Ebola virus VP35 interacts non-covalently with ubiquitin chains to promote viral replication.

Ebolavirus (EBOV) belongs to a family of highly pathogenic viruses that cause severe hemorrhagic fever in humans. EBOV replication requires the activity of the viral polymerase complex, which includes the cofactor and Interferon antagonist VP35. We previously showed that the covalent ubiquitination of VP35 promotes virus replication by regulating interactions with the polymerase complex. In addition, VP35 can also interact non-covalently with ubiquitin (Ub); however, the function of this interaction is unknown. Here, we report that VP35 interacts with free (unanchored) K63-linked polyUb chains. Ectopic expression of Isopeptidase T (USP5), which is known to degrade unanchored polyUb chains, reduced VP35 association with Ub and correlated with diminished polymerase activity in a minigenome assay. Using computational methods, we modeled the VP35-Ub non-covalent interacting complex, identified the VP35-Ub interacting surface, and tested mutations to validate the interface. Docking simulations identified chemical compounds that can block VP35-Ub interactions leading to reduced viral polymerase activity. Treatment with the compounds reduced replication of infectious EBOV in cells and in vivo in a mouse model. In conclusion, we identified a novel role of unanchored polyUb in regulating Ebola virus polymerase function and discovered compounds that have promising anti-Ebola virus activity.

Surfaces: a software to quantify and visualize interactions within and between proteins and ligands.

SUMMARY: Computational methods for the quantification and visualization of the relative contribution of molecular interactions to the stability of biomolecular structures and complexes are fundamental to understand, modulate and engineer biological processes. Here, we present Surfaces, an easy to use, fast and customizable software for quantification and visualization of molecular interactions based on the calculation of surface areas in contact. Surfaces calculations shows equivalent or better correlations with experimental data as computationally expensive methods based on molecular dynamics. AVAILABILITY AND IMPLEMENTATION: All scripts are available at https://github.com/NRGLab/Surfaces. Surface's documentation is available at https://surfaces-tutorial.readthedocs.io/en/latest/index.html.

Ebola Virus VP35 Interacts Non-Covalently with Ubiquitin Chains to Promote Viral Replication Creating New Therapeutic Opportunities.

Ebolavirus (EBOV) belongs to a family of highly pathogenic viruses that cause severe hemorrhagic fever in humans. EBOV replication requires the activity of the viral polymerase complex, which includes the co-factor and Interferon antagonist VP35. We previously showed that the covalent ubiquitination of VP35 promotes virus replication by regulating interactions with the polymerase complex. In addition, VP35 can also interact non-covalently with ubiquitin (Ub); however, the function of this interaction is unknown. Here, we report that VP35 interacts with free (unanchored) K63-linked polyUb chains. Ectopic expression of Isopeptidase T (USP5), which is known to degrade unanchored polyUb chains, reduced VP35 association with Ub and correlated with diminished polymerase activity in a minigenome assay. Using computational methods, we modeled the VP35-Ub non-covalent interacting complex, identified the VP35-Ub interacting surface and tested mutations to validate the interface. Docking simulations identified chemical compounds that can block VP35-Ub interactions leading to reduced viral polymerase activity that correlated with reduced replication of infectious EBOV. In conclusion, we identified a novel role of unanchored polyUb in regulating Ebola virus polymerase function and discovered compounds that have promising anti-Ebola virus activity.

The DynaSig-ML Python package: automated learning of biomolecular dynamics-function relationships.

The DynaSig-ML ('Dynamical Signatures-Machine Learning') Python package allows the efficient, user-friendly exploration of 3D dynamics-function relationships in biomolecules, using datasets of experimental measures from large numbers of sequence variants. It does so by predicting 3D structural dynamics for every variant using the Elastic Network Contact Model (ENCoM), a sequence-sensitive coarse-grained normal mode analysis model. Dynamical Signatures represent the fluctuation at every position in the biomolecule and are used as features fed into machine learning models of the user's choice. Once trained, these models can be used to predict experimental outcomes for theoretical variants. The whole pipeline can be run with just a few lines of Python and modest computational resources. The compute-intensive steps are easily parallelized in the case of either large biomolecules or vast amounts of sequence variants. As an example application, we use the DynaSig-ML package to predict the maturation efficiency of human microRNA miR-125a variants from high-throughput enzymatic assays. AVAILABILITY AND IMPLEMENTATION: DynaSig-ML is open-source software available at https://github.com/gregorpatof/dynasigml_package.

