Coevolution analysis of Hepatitis C virus genome to identify the structural and functional dependency network of viral proteins

Targeted barcoding of variable antibody domains and individual transcriptomes of the human B-cell repertoire using Link-Seq

Here, we present Link-Seq, a highly efficient droplet microfluidic method for combined sequencing of antibody-encoding genes and the transcriptome of individual B cells at large scale. The method is based on 3' barcoding of the transcriptome and subsequent single-molecule PCR in droplets, which freely shift the barcode along specific gene regions, such as the antibody heavy- and light-chain genes. Using the immune repertoire of COVID-19 patients and healthy donors as a model system, we obtain up to 91.7% correctly paired immunoglobulin heavy and light chains. Furthermore, we map the V(D)J usage and obtain sensitivities comparable with the current gold-standard 10× Genomics commercial systems while offering full flexibility in experimental setup and significant cost savings. A further unique feature of Link-Seq is the possibility of barcoding multiple target genes in a site-specific manner. Based on the open character of the platform and its conceptual advantages, we expect Link-Seq to become a versatile tool for single-cell analysis, especially for applications requiring additional processing steps that cannot be implemented on commercially available platforms.

Antiviral Drug Target Identification and Ligand Discovery

This chapter intends to provide a general overview of web-based resources available for antiviral drug discovery studies. First, we explain how the structure for a potential viral protein target can be obtained and then highlight some of the main considerations in preparing for the application of receptor-based molecular docking techniques. Thereafter, we discuss the resources to search for potential drug candidates (ligands) against this target protein receptor, how to screen them, and preparing their analogue library. We make specific reference to free, online, open-source tools and resources which can be applied for antiviral drug discovery studies.

Parallel evolution of picobirnaviruses from distinct ancestral origins

IMPORTANCE

Picobirnaviruses (PBVs) are highly heterogeneous viruses encoding a capsid and RdRp. Detected in a wide variety of animals with and without disease, their association with gastrointestinal and respiratory infections, and consequently their public health importance, has rightly been questioned. Determining the "true" host of Picobirnavirus lies at the center of this debate, as evidence exists for them having both vertebrate and prokaryotic origins. Using integrated and time-stamped phylogenetic approaches, we show they are contemporaneous viruses descending from two different ancestors: avian Reovirus and fungal Partitivirus. The fungal PBV-R2 species emerged with a single segment (RdRp) until it acquired a capsid from vertebrate PBV-R1 and PBV-R3 species. Protein and RNA folding analyses revealed how the former came to resemble the latter over time. Thus, parallel evolution from disparate hosts has driven the adaptation and genetic diversification of the Picobirnaviridae family.

Coevolution combined with molecular dynamics simulations provides structural and mechanistic insights into the interactions between the integrator complex subunits

Finding the 3D structure of large, multi-subunit complexes is difficult, despite recent advances in cryo-EM technology, due to remaining challenges to expressing and purifying subunits. Computational approaches that predict protein-protein interactions, including Direct Coupling Analysis (DCA), represent an attractive alternative for dissecting interactions within protein complexes. However, they are readily applicable only to small proteins due to high computational complexity and a high number of false positives. To solve this problem, we proposed a modified DCA approach, a powerful tool to predict the most likely interfaces of protein complexes. Since our modified approach cannot provide structural and mechanistic details of interacting peptides, we combine it with Molecular Dynamics (MD) simulations. To illustrate this novel approach, we predict interacting domains and structural details of interactions of two Integrator complex subunits, INTS9 and INTS11. Our predictions of interacting residues of INTS9/INTS11 are highly consistent with crystallographic structure. We then expand our procedure to two complexes whose structures are not well-studied: 1) The heterodimer formed by the Cleavage and Polyadenylation Specificity Factor 100-kD (CPSF100) and 73-kD (CPSF73); 2) The heterotrimer formed by INTS4/INTS9/INTS11. Experimental data supports our predictions of interactions within these two complexes, demonstrating that combining DCA and MD simulations is a powerful approach to revealing structural insights of large protein complexes.

