The structural role of SARS-CoV-2 genetic background in the emergence and success of spike mutations: The case of the spike A222V mutation

Real-space heterogeneous reconstruction, refinement, and disentanglement of CryoEM conformational states with HetSIREN

Single-particle analysis by Cryo-electron microscopy (CryoEM) provides direct access to the conformations of macromolecules. Traditional methods assume discrete conformations, while newer algorithms estimate conformational landscapes representing the different structural states a biomolecule explores. This work presents HetSIREN, a deep learning-based method that can fully reconstruct or refine a CryoEM volume in real space based on the structural information summarized in a conformational latent space. HetSIREN is defined as an accurate space-based method that allows spatially focused analysis and the introduction of sinusoidal hypernetworks with proven high analytics capacities. Continuing with innovations, HetSIREN can also refine the images' pose while conditioning the network with additional constraints to yield cleaner high-quality volumes, as well as addressing one of the most confusing issues in heterogeneity analysis, as it is the fact that structural heterogeneity estimations are entangled with pose estimation (and to a lesser extent with CTF estimation) thanks to its decoupling architecture.

Secondary structure of the SARS-CoV-2 genome is predictive of nucleotide substitution frequency

Accurate estimation of the effects of mutations on SARS-CoV-2 viral fitness can inform public-health responses such as vaccine development and predicting the impact of a new variant; it can also illuminate biological mechanisms including those underlying the emergence of variants of concern. Recently, Lan et al. reported a model of SARS-CoV-2 secondary structure and its underlying dimethyl sulfate reactivity data (Lan et al., 2022). I investigated whether base reactivities and secondary structure models derived from them can explain some variability in the frequency of observing different nucleotide substitutions across millions of patient sequences in the SARS-CoV-2 phylogenetic tree. Nucleotide basepairing was compared to the estimated 'mutational fitness' of substitutions, a measurement of the difference between a substitution's observed and expected frequency that is correlated with other estimates of viral fitness (Bloom and Neher, 2023). This comparison revealed that secondary structure is often predictive of substitution frequency, with significant decreases in substitution frequencies at basepaired positions. Focusing on the mutational fitness of C→U, the most common type of substitution, I describe C→U substitutions at basepaired positions that characterize major SARS-CoV-2 variants; such mutations may have a greater impact on fitness than appreciated when considering substitution frequency alone.

Real-space heterogeneous reconstruction, refinement, and disentanglement of CryoEM conformational states with HetSIREN

Single-particle analysis by Cryo-electron microscopy (CryoEM) provides direct access to the conformation of each macromolecule. However, the image's signal-to-noise ratio is low, and some form of classification is usually performed at the image processing level to allow structural modeling. Classical classification methods imply the existence of a discrete number of structural conformations. However, new heterogeneity algorithms introduce a novel reconstruction paradigm, where every state is represented by a lower number of particles, potentially just one, allowing the estimation of conformational landscapes representing the different structural states a biomolecule explores. In this work, we present a novel deep learning-based method called HetSIREN. HetSIREN can fully reconstruct or refine a CryoEM volume in real space based on the structural information summarized in a conformational latent space. The unique characteristics that set HetSIREN apart start with the definition of the approach as a real space-based only method, a fact that allows spatially focused analysis, but also the introduction of a novel network architecture specifically designed to make use of meta-sinusoidal activations, with proven high analytics capacities. Continuing with innovations, HetSIREN can also refine the pose parameters of the images at the same time that it conditions the network with prior information/constraints on the maps, such as Total Variation andL 1 denoising, ultimately yielding cleaner volumes with high-quality structural features. Finally, but very importantly, HetSIREN addresses one of the most confusing issues in heterogeneity analysis, as it is the fact that real structural heterogeneity estimation is entangled with pose estimation (and to a lesser extent with CTF estimation), in this way, HetSIREN introduces a novel encoding architecture able to decouple pose and CTF information from the conformational landscape, resulting in more accurate and interpretable conformational latent spaces. We present results on computer-simulated data, public data from EMPIAR, and data from experimental systems currently being studied in our laboratories. An important finding is the sensitivity of the structure and dynamics of the SARS-CoV-2 Spike protein on the storage temperature.

