information of each protein from freely accessible databases. The collected data include protein sequence, functional and structural properties, associated pathologies and drugs, as well as location and expression in human tissues. Based on this information, users can manipulate different filters in the web platform to identify candidate proteins to establish physical contacts with a target protein. Thus, our server offers a simple but powerful tool to detect novel protein-protein interactions, avoiding tedious and time-consuming data postprocessing. To test the web server, we employed the interactome of the TRPM4 and TMPRSS11a proteins as a use case. From these data, protein-protein interactions were identified, which have been validated through biochemical and bioinformatic studies. Accordingly, our web platform provides a comprehensive and complementary tool for identifying protein-protein complexes assisting the future design of associated therapies.

PathBIX-a web server for network-based pathway annotation with adaptive null models

Motivation

Pathway annotation is a vital tool for interpreting and giving meaning to experimental data in life sciences. Numerous tools exist for this task, where the most recent generation of pathway enrichment analysis tools, network-based methods, utilize biological networks to gain a richer source of information as a basis of the analysis than merely the gene content. Network-based methods use the network crosstalk between the query gene set and the genes in known pathways, and compare this to a null model of random expectation.

Results

We developed PathBIX, a novel web application for network-based pathway analysis, based on the recently published ANUBIX algorithm which has been shown to be more accurate than previous network-based methods. The PathBIX website performs pathway annotation for 21 species, and utilizes prefetched and preprocessed network data from FunCoup 5.0 networks and pathway data from three databases: KEGG, Reactome, and WikiPathways.

Availability

https://pathbix.sbc.su.se/.

Contact

erik.sonnhammer@scilifelab.se.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

MiMiC: a bioinformatic approach for generation of synthetic communities from metagenomes

Environmental and host-associated microbial communities are complex ecosystems, of which many members are still unknown. Hence, it is challenging to study community dynamics and important to create model systems of reduced complexity that mimic major community functions. Therefore, we developed MiMiC, a computational approach for data-driven design of simplified communities from shotgun metagenomes. We first built a comprehensive database of species-level bacterial and archaeal genomes (n = 22 627) consisting of binary (presence/absence) vectors of protein families (Pfam = 17 929). MiMiC predicts the composition of minimal consortia using an iterative scoring system based on maximal match-to-mismatch ratios between this database and the Pfam binary vector of any input metagenome. Pfam vectorization retained enough resolution to distinguish metagenomic profiles between six environmental and host-derived microbial communities (n = 937). The calculated number of species per minimal community ranged between 5 and 11, with MiMiC selected communities better recapitulating the functional repertoire of the original samples than randomly selected species. The inferred minimal communities retained habitat-specific features and were substantially different from communities consisting of most abundant members. The use of a mixture of known microbes revealed the ability to select 23 of 25 target species from the entire genome database. MiMiC is open source and available at https://github.com/ClavelLab/MiMiC.

Experimental and Genomic Evaluation of the Oestrogen Degrading Bacterium Rhodococcus equi ATCC13557

Rhodococcus equi ATCC13557 was selected as a model organism to study oestrogen degradation based on its previous ability to degrade 17α-ethinylestradiol (EE2). Biodegradation experiments revealed that R. equi ATCC13557 was unable to metabolise EE2. However, it was able to metabolise E2 with the major metabolite being E1 with no further degradation of E1. However, the conversion of E2 into E1 was incomplete, with 11.2 and 50.6% of E2 degraded in mixed (E1-E2-EE2) and E2-only conditions, respectively. Therefore, the metabolic pathway of E2 degradation by R. equi ATCC13557 may have two possible pathways. The genome of R. equi ATCC13557 was sequenced, assembled, and mapped for the first time. The genome analysis allowed the identification of genes possibly responsible for the observed biodegradation characteristics of R. equi ATCC13557. Several genes within R. equi ATCC13557 are similar, but not identical in sequence, to those identified within the genomes of other oestrogen degrading bacteria, including Pseudomonas putida strain SJTE-1 and Sphingomonas strain KC8. Homologous gene sequences coding for enzymes potentially involved in oestrogen degradation, most commonly a cytochrome P450 monooxygenase (oecB), extradiol dioxygenase (oecC), and 17β-hydroxysteroid dehydrogenase (oecA), were identified within the genome of R. equi ATCC13557. These searches also revealed a gene cluster potentially coding for enzymes involved in steroid/oestrogen degradation; 3-carboxyethylcatechol 2,3-dioxygenase, 2-hydroxymuconic semialdehyde hydrolase, 3-alpha-(or 20-beta)-hydroxysteroid dehydrogenase, 3-(3-hydroxy-phenyl)propionate hydroxylase, cytochrome P450 monooxygenase, and 3-oxosteroid 1-dehydrogenase. Further, the searches revealed steroid hormone metabolism gene clusters from the 9, 10-seco pathway, therefore R. equi ATCC13557 also has the potential to metabolise other steroid hormones such as cholesterol.

Combined Theoretical and Experimental Study to Unravel the Differences in Promiscuous Amidase Activity of Two Nonhomologous Enzymes

Convergent evolution has resulted in nonhomologous enzymes that contain similar active sites that catalyze the same primary and secondary reactions. Comparing how these enzymes achieve their reaction promiscuity can yield valuable insights to develop functions from the optimization of latent activities. In this work, we have focused on the promiscuous amidase activity in the esterase from Bacillus subtilis (Bs2) and compared with the same activity in the promiscuous lipase B from Candida antarctica (CALB). The study, combining multiscale quantum mechanics/molecular mechanics (QM/MM) simulations, deep machine learning approaches, and experimental characterization of Bs2 kinetics, confirms the amidase activity of Bs2 and CALB. The computational results indicate that both enzymes offer a slightly different reaction environment reflected by electrostatic effects within the active site, thus resulting in a different reaction mechanism during the acylation step. A convolutional neural network (CNN) has been used to understand the conserved amino acids among the evolved protein family and suggest that Bs2 provides a more robust protein scaffold to perform future mutagenesis studies. Results derived from this work will help reveal the origin of enzyme promiscuity, which will find applications in enzyme (re)design, particularly in creating a highly active amidase.

Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions

Background

Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level.

Results

Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k_{{cat}}$$\end{document}kcat values, predicting the differential expression of enzymes under different growth conditions.

Conclusions

This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-021-01614-2.

Up State of the SARS-COV-2 Spike Homotrimer Favors an Increased Virulence for New Variants

The COVID-19 pandemic has spread worldwide. However, as soon as the first vaccines-the only scientifically verified and efficient therapeutic option thus far-were released, mutations combined into variants of SARS-CoV-2 that are more transmissible and virulent emerged, raising doubts about their efficiency. This study aims to explain possible molecular mechanisms responsible for the increased transmissibility and the increased rate of hospitalizations related to the new variants. A combination of theoretical methods was employed. Constant-pH Monte Carlo simulations were carried out to quantify the stability of several spike trimeric structures at different conformational states and the free energy of interactions between the receptor-binding domain (RBD) and angiotensin-converting enzyme II (ACE2) for the most worrying variants. Electrostatic epitopes were mapped using the PROCEEDpKa method. These analyses showed that the increased virulence is more likely to be due to the improved stability to the S trimer in the opened state, in which the virus can interact with the cellular receptor, ACE2, rather than due to alterations in the complexation RBD-ACE2, since the difference observed in the free energy values was small (although more attractive in general). Conversely, the South African/Beta variant (B.1.351), compared with the SARS-CoV-2 wild type (wt), is much more stable in the opened state with one or two RBDs in the up position than in the closed state with three RBDs in the down position favoring the infection. Such results contribute to understanding the natural history of disease and indicate possible strategies for developing new therapeutic molecules and adjusting the vaccine doses for higher B-cell antibody production.