Comparative Genome Analysis of Old World and New World TYLCV Reveals a Biasness toward Highly Variable Amino Acids in Coat Protein

Begomoviruses, belonging to the family Geminiviridae and the genus Begomovirus, are DNA viruses that are transmitted by whitefly Bemisia tabaci (Gennadius) in a circulative persistent manner. They can easily adapt to new hosts and environments due to their wide host range and global distribution. However, the factors responsible for their adaptability and coevolutionary forces are yet to be explored. Among BGVs, TYLCV exhibits the broadest range of hosts. In this study, we have identified variable and coevolving amino acid sites in the proteins of Tomato yellow leaf curl virus (TYLCV) isolates from Old World (African, Indian, Japanese, and Oceania) and New World (Central and Southern America). We focused on mutations in the coat protein (CP), as it is highly variable and interacts with both vectors and host plants. Our observations indicate that some mutations were accumulating in Old World TYLCV isolates due to positive selection, with the S149N mutation being of particular interest. This mutation is associated with TYLCV isolates that have spread in Europe and Asia and is dominant in 78% of TYLCV isolates. On the other hand, the S149T mutation is restricted to isolates from Saudi Arabia. We further explored the implications of these amino acid changes through structural modeling. The results presented in this study suggest that certain hypervariable regions in the genome of TYLCV are conserved and may be important for adapting to different host environments. These regions could contribute to the mutational robustness of the virus, allowing it to persist in different host populations.

iBIS2Analyzer: a web server for a phylogeny-driven coevolution analysis of protein families

Residue coevolution within and between proteins is used as a marker of physical interaction and/or residue functional cooperation. Pairs or groups of coevolving residues are extracted from multiple sequence alignments based on a variety of computational approaches. However, coevolution signals emerging in subsets of sequences might be lost if the full alignment is considered. iBIS2Analyzer is a web server dedicated to a phylogeny-driven coevolution analysis of protein families with different evolutionary pressure. It is based on the iterative version, iBIS², of the coevolution analysis method BIS, Blocks in Sequences. iBIS² is designed to iteratively select and analyse subtrees in phylogenetic trees, possibly large and comprising thousands of sequences. With iBIS2Analyzer, openly accessible at http://ibis2analyzer.lcqb.upmc.fr/, the user visualizes, compares and inspects clusters of coevolving residues by mapping them onto sequences, alignments or structures of choice, greatly simplifying downstream analysis steps. A rich and interactive graphic interface facilitates the biological interpretation of the results.

Activation of the Ca2+/NFAT Pathway by Assembly of Hepatitis C Virus Core Protein into Nucleocapsid-like Particles

Hepatitis C virus (HCV) is the primary pathogen responsible for liver cirrhosis and hepatocellular carcinoma. The main virion component, the core (C) protein, has been linked to several aspects of HCV pathology, including oncogenesis, immune evasion and stress responses. We and others have previously shown that C expression in various cell lines activates Ca²⁺ signaling and alters Ca²⁺ homeostasis. In this study, we identified two distinct C protein regions that are required for the activation of Ca²⁺/NFAT signaling. In the basic N-terminal domain, which has been implicated in self-association of C, amino acids 1–68 were critical for NFAT activation. Sedimentation analysis of four mutants in this domain revealed that association of the C protein into nucleocapsid-like particles correlated with NFAT-activated transcription. The internal, lipid droplet-targeting domain was not required for NFAT-activated transcription. Finally, the C-terminal ER-targeting domain was required in extenso for the C protein to function. Our results indicate that targeting of HCV C to the ER is necessary but not sufficient for inducing Ca²⁺/NFAT signaling. Taken together, our data are consistent with a model whereby proteolytic intermediates of C with an intact transmembrane ER-anchor assemble into pore-like structures in the ER membrane.