Whole-Genome Sequencing and Mutation Analyses of SARS-CoV-2 Isolates from Indonesia

The SARS-CoV-2 infection that caused the COVID-19 pandemic has become a significant public health concern. New variants with distinct mutations have emerged, potentially impacting its infectivity, immune evasion capacity, and vaccine response. A whole-genome sequencing study of 292 SARS-CoV-2 isolates collected from selected regions of Indonesia between January and October 2021 was performed to identify the distribution of SARS-CoV-2 variants and common mutations in Indonesia. During January-April 2021, Indonesian lineages B.1.466.2 and B.1.470 dominated, but from May 2021, Delta's AY.23 lineage outcompeted them. An analysis of 7515 published sequences from January 2021 to June 2022 revealed a decline in Delta in November 2021, followed by the emergence of Omicron variants in December 2021. We identified C241T (5'UTR), P314L (NSP12b), F106F (NSP3), and D614G (Spike) mutations in all sequences. The other common substitutions included P681R (76.4%) and T478K (60%) in Spike, D377Y in Nucleocapsid (61%), and I82T in Membrane (60%) proteins. Breakthrough infection and prolonged viral shedding cases were associated with Delta variants carrying the Spike T19R, G142D, L452R, T478K, D614G, P681R, D950N, and V1264L mutations. The dynamic of SARS-CoV-2 variants in Indonesia highlights the importance of continuous genomic surveillance in monitoring and identifying potential strains leading to disease outbreaks.

Design of protein-binding peptides with controlled binding affinity: the case of SARS-CoV-2 receptor binding domain and angiotensin-converting enzyme 2 derived peptides

The development of methods able to modulate the binding affinity between proteins and peptides is of paramount biotechnological interest in view of a vast range of applications that imply designed polypeptides capable to impair or favour Protein-Protein Interactions. Here, we applied a peptide design algorithm based on shape complementarity optimization and electrostatic compatibility and provided the first experimental in vitro proof of the efficacy of the design algorithm. Focusing on the interaction between the SARS-CoV-2 Spike Receptor-Binding Domain (RBD) and the human angiotensin-converting enzyme 2 (ACE2) receptor, we extracted a 23-residues long peptide that structurally mimics the major interacting portion of the ACE2 receptor and designed in silico five mutants of such a peptide with a modulated affinity. Remarkably, experimental KD measurements, conducted using biolayer interferometry, matched the in silico predictions. Moreover, we investigated the molecular determinants that govern the variation in binding affinity through molecular dynamics simulation, by identifying the mechanisms driving the different values of binding affinity at a single residue level. Finally, the peptide sequence with the highest affinity, in comparison with the wild type peptide, was expressed as a fusion protein with human H ferritin (HFt) 24-mer. Solution measurements performed on the latter constructs confirmed that peptides still exhibited the expected trend, thereby enhancing their efficacy in RBD binding. Altogether, these results indicate the high potentiality of this general method in developing potent high-affinity vectors for hindering/enhancing protein-protein associations.

Learning from prepandemic data to forecast viral escape

Effective pandemic preparedness relies on anticipating viral mutations that are able to evade host immune responses to facilitate vaccine and therapeutic design. However, current strategies for viral evolution prediction are not available early in a pandemic-experimental approaches require host polyclonal antibodies to test against1-16, and existing computational methods draw heavily from current strain prevalence to make reliable predictions of variants of concern17-19. To address this, we developed EVEscape, a generalizable modular framework that combines fitness predictions from a deep learning model of historical sequences with biophysical and structural information. EVEscape quantifies the viral escape potential of mutations at scale and has the advantage of being applicable before surveillance sequencing, experimental scans or three-dimensional structures of antibody complexes are available. We demonstrate that EVEscape, trained on sequences available before 2020, is as accurate as high-throughput experimental scans at anticipating pandemic variation for SARS-CoV-2 and is generalizable to other viruses including influenza, HIV and understudied viruses with pandemic potential such as Lassa and Nipah. We provide continually revised escape scores for all current strains of SARS-CoV-2 and predict probable further mutations to forecast emerging strains as a tool for continuing vaccine development ( evescape.org ).

