Natalia Teruel

Summary: We introduce NRGSuite-Qt, a PyMOL plugin that provides a comprehensive toolkit for protein modeling, virtual screening, normal mode analysis, and binding-site similarity calculations. Building on the original NRGSuite plugin for FlexAID, this updated version integrates five new functionalities: protein-protein and protein-ligand interaction analysis using Surfaces, ultra-massive virtual screening with NRGRank, binding-site similarity detection with IsoMIF, normal mode analysis using NRGTEN, and mutational studies through integration with the Modeller Suite. By merging these advanced tools into a cohesive platform, NRGSuite-Qt streamlines complex workflows and facilitates high-throughput computational studies within a single interface. Additionally, we benchmark a newer version of the Elastic Network Contact Model for normal mode analysis method ENCoM, utilizing the same 40 atom-type pairwise interaction matrix that is used in all other software. This version outperforms the default model in multiple benchmarking tests. Availability: The Installation guide and tutorial is available at https://nrg-qt.readthedocs.io/ Contact: rafael.najmanovich@umontreal.ca

Summary: We introduce NRGSuite-Qt, a PyMOL plugin, that provides a comprehensive toolkit for macromolecular cavity detection, virtual screening, small-molecule docking, normal mode analysis, analyses of molecular interactions, and detection of binding-site similarities. This complete redesign of the original NRGSuite (restricted to cavity detection and small-molecule docking) integrates five new functionalities: protein–protein and protein–ligand interaction analysis using Surfaces, ultra-massive virtual screening with NRGRank, binding-site similarity detection with IsoMIF, normal mode analysis using NRGTEN, and mutational studies through integration with the Modeler Suite. By merging these advanced tools into a cohesive platform, NRGSuite-Qt simplifies visualization and streamlines complex workflows within a single interface. Additionally, we benchmark a newer version of the Elastic Network Contact Model (ENCoM) for normal mode analysis method, utilizing the same 40 atom-type pairwise interaction matrix that is used in all other software. This version outperforms the default model in multiple benchmarking tests. Avalilability and implementation: The Installation guide and tutorial is available at https://nrg-qt.readthedocs.io/en/latest/index.html. The NRGSuite-Qt is implement in Python.

The evolution of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has produced unprecedented numbers of structures of the Spike protein. This study presents a comprehensive analysis of 1,560 published Spike protein structures, capturing most variants that emerged throughout the pandemic and covering diverse heteromerization and interacting complexes. We employ an interaction-energy informed geometric clustering to identify 14 epitopes characterized by their conformational specificity, shared interface with ACE2 binding, and glycosylation patterns. Our per-residue interaction evaluations accurately predict each residue's role in antibody recognition and as well as experimental measurements of immune escape, showing strong correlations with DMS data, thus making it possible to predict the behaviour of future variants. We integrate the structural analysis with a longitudinal analysis of nearly 3 million viral sequences. This broad-ranging structural and longitudinal analysis provides insight into the effect of specific mutations on the energetics of interactions and dynamics of the SARS-CoV-2 Spike protein during the course of the pandemic. Specifically, with the emergence of widespread immunity, we observe an enthalpic trade-off in which mutations in the receptor binding motif (RBM) that promote immune escape also weaken the interaction with ACE2. Additionally, we also observe a second mechanism, that we call entropic trade-off, in which mutations outside of the RBM contribute to decrease the occupancy of the open state of SARS-CoV-2 Spike, thus also contributing to immune escape at the expense of ACE2 binding but without changes on the ACE2 binding interface. This work not only highlights the role of mutations across SARS-CoV-2 Spike variants but also reveals the complex interplay of evolutionary forces shaping the evolution of the SARS-CoV-2 Spike protein over the course of the pandemic.

