Inferring the stabilization effects of SARS-CoV-2 variants on the binding with ACE2 receptor

Computational evidences of a misfolding event in an aggregation-prone light chain preceding the formation of the non-native pathogenic dimer

Antibody light chain amyloidosis is a disorder in which protein aggregates, mainly composed of immunoglobulin light chains, deposit in diverse tissues impairing the correct functioning of organs. Interestingly, due to the high susceptibility of antibodies to mutations, AL amyloidosis appears to be strongly patient-specific. Indeed, every patient will display their own mutations that will make the proteins involved prone to aggregation thus hindering the study of this disease on a wide scale. In this framework, determining the molecular mechanisms that drive the aggregation could pave the way to the development of patient-specific therapeutics. Here, we focus on a particular patient-derived light chain, which has been experimentally characterized. We investigated the early phases of the aggregation pathway through extensive full-atom molecular dynamics simulations, highlighting a structural rearrangement and the exposure of two hydrophobic regions in the aggregation-prone species. Next, we moved to consider the pathological dimerization process through docking and molecular dynamics simulations, proposing a dimeric structure as a candidate pathological first assembly. Overall, our results shed light on the first phases of the aggregation pathway for a light chain at an atomic level detail, offering new structural insights into the corresponding aggregation process.

Single-Molecule Investigation of the Binding Interface Stability of SARS-CoV-2 Variants with ACE2

The SARS-CoV-2 pandemic spurred numerous research endeavors to comprehend the virus and mitigate its global severity. Understanding the binding interface between the virus and human receptors is pivotal to these efforts and paramount to curbing infection and transmission. Here we employ atomic force microscopy and steered molecular dynamics simulation to explore SARS-CoV-2 receptor binding domain (RBD) variants and angiotensin-converting enzyme 2 (ACE2), examining the impact of mutations at key residues upon binding affinity. Our results show that the Omicron and Delta variants possess strengthened binding affinity in comparison to the Mu variant. Further, using sera from individuals either vaccinated or with acquired immunity following Delta strain infection, we assess the impact of immunity upon variant RBD/ACE2 complex formation. Single-molecule force spectroscopy analysis suggests that vaccination before infection may provide stronger protection across variants. These results underscore the need to monitor antigenic changes in order to continue developing innovative and effective SARS-CoV-2 abrogation strategies.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Two Receptor Binding Strategy of SARS-CoV-2 Is Mediated by Both the N-Terminal and Receptor-Binding Spike Domain

It is not well understood why severe acute respiratory syndrome (SARS)-CoV-2 spreads much faster than other β-coronaviruses such as SARS-CoV and Middle East respiratory syndrome (MERS)-CoV. In a previous publication, we predicted the binding of the N-terminal domain (NTD) of SARS-CoV-2 spike to sialic acids (SAs). Here, we experimentally validate this interaction and present simulations that reveal a second possible interaction between SAs and the spike protein via a binding site located in the receptor-binding domain (RBD). The predictions from molecular-dynamics simulations and the previously-published 2D-Zernike binding-site recognition approach were validated through flow-induced dispersion analysis (FIDA)─which reveals the capability of the SARS-CoV-2 spike to bind to SA-containing (glyco)lipid vesicles, and flow-cytometry measurements─which show that spike binding is strongly decreased upon inhibition of SA expression on the membranes of angiotensin converting enzyme-2 (ACE2)-expressing HEK cells. Our analyses reveal that the SA binding of the NTD and RBD strongly enhances the infection-inducing ACE2 binding. Altogether, our work provides in silico, in vitro, and cellular evidence that the SARS-CoV-2 virus utilizes a two-receptor (SA and ACE2) strategy. This allows the SARS-CoV-2 spike to use SA moieties on the cell membrane as a binding anchor, which increases the residence time of the virus on the cell surface and aids in the binding of the main receptor, ACE2, via 2D diffusion.

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.