Structure-Based Virtual Screening Reveals Ibrutinib and Zanubrutinib as Potential Repurposed Drugs against COVID-19

Coronavirus disease (COVID)-19 is the leading global health threat to date caused by a severe acute respiratory syndrome coronavirus (SARS-CoV-2). Recent clinical trials reported that the use of Bruton's tyrosine kinase (BTK) inhibitors to treat COVID-19 patients could reduce dyspnea and hypoxia, thromboinflammation, hypercoagulability and improve oxygenation. However, the mechanism of action remains unclear. Thus, this study employs structure-based virtual screening (SBVS) to repurpose BTK inhibitors acalabrutinib, dasatinib, evobrutinib, fostamatinib, ibrutinib, inositol 1,3,4,5-tetrakisphosphate, spebrutinib, XL418 and zanubrutinib against SARS-CoV-2. Molecular docking is conducted with BTK inhibitors against structural and nonstructural proteins of SARS-CoV-2 and host targets (ACE2, TMPRSS2 and BTK). Molecular mechanics-generalized Born surface area (MM/GBSA) calculations and molecular dynamics (MD) simulations are then carried out on the selected complexes with high binding energy. Ibrutinib and zanubrutinib are found to be the most potent of the drugs screened based on the results of computational studies. Results further show that ibrutinib and zanubrutinib could exploit different mechanisms at the viral entry and replication stage and could be repurposed as potential inhibitors of SARS-CoV-2 pathogenesis.

Molecular Insights into the Oxygen-Sensing Pathway and Erythropoietin Expression Regulation in Erythropoiesis

Erythropoiesis is regulated by several factors, including the oxygen-sensing pathway as the main regulator of erythropoietin (EPO) synthesis in the kidney. The release of EPO from the kidney and its binding to the EPO receptor (EPOR) on erythrocyte progenitor cells in the bone marrow results in increased erythropoiesis. Any imbalance in these homeostatic mechanisms can lead to dysregulated erythropoiesis and hematological disorders. For example, mutations in genes encoding key players of oxygen-sensing pathway and regulation of EPO production (HIF-EPO pathway), namely VHL, EGLN, EPAS1 and EPO, are well known causative factors that contribute to the development of erythrocytosis. We aimed to investigate additional molecular mechanisms involved in the HIF-EPO pathway that correlate with erythropoiesis. To this end, we conducted an extensive literature search and used several in silico tools. We identified genes encoding transcription factors and proteins that control transcriptional activation or repression; genes encoding kinases, deacetylases, methyltransferases, conjugating enzymes, protein ligases, and proteases involved in post-translational modifications; and genes encoding nuclear transport receptors that regulate nuclear transport. All these genes may modulate the stability or activity of HIF2α and its partners in the HIF-EPO pathway, thus affecting EPO synthesis. The theoretical information we provide in this work can be a valuable tool for a better understanding of one of the most important regulatory pathways in the process of erythropoiesis. This knowledge is necessary to discover the causative factors that may contribute to the development of hematological diseases and improve current diagnostic and treatment solutions in this regard.

Broadening INPP5E phenotypic spectrum: detection of rare variants in syndromic and non-syndromic IRD

Pathogenic variants in INPP5E cause Joubert syndrome (JBTS), a ciliopathy with retinal involvement. However, despite sporadic cases in large cohort sequencing studies, a clear association with non-syndromic inherited retinal degenerations (IRDs) has not been made. We validate this association by reporting 16 non-syndromic IRD patients from ten families with bi-allelic mutations in INPP5E. Additional two patients showed early onset IRD with limited JBTS features. Detailed phenotypic description for all probands is presented. We report 14 rare INPP5E variants, 12 of which have not been reported in previous studies. We present tertiary protein modeling and analyze all INPP5E variants for deleteriousness and phenotypic correlation. We observe that the combined impact of INPP5E variants in JBTS and non-syndromic IRD patients does not reveal a clear genotype-phenotype correlation, suggesting the involvement of genetic modifiers. Our study cements the wide phenotypic spectrum of INPP5E disease, adding proof that sequence defects in this gene can lead to early-onset non-syndromic IRD.

Transamidation Down-Regulates Intestinal Immunity of Recombinant α-Gliadin in HLA-DQ8 Transgenic Mice

Enzymatic transamidation of gliadins by microbial transglutaminase (mTG) inhibits interferon-γ (IFN-γ) secretion by intestinal T cell lines in patients with celiac disease (CD). To gain insight into the cellular mechanisms underlying the down-regulatory effects of transamidation, we tested a single recombinant α-gliadin (r-gliadin) harbouring two immunodominant peptides, p13 (aa. 120–139) and p23 (aa. 220–239), in HLA-DQ8 transgenic mice, a model of gluten sensitivity. Mice were intranasally immunised with r-gliadin or r-gliadin transamidated by mTG (K-r-gliadin) along with cholera toxin, and the response of mesenteric lymph node cells was analysed by cytokine multiplex assay. An in vitro challenge with r-gliadin was characterised by secretion of specific cytokines featuring both innate immunity and the Th1/Th2/Th17 pattern of the adaptive response. Notably, transamidation specifically down-regulated the Th1 response. Structural studies performed on K-r-gliadin confirmed that specific glutamine residues in p13 and p23, previously found to be deamidated by tissue transglutaminase, were also transamidated by mTG. In silico analysis, simulating p13 and p23 peptide binding to HLA-DQ8 showed that these glutamines, in the form of glutamate, could interact by means of salt bridges with peculiar amino acids of the alpha chain of HLA-DQ8, suggesting that their transamidation may influence the HLA-restricted recognition of these peptides. Thus, the structural findings provided a rationale to explain the down-regulation of the r-gliadin-specific Th1 response following transamidation.

The Role of Microbiota in Primary Sclerosing Cholangitis and Related Biliary Malignancies

Primary sclerosing cholangitis (PSC) is an immune-related cholangiopathy characterized by biliary inflammation, cholestasis, and multifocal bile duct strictures. It is associated with high rates of progression to end-stage liver disease as well as a significant risk of cholangiocarcinoma (CCA), gallbladder cancer, and colorectal carcinoma. Currently, no effective medical treatment with an impact on the overall survival is available, and liver transplantation is the only curative treatment option. Emerging evidence indicates that gut microbiota is associated with disease pathogenesis. Several studies analyzing fecal and mucosal samples demonstrate a distinct gut microbiome in individuals with PSC compared to healthy controls and individuals with inflammatory bowel disease (IBD) without PSC. Experimental mouse and observational human data suggest that a diverse set of microbial functions may be relevant, including microbial metabolites and bacterial processing of pharmacological agents, bile acids, or dietary compounds, altogether driving the intrahepatic inflammation. Despite critical progress in this field over the past years, further functional characterization of the role of the microbiota in PSC and related malignancies is needed. In this review, we discuss the available data on the role of the gut microbiome and elucidate important insights into underlying pathogenic mechanisms and possible microbe-altering interventions.