Sequence-sensitive elastic network captures dynamical features necessary for miR-125a maturation.

The Elastic Network Contact Model (ENCoM) is a coarse-grained normal mode analysis (NMA) model unique in its all-atom sensitivity to the sequence of the studied macromolecule and thus to the effect of mutations. We adapted ENCoM to simulate the dynamics of ribonucleic acid (RNA) molecules, benchmarked its performance against other popular NMA models and used it to study the 3D structural dynamics of human microRNA miR-125a, leveraging high-throughput experimental maturation efficiency data of over 26 000 sequence variants. We also introduce a novel way of using dynamical information from NMA to train multivariate linear regression models, with the purpose of highlighting the most salient contributions of dynamics to function. ENCoM has a similar performance profile on RNA than on proteins when compared to the Anisotropic Network Model (ANM), the most widely used coarse-grained NMA model; it has the advantage on predicting large-scale motions while ANM performs better on B-factors prediction. A stringent benchmark from the miR-125a maturation dataset, in which the training set contains no sequence information in common with the testing set, reveals that ENCoM is the only tested model able to capture signal beyond the sequence. This ability translates to better predictive power on a second benchmark in which sequence features are shared between the train and test sets. When training the linear regression model using all available data, the dynamical features identified as necessary for miR-125a maturation point to known patterns but also offer new insights into the biogenesis of microRNAs. Our novel approach combining NMA with multivariate linear regression is generalizable to any macromolecule for which relatively high-throughput mutational data is available.

Selective CDK9 Inhibition by Natural Compound Toyocamycin in Cancer Cells.

Aberrant transcription in cancer cells involves the silencing of tumor suppressor genes (TSGs) and activation of oncogenes. Transcriptomic changes are associated with epigenomic alterations such as DNA-hypermethylation, histone deacetylation, and chromatin condensation in promoter regions of silenced TSGs. To discover novel drugs that trigger TSG reactivation in cancer cells, we used a GFP-reporter system whose expression is silenced by promoter DNA hypermethylation and histone deacetylation. After screening a natural product drug library, we identified that toyocamycin, an adenosine-analog, induces potent GFP reactivation and loss of clonogenicity in human colon cancer cells. Connectivity-mapping analysis revealed that toyocamycin produces a pharmacological signature mimicking cyclin-dependent kinase (CDK) inhibitors. RNA-sequencing revealed that the toyocamycin transcriptomic signature resembles that of a specific CDK9 inhibitor (HH1). Specific inhibition of RNA Pol II phosphorylation level and kinase assays confirmed that toyocamycin specifically inhibits CDK9 (IC50 = 79 nM) with a greater efficacy than other CDKs (IC50 values between 0.67 and 15 µM). Molecular docking showed that toyocamycin efficiently binds the CDK9 catalytic site in a conformation that differs from other CDKs, explained by the binding contribution of specific amino acids within the catalytic pocket and protein backbone. Altogether, we demonstrated that toyocamycin exhibits specific CDK9 inhibition in cancer cells, highlighting its potential for cancer chemotherapy.

SPEAR: Systematic ProtEin AnnotatoR.

SUMMARY: We present Systematic ProtEin AnnotatoR (SPEAR), a lightweight and rapid SARS-CoV-2 variant annotation and scoring tool, for identifying mutations contributing to potential immune escape and transmissibility (ACE2 binding) at point of sequencing. SPEAR can be used in the field to evaluate genomic surveillance results in real time and features a powerful interactive data visualization report. AVAILABILITY AND IMPLEMENTATION: SPEAR and documentation are freely available on GitHub: https://github.com/m-crown/SPEAR and are implemented in Python and installable via Conda environment. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants.