Structural plasticity of omicron BA.5 and BA.2.75 for enhanced ACE-dependent entry into cells

The current study investigated the binding variations among the wilt type, Omicron sub-variants BA.2.75 and BA.5, using protein-protein docking, protein structural graphs (P SG), and molecular simulation methods. HADDOCK predicted docking scores and dissociation constant (KD) revealed tighter binding of these sub-variants in contrast to the WT. Further investigation revealed variations in the hub residues, protein sub-networks, and GlobalMetapath in these variants as compared to the WT. A very unusual dynamic for BA.2.75 and BA.5 was observed, and secondary structure transition can also be witnessed in the loops (44-505). The results show that the flexibility of these three loops is increased by the mutations as an allosteric effect and thus enhances the chances of bonding with the nearby residues to connect and form a stable connection. Furthermore, the additional hydrogen bonding contacts steer the robust binding of these variants in contrast to the wild type. The total binding free energy for the wild type was calculated to be -61.38 kcal/mol, while for BA.2.75 and BA.5 variants the T BE was calculated to be -70.42 kcal/mol and 69.78 kcal/mol, respectively. We observed that the binding of BA.2.75 is steered by the electrostatic interactions while the BA.5 additional contacts are due to the vdW (Van der Waal) energy. From these findings, it can be observed the Spike (S) protein is undergoing structural adjustments to bind efficiently to the hACE2 (human angiotensin-converting enzyme 2) receptor and, in turn, increase entry to the host cells. The current study will aid the development of structure-based drugs against these variants.Communicated by Ramaswamy H. Sarma.

Molecular dynamics analysis of superoxide dismutase 1 mutations suggests decoupling between mechanisms underlying ALS onset and progression

Mutations in the superoxide dismutase 1 (SOD1) gene are the second most common known cause of ALS. SOD1 variants express high phenotypic variability and over 200 have been reported in people with ALS. It was previously proposed that variants can be broadly classified in two groups, 'wild-type like' (WTL) and 'metal binding region' (MBR) variants, based on their structural location and biophysical properties. MBR variants, but not WTL variants, were associated with a reduction of SOD1 enzymatic activity. In this study we used molecular dynamics and large clinical datasets to characterise the differences in the structural and dynamic behaviour of WTL and MBR variants with respect to the wild-type SOD1, and how such differences influence the ALS clinical phenotype. Our study identified marked structural differences, some of which are observed in both variant groups, while others are group specific. Moreover, collecting clinical data of approximately 500 SOD1 ALS patients carrying variants, we showed that the survival time of patients carrying an MBR variant is generally longer (∼6 years median difference, p < 0.001) with respect to patients with a WTL variant. In conclusion, our study highlighted key differences in the dynamic behaviour between WTL and MBR SOD1 variants, and between variants and wild-type SOD1 at an atomic and molecular level, that could be further investigated to explain the associated phenotypic variability. Our results support the hypothesis of a decoupling between mechanisms of onset and progression of SOD1 ALS, and an involvement of loss-of-function of SOD1 with the disease progression.

Differences in the organization of interface residues tunes the stability of the SARS-CoV-2 spike-ACE2 complex

The continuous emergence of novel variants represents one of the major problems in dealing with the SARS-CoV-2 virus. Indeed, also due to its prolonged circulation, more than ten variants of concern emerged, each time rapidly overgrowing the current viral version due to improved spreading features. As, up to now, all variants carry at least one mutation on the spike Receptor Binding Domain, the stability of the binding between the SARS-CoV-2 spike protein and the human ACE2 receptor seems one of the molecular determinants behind the viral spreading potential. In this framework, a better understanding of the interplay between spike mutations and complex stability can help to assess the impact of novel variants. Here, we characterize the peculiarities of the most representative variants of concern in terms of the molecular interactions taking place between the residues of the spike RBD and those of the ACE2 receptor. To do so, we performed molecular dynamics simulations of the RBD-ACE2 complexes of the seven variants of concern in comparison with a large set of complexes with different single mutations taking place on the RBD solvent-exposed residues and for which the experimental binding affinity was available. Analyzing the strength and spatial organization of the intermolecular interactions of the binding region residues, we found that (i) mutations producing an increase of the complex stability mainly rely on instaurating more favorable van der Waals optimization at the cost of Coulombic ones. In particular, (ii) an anti-correlation is observed between the shape and electrostatic complementarities of the binding regions. Finally, (iii) we showed that combining a set of dynamical descriptors is possible to estimate the outcome of point mutations on the complex binding region with a performance of 0.7. Overall, our results introduce a set of dynamical observables that can be rapidly evaluated to probe the effects of novel isolated variants or different molecular systems.