AutoCoEv—A High-Throughput In Silico Pipeline for Predicting Inter-Protein Coevolution

Protein–protein interactions govern cellular processes via complex regulatory networks, which are still far from being understood. Thus, identifying and understanding connections between proteins can significantly facilitate our comprehension of the mechanistic principles of protein functions. Coevolution between proteins is a sign of functional communication and, as such, provides a powerful approach to search for novel direct or indirect molecular partners. However, an evolutionary analysis of large arrays of proteins in silico is a highly time-consuming effort that has limited the usage of this method for protein pairs or small protein groups. Here, we developed AutoCoEv, a user-friendly, open source, computational pipeline for the search of coevolution between a large number of proteins. By driving 15 individual programs, culminating in CAPS2 as the software for detecting coevolution, AutoCoEv achieves a seamless automation and parallelization of the workflow. Importantly, we provide a patch to the CAPS2 source code to strengthen its statistical output, allowing for multiple comparison corrections and an enhanced analysis of the results. We apply the pipeline to inspect coevolution among 324 proteins identified to be located at the vicinity of the lipid rafts of B lymphocytes. We successfully detected multiple coevolutionary relations between the proteins, predicting many novel partners and previously unidentified clusters of functionally related molecules. We conclude that AutoCoEv, can be used to predict functional interactions from large datasets in a time- and cost-efficient manner.

Protein sequence-to-structure learning: Is this the end(-to-end revolution)?

The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13. In CASP14, deep learning has boosted the field to unanticipated levels reaching near-experimental accuracy. This success comes from advances transferred from other machine learning areas, as well as methods specifically designed to deal with protein sequences and structures, and their abstractions. Novel emerging approaches include (i) geometric learning, that is, learning on representations such as graphs, three-dimensional (3D) Voronoi tessellations, and point clouds; (ii) pretrained protein language models leveraging attention; (iii) equivariant architectures preserving the symmetry of 3D space; (iv) use of large meta-genome databases; (v) combinations of protein representations; and (vi) finally truly end-to-end architectures, that is, differentiable models starting from a sequence and returning a 3D structure. Here, we provide an overview and our opinion of the novel deep learning approaches developed in the last 2 years and widely used in CASP14.

Interaction between matrix metalloproteinase-9 (MMP-9) and neutrophil gelatinase-associated lipocalin (NGAL): A recent evolutionary event in primates

Matrix metalloproteases are known to represent an early step in the evolution of the immune system. Similarly, neutrophil gelatinase-associated lipocalin is known to be a key effector in immune response. MMP-9 interacts with NGAL, but their interaction mechanisms remain unclear. Functional interaction between proteins is ensured by coevolution. Protein coevolution was inferred by calculating the linear correlation coefficients between inter-protein distance matrices using MirrorTree. Among examined mammal species, we found a robust signal of MMP-9/NGAL coevolution exclusively within Primates (R = 0.96, p < 1e-06). Owing to the high conservation of these proteins among Mammals, we chose to utilize a recent version of Blocks in Sequences (BIS2) algorithm implemented in BIS2Analyzer webserver. Coevolution clusters between the two proteins were identified in MMP-9 fibronectin and hemopexin domains. Our results suggest that MMP-9/NGAL interaction is a recent evolutionary acquisition in Primates. Furthermore, MMP-9 hemopexin domain would represent a promising target for drug design against these molecules.

NGlyAlign: an automated library building tool to align highly divergent HIV envelope sequences

BACKGROUND

The high variability in envelope regions of some viruses such as HIV allow the virus to establish infection and to escape subsequent immune surveillance. This variability, as well as increasing incorporation of N-linked glycosylation sites, is fundamental to this evasion. It also creates difficulties for multiple sequence alignment methods (MSA) that provide the first step in their analysis. Existing MSA tools often fail to properly align highly variable HIV envelope sequences requiring extensive manual editing that is impractical with even a moderate number of these variable sequences.