The SARS-CoV-2 Spike protein needs to be in an open-state conformation to interact with ACE2 to initiate viral entry. We utilise coarse-grained normal mode analysis to model the dynamics of Spike and calculate transition probabilities between states for 17081 variants including experimentally observed variants. Our results correctly model an increase in open-state occupancy for the more infectious D614G via an increase in flexibility of the closed-state and decrease of flexibility of the open-state. We predict the same effect for several mutations on glycine residues (404, 416, 504, 252) as well as residues K417, D467 and N501, including the N501Y mutation recently observed within the B.1.1.7, 501.V2 and P1 strains. This is, to our knowledge, the first use of normal mode analysis to model conformational state transitions and the effect of mutations on such transitions. The specific mutations of Spike identified here may guide future studies to increase our understanding of SARS-CoV-2 infection mechanisms and guide public health in their surveillance efforts.

Repurposing proscillaridin A in combination with decitabine against embryonal rhabdomyosarcoma RD cells.

PURPOSE: Embryonal rhabdomyosarcoma (eRMS) is the most common type of rhabdomyosarcoma in children. eRMS is characterized by malignant skeletal muscle cells driven by hyperactivation of several oncogenic pathways including the MYC pathway. Targeting MYC in cancer has been extremely challenging. Recently, we have demonstrated that the heart failure drug, proscillaridin A, produced anticancer effects with specificity toward MYC expressing leukemia cells. We also reported that decitabine, a hypomethylating drug, synergizes with proscillaridin A in colon cancer cells. Here, we investigated whether proscillaridin A exhibits epigenetic and anticancer activity against eRMS RD cells, overexpressing MYC oncogene, and its combination with decitabine. METHODS: We investigated the anticancer effects of proscillaridin A in eRMS RD cells in vitro. In response to drug treatment, we measured growth inhibition, cell cycle arrest, loss of clonogenicity and self-renewal capacity. We further evaluated the impact of proscillaridin A on MYC expression and its downstream transcriptomic effects by RNA sequencing. Then, we measured protein expression of epigenetic regulators and their associated chromatin post-translational modifications in response to drug treatment. Chromatin immunoprecipitation sequencing data sets were coupled with transcriptomic results to pinpoint the impact of proscillaridin A on gene pathways associated with specific chromatin modifications. Lastly, we evaluated the effect of the combination of proscillaridin A and the DNA demethylating drug decitabine on eRMS RD cell growth and clonogenic potential. RESULTS: Clinically relevant concentration of proscillaridin A (5 nM) produced growth inhibition, cell cycle arrest and loss of clonogenicity in eRMS RD cells. Proscillaridin A produced a significant downregulation of MYC protein expression and inhibition of oncogenic transcriptional programs controlled by MYC, involved in cell replication. Interestingly, significant reduction in total histone 3 acetylation and on specific lysine residues (lysine 9, 14, 18, and 27 on histone 3) was associated with significant protein downregulation of a series of lysine acetyltransferases (KAT3A, KAT3B, KAT2A, KAT2B, and KAT5). In addition, proscillaridin A produced synergistic growth inhibition and loss of clonogenicity when combined with the approved DNA demethylating drug decitabine. CONCLUSION: Proscillaridin A produces anticancer and epigenetic effects in the low nanomolar range and its combination with decitabine warrants further investigation for the treatment of eRMS.

The NRGTEN Python package: an extensible toolkit for coarse-grained normal mode analysis of proteins, nucleic acids, small molecules and their complexes.

