Rapid response to fast viral evolution using AlphaFold 3-assisted topological deep learning

Severe acute respiratory syndrome coronavirus (SARS-CoV-2), a novel coronavirus, has brought an unprecedented pandemic to the world and affected over 64 million people. The virus infects human using its spike glycoprotein mediated by a crucial area, receptor-binding domain (RBD), to bind to the human ACE2 (hACE2) receptor. Mutations on RBD have been observed in different countries and classified into nine types: A435S, D364Y, G476S, N354D/D364Y, R408I, V341I, V367F, V483A and W436R. Employing molecular dynamics (MD) simulation, we investigated dynamics and structures of the complexes of the prototype and mutant types of SARS-CoV-2 spike RBDs and hACE2. We then probed binding free energies of the prototype and mutant types of RBD with hACE2 protein by using an end-point molecular mechanics Poisson Boltzmann surface area (MM-PBSA) method. According to the result of MM-PBSA binding free energy calculations, we found that V367F and N354D/D364Y mutant types showed enhanced binding affinities with hACE2 compared to the prototype. Our computational protocols were validated by the successful prediction of relative binding free energies between prototype and three mutants: N354D/D364Y, V367F and W436R. Thus, this study provides a reliable computational protocol to fast assess the existing and emerging RBD mutations. More importantly, the binding hotspots identified by using the molecular mechanics generalized Born surface area (MM-GBSA) free energy decomposition approach can guide the rational design of small molecule drugs or vaccines free of drug resistance, to interfere with or eradicate spike-hACE2 binding.

Predicting the effects of protein and DNA mutations on the binding free energy of protein-DNA complexes is crucial for understanding how DNA variants impact wild-type cellular function. As many cellular interactions involve protein-DNA binding, accurately predicting changes in binding free energy (ΔΔG) is valuable for distinguishing pathogenic mutations from benign ones. This study describes the development and optimization of the SAMPDI-3Dv2 machine learning method, which is trained on an expanded database of experimentally measured ΔΔGs. This enhanced model incorporates new features, including the 3D structure of the mutant protein, features of the mutant structure, and a position-specific scoring matrix (PSSM). Benchmarking was conducted using 5-fold cross-validation. The updated SAMPDI-3D model (SAMPDI-3Dv2) achieved Pearson correlation coefficients (PCCs) of 0.68 for protein and 0.80 for DNA mutations. These results represent significant improvements over existing tools. Additionally, the method's rapid execution time enables genome-scale predictions. The improved SAMPDI-3Dv2 shows enhanced predictive performance for analyzing mutations in protein-DNA complexes. By leveraging structural information and an expanded training dataset, SAMPDI-3Dv2 provides researchers with a more accurate and efficient tool for mutation analysis, contributing to identifying pathogenic variants and improving our understanding of cellular function.

AlphaFold 3 (AF3), the latest version of protein structure prediction software, goes beyond its predecessors by predicting protein-protein complexes. It could revolutionize drug discovery and protein engineering, marking a major step toward comprehensive, automated protein structure prediction. However, independent validation of AF3's predictions is necessary. In this work, we evaluate AF3 complex structures using the SKEMPI 2.0 database which involves 317 protein-protein complexes and 8338 mutations. AF3 complex structures when applied to the most advanced TDL model, MT-TopLap (MultiTask-Topological Laplacian), give rise to a very good Pearson correlation coefficient of 0.86 for predicting protein-protein binding free energy changes upon mutation, which is slightly less than the 0.88 achieved earlier with the Protein Data Bank (PDB) structures. Nonetheless, AF3 complex structures led to a 8.6% increase in the prediction RMSE compared to original PDB complex structures. Additionally, some of AF3's complex structures have large errors, which were not captured in its ipTM performance metric. Finally, it is found that AF3's complex structures are not reliable for intrinsically flexible regions or domains.

Understanding and predicting the energetics of protein-protein interactions is fundamental to the structural modeling of protein complexes. Binding free energy can be approximated as a sum of pairwise atomic or residue contact energies, which are commonly inferred from contact frequencies observed in experimental protein structures. However, such statistically inferred potentials require certain assumptions and approximation. Here, we explore the possibility of deriving atomic and residue contact potentials directly from experimental binding free energy changes following mutation and present a number of such potentials. The first set of potentials is obtained by unweighted least-squares fitting and bootsrap aggregating. The second set is calculated using a weighting scheme optimized against absolute binding affinity data, so as to account for the over-representation of certain complexes, residues, and families of interactions. The congruence of the potentials with known physical chemistry is investigated. The potentials are further validated by ranking and clustering protein-protein docking poses.