Scipion-EM-ProDy: A Graphical Interface for the ProDy Python Package within the Scipion Workflow Engine Enabling Integration of Databases, Simulations and Cryo-Electron Microscopy Image Processing

Macromolecular assemblies, such as protein complexes, undergo continuous structural dynamics, including global reconfigurations critical for their function. Two fast analytical methods are widely used to study these global dynamics, namely elastic network model normal mode analysis and principal component analysis of ensembles of structures. These approaches have found wide use in various computational studies, driving the development of complex pipelines in several software packages. One common theme has been conformational sampling through hybrid simulations incorporating all-atom molecular dynamics and global modes of motion. However, wide functionality is only available for experienced programmers with limited capabilities for other users. We have, therefore, integrated one popular and extensively developed software for such analyses, the ProDy Python application programming interface, into the Scipion workflow engine. This enables a wider range of users to access a complete range of macromolecular dynamics pipelines beyond the core functionalities available in its command-line applications and the normal mode wizard in VMD. The new protocols and pipelines can be further expanded and integrated into larger workflows, together with other software packages for cryo-electron microscopy image analysis and molecular simulations. We present the resulting plugin, Scipion-EM-ProDy, in detail, highlighting the rich functionality made available by its development.

C-2 Thiophenyl Tryptophan Trimers Inhibit Cellular Entry of SARS-CoV-2 through Interaction with the Viral Spike (S) Protein

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19, by infecting cells via the interaction of its spike protein (S) with the primary cell receptor angiotensin-converting enzyme (ACE2). To search for inhibitors of this key step in viral infection, we screened an in-house library of multivalent tryptophan derivatives. Using VSV-S pseudoparticles, we identified compound 2 as a potent entry inhibitor lacking cellular toxicity. Chemical optimization of 2 rendered compounds 63 and 65, which also potently inhibited genuine SARS-CoV-2 cell entry. Thermofluor and microscale thermophoresis studies revealed their binding to S and to its isolated receptor binding domain (RBD), interfering with the interaction with ACE2. High-resolution cryoelectron microscopy structure of S, free or bound to 2, shed light on cell entry inhibition mechanisms by these compounds. Overall, this work identifies and characterizes a new class of SARS-CoV-2 entry inhibitors with clear potential for preventing and/or fighting COVID-19.

A SARS-CoV-2 full genome sequence of the B.1.1 lineage sheds light on viral evolution in Sicily in late 2020

To investigate the influence of geographic constrains to mobility on SARS-CoV-2 circulation before the advent of vaccination, we recently characterized the occurrence in Sicily of viral lineages in the second pandemic wave (September to December 2020). Our data revealed wide prevalence of the then widespread through Europe B.1.177 variant, although some viral samples could not be classified with the limited Sanger sequencing tools used. A particularly interesting sample could not be fitted to a major variant then circulating in Europe and has been subjected here to full genome sequencing in an attempt to clarify its origin, lineage and relations with the seven full genome sequences deposited for that period in Sicily, hoping to provide clues on viral evolution. The obtained genome is unique (not present in databases). It hosts 20 single-base substitutions relative to the original Wuhan-Hu-1 sequence, 8 of them synonymous and the other 12 encoding 11 amino acid substitutions, all of them already reported one by one. They include four highly prevalent substitutions, NSP12:P323L, S:D614G, and N:R203K/G204R; the much less prevalent S:G181V, ORF3a:G49V and N:R209I changes; and the very rare mutations NSP3:L761I, NSP6:S106F, NSP8:S41F and NSP14:Y447H. GISAID labeled this genome as B.1.1 lineage, a lineage that appeared early on in the pandemic. Phylogenetic analysis also confirmed this lineage diagnosis. Comparison with the seven genome sequences deposited in late 2020 from Sicily revealed branching leading to B.1.177 in one branch and to Alpha in the other branch, and suggested a local origin for the S:G118V mutation.

In-depth genetic characterization of the SARS-CoV-2 pandemic in a two-year frame in North Macedonia using second and third generation sequencing technologies