SARS-CoV-2 is a highly transmissible virus that causes COVID-19 disease. Mechanisms of viral pathogenesis include excessive inflammation and viral-induced cell death, resulting in tissue damage. We identified the host E3-ubiquitin ligase TRIM7 as an inhibitor of apoptosis and SARS-CoV-2 replication via ubiquitination of the viral membrane (M) protein. Trim7 ^-/- mice exhibited increased pathology and virus titers associated with epithelial apoptosis and dysregulated immune responses. Mechanistically, TRIM7 ubiquitinates M on K14, which protects cells from cell death. Longitudinal SARS-CoV-2 sequence analysis from infected patients revealed that mutations on M-K14 appeared in circulating variants during the pandemic. The relevance of these mutations was tested in a mouse model. A recombinant M- K14/K15R virus showed reduced viral replication, consistent with the role of K15 in virus assembly, and increased levels of apoptosis associated with the loss of ubiquitination on K14. TRIM7 antiviral activity requires caspase-6 inhibition, linking apoptosis with viral replication and pathology.

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. Significance Statement: Ebola virus infection can result in high mortality rates with extreme risk of person-to-person transmission. Sporadic outbreaks in Africa have resulted in thousands of fatal cases, highlighting that there is still insufficient knowledge to develop effective antiviral therapies. Like other viruses, Ebola utilizes the host machinery to replicate. Understanding how viral and host proteins interact can help identifying targets for the rational design of antiviral drugs. Here, we identified interactions between the cellular ubiquitin machinery and the Ebola virus polymerase cofactor protein VP35, which are important for efficient virus replication. We developed an approach to identify and block these virus-host interactions using small chemical compounds, which provides a useful tool to study functional molecular mechanisms and at the same time a potential approach to antiviral therapies.

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 levels of correlations with experimental data as computationally expensive methods based on molecular dynamics. All scripts are available at https://github.com/nataliateruel/Surfaces Documentation is available at https://surfaces-tutorial.readthedocs.io/en/latest/index.html

The recently reported Omicron (B.1.1.529) SARS-CoV-2 variant has a large number of mutations in the Spike (S) protein compared to previous variants. Here we evaluate the potential effect of Omicron S mutations on S protein dynamics and ACE2 binding as contributing factors to infectivity as well as propensity for immune escape. We define a consensus set of mutations from 77 sequences assigned as Omicron in GISAID as of November 25. We create structural models of the Omicron S protein in the open and closed states, as part of a complex with ACE2 and for each of 77 complexes of S bound to different antibodies with known structures. We have previously utilized Dynamical Signatures (DS) and the Vibrational Entropy Score (VDS) to evaluate the propensity of S variants to favour the open state. Here, we introduce the Binding Influence Score (BIS) to evaluate the influence of mutations on binding affinity based on the net gain or loss of interactions within the protein-protein interface. BIS shows excellent correlation with experimental data (Pearson correlation coefficient of 0.87) on individual mutations in the ACE2 interface for the Alpha, Beta, Gamma, Delta and Omicron variants combined. On the one hand, the DS of Omicron highly favours a more rigid open state and a more flexible closed state with the largest VDS of all variants to date, suggesting a large increase in the chances to interact with ACE2. On the other hand, the BIS shows that apart from N501Y, all other mutations in the interface reduce ACE2 binding affinity. VDS and BIS show opposing effects on the overall effectiveness of Omicron mutations to promote binding to ACE2 and therefore initiate infection. To evaluate the propensity for immune escape we calculated the net change of favourable and unfavourable interactions within each S-antibody interface. The net change of interactions shows a positive score (a net increase of favourable interactions and decrease of unfavourable ones) for 41 out of 77 antibodies, a nil score for 15 and a negative score for 21 antibodies. Therefore, in only 28% of S-antibody complexes (21/77) we predict some level of immune escape due to a weakening of the interactions with Omicron S. Considering that most antibody epitopes and the mutations are within the S-ACE2 interface our results suggest that mutations within the RBD of Omicron may give rise to only partial immune escape, which comes at the expense of reduced ACE2 binding affinity. However, this reduced ACE2 affinity appears to have been offset by increasing the occupancy of the open state of the Spike protein.

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.

The SARS-CoV-2 Spike protein needs to be in an open-state conformation to interact with ACE2 as part of the viral entry mechanism. We utilise coarse-grained normal-mode analyses to model the dynamics of Spike and calculate transition probabilities between states for 17081 Spike 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, explaining the higher infectivity of the B.1.1.7 and 501.V2 strains. This is, to our knowledge, the first use of normal-mode analysis to model conformational state transitions and the effect of mutations thereon. 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.