Structure-based virtual screening of CYP1A1 inhibitors: towards rapid tier-one assessment of potential developmental toxicants

Cytochrome P450 1A1 (CYP1A1) metabolizes estrogens, melatonin, and other key endogenous signaling molecules critical for embryonic/fetal development. The enzyme has increasing expression during pregnancy, and its inhibition or knockout increases embryonic/fetal lethality and/or developmental problems. Here, we present a virtual screening model for CYP1A1 inhibitors based on the orthosteric and predicted allosteric sites of the enzyme. Using 1001 reference compounds with CYP1A1 activity data, we optimized the decision thresholds of our model and classified the training compounds with 68.3% balanced accuracy (91.0% sensitivity and 45.7% specificity). We applied our final model to 11 known CYP1A1 orthosteric binders and related compounds, and found that our ranking of the known orthosteric binders generally agrees with the relative activity of CYP1A1 in metabolizing these compounds. We also applied the model to 22 new test compounds with unknown/unclear CYP1A1 inhibitory activity, and predicted 16 of them are CYP1A1 inhibitors. The CYP1A1 potency and modes of inhibition of these 22 compounds were experimentally determined. We confirmed that most predicted inhibitors, including drugs contraindicated during pregnancy (amiodarone, bicalutamide, cyproterone acetate, ketoconazole, and tamoxifen) and environmental agents suspected to be endocrine disruptors (bisphenol A, diethyl and dibutyl phthalates, and zearalenone), are indeed potent inhibitors of CYP1A1. Our results suggest that virtual screening may be used as a rapid tier-one method to screen for potential CYP1A1 inhibitors, and flag them out for further experimental evaluations.

A Human Endogenous Bornavirus-Like Nucleoprotein Encodes a Mitochondrial Protein Associated with Cell Viability

Endogenous retroviruses (ERVs) are sequences in animal genomes that originated from ancient retrovirus infections; they provide genetic novelty in hosts by being coopted as functional genes or elements during evolution. Recently, we demonstrated that endogenous elements from not only from retroviruses but also nonretroviral RNA viruses are a possible source of functional genes in host animals. The remnants of ancient bornavirus infections, called endogenous bornavirus-like elements (EBLs), are present in the genomes of a wide variety of vertebrate species, and some express functional products in host cells. Previous studies have predicted that the human EBL locus derived from bornavirus nucleoprotein, termed hsEBLN-2, expresses mRNA encoding a protein, suggesting that hsEBLN-2 has acquired a cellular function during evolution. However, the detailed function of the hsEBLN-2-derived product remains to be elucidated. In this study, we show that the hsEBLN-2-derived protein E2 acts as a mitochondrial protein that interacts with mitochondrial host factors associated with apoptosis, such as HAX-1. We also demonstrate that knockdown of hsEBLN-2-derived RNA increased the levels of PARP and caspase-3 cleavage and markedly decreased cell viability. In contrast, overexpression of E2 enhanced cell viability, as well as the intracellular stability of HAX-1, under stress conditions. Our results suggest that hsEBLN-2 has been coopted as a host gene, the product of which is involved in cell viability by interacting with mitochondrial proteins. IMPORTANCE Our genomes contain molecular fossils of ancient viruses, called endogenous virus elements (EVEs). Mounting evidence suggests that EVEs derived from nonretroviral RNA viruses have acquired functions in host cells during evolution. Previous studies have revealed that a locus encoding a bornavirus-derived EVE, hsEBLN-2, which was generated approximately 43 million years ago in a human ancestor, may be linked to the development of some tumors. However, the function of hsEBLN-2 has not been determined. In this study, we found that the E2 protein, an expression product of hsEBLN-2, interacts with apoptosis-related host proteins as a mitochondrial protein and affects cell viability. This study suggests that nonretroviral RNA viral EVEs have been coopted by hosts with more diverse functions than previously thought, showing a pivotal role for RNA virus infection in evolution.

Open Issues for Protein Function Assignment in Haloferax volcanii and Other Halophilic Archaea

Background: Annotation ambiguities and annotation errors are a general challenge in genomics. While a reliable protein function assignment can be obtained by experimental characterization, this is expensive and time-consuming, and the number of such Gold Standard Proteins (GSP) with experimental support remains very low compared to proteins annotated by sequence homology, usually through automated pipelines. Even a GSP may give a misleading assignment when used as a reference: the homolog may be close enough to support isofunctionality, but the substrate of the GSP is absent from the species being annotated. In such cases, the enzymes cannot be isofunctional. Here, we examined a variety of such issues in halophilic archaea (class Halobacteria), with a strong focus on the model haloarchaeon Haloferax volcanii. Results: Annotated proteins of Hfx. volcanii were identified for which public databases tend to assign a function that is probably incorrect. In some cases, an alternative, probably correct, function can be predicted or inferred from the available evidence, but this has not been adopted by public databases because experimental validation is lacking. In other cases, a probably invalid specific function is predicted by homology, and while there is evidence that this assigned function is unlikely, the true function remains elusive. We listed 50 of those cases, each with detailed background information, so that a conclusion about the most likely biological function can be drawn. For reasons of brevity and comprehension, only the key aspects are listed in the main text, with detailed information being provided in a corresponding section of the Supplementary Materials. Conclusions: Compiling, describing and summarizing these open annotation issues and functional predictions will benefit the scientific community in the general effort to improve the evaluation of protein function assignments and more thoroughly detail them. By highlighting the gaps and likely annotation errors currently in the databases, we hope this study will provide a framework for experimentalists to systematically confirm (or disprove) our function predictions or to uncover yet more unexpected functions.

Expression, Purification, and Characterization of a Well-Adapted Tyrosinase from Peatlands Identified by Partial Community Analysis

In peatlands, bacterial tyrosinases (TYRs) are proposed to act as key regulators of carbon storage by removing phenolic compounds, which inhibit the degradation of organic carbon. Historically, TYR activity has been blocked by anoxia resulting from persistent waterlogging; however, recent events of prolonged summer drought have boosted TYR activity and, consequently, the release of carbon stored in the form of organic compounds from peatlands. Since 30% of the global soil carbon stock is stored in peatlands, a profound understanding of the production and activity of TYRs is essential to assess the impact of carbon dioxide emitted from peatlands on climate change. TYR partial sequences identified by degenerated primers suggest a versatile TYR enzyme community naturally present in peatlands, which is produced by a phylogenetically diverse spectrum of bacteria, including Proteobacteria and Actinobacteria. One full-length sequence of an extracellular TYR (SzTYR) identified from a soda-rich inland salt marsh has been heterologously expressed and purified. SzTYR exhibits a molecular mass of 30 891.8 Da and shows a pH optimum of 9.0. Spectroscopic studies and kinetic investigations characterized SzTYR as a tyrosinase and proved its activity toward monophenols (coumaric acid), diphenols (caffeic acid, protocatechuic acid), and triphenols (gallic acid) naturally present in peatlands.