Substitutions that fix between SARS-CoV-2 variants can transform the mutational landscape of future evolution via epistasis. For example, large epistatic shifts in mutational effects caused by N501Y underlied the original emergence of Omicron, but whether such epistatic saltations continue to define ongoing SARS-CoV-2 evolution remains unclear. We conducted deep mutational scans to measure the impacts of all single amino acid mutations and single-codon deletions in the spike receptor-binding domain (RBD) on ACE2-binding affinity and protein expression in the recent Omicron BQ.1.1 and XBB.1.5 variants, and we compared mutational patterns to earlier viral strains that we have previously profiled. As with previous deep mutational scans, we find many mutations that are tolerated or even enhance binding to ACE2 receptor. The tolerance of sites to single-codon deletion largely conforms with tolerance to amino acid mutation. Though deletions in the RBD have not yet been seen in dominant lineages, we observe tolerated deletions including at positions that exhibit indel variation across broader sarbecovirus evolution and in emerging SARS-CoV-2 variants of interest, most notably the well-tolerated Δ483 deletion in BA.2.86. The substitutions that distinguish recent viral variants have not induced as dramatic of epistatic perturbations as N501Y, but we identify ongoing epistatic drift in SARS-CoV-2 variants, including interaction between R493Q reversions and mutations at positions 453, 455, and 456, including F456L that defines the XBB.1.5-derived EG.5 lineage. Our results highlight ongoing drift in the effects of mutations due to epistasis, which may continue to direct SARS-CoV-2 evolution into new regions of sequence space.

The SARS-CoV-2 Delta and Lambda variants had been named variants of concern (VOC) and variants of interest (VOI), respectively, by the World Health Organization (WHO). Both variants have two mutations in the spike receptor binding domain (RBD) region, with L452R and T478K mutations in the Delta variant, and L452Q and F490S mutations in the Lambda variant. We used surface plasmon resonance (SPR)-based technology to evaluate the effect of these mutations on human angiotensin-converting enzyme 2 (ACE2) and Bamlanivimab binding. The affinity for the RBD ligand, ACE2, of the Delta RBD is approximately twice as strong as that of the wild type RBD, an increase that accounts for the increased infectivity of the Delta variant. On the other hand, in spite of its amino acid changes, the Lambda RBD has similar affinity to ACE2 as the wild type RBD. The protective anti-wild type RBD antibody Bamlanivimab binds very poorly to the Delta RBD and not at all to the Lambda RBD. Nevertheless, serum antibodies from individuals immunized with the BNT162b2 vaccine were found to bind well to the Delta RBD, but less efficiently to the Lambda RBD in contrast. As a result, the blocking ability of ACE2 binding by serum antibodies was decreased more by the Lambda than the Delta RBD. Titers of sera from BNT162b2 mRNA vaccinated individuals dropped 3-fold within six months of vaccination regardless of whether the target RBD was wild type, Delta or Lambda. This may account partially for the fall off with time in the protective effect of vaccines against any variant.

An approach using the finite difference solution of the Poisson‐Boltzmann equation to estimate binding free energy changes for two receptor–ligand systems, arabinose binding protein and sulfate binding protein, is presented. The eight calculated binding free energy changes agree with experiment, showing a correlation coefficient of 0.92 and energy deviations of 1 kcal/mol or less. More importantly, the decomposition of solvation and assembly energies in this approach provides an understanding of binding mechanisms and therefore could suggest directions to alter binding affinities. The method is demonstrated to be useful in analyzing experimental binding structures and predicting binding effects of mutants or modified ligands for macromolecular systems, in which the electrostatic forces dominate the overall interaction and the structural perturbations upon modifications are small. © 1995 by John Wiley & Sons, Inc.