SUMMARY: The Najmanovich Research Group Toolkit for Elastic Networks (NRGTEN) is a Python toolkit that implements four different NMA models in addition to popular and novel metrics to benchmark and measure properties from these models. Furthermore, the toolkit is available as a public Python package and is easily extensible for the development or implementation of additional normal mode analysis models. The inclusion of the Elastic Network Contact Model developed in our group within NRGTEN is noteworthy, owing to its account for the specific chemical nature of atomic interactions. AVAILABILITY AND IMPLEMENTATION: https://github.com/gregorpatof/nrgten_package/.

Identification of an alternative translation initiation site in the sequence of the commonly used Glutathione S-Transferase tag.

A large number of proteins are expressed in fusion with a tag to perform their purification. Glutathione-S-Transferase (GST) is a widely used tag to achieve passenger protein purification. Accordingly, commonly used commercial expression vectors contain the coding sequence of GST in order to express fusion proteins. However, fusion proteins are sometimes expressed in a truncated form that may result from an incomplete synthesis, proteolytic cleavage or the presence of a functional alternative translation initiation site. In particular, a truncated as well as a full-length fusion protein were observed when expressing RGS9-1 Anchor Protein without its C-terminal segment (bR9AP) in fusion with GST. Moreover, this truncated protein was found to be purified together with the full-length fusion protein. Here, we identified for the first time an alternative translation initiation site within the sequence of GST that likely becomes accessible for translation only when it is fused with a passenger protein. Indeed, bioinformatics analyses suggest that the secondary structure of the mRNA of the GST-bR9AP fusion protein is different from that of GST alone, which likely allows accessibility of an alternative Shine-Dalgarno sequence coupled with an additional initiation codon within the sequence of GST. The functionality of this alternative translation initiation site was confirmed by site-directed mutagenesis, which resulted in the absence of a truncated fusion protein and, consequently, only a purified full-length fusion protein. This is an extremely important finding in view of the wide use of GST as a purification and solubility-enhancing tag.

Functional diversity of TMPRSS6 isoforms and variants expressed in hepatocellular carcinoma cell lines.

TMPRSS6, also known as matriptase-2, is a type II transmembrane serine protease that plays a major role in iron homeostasis by acting as a negative regulator of hepcidin production through cleavage of the BMP co-receptor haemojuvelin. Iron-refractory iron deficiency anaemia (IRIDA), an iron metabolism disorder, is associated with mutations in the TMPRSS6 gene. By analysing RNA-seq data encoding TMPRSS6 isoforms and other proteins involved in hepcidin production, we uncovered significant differences in expression levels between hepatocellular carcinoma (HCC) cell lines and normal human liver samples. Most notably, TMPRSS6 and HAMP expression was found to be much lower in HepG2 and Huh7 cells when compared to human liver samples. Furthermore, we characterized the common TMPRSS6 polymorphism V736A identified in Hep3B cells, the V795I mutation found in HepG2 cells, also associated with IRIDA, and the G603R substitution recently detected in two IRIDA patients. While variant V736A is as active as wild-type TMPRSS6, mutants V795I and G603R displayed significantly reduced proteolytic activity. Our results provide important information about commonly used liver cell models and shed light on the impact of two TMPRSS6 mutations associated with IRIDA.

Applications of the NRGsuite and the Molecular Docking Software FlexAID in Computational Drug Discovery and Design.

Docking simulations help us understand molecular interactions. Here we present a hands-on tutorial to utilize FlexAID (Flexible Artificial Intelligence Docking), an open source molecular docking software between ligands such as small molecules or peptides and macromolecules such as proteins and nucleic acids. The tutorial uses the NRGsuite PyMOL plugin graphical user interface to set up and visualize docking simulations in real time as well as detect and refine target cavities. The ease of use of FlexAID and the NRGsuite combined with its superior performance relative to widely used docking software provides nonexperts with an important tool to understand molecular interactions with direct applications in structure-based drug design and virtual high-throughput screening.

A three-way inter-molecular network accounts for the CaVα2δ1-induced functional modulation of the pore-forming CaV1.2 subunit.