Electrostatic complementarity at the interface drives transient protein-protein interactions

Understanding the mechanisms driving bio-molecules binding and determining the resulting complexes' stability is fundamental for the prediction of binding regions, which is the starting point for drug-ability and design. Characteristics like the preferentially hydrophobic composition of the binding interfaces, the role of van der Waals interactions, and the consequent shape complementarity between the interacting molecular surfaces are well established. However, no consensus has yet been reached on the role of electrostatic. Here, we perform extensive analyses on a large dataset of protein complexes for which both experimental binding affinity and pH data were available. Probing the amino acid composition, the disposition of the charges, and the electrostatic potential they generated on the protein molecular surfaces, we found that (i) although different classes of dimers do not present marked differences in the amino acid composition and charges disposition in the binding region, (ii) homodimers with identical binding region show higher electrostatic compatibility with respect to both homodimers with non-identical binding region and heterodimers. Interestingly, (iii) shape and electrostatic complementarity, for patches defined on short-range interactions, behave oppositely when one stratifies the complexes by their binding affinity: complexes with higher binding affinity present high values of shape complementarity (the role of the Lennard-Jones potential predominates) while electrostatic tends to be randomly distributed. Conversely, complexes with low values of binding affinity exploit Coulombic complementarity to acquire specificity, suggesting that electrostatic complementarity may play a greater role in transient (or less stable) complexes. In light of these results, (iv) we provide a novel, fast, and efficient method, based on the 2D Zernike polynomial formalism, to measure electrostatic complementarity without the need of knowing the complex structure. Expanding the electrostatic potential on a basis of 2D orthogonal polynomials, we can discriminate between transient and permanent protein complexes with an AUC of the ROC of [Formula: see text] 0.8. Ultimately, our work helps shedding light on the non-trivial relationship between the hydrophobic and electrostatic contributions in the binding interfaces, thus favoring the development of new predictive methods for binding affinity characterization.

Investigating the competition between ACE2 natural molecular interactors and SARS-CoV-2 candidate inhibitors

The SARS-CoV-2 pandemic still poses a threat to the global health as the virus continues spreading in most countries. Therefore, the identification of molecules capable of inhibiting the binding between the ACE2 receptor and the SARS-CoV-2 spike protein is of paramount importance. Recently, two DNA aptamers were designed with the aim to inhibit the interaction between the ACE2 receptor and the spike protein of SARS-CoV-2. Indeed, the two molecules interact with the ACE2 receptor in the region around the K353 residue, preventing its binding of the spike protein. If on the one hand this inhibition process hinders the entry of the virus into the host cell, it could lead to a series of side effects, both in physiological and pathological conditions, preventing the correct functioning of the ACE2 receptor. Here, we discuss through a computational study the possible effect of these two very promising DNA aptamers, investigating all possible interactions between ACE2 and its experimentally known molecular partners. Our in silico predictions show that some of the 10 known molecular partners of ACE2 could interact, physiologically or pathologically, in a region adjacent to the K353 residue. Thus, the curative action of the proposed DNA aptamers could recruit ACE2 from its biological functions.

Is BF.7 more infectious than other Omicron subtypes: Insights from structural and simulation studies of BF.7 spike RBD variant

Fear of a fresh infection wave and a global health issue in the ongoing COVID-19 pandemic have been rekindled by the appearance of two new novel variants BF.7 and BA.4/5 of Omicron lineages. Predictions of increased antibody evasion capabilities and transmissibility have been recognised in addition to the existing lineages (BA.1.1, BA.2, BA.2.12.1 and BA.3) as cause for worry. In comparison to Omicron, BA.4 and BF.7 share nine mutations in the spike protein, Leu371Phe, Thr376Ala, Asp405Asn, Arg408Ser, Ser446Gly, Leu452Arg, Phe486Val, Arg493Gln, Ser496Gly, whereas BF.7 contains an additional mutation, Arg346Thr, in the receptor binding domain (RBD) region. Due to the critical need for analysis and data on the BA.4 and BF.7 variants, we have computationally analyzed the interaction pattern between the Omicron, BA.4 and BF.7 RBD and angiotensin-converting enzyme 2 (ACE2) to determine the influence of these unique mutations on the structures, functions, and binding affinity of RBD towards ACE2. These analyses also allow to compare molecular models to previously reported data to evaluate the robustness of our methods for quick prediction of emerging future variants. The docking results reveal that BA.4 and BF.7 have particularly strong interactions with ACE2 when compared to Omicron, as shown by several parameters such as salt bridge, hydrogen bond, and non-bonded interactions. In addition, the estimations of binding free energy corroborated the findings further. BA.4 and BF.7 were found to bind to ACE2 with similar affinities (-72.14 and - 71.54 kcal/mol, respectively) and slightly stronger than Omicron (-70.04 kcal/mol). The differences in the binding pattern between the Omicron, BA.4 and BF.7 variant complexes indicated that the BA.4 and BF.7 RBD substitutions Asp405Asn, Ser446Gly, Leu452Arg, Phe486Val and Arg493Gln caused additional interactions with ACE2. In addition, normal mode analyses also indicate more stable conformations of BA.4 and BF.7 RBDs against human ACE2. Based on these structural and simulation analyses, we hypothesized that these changes may affect the binding affinity of BA.4 and BF.7 with ACE2.