RESULTS

We developed an automated library building tool NGlyAlign, that organizes similar N-linked glycosylation sites as block constraints and statistically conserved global sites as single site constraints to automatically enforce partial columns in consistency-based MSA methods such as Dialign. This combined method accurately aligns variable HIV-1 envelope sequences. We tested the method on two datasets: a set of 156 founder and chronic gp160 HIV-1 subtype B sequences as well as a set of reference sequences of gp120 in the highly variable region 1. On measures such as entropy scores, sum of pair scores, column score, and similarity heat maps, NGlyAlign+Dialign proved superior against methods such as T-Coffee, ClustalOmega, ClustalW, Praline, HIValign and Muscle. The method is scalable to large sequence sets producing accurate alignments without requiring manual editing. As well as this application to HIV, our method can be used for other highly variable glycoproteins such as hepatitis C virus envelope.

CONCLUSIONS

NGlyAlign is an automated tool for mapping and building glycosylation motif libraries to accurately align highly variable regions in HIV sequences. It can provide the basis for many studies reliant on single robust alignments. NGlyAlign has been developed as an open-source tool and is freely available at https://github.com/UNSW-Mathematical-Biology/NGlyAlign_v1.0 .

Frustration and Direct-Coupling Analyses to Predict Formation and Function of Adeno-Associated Virus

Adeno-associated virus (AAV) is a promising gene therapy vector because of its efficient gene delivery and relatively mild immunogenicity. To improve delivery target specificity, researchers use combinatorial and rational library design strategies to generate novel AAV capsid variants. These approaches frequently propose high proportions of nonforming or noninfective capsid protein sequences that reduce the effective depth of synthesized vector DNA libraries, thereby raising the discovery cost of novel vectors. We evaluated two computational techniques for their ability to estimate the impact of residue mutations on AAV capsid protein-protein interactions and thus predict changes in vector fitness, reasoning that these approaches might inform the design of functionally enriched AAV libraries and accelerate therapeutic candidate identification. The Frustratometer computes an energy function derived from the energy landscape theory of protein folding. Direct-coupling analysis (DCA) is a statistical framework that captures residue coevolution within proteins. We applied the Frustratometer to select candidate protein residues predicted to favor assembled or disassembled capsid states, then predicted mutation effects at these sites using the Frustratometer and DCA. Capsid mutants were experimentally assessed for changes in virus formation, stability, and transduction ability. The Frustratometer-based metric showed a counterintuitive correlation with viral stability, whereas a DCA-derived metric was highly correlated with virus transduction ability in the small population of residues studied. Our results suggest that coevolutionary models may be able to elucidate complex capsid residue-residue interaction networks essential for viral function, but further study is needed to understand the relationship between protein energy simulations and viral capsid metastability.

Mutational networks of escape from transmitted HIV-1 infection

Human immunodeficiency virus (HIV) is subject to immune selective pressure soon after it establishes infection at the founder stage. As an individual progresses from the founder to chronic stage of infection, immune pressure forces a history of mutations that are embedded in envelope sequences. Determining this pathway of coevolving mutations can assist in understanding what is different with the founder virus and the essential pathways it takes to maintain infection. We have combined operations research and bioinformatics methods to extract key networks of mutations that differentiate founder and chronic stages for 156 subtype B and 107 subtype C envelope (gp160) sequences. The chronic networks for both subtypes revealed strikingly different hub-and-spoke topologies compared to the less structured transmission networks. This suggests that the hub nodes are impacted by the immune response and the resulting loss of fitness is compensated by mutations at the spoke positions. The major hubs in the chronic C network occur at positions 12, 137 (within the N136 glycan), and 822, and at position 306 for subtype B. While both founder networks had a more heterogeneous connected network structure, interestingly founder B subnetworks around positions 640 and 837 preferentially contained CD4 and coreceptor binding domains. Finally, we observed a differential effect of glycosylation between founder and chronic subtype B where the latter had mutational pathways significantly driven by N-glycosylation. Our study provides insights into the mutational pathways HIV takes to evade the immune response, and presents features more likely to establish founder infection, valuable for effective vaccine design.