L-type CaV1.2 channels are essential for the excitation-contraction coupling in cardiomyocytes and are hetero-oligomers of a pore-forming CaVα1C assembled with CaVß and CaVα2δ1 subunits. A direct interaction between CaVα2δ1 and Asp-181 in the first extracellular loop of CaVα1 reproduces the native properties of the channel. A 3D model of the von Willebrand factor type A (VWA) domain of CaVα2δ1 complexed with the voltage sensor domain of CaVα1C suggests that Ser-261 and Ser-263 residues in the metal ion-dependent adhesion site (MIDAS) motif are determinant in this interaction, but this hypothesis is untested. Here, coimmunoprecipitation assays and patch-clamp experiments of single-substitution variants revealed that CaVα2δ1 Asp-259 and Ser-261 are the two most important residues in regard to protein interactions and modulation of CaV1.2 currents. In contrast, mutating the side chains of CaVα2δ1 Ser-263, Thr-331, and Asp-363 with alanine did not completely prevent channel function. Molecular dynamics simulations indicated that the carboxylate side chain of CaVα2δ1 Asp-259 coordinates the divalent cation that is further stabilized by the oxygen atoms from the hydroxyl side chain of CaVα2δ1 Ser-261 and the carboxylate group of CaVα1C Asp-181. In return, the hydrogen atoms contributed by the side chain of Ser-261 and the main chain of Ser-263 bonded the oxygen atoms of CaV1.2 Asp-181. We propose that CaVα2δ1 Asp-259 promotes Ca2+ binding necessary to produce the conformation of the VWA domain that locks CaVα2δ1 Ser-261 and Ser-263 within atomic distance of CaVα1C Asp-181. This three-way network appears to account for the CaVα2δ1-induced modulation of CaV1.2 currents.

Purine analogs targeting the guanine riboswitch as potential antibiotics against Clostridioides difficile.

Riboswitches recently emerged as possible targets for the development of alternative antimicrobial approaches. Guanine-sensing riboswitches in the bacterial pathogen Clostridioides difficile (formerly known as Clostridium difficile) constitute potential targets based on their involvement in the regulation of basal metabolic control of purine compounds. In this study, we deciphered the structure-activity relationship of several guanine derivatives on the guanine riboswitch and determined their antimicrobial activity. We describe the synthesis of purine analogs modified in ring B as well as positions 2 and 6. Their biological activity was determined by measuring their affinity for the C. difficile guanine riboswitch and their inhibitory effect on bacterial growth, including a counter-screen to discriminate against riboswitch-independent antibacterial effects. Altogether, our results suggest that improvements in riboswitch binding affinity in vitro do not necessarily translate into improved antibacterial activity in bacteria, despite the fact that some structure-activity relationship was observed at least with respect to binding affinity.

Remodeling adipose tissue through in silico modulation of fat storage for the prevention of type 2 diabetes.

BACKGROUND: Type 2 diabetes is one of the leading non-infectious diseases worldwide and closely relates to excess adipose tissue accumulation as seen in obesity. Specifically, hypertrophic expansion of adipose tissues is related to increased cardiometabolic risk leading to type 2 diabetes. Studying mechanisms underlying adipocyte hypertrophy could lead to the identification of potential targets for the treatment of these conditions. RESULTS: We present iTC1390adip, a highly curated metabolic network of the human adipocyte presenting various improvements over the previously published iAdipocytes1809. iTC1390adip contains 1390 genes, 4519 reactions and 3664 metabolites. We validated the network obtaining 92.6% accuracy by comparing experimental gene essentiality in various cell lines to our predictions of biomass production. Using flux balance analysis under various test conditions, we predict the effect of gene deletion on both lipid droplet and biomass production, resulting in the identification of 27 genes that could reduce adipocyte hypertrophy. We also used expression data from visceral and subcutaneous adipose tissues to compare the effect of single gene deletions between adipocytes from each compartment. CONCLUSIONS: We generated a highly curated metabolic network of the human adipose tissue and used it to identify potential targets for adipose tissue metabolic dysfunction leading to the development of type 2 diabetes.