No Evidence Known Viruses Play a Role in the Pathogenesis of Onchocerciasis-Associated Epilepsy. An Explorative Metagenomic Case-Control Study

Despite the increasing epidemiological evidence that the Onchocerca volvulus parasite is strongly associated with epilepsy in children, hence the name onchocerciasis-associated epilepsy (OAE), the pathophysiological mechanism of OAE remains to be elucidated. In June 2014, children with unprovoked convulsive epilepsy and healthy controls were enrolled in a case control study in Titule, Bas-Uélé Province in the Democratic Republic of the Congo (DRC) to identify risk factors for epilepsy. Using a subset of samples collected from individuals enrolled in this study (16 persons with OAE and 9 controls) plasma, buffy coat, and cerebrospinal fluid (CSF) were subjected to random-primed next-generation sequencing. The resulting sequences were analyzed using sensitive computational methods to identify viral DNA and RNA sequences. Anneloviridae, Flaviviridae, Hepadnaviridae (Hepatitis B virus), Herpesviridae, Papillomaviridae, Polyomaviridae (Human polyomavirus), and Virgaviridae were identified in cases and in controls. Not unexpectedly, a variety of bacteriophages were also detected in all cases and controls. However, none of the identified viral sequences were found enriched in OAE cases, which was our criteria for agents that might play a role in the etiology or pathogenesis of OAE.

Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks

Characterization of protein complexes, i.e. sets of proteins assembling into a single larger physical entity, is important, as such assemblies play many essential roles in cells such as gene regulation. From networks of protein-protein interactions, potential protein complexes can be identified computationally through the application of community detection methods, which flag groups of entities interacting with each other in certain patterns. Most community detection algorithms tend to be unsupervised and assume that communities are dense network subgraphs, which is not always true, as protein complexes can exhibit diverse network topologies. The few existing supervised machine learning methods are serial and can potentially be improved in terms of accuracy and scalability by using better-suited machine learning models and parallel algorithms. Here, we present Super.Complex, a distributed, supervised AutoML-based pipeline for overlapping community detection in weighted networks. We also propose three new evaluation measures for the outstanding issue of comparing sets of learned and known communities satisfactorily. Super.Complex learns a community fitness function from known communities using an AutoML method and applies this fitness function to detect new communities. A heuristic local search algorithm finds maximally scoring communities, and a parallel implementation can be run on a computer cluster for scaling to large networks. On a yeast protein-interaction network, Super.Complex outperforms 6 other supervised and 4 unsupervised methods. Application of Super.Complex to a human protein-interaction network with ~8k nodes and ~60k edges yields 1,028 protein complexes, with 234 complexes linked to SARS-CoV-2, the COVID-19 virus, with 111 uncharacterized proteins present in 103 learned complexes. Super.Complex is generalizable with the ability to improve results by incorporating domain-specific features. Learned community characteristics can also be transferred from existing applications to detect communities in a new application with no known communities. Code and interactive visualizations of learned human protein complexes are freely available at: https://sites.google.com/view/supercomplex/super-complex-v3-0.

Key Technologies for Progressing Discovery of Microbiome-Based Medicines

A growing number of experimental and computational approaches are illuminating the “microbial dark matter” and uncovering the integral role of commensal microbes in human health. Through this work, it is now clear that the human microbiome presents great potential as a therapeutic target for a plethora of diseases, including inflammatory bowel disease, diabetes and obesity. The development of more efficacious and targeted treatments relies on identification of causal links between the microbiome and disease; with future progress dependent on effective links between state-of-the-art sequencing approaches, computational analyses and experimental assays. We argue determining causation is essential, which can be attained by generating hypotheses using multi-omic functional analyses and validating these hypotheses in complex, biologically relevant experimental models. In this review we discuss existing analysis and validation methods, and propose best-practice approaches required to enable the next phase of microbiome research.

Residue-Residue Interaction Prediction via Stacked Meta-Learning

Protein-protein interactions (PPIs) are the basis of most biological functions determined by residue-residue interactions (RRIs). Predicting residue pairs responsible for the interaction is crucial for understanding the cause of a disease and drug design. Computational approaches that considered inexpensive and faster solutions for RRI prediction have been widely used to predict protein interfaces for further analysis. This study presents RRI-Meta, an ensemble meta-learning-based method for RRI prediction. Its hierarchical learning structure comprises four base classifiers and one meta-classifier to integrate predictive strengths from different classifiers. It considers multiple feature types, including sequence-, structure-, and neighbor-based features, for characterizing other properties of a residue interaction environment to better distinguish between noninteracting and interacting residues. We conducted the same experiments using the same data as previously reported in the literature to demonstrate RRI-Meta's performance. Experimental results show that RRI-Meta is superior to several current prediction tools. Additionally, to analyze the factors that affect the performance of RRI-Meta, we conducted a comparative case study using different protein complexes.

Determinants for α4β2 vs. α3β4 Subtype Selectivity of Pyrrolidine-Based nAChRs Ligands: A Computational Perspective with Focus on Recent cryo-EM Receptor Structures

The selectivity of α4β2 nAChR agonists over the α3β4 nicotinic receptor subtype, predominant in ganglia, primarily conditions their therapeutic range and it is still a complex and challenging issue for medicinal chemists and pharmacologists. Here, we investigate the determinants for such subtype selectivity in a series of more than forty α4β2 ligands we have previously reported, docking them into the structures of the two human subtypes, recently determined by cryo-electron microscopy. They are all pyrrolidine based analogues of the well-known α4β2 agonist N-methylprolinol pyridyl ether A-84543 and differ in the flexibility and pattern substitution of their aromatic portion. Indeed, the direct or water mediated interaction with hydrophilic residues of the relatively narrower β2 minus side through the elements decorating the aromatic ring and the stabilization of the latter by facing to the not conserved β2-Phe119 result as key distinctive features for the α4β2 affinity. Consistently, these compounds show, despite the structural similarity, very different α4β2 vs. α3β4 selectivities, from modest to very high, which relate to rigidity/extensibility degree of the portion containing the aromatic ring and to substitutions at the latter. Furthermore, the structural rationalization of the rat vs. human differences of α4β2 vs. α3β4 selectivity ratios is here proposed.

The genome assembly and annotation of the Apollo butterfly Parnassius apollo, a flagship species for conservation biology

Conservation genomics has made dramatic improvements over the past decade, leveraging the power of genomes to infer diverse parameters central to conservation management questions. However, much of this effort has focused upon vertebrate species, despite insects providing similar flagship status with the added benefit of smaller genomes, shorter generation times and extensive historical collections in museums. Here we present the genome of the Apollo butterfly (Parnassius apollo, Papilionidae), an iconic endangered butterfly, which like many species in this genus, needs conservation genomic attention yet lacks a genome. Using 68.7 Gb of long-read data (N50 = 15.2 kb) we assembled a 1.4 Gb genome for the Apollo butterfly, making this the largest sequenced Lepidopteran genome to date. The assembly was highly contiguous (N50 = 7.1 Mb) and complete (97% of Lepidopteran BUSCOs were single-copy and complete) and consisted of 1,707 contigs. Using RNAseq data and Arthropoda proteins, we annotated 28.3K genes. Alignment with the closest-related chromosome-level assembly, Papilio bianor, reveals a highly conserved chromosomal organization, albeit genome size is highly expanded in the Apollo butterfly, due primarily to a dramatic increase in repetitive element content. Using this alignment for superscaffolding places the P. apollo genome in to 31 chromosomal scaffolds, and together with our functional annotation, provides an essential resource for advancing conservation genomics in a flagship species for insect conservation.