COVTree: Coevolution in OVerlapped sequences by Tree analysis server

Overlapping genes are commonplace in viruses and play an important role in their function and evolution. For these genes, molecular coevolution may be seen as a mechanism to decrease the evolutionary constraints of amino acid positions in the overlapping regions and to tolerate or compensate unfavorable mutations. Tracing these mutational sites, could help to gain insight on the direct or indirect effect of the mutations in the corresponding overlapping proteins. In the past, coevolution analysis has been used to identify residue pairs and coevolutionary signatures within or between proteins that served as markers of physical interactions and/or functional relationships. Coevolution in OVerlapped sequences by Tree analysis (COVTree) is a web server providing the online analysis of coevolving amino-acid pairs in overlapping genes, where residues might be located inside or outside the overlapping region. COVTree is designed to handle protein families with various characteristics, among which those that typically display a small number of highly conserved sequences. It is based on BIS2, a fast version of the coevolution analysis tool Blocks in Sequences (BIS). COVTree provides a rich and interactive graphical interface to ease biological interpretation of the results and it is openly accessible at http://www.lcqb.upmc.fr/COVTree/.

CoevDB: a database of intramolecular coevolution among protein-coding genes of the bony vertebrates

The study of molecular coevolution, due to its potential to identify gene regions under functional or structural constraints, has recently been subject to numerous scientific inquiries. Particular efforts have been conducted to develop methods predicting the presence of coevolution in molecular sequences. Among these methods, a few aim to model the underlying evolutionary process of coevolution, which enable to differentiate the shared history of genes to coevolution and thus improve their accuracy. However, the usage of such methods remains sparse due to their expensive computational cost and the lack of resources alleviating this issue. Here we present CoevDB (http://phylodb.unil.ch/CoevDB), a database containing the result of a large-scale analysis of intramolecular coevolution of 8201 protein-coding genes of bony vertebrates. The web interface of CoevDB gives access to the results to 800 millions of statistical tests corresponding to all the pairs of sites analyzed. Several type of queries enable users to explore the database by either targeting specific genes or by discovering genes having promising estimations of coevolution.

S100A7/Ran-binding protein 9 coevolution in mammals

S100A7 has been suggested to interact with Ran-binding protein 9. Both proteins are nowadays considered key effectors in immune response. Functional interaction between proteins is ensured by coevolution. The mechanisms of vertebrate coevolution between S100A7 and RanBP9 remain unclear. Several approaches for studying coevolution have been developed. Protein coevolution was inferred by calculating the linear correlation coefficients between inter-protein distance matrices using Mirrortree. We found an overall moderate correlation value (R = 0.53, p < 1e-06). Moreover, owing to the high conservation of RanBP9 protein among vertebrates, we chose to utilize a recent version of Blocks in Sequences (BIS2) algorithm implemented in BIS2Analyzer webserver. A coevolution cluster was identified between the two proteins (p < 8.10e-05). In conclusion, our coevolutionary analysis suggests that amino acid variations may modulate S100A7/RanBP9 interaction with potential pathogenic effects. Such findings could guide further analysis to better elucidate the function of S100A7 and RanBP9 and to design drugs targeting for these molecules in diseases.

Disease-relevant mutations alter amino acid co-evolution networks in the second nucleotide binding domain of CFTR

Cystic Fibrosis (CF) is an inherited disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) ion channel. Mutations in CFTR cause impaired chloride ion transport in the epithelial tissues of patients leading to cardiopulmonary decline and pancreatic insufficiency in the most severely affected patients. CFTR is composed of twelve membrane-spanning domains, two nucleotide-binding domains (NBDs), and a regulatory domain. The most common mutation in CFTR is a deletion of phenylalanine at position 508 (ΔF508) in NBD1. Previous research has primarily concentrated on the structure and dynamics of the NBD1 domain; However numerous pathological mutations have also been found in the lesser-studied NBD2 domain. We have investigated the amino acid co-evolved network of interactions in NBD2, and the changes that occur in that network upon the introduction of CF and CF-related mutations (S1251N(T), S1235R, D1270N, N1303K(T)). Extensive coupling between the α- and β-subdomains were identified with residues in, or near Walker A, Walker B, H-loop and C-loop motifs. Alterations in the predicted residue network varied from moderate for the S1251T perturbation to more severe for N1303T. The S1235R and D1270N networks varied greatly compared to the wildtype, but these CF mutations only affect ion transport preference and do not severely disrupt CFTR function, suggesting dynamic flexibility in the network of interactions in NBD2. Our results also suggest that inappropriate interactions between the β-subdomain and Q-loop could be detrimental. We also identified mutations predicted to stabilize the NBD2 residue network upon introduction of the CF and CF-related mutations, and these predicted mutations are scored as benign by the MUTPRED2 algorithm. Our results suggest the level of disruption of the co-evolution predictions of the amino acid networks in NBD2 does not have a straightforward correlation with the severity of the CF phenotypes observed.