Molecular dynamics simulations highlight the altered binding landscape at the spike-ACE2 interface between the Delta and Omicron variants compared to the SARS-CoV-2 original strain

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.1.529 variant (Omicron), represents a significant deviation in genetic makeup and function compared to previous variants. Following the BA.1 sublineage, the BA.2 and BA.3 Omicron subvariants became dominant, and currently the BA.4 and BA.5, which are quite distinct variants, have emerged. Using molecular dynamics simulations, we investigated the binding characteristics of the Delta and Omicron (BA.1) variants in comparison to wild-type (WT) at the interface of the spike protein receptor binding domain (RBD) and human angiotensin converting enzyme-2 (ACE2) ectodomain. The primary aim was to compare our molecular modelling systems with previously published observations, to determine the robustness of our approach for rapid prediction of emerging future variants. Delta and Omicron were found to bind to ACE2 with similar affinities (−39.4 and −43.3 kcal/mol, respectively) and stronger than WT (−33.5 kcal/mol). In line with previously published observations, the energy contributions of the non-mutated residues at the interface were largely retained between WT and the variants, with F456, F486, and Y489 having the strongest energy contributions to ACE2 binding. Further, residues N440K, Q498R, and N501Y were predicted to be energetically favourable in Omicron. In contrast to Omicron, which had the E484A and K417N mutations, intermolecular bonds were detected for the residue pairs E484:K31 and K417:D30 in WT and Delta, in accordance with previously published findings. Overall, our simplified molecular modelling approach represents a step towards predictive model systems for rapidly analysing arising variants of concern.

Tracking mutational semantics of SARS-CoV-2 genomes

Natural language processing (NLP) algorithms process linguistic data in order to discover the associated word semantics and develop models that can describe or even predict the latent meanings of the data. The applications of NLP become multi-fold while dealing with dynamic or temporally evolving datasets (e.g., historical literature). Biological datasets of genome-sequences are interesting since they are sequential as well as dynamic. Here we describe how SARS-CoV-2 genomes and mutations thereof can be processed using fundamental algorithms in NLP to reveal the characteristics and evolution of the virus. We demonstrate applicability of NLP in not only probing the temporal mutational signatures through dynamic topic modelling, but also in tracing the mutation-associations through tracing of semantic drift in genomic mutation records. Our approach also yields promising results in unfolding the mutational relevance to patient health status, thereby identifying putative signatures linked to known/highly speculated mutations of concern.

Spatial organization of hydrophobic and charged residues affects protein thermal stability and binding affinity

What are the molecular determinants of protein–protein binding affinity and whether they are similar to those regulating fold stability are two major questions of molecular biology, whose answers bring important implications both from a theoretical and applicative point of view. Here, we analyze chemical and physical features on a large dataset of protein–protein complexes with reliable experimental binding affinity data and compare them with a set of monomeric proteins for which melting temperature data was available. In particular, we probed the spatial organization of protein (1) intramolecular and intermolecular interaction energies among residues, (2) amino acidic composition, and (3) their hydropathy features. Analyzing the interaction energies, we found that strong Coulombic interactions are preferentially associated with a high protein thermal stability, while strong intermolecular van der Waals energies correlate with stronger protein–protein binding affinity. Statistical analysis of amino acids abundances, exposed to the molecular surface and/or in interaction with the molecular partner, confirmed that hydrophobic residues present on the protein surfaces are preferentially located in the binding regions, while charged residues behave oppositely. Leveraging on the important role of van der Waals interface interactions in binding affinity, we focused on the molecular surfaces in the binding regions and evaluated their shape complementarity, decomposing the molecular patches in the 2D Zernike basis. For the first time, we quantified the correlation between local shape complementarity and binding affinity via the Zernike formalism. In addition, considering the solvent interactions via the residue hydropathy, we found that the hydrophobicity of the binding regions dictates their shape complementary as much as the correlation between van der Waals energy and binding affinity. In turn, these relationships pave the way to the fast and accurate prediction and design of optimal binding regions as the 2D Zernike formalism allows a rapid and superposition-free comparison between possible binding surfaces.