A Tale of Loops and Tails: The Role of Intrinsically Disordered Protein Regions in R-Loop Recognition and Phase Separation

R-loops are non-canonical, three-stranded nucleic acid structures composed of a DNA:RNA hybrid, a displaced single-stranded (ss)DNA, and a trailing ssRNA overhang. R-loops perform critical biological functions under both normal and disease conditions. To elucidate their cellular functions, we need to understand the mechanisms underlying R-loop formation, recognition, signaling, and resolution. Previous high-throughput screens identified multiple proteins that bind R-loops, with many of these proteins containing folded nucleic acid processing and binding domains that prevent (e.g., topoisomerases), resolve (e.g., helicases, nucleases), or recognize (e.g., KH, RRMs) R-loops. However, a significant number of these R-loop interacting Enzyme and Reader proteins also contain long stretches of intrinsically disordered regions (IDRs). The precise molecular and structural mechanisms by which the folded domains and IDRs synergize to recognize and process R-loops or modulate R-loop-mediated signaling have not been fully explored. While studying one such modular R-loop Reader, the Fragile X Protein (FMRP), we unexpectedly discovered that the C-terminal IDR (C-IDR) of FMRP is the predominant R-loop binding site, with the three N-terminal KH domains recognizing the trailing ssRNA overhang. Interestingly, the C-IDR of FMRP has recently been shown to undergo spontaneous Liquid-Liquid Phase Separation (LLPS) assembly by itself or in complex with another non-canonical nucleic acid structure, RNA G-quadruplex. Furthermore, we have recently shown that FMRP can suppress persistent R-loops that form during transcription, a process that is also enhanced by LLPS via the assembly of membraneless transcription factories. These exciting findings prompted us to explore the role of IDRs in R-loop processing and signaling proteins through a comprehensive bioinformatics and computational biology study. Here, we evaluated IDR prevalence, sequence composition and LLPS propensity for the known R-loop interactome. We observed that, like FMRP, the majority of the R-loop interactome, especially Readers, contains long IDRs that are highly enriched in low complexity sequences with biased amino acid composition, suggesting that these IDRs could directly interact with R-loops, rather than being “mere flexible linkers” connecting the “functional folded enzyme or binding domains”. Furthermore, our analysis shows that several proteins in the R-loop interactome are either predicted to or have been experimentally demonstrated to undergo LLPS or are known to be associated with phase separated membraneless organelles. Thus, our overall results present a thought-provoking hypothesis that IDRs in the R-loop interactome can provide a functional link between R-loop recognition via direct binding and downstream signaling through the assembly of LLPS-mediated membrane-less R-loop foci. The absence or dysregulation of the function of IDR-enriched R-loop interactors can potentially lead to severe genomic defects, such as the widespread R-loop-mediated DNA double strand breaks that we recently observed in Fragile X patient-derived cells.

Complete Genome Sequence and Function Gene Identify of Prometryne-Degrading Strain Pseudomonas sp. DY-1

The genus Pseudomonas is widely recognized for its potential for environmental remediation and plant growth promotion. Pseudomonas sp. DY-1 was isolated from the agricultural soil contaminated five years by prometryne, it manifested an outstanding prometryne degradation efficiency and an untapped potential for plant resistance improvement. Thus, it is meaningful to comprehend the genetic background for strain DY-1. The whole genome sequence of this strain revealed a series of environment adaptive and plant beneficial genes which involved in environmental stress response, heavy metal or metalloid resistance, nitrate dissimilatory reduction, riboflavin synthesis, and iron acquisition. Detailed analyses presented the potential of strain DY-1 for degrading various organic compounds via a homogenized pathway or the protocatechuate and catechol branches of the β-ketoadipate pathway. In addition, heterologous expression, and high efficiency liquid chromatography (HPLC) confirmed that prometryne could be oxidized by a Baeyer-Villiger monooxygenase (BVMO) encoded by a gene in the chromosome of strain DY-1. The result of gene knock-out suggested that the sulfate starvation-induced (SSI) genes in this strain might also involve in the process of prometryne degradation. These results would provide the molecular basis for the application of strain DY-1 in various fields and would contribute to the study of prometryne biodegradation mechanism as well.

Sec14 family of lipid transfer proteins in yeasts

The hydrophobicity of lipids prevents their free movement across the cytoplasm. To achieve highly heterogeneous and precisely regulated lipid distribution in different cellular membranes, lipids are transported by lipid transfer proteins (LTPs) in addition to their transport by vesicles. Sec14 family is one of the most extensively studied groups of LTPs. Here we provide an overview of Sec14 family of LTPs in the most studied yeast Saccharomyces cerevisiae as well as in other selected non-Saccharomyces yeasts-Schizosaccharomyces pombe, Kluyveromyces lactis, Candida albicans, Candida glabrata, Cryptococcus neoformans, and Yarrowia lipolytica. Discussed are specificities of Sec14-domain LTPs in various yeasts, their mode of action, subcellular localization, and physiological function. In addition, quite few Sec14 family LTPs are target of antifungal drugs, serve as modifiers of drug resistance or influence virulence of pathologic yeasts. Thus, they represent an important object of study from the perspective of human health.

Investigating the active compounds and mechanism of HuaShi XuanFei formula for prevention and treatment of COVID-19 based on network pharmacology and molecular docking analysis

Traditional Chinese medicine (TCM) has exerted positive effects in controlling the COVID-19 pandemic. HuaShi XuanFei Formula (HSXFF) was developed to treat patients with mild and general COVID-19 in Zhejiang Province, China. The present study seeks to explore its potentially active compounds and pharmacological mechanisms against COVID-19 based on network pharmacology, molecular docking, and molecular dynamics (MD) simulation. All components of HSXFF were harvested from the pharmacology database of the TCMSP system. COVID-19-related targets were retrieved from using OMIM and GeneCards databases. The herb-compound-targets network was constructed by Cytoscape. The target protein–protein interaction (PPI) network, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to discover the potential key target genes and mechanism. The main active compounds of HSXFF were docked with 3C-like (3CL) protease hydrolase and angiotensin-converting enzyme 2 (ACE2). The MD simulation confirmed the binding stability of docking results. The herbs-targets network mainly contained 52 compounds and 70 corresponding targets, including key targets such as RELA, TNF, TP53, IL6, MAPK1, CXCL8, IL-1β, and MAPK14. The GO and KEGG indicated that HSXFF may be mainly acting on the IL-17 signaling pathway, TNF signaling pathway, NF-κB signaling pathway, etc. The molecular docking results indicated that isovitexin and procyanidin B1 showed the highest affinity with 3CL and ACE2, respectively, which were confirmed by MD simulation. These findings suggested HSXFF exerted therapeutic effects involving “multi-compounds and multi-targets.” It might be working through directly inhibiting the virus, improving immune function, and reducing the inflammatory in response to anti-COVID-19. In summary, the present study would provide a valuable direction for further research of HSXFF.

Interpreting the lipidome: bioinformatic approaches to embrace the complexity

BACKGROUND

Improvements in mass spectrometry (MS) technologies coupled with bioinformatics developments have allowed considerable advancement in the measurement and interpretation of lipidomics data in recent years. Since research areas employing lipidomics are rapidly increasing, there is a great need for bioinformatic tools that capture and utilize the complexity of the data. Currently, the diversity and complexity within the lipidome is often concealed by summing over or averaging individual lipids up to (sub)class-based descriptors, losing valuable information about biological function and interactions with other distinct lipids molecules, proteins and/or metabolites.