Coevolution analysis of amino-acids reveals diversified drug-resistance solutions in viral sequences: a case study of hepatitis B virus

The study of mutational landscapes of viral proteins is fundamental for the understanding of the mechanisms of cross-resistance to drugs and the design of effective therapeutic strategies based on several drugs. Antiviral therapy with nucleos(t)ide analogues targeting the hepatitis B virus (HBV) polymerase protein (Pol) can inhibit disease progression by suppression of HBV replication and makes it an important case study. In HBV, treatment may fail due to the emergence of drug-resistant mutants. Primary and compensatory mutations have been associated with lamivudine resistance, whereas more complex mutational patterns are responsible for resistance to other HBV antiviral drugs. So far, all known drug-resistance mutations are located in one of the four Pol domains, called reverse transcriptase. We demonstrate that sequence covariation identifies drug-resistance mutations in viral sequences. A new algorithmic strategy, BIS2TreeAnalyzer, is designed to apply the coevolution analysis method BIS2, successfully used in the past on small sets of conserved sequences, to large sets of evolutionary related sequences. When applied to HBV, BIS2TreeAnalyzer highlights diversified viral solutions by discovering thirty-seven positions coevolving with residues known to be associated with drug resistance and located on the four Pol domains. These results suggest a sequential mechanism of emergence for some mutational patterns. They reveal complex combinations of positions involved in HBV drug resistance and contribute with new information to the landscape of HBV evolutionary solutions. The computational approach is general and can be applied to other viral sequences when compensatory mutations are presumed.

Coevolution of Residues Provides Evidence of a Functional Heterodimer of 5-HT2AR and 5-HT2CR Involving Both Intracellular and Extracellular Domains

Serotonin is a neurotransmitter that plays a role in regulating activities such as sleep, appetite, mood and substance abuse disorders; serotonin receptors 5-HT2AR and 5-HT2CR are active within pathways associated with substance abuse. It has been suggested that 5-HT2AR and 5-HT2CR may form a dimer that affects behavioral processes. Here we study the coevolution of residues in 5-HT2AR and 5-HT2CR to identify potential interactions between residues in both proteins. Coevolution studies can detect protein interactions, and since the thus uncovered interactions are subject to evolutionary pressure, they are likely functional. We assessed the significance of the 5-HT2AR/5-HT2CR interactions using randomized phylogenetic trees and found the coevolution significant (p-value = 0.01). We also discuss how co-expression of the receptors suggests the predicted interaction is functional. Finally, we analyze how several single nucleotide polymorphisms for the 5-HT2AR and 5-HT2CR genes affect their interaction. Our findings are the first to characterize the binding interface of 5-HT2AR/5-HT2CR and indicate a correlation between this interface and location of SNPs in both proteins.