Deep mutational scanning experiments aid in the surveillance and forecasting of viral evolution by providing prospective measurements of mutational effects on viral traits, but epistatic shifts in the impacts of mutations can hinder viral forecasting when measurements were made in outdated strain backgrounds. Here, we report measurements of the impact of all single amino acid mutations on ACE2-binding affinity and protein folding and expression in the SARS-CoV-2 Omicron BA.2.86 spike receptor-binding domain (RBD). As with other SARS-CoV-2 variants, we find a plastic and evolvable basis for receptor binding, with many mutations at the ACE2 interface maintaining or even improving ACE2-binding affinity. Despite its large genetic divergence, mutational effects in BA.2.86 have not diverged greatly from those measured in its Omicron BA.2 ancestor. However, we do identify strong positive epistasis among subsequent mutations that have accrued in BA.2.86 descendants. Specifically, the Q493E mutation that decreased ACE2-binding affinity in all previous SARS-CoV-2 backgrounds is reversed in sign to enhance human ACE2-binding affinity when coupled with L455S and F456L in the currently emerging KP.3 variant. Our results point to a modest degree of epistatic drift in mutational effects during recent SARS-CoV-2 evolution but highlight how these small epistatic shifts can have important consequences for the emergence of new SARS-CoV-2 variants.

Protein–protein interactions (PPIs) play a critical role in all biological processes. Menin is tumor suppressor protein, mutated in multiple endocrine neoplasia type 1 syndrome and has been shown to interact with multiple transcription factors including (RPA2) subunit of replication protein A (RPA). RPA2, heterotrimeric protein required for DNA repair, recombination and replication. However, it’s still remains unclear the specific amino acid residues that have been involved in Menin-RPA2 interaction. Thus, accurately predicting the specific amino acid involved in interaction and effects of MEN1 mutations on biological systems is of great interests. The experimental approaches for identifying amino acids in menin-RPA2 interactions are expensive, time-consuming, and challenging. This study leverages computational tools, free energy decomposition and configurational entropy scheme to annotate the menin-RPA2 interaction and effect on menin point mutation, thereby proposing a viable model of menin-RPA2 interaction. The menin–RPA2 interaction pattern was calculated on the basis of different 3D structures of menin and RPA2 complexes, constructed using homology modeling and docking strategy, generating three best-fit models: Model 8 (− 74.89 kJ/mol), Model 28 (− 92.04 kJ/mol) and Model 9 (− 100.4 kJ/mol). The molecular dynamic (MD) was performed for 200 ns and binding free energies and energy decomposition analysis were calculated using Molecular Mechanics Poisson–Boltzmann Surface Area (MM/PBSA) in GROMACS. From binding free energy change, model 8 of Menin-RPA2 exhibited most negative binding energy of − 205.624 kJ/mol, followed by model 28 of Menin-RPA2 with − 177.382 kJ/mol. After S606F point mutation in Menin, increase of BFE (ΔGbind) by − 34.09 kJ/mol in Model 8 of mutant Menin-RPA2 occurs. Interestingly, we found a significant reduction of BFE (ΔGbind) and configurational entropy by − 97.54 kJ/mol and − 2618 kJ/mol in mutant model 28 as compared the o wild type. Collectively, this is the first study to highlight the configurational entropy of protein–protein interactions thereby strengthening the prediction of two significant important interaction sites in menin for the binding of RPA2. These predicted sites could be vulnerable for structural alternation in terms of binding free energy and configurational entropy after missense mutation in menin.

BackgroundMalaria remains a major global health concern. The development of novel therapeutic strategies is critical to overcome the selection of multiresistant parasites. The subtilisin-like protease (SUB1) involved in the egress of daughter Plasmodium parasites from infected erythrocytes and in their subsequent invasion into fresh erythrocytes has emerged as an interesting new drug target.FindingsUsing a computational approach based on homology modeling, protein–protein docking and mutation scoring, we designed protein–based inhibitors of Plasmodium vivax SUB1 (PvSUB1) and experimentally evaluated their inhibitory activity. The small peptidic trypsin inhibitor EETI-II was used as scaffold. We mutated residues at specific positions (P4 and P1) and calculated the change in free-energy of binding with PvSUB1. In agreement with our predictions, we identified a mutant of EETI-II (EETI-II-P4LP1W) with a Ki in the medium micromolar range.ConclusionsDespite the challenges related to the lack of an experimental structure of PvSUB1, the computational protocol we developed in this study led to the design of protein-based inhibitors of PvSUB1. The approach we describe in this paper, together with other examples, demonstrates the capabilities of computational procedures to accelerate and guide the design of novel proteins with interesting therapeutic applications.