AIM OF REVIEW

To address this gap in knowledge, novel bioinformatics methods are needed to improve identification, quantification, integration and interpretation of lipidomics data. The purpose of this mini-review is to summarize exemplary methods to explore the complexity of the lipidome.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Here we describe six approaches that capture three core focus areas for lipidomics: (1) lipidome annotation including a resolvable database identifier, (2) interpretation via pathway- and enrichment-based methods, and (3) understanding complex interactions to emphasize specific steps in the analytical process and highlight challenges in analyses associated with the complexity of lipidome data.

Molecular Details of the Frataxin-Scaffold Interaction during Mitochondrial Fe-S Cluster Assembly

Iron-sulfur clusters are essential to almost every life form and utilized for their unique structural and redox-targeted activities within cells during many cellular pathways. Although there are three different Fe-S cluster assembly pathways in prokaryotes (the NIF, SUF and ISC pathways) and two in eukaryotes (CIA and ISC pathways), the iron-sulfur cluster (ISC) pathway serves as the central mechanism for providing 2Fe-2S clusters, directly and indirectly, throughout the entire cell in eukaryotes. Proteins central to the eukaryotic ISC cluster assembly complex include the cysteine desulfurase, a cysteine desulfurase accessory protein, the acyl carrier protein, the scaffold protein and frataxin (in humans, NFS1, ISD11, ACP, ISCU and FXN, respectively). Recent molecular details of this complex (labeled NIAUF from the first letter from each ISC protein outlined earlier), which exists as a dimeric pentamer, have provided real structural insight into how these partner proteins arrange themselves around the cysteine desulfurase, the core dimer of the (NIAUF)2 complex. In this review, we focus on both frataxin and the scaffold within the human, fly and yeast model systems to provide a better understanding of the biophysical characteristics of each protein alone and within the FXN/ISCU complex as it exists within the larger NIAUF construct. These details support a complex dynamic interaction between the FXN and ISCU proteins when both are part of the NIAUF complex and this provides additional insight into the coordinated mechanism of Fe-S cluster assembly.

Vascular Endothelial Galectins in Leukocyte Trafficking

Leukocyte recruitment to the site of injury is a crucial event in the regulation of an inflammatory response. Tight regulation of interactions between the endothelium and circulating leukocytes is necessary to ensure a protective response to injury does not result in inflammatory disease. Rising interest in the broad immunoregulatory roles displayed by members of the glycan-binding galectin family suggests that these proteins could be an attractive target for therapeutic intervention, since their expression is significantly altered in disease. The focus of this review is to summarize current knowledge on the role of galectins in leukocyte trafficking during inflammation and the clinical approaches being taken to target these interactions for treatment of inflammatory disease.

H3K14 hyperacetylation‑mediated c‑Myc binding to the miR‑30a‑5p gene promoter under hypoxia postconditioning protects senescent cardiomyocytes from hypoxia/reoxygenation injury

Our previous study reported that microRNA (miR)‑30a‑5p upregulation under hypoxia postconditioning (HPostC) exert a protective effect on aged H9C2 cells against hypoxia/reoxygenation injury via DNA methyltransferase 3B‑induced DNA hypomethylation at the miR‑30a‑5p gene promoter. This suggests that miR‑30a‑5p may be a potential preventative and therapeutic target for ischemic heart disease in aged myocardium. The present study aimed to investigate the underlying mechanisms of miR‑30a‑5p transcription in aged myocardium in ischemic heart disease. Cardiomyocytes were treated with 8 mg/ml D‑galactose for 9 days, and then exposed to hypoxic conditions. Cell viability was determined using a cell viability assay. Expression levels of histone deacetylase 2 (HDAC2), LC3B‑II/I, beclin‑1 and p62 were detected via reverse transcription‑quantitative PCR and western blotting. Chromatin immunoprecipitation‑PCR and luciferase reporter assays were performed to evaluate the effect of c‑Myc binding and activity on the miR‑30a‑5p promoter in senescent cardiomyocytes following HPostC. It was found that HPostC enhanced the acetylation levels of H3K14 at the miR‑30a‑5p gene promoter in senescent cardiomyocytes, which attributed to the decreased expression of HDAC2. In addition, c‑Myc could positively regulate miR‑30a‑5p transcription to inhibit senescent cardiomyocyte autophagy. Mechanically, it was observed that increased H3K14 acetylation level exposed to romidepsin facilitated c‑Myc binding to the miR‑30a‑5p gene promoter region, which led to the increased transcription of miR‑30a‑5p. Taken together, these results demonstrated that HDAC2‑mediated H3K14 hyperacetylation promoted c‑Myc binding to the miR‑30a‑5p gene promoter, which contributed to HPostC senescent cardioprotection.

Nitrate- and Nitrite-Sensing Histidine Kinases: Function, Structure, and Natural Diversity

Under anaerobic conditions, bacteria may utilize nitrates and nitrites as electron acceptors. Sensitivity to nitrous compounds is achieved via several mechanisms, some of which rely on sensor histidine kinases (HKs). The best studied nitrate- and nitrite-sensing HKs (NSHKs) are NarQ and NarX from Escherichia coli. Here, we review the function of NSHKs, analyze their natural diversity, and describe the available structural information. In particular, we show that around 6000 different NSHK sequences forming several distinct clusters may now be found in genomic databases, comprising mostly the genes from Beta- and Gammaproteobacteria as well as from Bacteroidetes and Chloroflexi, including those from anaerobic ammonia oxidation (annamox) communities. We show that the architecture of NSHKs is mostly conserved, although proteins from Bacteroidetes lack the HAMP and GAF-like domains yet sometimes have PAS. We reconcile the variation of NSHK sequences with atomistic models and pinpoint the structural elements important for signal transduction from the sensor domain to the catalytic module over the transmembrane and cytoplasmic regions spanning more than 200 Å.

De Novo Transcriptome Meta-Assembly of the Mixotrophic Freshwater Microalga Euglena gracilis

Euglena gracilis is a well-known photosynthetic microeukaryote considered as the product of a secondary endosymbiosis between a green alga and a phagotrophic unicellular belonging to the same eukaryotic phylum as the parasitic trypanosomatids. As its nuclear genome has proven difficult to sequence, reliable transcriptomes are important for functional studies. In this work, we assembled a new consensus transcriptome by combining sequencing reads from five independent studies. Based on a detailed comparison with two previously released transcriptomes, our consensus transcriptome appears to be the most complete so far. Remapping the reads on it allowed us to compare the expression of the transcripts across multiple culture conditions at once and to infer a functionally annotated network of co-expressed genes. Although the emergence of meaningful gene clusters indicates that some biological signal lies in gene expression levels, our analyses confirm that gene regulation in euglenozoans is not primarily controlled at the transcriptional level. Regarding the origin of E. gracilis, we observe a heavily mixed gene ancestry, as previously reported, and rule out sequence contamination as a possible explanation for these observations. Instead, they indicate that this complex alga has evolved through a convoluted process involving much more than two partners.