BIS2Analyzer: a server for co-evolution analysis of conserved protein families

Along protein sequences, co-evolution analysis identifies residue pairs demonstrating either a specific co-adaptation, where changes in one of the residues are compensated by changes in the other during evolution or a less specific external force that affects the evolutionary rates of both residues in a similar magnitude. In both cases, independently of the underlying cause, co-evolutionary signatures within or between proteins serve as markers of physical interactions and/or functional relationships. Depending on the type of protein under study, the set of available homologous sequences may greatly differ in size and amino acid variability. BIS2Analyzer, openly accessible at http://www.lcqb.upmc.fr/BIS2Analyzer/, is a web server providing the online analysis of co-evolving amino-acid pairs in protein alignments, especially designed for vertebrate and viral protein families, which typically display a small number of highly similar sequences. It is based on BIS2, a re-implemented fast version of the co-evolution analysis tool Blocks in Sequences (BIS). BIS2Analyzer provides a rich and interactive graphical interface to ease biological interpretation of the results.

HCV adaptation to HIV coinfection

Human immunodeficiency virus (HIV) infection is rising as a leading cause of morbidity and mortality among hepatitis C virus (HCV)-infected patients. Both viruses interact in co-infected hosts, which may affect their intra-host evolution, potentially leading to differing genetic composition of viral populations in co-infected (CIP) and mono-infected (MIP) patients. Here, we investigate genetic differences between intra-host variants of the HCV hypervariable region 1 (HVR1) sampled from CIP and MIP. Nucleotide (nt) sequences of intra-host HCV HVR1 variants (N = 28,622) obtained from CIP (N = 112) and MIP (n = 176) were represented using 148 physical-chemical (PhyChem) indexes of DNA nt dimers. Significant (p < .0001) differences in the means and frequency distributions of 7 PhyChem properties were found between HVR1 variants from both groups. Linear projection analysis of 29 PhyChem features extracted from such PhyChem properties showed that the CIP and MIP HVR1 variants have a distinct distribution in the modeled 2D-space, with only ~1.3% of PhyChem profiles (N = 6782), shared by all HVR1 variants, being found in both groups. Probabilistic neural network (PNN) and naïve Bayesian (NB) classifiers trained on the PhyChem features accurately classified HVR1 variants by the group in cross-validation experiments (AUROC ≥ 0.96). Similarly, both models showed a high accuracy (AUROC ≥ 0.95) when evaluated on a test dataset of HVR1 sequences obtained from 10 patients, data from whom were not used for model building. Both models performed at the expected lower accuracy on randomly labeled datasets in cross-validation experiments (AUROC = 0.50). The random-label trained PNN showed a similar drop in accuracy on the test dataset (AUROC = 0.48), indicating that the detected associations were unlikely due to random correlations. Marked differences in genetic composition of HCV HVR1 variants sampled from CIP and MIP suggest differing intra-host HCV evolution in the presence of HIV infection. PhyChem features identified here may be used for detection of HIV infection from intra-host HCV variants alone in co-infected patients, thus facilitating monitoring for HIV introduction to high-risk populations with high HCV prevalence.

Beyond Channel Activity: Protein-Protein Interactions Involving Viroporins

Viroporins are short polypeptides encoded by viruses. These small membrane proteins assemble into oligomers that can permeabilize cellular lipid bilayers, disrupting the physiology of the host to the advantage of the virus. Consequently, efforts during the last few decades have been focused towards the discovery of viroporin channel inhibitors, but in general these have not been successful to produce licensed drugs. Viroporins are also involved in viral pathogenesis by engaging in critical interactions with viral proteins, or disrupting normal host cellular pathways through coordinated interactions with host proteins. These protein-protein interactions (PPIs) may become alternative attractive drug targets for the development of antivirals. In this sense, while thus far most antiviral molecules have targeted viral proteins, focus is moving towards targeting host proteins that are essential for virus replication. In principle, this largely would overcome the problem of resistance, with the possibility of using repositioned existing drugs. The precise role of these PPIs, their strain- and host- specificities, and the structural determination of the complexes involved, are areas that will keep the fields of virology and structural biology occupied for years to come. In the present review, we provide an update of the efforts in the characterization of the main PPIs for most viroporins, as well as the role of viroporins in these PPIs interactions.