Ferritin nanocages with spherical shells carry proteins or antigens that enable their use as highly efficient nanoreactors and nanocarriers. Mimicking the surface Spike (S) receptor-binding domain (RBD) from SARS-CoV-2, ferritin nanocages induce neutralizing antibody production or block viral entry. Herein, by implementing molecular dynamics simulation, we evaluate the efficiency in the interaction pattern (active or alternative sites) of H-ferritin displaying the 24 S RBDs with host-cell-receptor or monoclonal antibodies (mAbs; B38 or VVH-72). Our constructed nanocage targeted the receptor- or antibody-binding interfaces, suggesting that mAbs demonstrate an enhanced binding affinity with the RBD, with key interactions originating from its variable heavy chain. The S RBD interactions with ACE2 and B38 involved the same binding site but led to divergent dynamic responses. In particular, both B38 chains showed that asymmetric fluctuations had a major effect on their engagement with the Spike RBD. Although the receptor increased the binding affinity of VVH-72 for the RBD, the mAb structural orientation on the nanocage remained identical to its conformation when bound to the host receptor. Overall, our findings characterize the essential pharmacophore formed by Spike RBD residues over nanocage molecules, which mediates high-affinity interactions with either binding partner. Importantly, the ferritin-displayed RBD maintained native receptor and antibody binding profiles, positioning it as a promising scaffold for pre-fusion stabilization and protective RBD vaccine design.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative pathogen of the COVID-19 pandemic. The receptor-binding domain (RBD) of the SARS-CoV-2 spike protein is considered an important antigen of SARS-CoV-2 vaccines, while the cytotoxic T lymphocyte (CTL) activity is critical for evaluating cell-mediated immune response for vaccine efficacy. Human angiotensin-converting enzyme 2 (hACE2)-expressing transgenic mice with a C57BL/6 genetic background have widely used as an animal model of SARS-CoV-2 study. However, the CTL epitope of SARS-CoV-2 RBD has not yet been identified in C57BL/6 mice and quantifying the CTL activity has used RBD peptide pools with limitations such as expensive and time-consuming synthesis of peptides. In this study, we identified a murine CD8+ T cell epitope of the SARS-CoV-2 spike RBD using C57BL/6 wild-type and hACE2-expressing transgenic (K18-hACE2) mice. Mice were immunized with the adjuvanted SARS-CoV-2 RBD recombinant protein and then the CTL activities were evaluated by determining numbers of the IFN-γ-producing T cells in the splenocytes of the immunized mice upon exposure to overlapping peptide pools or individual peptides via an enzyme-linked immunospot assay and flow cytometry. We found SARS-CoV-2 S395–404 (VYADSFVIRG) as a major histocompatibility complex class I-restricted epitope for the RBD-specific CTL activity in C57BL/6 mice. Supported by a grant from Korea Research Institute of Bioscience and Biotechnology (KRIBB) Research Initiative Program (KGM9942112).

Identification of hACE2-interacting sites in SARS-CoV-2 spike receptor binding domain for antiviral drugs screening

Hydration free energy (HFE) is a key factor in improving protein–ligand binding free energy (BFE) prediction accuracy. The HFE itself can be calculated using the three-dimensional reference interaction model (3D-RISM); however, the BFE predictions solely evaluated using 3D-RISM are not correlated to the experimental BFE for abundant protein–ligand pairs. In this study, to predict the BFE for multiple sets of protein–ligand pairs, we propose a machine learning approach incorporating the HFEs obtained using 3D-RISM, termed 3D-RISM-AI. In the learning process, structural metrics, intra-/intermolecular energies, and HFEs obtained via 3D-RISM of ∼4000 complexes in the PDBbind database (ver. 2018) were used. The BFEs predicted using 3D-RISM-AI were well correlated to the experimental data (Pearson’s correlation coefficient of 0.80 and root-mean-square error of 1.91 kcal/mol). As important factors for the prediction, the difference in the solvent accessible surface area between the bound and unbound structures and the hydration properties of the ligands were detected during the learning process.

Exploring <i>Artocarpus heterophyllus</i> phytochemicals as novel mineralocorticoid receptor inhibitors: A computational approach to hypertension therapy