DNA Damage Response in the Adaptive Arm of the Immune System: Implications for Autoimmunity

In complex environments, cells have developed molecular responses to confront threats against the genome and achieve the maintenance of genomic stability assuring the transfer of undamaged DNA to their progeny. DNA damage response (DDR) mechanisms may be activated upon genotoxic or environmental agents, such as cytotoxic drugs or ultraviolet (UV) light, and during physiological processes requiring DNA transactions, to restore DNA alterations that may cause cellular malfunction and affect viability. In addition to the DDR, multicellular organisms have evolved specialized immune cells to respond and defend against infections. Both adaptive and innate immune cells are subjected to DDR processes, either as a prerequisite to the immune response, or as a result of random endogenous and exogenous insults. Aberrant DDR activities have been extensively studied in the immune cells of the innate arm, but not in adaptive immune cells. Here, we discuss how the aberrant DDR may lead to autoimmunity, with emphasis on the adaptive immune cells and the potential of therapeutic targeting.

AniProtDB: A Collection of Consistently Generated Metazoan Proteomes for Comparative Genomics Studies

To address the void in the availability of high-quality proteomic data traversing the animal tree, we have implemented a pipeline for generating de novo assemblies based on publicly available data from the NCBI Sequence Read Archive, yielding a comprehensive collection of proteomes from 100 species spanning 21 animal phyla. We have also created the Animal Proteome Database (AniProtDB), a resource providing open access to this collection of high-quality metazoan proteomes, along with information on predicted proteins and protein domains for each taxonomic classification and the ability to perform sequence similarity searches against all proteomes generated using this pipeline. This solution vastly increases the utility of these data by removing the barrier to access for research groups who do not have the expertise or resources to generate these data themselves and enables the use of data from non-traditional research organisms that have the potential to address key questions in biomedicine.

Influence of charge configuration on substrate binding to SARS-CoV-2 main protease

While the state-of-the-art computational simulations support the neutral state for the catalytic dyad of the SARS-CoV-2 main protease, the recently-reported neutron structure exhibits a zwitterionic form. To better compare the structural and dynamical features of the two charge configurations, we perform a Molecular Dynamics study of the dimeric enzyme in complex with a peptide substrate. The simulations show that the enzyme charge configuration from the neutron structure is not compatible with a catalytically-competent binding mode for peptide substrates.

Protein sequence design with deep generative models

Protein engineering seeks to identify protein sequences with optimized properties. When guided by machine learning, protein sequence generation methods can draw on prior knowledge and experimental efforts to improve this process. In this review, we highlight recent applications of machine learning to generate protein sequences, focusing on the emerging field of deep generative methods.

Common pathways and functional profiles reveal underlying patterns in Breast, Kidney and Lung cancers

Background

Cancer is a major health problem which presents a high heterogeneity. In this work we explore omics data from Breast, Kidney and Lung cancers at different levels as signalling pathways, functions and miRNAs, as part of the CAMDA 2019 Hi-Res Cancer Data Integration Challenge. Our goal is to find common functional patterns which give rise to the generic microenvironment in these cancers and contribute to a better understanding of cancer pathogenesis and a possible clinical translation down further studies.

Results

After a tumor versus normal tissue comparison of the signaling pathways and cell functions, we found 828 subpathways, 912 Gene Ontology terms and 91 Uniprot keywords commonly significant to the three studied tumors. Such features interestingly show the power to classify tumor samples into subgroups with different survival times, and predict tumor state and tissue of origin through machine learning techniques. We also found cancer-specific alternative activation subpathways, such as the ones activating STAT5A in ErbB signaling pathway. miRNAs evaluation show the role of miRNAs, such as mir-184 and mir-206, as regulators of many cancer pathways and their value in prognoses.

Conclusions

The study of the common functional and pathway activities of different cancers is an interesting approach to understand molecular mechanisms of the tumoral process regardless of their tissue of origin. The existence of platforms as the CAMDA challenges provide the opportunity to share knowledge and improve future scientific research and clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13062-021-00293-8.

Molecular mechanisms of phenotypic variability in monogenic autoinflammatory diseases

Monogenic autoinflammatory diseases are a group of rheumatologic disorders caused by dysregulation in the innate immune system. The molecular mechanisms of these disorders are linked to defects in inflammasome-mediated, NF-κB-mediated or interferon-mediated inflammatory signalling pathways, cytokine receptors, the actin cytoskeleton, proteasome complexes and various enzymes. As with other human disorders, disease-causing variants in a single gene can present with variable expressivity and incomplete penetrance. In some cases, pathogenic variants in the same gene can be inherited either in a recessive or dominant manner and can cause distinct and seemingly unrelated phenotypes, although they have a unifying biochemical mechanism. With an enhanced understanding of protein structure and functionality of protein domains, genotype-phenotype correlations are beginning to be unravelled. Many of the mutated proteins are primarily expressed in haematopoietic cells, and their malfunction leads to systemic inflammation. Disease presentation is also defined by a specific effect of the mutant protein in a particular cell type and, therefore, the resulting phenotype might be more deleterious in one tissue than in another. Many patients present with the expanded immunological disease continuum that includes autoinflammation, immunodeficiency, autoimmunity and atopy, which necessitate genetic testing.

Deep Learning-Based Advances in Protein Structure Prediction

Obtaining an accurate description of protein structure is a fundamental step toward understanding the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine protein structures, the gap between the number of protein sequences and known protein structures is ever increasing. Computational protein structure prediction is one of the ways to fill this gap. Recently, the protein structure prediction field has witnessed a lot of advances due to Deep Learning (DL)-based approaches as evidenced by the success of AlphaFold2 in the most recent Critical Assessment of protein Structure Prediction (CASP14). In this article, we highlight important milestones and progresses in the field of protein structure prediction due to DL-based methods as observed in CASP experiments. We describe advances in various steps of protein structure prediction pipeline viz. protein contact map prediction, protein distogram prediction, protein real-valued distance prediction, and Quality Assessment/refinement. We also highlight some end-to-end DL-based approaches for protein structure prediction approaches. Additionally, as there have been some recent DL-based advances in protein structure determination using Cryo-Electron (Cryo-EM) microscopy based, we also highlight some of the important progress in the field. Finally, we provide an outlook and possible future research directions for DL-based approaches in the protein structure prediction arena.

Representation learning applications in biological sequence analysis

Although remarkable advances have been reported in high-throughput sequencing, the ability to aptly analyze a substantial amount of rapidly generated biological (DNA/RNA/protein) sequencing data remains a critical hurdle. To tackle this issue, the application of natural language processing (NLP) to biological sequence analysis has received increased attention. In this method, biological sequences are regarded as sentences while the single nucleic acids/amino acids or k-mers in these sequences represent the words. Embedding is an essential step in NLP, which performs the conversion of these words into vectors. Specifically, representation learning is an approach used for this transformation process, which can be applied to biological sequences. Vectorized biological sequences can then be applied for function and structure estimation, or as input for other probabilistic models. Considering the importance and growing trend for the application of representation learning to biological research, in the present study, we have reviewed the existing knowledge in representation learning for biological sequence analysis.