Large-scale detection of drug off-targets: hypotheses for drug repurposing and understanding side-effects.

BACKGROUND: Promiscuity in molecular interactions between small-molecules, including drugs, and proteins is widespread. Such unintended interactions can be exploited to suggest drug repurposing possibilities as well as to identify potential molecular mechanisms responsible for observed side-effects. METHODS: We perform a large-scale analysis to detect binding-site molecular interaction field similarities between the binding-sites of the primary target of 400 drugs against a dataset of 14082 cavities within 7895 different proteins representing a non-redundant dataset of all proteins with known structure. Statistically-significant cases with high levels of similarities represent potential cases where the drugs that bind the original target may in principle bind the suggested off-target. Such cases are further analysed with docking simulations to verify if indeed the drug could, in principle, bind the off-target. Diverse sources of data are integrated to associated potential cross-reactivity targets with side-effects. RESULTS: We observe that promiscuous binding-sites tend to display higher levels of hydrophobic and aromatic similarities. Focusing on the most statistically significant similarities (Z-score ≥ 3.0) and corroborating docking results (RMSD < 2.0 Å), we find 2923 cases involving 140 unique drugs and 1216 unique potential cross-reactivity protein targets. We highlight a few cases with a potential for drug repurposing (acetazolamide as a chorismate pyruvate lyase inhibitor, raloxifene as a bacterial quorum sensing inhibitor) as well as to explain the side-effects of zanamivir and captopril. A web-interface permits to explore the detected similarities for each of the 400 binding-sites of the primary drug targets and visualise them for the most statistically significant cases. CONCLUSIONS: The detection of molecular interaction field similarities provide the opportunity to suggest drug repurposing opportunities as well as to identify potential molecular mechanisms responsible for side-effects. All methods utilized are freely available and can be readily applied to new query binding-sites. All data is freely available and represents an invaluable source to identify further candidates for repurposing and suggest potential mechanisms responsible for side-effects.

Modulating the selectivity of matriptase-2 inhibitors with unnatural amino acids.

Matriptase-2, a type II transmembrane serine protease (TTSP), is expressed in the liver and regulates iron homeostasis via the cleavage of hemojuvelin. Matriptase-2 emerges as an attractive target for the treatment of conditions associated with iron overload, such as hemochromatosis or beta-thalassemia. Starting from the crystal structure of its closest homolog matriptase, we constructed a homology model of matriptase-2 in order to further optimize the selectivity of serine trap peptidomimetic inhibitors for matriptase-2 vs matriptase. Careful modifications of the P4, P3 and P2 positions with the help of unnatural amino acids led to a thorough understanding of Structure-Activity Relationship and a >60-fold increase in selectivity for matriptase-2 vs matriptase. Additionally, the introduction of unnatural amino acids led to significant increases in plasma stability. Such compounds represent useful pharmacological tools to test matriptase-2 inhibition in a context of iron overload.

Evolutionary studies of ligand binding sites in proteins.

Biological processes at their most fundamental molecular aspects are defined by molecular interactions with ligand-protein interactions in particular at the core of cellular functions such as metabolism and signalling. Divergent and convergent processes shape the evolution of ligand binding sites. The competition between similar ligands and binding sites across protein families create evolutionary pressures that affect the specificity and selectivity of interactions. This short review showcases recent studies of the evolution of ligand binding-sites and methods used to detect binding-site similarities.

Applications of Normal Mode Analysis Methods in Computational Protein Design.

Recent advances in coarse-grained normal mode analysis methods make possible the large-scale prediction of the effect of mutations on protein stability and dynamics as well as the generation of biologically relevant conformational ensembles. Given the interplay between flexibility and enzymatic activity, the combined analysis of stability and dynamics using the Elastic Network Contact Model (ENCoM) method has ample applications in protein engineering in industrial and medical applications such as in computational antibody design. Here, we present a detailed tutorial on how to perform such calculations using ENCoM.