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a persistent negative impact on both the public health and the global economy. To comprehend the origin, transmission routes and discover the mutations that alter the virus’s transmissibility and pathogenicity, full-length SARS-CoV-2 genomes have to be molecularly characterized. Focusing on a two-year time frame (2020-2021), we provide an in-depth virologic and epidemiological overview of the SARS-CoV-2 pandemic in the Republic of North Macedonia by assessing the frequency and distribution of the circulating SARS-CoV-2 variants. Using genetic characterization and phylogenetic analysis we shed light on the molecular evolution of the virus as well as test for a possible connection between specific SARS-CoV-2 haplotypes and the severity of the clinical symptoms. Our results show that one fifth (21.51%) of the tested respiratory samples for SARS-CoV-2 were positive. A noticeable trend in the incidence and severity of the COVID-19 infections was observed in the 60+ age group between males and females. Of the total number of positive cases, the highest incidence of SARS-CoV-2 was noticed in 60+ males (4,170.4/100,000), with a statistically significant (0,0001) difference between the two sexes. Additionally, a 1.8x increase in male mortality and consequentially significantly higher number of death cases was observed compared to females of the same age group (0.001). A total of 327 samples were sequenced in the period March 2020 - August 2021, showing the temporal distribution of SARS-CoV-2 variants circulating in North Macedonia. The phylogenetic analysis showed that most of the viral genomes were closely related and clustered in four distinctive lineages, B.1, B.1.1.7, B.1.351 and B.1.617.2. A statistically significant difference was observed in the 2C_1 haplotype (p=0.0013), where 10.5% of the patients were hospitalized due to severe clinical condition. By employing genetic sequencing, coupled with epidemiological investigations, we investigated viral distribution patterns, identified emerging variants and detected vaccine breakthrough infections. The present work is the first molecular study giving a comprehensive overview of the genetic landscape of circulating SARS-CoV-2 viruses in North Macedonia in a period of two years.

Correction: The structural role of SARS-CoV-2 genetic background in the emergence and success of spike mutations: The case of the spike A222V mutation

[This corrects the article DOI: 10.1371/journal.ppat.1010631.].

Genetic, Structural, Physicochemical, and Molecular Epidemiological Landscape of Three New Mutations Found in the Spike (S) Protein of Highly Transmissible Sri Lankan Delta Variant Sub-lineage AY.28: A222V, A701S, and A1078S

Recently, three new mutations were identified in Sri Lanka in the spike protein of the rapidly spreading delta variant of the SARS-CoV-2 virus identified by the sublineage AY.28. They were A222V, A701S, and A1078S. The primary focus here is on the A701S mutation that is (1) found in the immediate vicinity of the S1/S2 cleavage site (PRRAR∗SV) that separates S1 and S2 subunits of the spike protein; (2) has high structural disorder of the region spanning Serine-701 (Ser-701), which promotes a longer flexible loop forming a better substrate; (3) collapses a loose, short, unstable, tripeptide beta strand (ENS), which is likely to assist the host proteases to cleave the S1-S2 interface easily than when an alanine is present. The same A701S mutation is found in at least 10 other strains of SARS-CoV-2 found in India, USA (East and West Coasts), and China, which classifies this mutation as geographically widespread and convergent in etiology. Conversely, the A1078S mutation is a highly present (>60 strains) mutation in terms of the SARS-CoV-2 coronaviruses, while the highly abundant A222V mutation is inferred by the genetic code, structural and topological features, and placement (in a beta strand) to be an innocuous, conservative mutation. The critical nature of S1/S2-dependent cleavage of S1 and S2 subunits of the spike protein makes the A701S mutation one of significance not just for possible higher virus transmission but also for subsequent fates such as viral load and vaccine effectivity against the delta variant.

The Comparison of Mutational Progression in SARS-CoV-2: A Short Updated Overview

The COVID-19 pandemic has impacted the world population adversely, posing a threat to human health. In the past few years, various strains of SARS-CoV-2, each with different mutations in its structure, have impacted human health in negative ways. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations influence the virulence, antibody evasion, and Angiotensin-converting enzyme 2 (ACE2) affinity of the virus. These mutations are essential to understanding how a new strain of SARS-CoV-2 has changed and its possible effects on the human body. This review provides an insight into the spike mutations of SARS-CoV-2 variants. As the current scientific data offer a scattered outlook on the various type of mutations, we aimed to categorize the mutations of Beta (B.1.351), Gamma (P.1), Delta (B.1.612.2), and Omicron (B.1.1.529) systematically according to their location in the subunit 1 (S1) and subunit 2 (S2) domains and summarized their consequences as a result. We also compared the miscellany of mutations that have emerged in all four variants to date. The comparison shows that mutations such as D614G and N501Y have emerged in all four variants of concern and that all four variants have multiple mutations within the N-terminal domain (NTD), as in the case of the Delta variant. Other mutations are scattered in the receptor binding domain (RBD) and subdomain 2 (SD2) of the S1 domain. Mutations in RBD or NTD are often associated with antibody evasion. Few mutations lie in the S2 domain in the Beta, Gamma, and Delta variants. However, in the Omicron variant many mutations occupy the S2 domain, hinting towards a much more evasive virus.