Sherlock: an open-source data platform to store, analyze and integrate Big Data for computational biologists

In the era of Big Data, data collection underpins biological research more than ever before. In many cases, this can be as time-consuming as the analysis itself. It requires downloading multiple public databases with various data structures, and in general, spending days preparing the data before answering any biological questions. Here, we introduce Sherlock, an open-source, cloud-based big data platform ( https://earlham-sherlock.github.io/) to solve this problem. Sherlock provides a gap-filling way for computational biologists to store, convert, query, share and generate biology data while ultimately streamlining bioinformatics data management. The Sherlock platform offers a simple interface to leverage big data technologies, such as Docker and PrestoDB. Sherlock is designed to enable users to analyze, process, query and extract information from extremely complex and large data sets. Furthermore, Sherlock can handle different structured data (interaction, localization, or genomic sequence) from several sources and convert them to a common optimized storage format, for example, the Optimized Row Columnar (ORC). This format facilitates Sherlock's ability to quickly and efficiently execute distributed analytical queries on extremely large data files and share datasets between teams. The Sherlock platform is freely available on GitHub, and contains specific loader scripts for structured data sources of genomics, interaction and expression databases. With these loader scripts, users can easily and quickly create and work with specific file formats, such as JavaScript Object Notation (JSON) or ORC. For computational biology and large-scale bioinformatics projects, Sherlock provides an open-source platform empowering data management, analytics, integration and collaboration through modern big data technologies.

Sherlock: an open-source data platform to store, analyze and integrate Big Data for computational biologists

In the era of Big Data, data collection underpins biological research more than ever before. In many cases, this can be as time-consuming as the analysis itself. It requires downloading multiple public databases with various data structures, and in general, spending days preparing the data before answering any biological questions. Here, we introduce Sherlock, an open-source, cloud-based big data platform ( https://earlham-sherlock.github.io/) to solve this problem. Sherlock provides a gap-filling way for computational biologists to store, convert, query, share and generate biology data while ultimately streamlining bioinformatics data management. The Sherlock platform offers a simple interface to leverage big data technologies, such as Docker and PrestoDB. Sherlock is designed to enable users to analyze, process, query and extract information from extremely complex and large data sets. Furthermore, Sherlock can handle different structured data (interaction, localization, or genomic sequence) from several sources and convert them to a common optimized storage format, for example, the Optimized Row Columnar (ORC). This format facilitates Sherlock's ability to quickly and efficiently execute distributed analytical queries on extremely large data files and share datasets between teams. The Sherlock platform is freely available on GitHub, and contains specific loader scripts for structured data sources of genomics, interaction and expression databases. With these loader scripts, users can easily and quickly create and work with specific file formats, such as JavaScript Object Notation (JSON) or ORC. For computational biology and large-scale bioinformatics projects, Sherlock provides an open-source platform empowering data management, analytics, integration and collaboration through modern big data technologies.

Sherlock: an open-source data platform to store, analyze and integrate Big Data for biology

In the era of Big Data, data collection underpins biological research more so than ever before. In many cases this can be as time-consuming as the analysis itself, requiring downloading multiple different public databases, with different data structures, and in general, spending days before answering any biological questions. To solve this problem, we introduce an open-source, cloud-based big data platform, called Sherlock ( https://earlham-sherlock.github.io/). Sherlock provides a gap-filling way for biologists to store, convert, query, share and generate biology data, while ultimately streamlining bioinformatics data management. The Sherlock platform provides a simple interface to leverage big data technologies, such as Docker and PrestoDB. Sherlock is designed to analyse, process, query and extract the information from extremely complex and large data sets. Furthermore, Sherlock is capable of handling different structured data (interaction, localization, or genomic sequence) from several sources and converting them to a common optimized storage format, for example to the Optimized Row Columnar (ORC). This format facilitates Sherlock's ability to quickly and easily execute distributed analytical queries on extremely large data files as well as share datasets between teams. The Sherlock platform is freely available on Github, and contains specific loader scripts for structured data sources of genomics, interaction and expression databases. With these loader scripts, users are able to easily and quickly create and work with the specific file formats, such as JavaScript Object Notation (JSON) or ORC. For computational biology and large-scale bioinformatics projects, Sherlock provides an open-source platform empowering data management, data analytics, data integration and collaboration through modern big data technologies.

Interplay of RNA-Binding Proteins and microRNAs in Neurodegenerative Diseases

The number of patients with neurodegenerative diseases (NDs) is increasing, along with the growing number of older adults. This escalation threatens to create a medical and social crisis. NDs include a large spectrum of heterogeneous and multifactorial pathologies, such as amyotrophic lateral sclerosis, frontotemporal dementia, Alzheimer’s disease, Parkinson’s disease, Huntington’s disease and multiple system atrophy, and the formation of inclusion bodies resulting from protein misfolding and aggregation is a hallmark of these disorders. The proteinaceous components of the pathological inclusions include several RNA-binding proteins (RBPs), which play important roles in splicing, stability, transcription and translation. In addition, RBPs were shown to play a critical role in regulating miRNA biogenesis and metabolism. The dysfunction of both RBPs and miRNAs is often observed in several NDs. Thus, the data about the interplay among RBPs and miRNAs and their cooperation in brain functions would be important to know for better understanding NDs and the development of effective therapeutics. In this review, we focused on the connection between miRNAs, RBPs and neurodegenerative diseases.

To Ubiquitinate or Not to Ubiquitinate: TRIM17 in Cell Life and Death

TRIM17 is a member of the TRIM family, a large class of RING-containing E3 ubiquitin-ligases. It is expressed at low levels in adult tissues, except in testis and in some brain regions. However, it can be highly induced in stress conditions which makes it a putative stress sensor required for the triggering of key cellular responses. As most TRIM members, TRIM17 can act as an E3 ubiquitin-ligase and promote the degradation by the proteasome of substrates such as the antiapoptotic protein MCL1. Intriguingly, TRIM17 can also prevent the ubiquitination of other proteins and stabilize them, by binding to other TRIM proteins and inhibiting their E3 ubiquitin-ligase activity. This duality of action confers several pivotal roles to TRIM17 in crucial cellular processes such as apoptosis, autophagy or cell division, but also in pathological conditions as diverse as Parkinson's disease or cancer. Here, in addition to recent data that endorse this duality, we review what is currently known from public databases and the literature about TRIM17 gene regulation and expression, TRIM17 protein structure and interactions, as well as its involvement in cell physiology and human disorders.

Insight on a new indolinone derivative as an orally bioavailable lead compound against renal cell carcinoma

A series of novel 3-indolinone-thiazolidinones and oxazolidinones 4a-k was synthesized via molecular hybridization approach and sequentially evaluated to explore its cytotoxic activity. The cytotoxicity screening pointed toward the N-cyclohexyl thiazolidinone derivative 4f that revealed promising renal cytotoxicity against CAKI-1 and UO-31 renal cancer cell lines with IC₅₀ values 4.74 and 3.99 µM, respectively, which were comparable to those of sunitinib along with good safety threshold against normal renal cells. Further emphasis on compound 4f renal cytotoxicity was achieved via different enzyme assays and CAKI-1 and UO-31 cell cycle analysis. The results were supported by in silico studies to explore its physicochemical, pharmacokinetic and drug-likeness properties. Finally, compound 4f was subjected to an in vivo pharmacokinetic study through two different routes of administration showing excellent oral bioavailability. This research represents compound 4f as a promising candidate against renal cell carcinoma.