A protein coevolution method uncovers critical features of the Hepatitis C Virus fusion mechanism

Amino-acid coevolution can be referred to mutational compensatory patterns preserving the function of a protein. Viral envelope glycoproteins, which mediate entry of enveloped viruses into their host cells, are shaped by coevolution signals that confer to viruses the plasticity to evade neutralizing antibodies without altering viral entry mechanisms. The functions and structures of the two envelope glycoproteins of the Hepatitis C Virus (HCV), E1 and E2, are poorly described. Especially, how these two proteins mediate the HCV fusion process between the viral and the cell membrane remains elusive. Here, as a proof of concept, we aimed to take advantage of an original coevolution method recently developed to shed light on the HCV fusion mechanism. When first applied to the well-characterized Dengue Virus (DENV) envelope glycoproteins, coevolution analysis was able to predict important structural features and rearrangements of these viral protein complexes. When applied to HCV E1E2, computational coevolution analysis predicted that E1 and E2 refold interdependently during fusion through rearrangements of the E2 Back Layer (BL). Consistently, a soluble BL-derived polypeptide inhibited HCV infection of hepatoma cell lines, primary human hepatocytes and humanized liver mice. We showed that this polypeptide specifically inhibited HCV fusogenic rearrangements, hence supporting the critical role of this domain during HCV fusion. By combining coevolution analysis and in vitro assays, we also uncovered functionally-significant coevolving signals between E1 and E2 BL/Stem regions that govern HCV fusion, demonstrating the accuracy of our coevolution predictions. Altogether, our work shed light on important structural features of the HCV fusion mechanism and contributes to advance our functional understanding of this process. This study also provides an important proof of concept that coevolution can be employed to explore viral protein mediated-processes, and can guide the development of innovative translational strategies against challenging human-tropic viruses.

Several virus-mediated molecular processes remain poorly described, which dampen the development of potent anti-viral therapies. Hence, new experimental strategies need to be undertaken to improve and accelerate our understanding of these processes. Here, as a proof of concept, we employ amino-acid coevolution as a tool to gain insights into the structural rearrangements of Hepatitis C Virus (HCV) envelope glycoproteins E1 and E2 during virus fusion with the cell membrane, and provide a basis for the inhibition of this process. Our coevolution analysis predicted that a specific domain of E2, the Back Layer (BL) is involved into significant conformational changes with E1 during the fusion of the HCV membrane with the cellular membrane. Consistently, a recombinant, soluble form of the BL was able to inhibit E1E2 fusogenic rearrangements and HCV infection. Moreover, predicted coevolution networks involving E1 and BL residues, as well as E1 and BL-adjacent residues, were found to modulate virus fusion. Our data shows that coevolution analysis is a powerful and underused approach that can provide significant insights into the functions and structural rearrangements of viral proteins. Importantly, this approach can also provide structural and molecular basis for the design of effective anti-viral drugs, and opens new perspectives to rapidly identify effective antiviral strategies against emerging and re-emerging viral pathogens.

Synonymous Co-Variation across the E1/E2 Gene Junction of Hepatitis C Virus Defines Virion Fitness

Hepatitis C virus is a positive-sense single-stranded RNA virus. The gene junction partitioning the viral glycoproteins E1 and E2 displays concurrent sequence evolution with the 3'-end of E1 highly conserved and the 5'-end of E2 highly heterogeneous. This gene junction is also believed to contain structured RNA elements, with a growing body of evidence suggesting that such structures can act as an additional level of viral replication and transcriptional control. We have previously used ultradeep pyrosequencing to analyze an amplicon library spanning the E1/E2 gene junction from a treatment naïve patient where samples were collected over 10 years of chronic HCV infection. During this timeframe maintenance of an in-frame insertion, recombination and humoral immune targeting of discrete virus sub-populations was reported. In the current study, we present evidence of epistatic evolution across the E1/E2 gene junction and observe the development of co-varying networks of codons set against a background of a complex virome with periodic shifts in population dominance. Overtime, the number of codons actively mutating decreases for all virus groupings. We identify strong synonymous co-variation between codon sites in a group of sequences harbouring a 3 bp in-frame insertion and propose that synonymous mutation acts to stabilize the RNA structural backbone.