Bioinformatic and Experimental Analysis of T Cell Immune Reactivity to SARS-CoV-2 and its Variants

Definition of the T cells responses to SARS-CoV-2 and associated variants is critical to understanding the complexity of adaptive immunity against SARS-CoV-2 infection. Several groups have investigated the T cells responses by both experimental and bioinformatical approaches. Here we summarize recent findings on CD4 and CD8 T cell responses to SARS-CoV-2 with particular emphasis on emerging variants of concern, consolidating the results on the impact of SARS-CoV-2 variants on T cell responses by performing an additional metanalysis emphasizing the lower impact of variant mutations in dominant T cell epitopes. The consensus is that the majority of T cell responses are conserved across all current SARS-CoV-2 variants, including Delta and Omicron. Thus, even in concomitance with reduced antibody and B cell responses, T cells can still provide a second line of antiviral immunity.

Assessment of mutations on RBD in the Spike protein of SARS-CoV-2 Alpha, Delta and Omicron variants

The severe acute respiratory syndrome (SARS) coronavirus 2 (CoV-2) variant Omicron spread more rapid than the other variants of SARS-CoV-2 virus. Mutations on the Spike (S) protein receptor-binding domain (RBD) are critical for the antibody resistance and infectivity of the SARS-CoV-2 variants. In this study, we have used accelerated molecular dynamics (aMD) simulations and free energy calculations to present a systematic analysis of the affinity and conformational dynamics along with the interactions that drive the binding between Spike protein RBD and human angiotensin-converting enzyme 2 (ACE2) receptor. We evaluate the impacts of the key mutation that occur in the RBDs Omicron and other variants in the binding with the human ACE2 receptor. The results show that S protein Omicron has stronger binding to the ACE2 than other variants. The evaluation of the decomposition energy per residue shows the mutations N440K, T478K, Q493R and Q498R observed in Spike protein of SARS-CoV-2 provided a stabilization effect for the interaction between the SARS-CoV-2 RBD and ACE2. Overall, the results demonstrate that faster spreading of SARS-CoV-2 Omicron may be correlated with binding affinity of S protein RBD to ACE2 and mutations of uncharged residues to positively charged residues such as Lys and Arg in key positions in the RBD.

Shape Complementarity Optimization of Antibody–Antigen Interfaces: The Application to SARS-CoV-2 Spike Protein

Many factors influence biomolecule binding, and its assessment constitutes an elusive challenge in computational structural biology. In this aspect, the evaluation of shape complementarity at molecular interfaces is one of the main factors to be considered. We focus on the particular case of antibody–antigen complexes to quantify the complementarities occurring at molecular interfaces. We relied on a method we recently developed, which employs the 2D Zernike descriptors, to characterize the investigated regions with an ordered set of numbers summarizing the local shape properties. Collecting a structural dataset of antibody–antigen complexes, we applied this method and we statistically distinguished, in terms of shape complementarity, pairs of the interacting regions from the non-interacting ones. Thus, we set up a novel computational strategy based on in silico mutagenesis of antibody-binding site residues. We developed a Monte Carlo procedure to increase the shape complementarity between the antibody paratope and a given epitope on a target protein surface. We applied our protocol against several molecular targets in SARS-CoV-2 spike protein, known to be indispensable for viral cell invasion. We, therefore, optimized the shape of template antibodies for the interaction with such regions. As the last step of our procedure, we performed an independent molecular docking validation of the results of our Monte Carlo simulations.

Mutational Effect of Some Major COVID-19 Variants on Binding of the S Protein to ACE2

COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has many variants that accelerated the spread of the virus. In this study, we investigated the quantitative effect of some major mutants of the spike protein of SARS-CoV-2 binding to the human angiotensin-converting enzyme 2 (ACE2). These mutations are directly related to the Variant of Concern (VOC) including Alpha, Beta, Gamma, Delta and Omicron. Our calculations show that five major mutations (N501Y, E484K, L452R, T478K and K417N), first reported in Alpha, Beta, Gamma and Delta variants, all increase the binding of the S protein to ACE2 (except K417N), consistent with the experimental findings. We also studied an additional eight mutations of the Omicron variant that are located on the interface of the receptor binding domain (RDB) and have not been reported in other VOCs. Our study showed that most of these mutations (except Y505H and G446S) enhance the binding of the S protein to ACE2. The computational predictions helped explain why the Omicron variant quickly became dominant worldwide. Finally, comparison of several different computational methods for binding free energy calculation of these mutants was made. The alanine scanning method used in the current calculation helped to elucidate the residue-specific interactions responsible for the enhanced binding affinities of the mutants. The results show that the ASGB (alanine scanning with generalized Born) method is an efficient and reliable method for these binding free energy calculations due to mutations.