Fast Viral Diagnostics: FTIR-Based Identification, Strain-Typing, and Structural Characterization of SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has triggered an ongoing global pandemic, necessitating rapid and accurate diagnostic tools to monitor emerging variants and preparedness for the next outbreak. This study introduces a multidisciplinary approach combining Fourier Transform Infrared (FTIR) microspectroscopy and Machine learning to comprehensively characterize and strain-type SARS-CoV-2 variants. FTIR analysis of pharyngeal swabs from different pandemic waves revealed distinct vibrational profiles, particularly in nucleic acid and protein vibrations. The spectral wavenumber range between 1150 and 1240 cm-1 was identified as the classification marker, distinguishing Healthy (noninfected) and infected samples. Machine learning algorithms, with neural networks exhibiting superior performance, successfully classified SARS-CoV-2 variants with a remarkable accuracy of 98.6%. Neural networks were also able to identify and differentiate a small cohort infected with influenza A variants, H1N1 and H3N2, from SARS-CoV-2-infected and Healthy samples. FTIR measurements further show distinct red shifts in vibrational energy and secondary structural alterations in the spike proteins of more transmissible forms of SARS-CoV-2 variants, providing experimental validation of the computational data. This integrated approach presents a promising avenue for rapid and reliable SARS-CoV-2 variant identification, enhancing our understanding of viral evolution and aiding in diagnostic advancements, particularly for an infectious disease with unknown etiology.

Predicting virus Fitness: Towards a structure-based computational model.

Predicting the impact of new emerging virus mutations is of major interest in surveillance and for understanding the evolutionary forces of the pathogens. The SARS-CoV-2 surface spike-protein (S-protein) binds to human ACE2 receptors as a critical step in host cell infection. At the same time, S-protein binding to human antibodies neutralizes the virus and prevents interaction with ACE2. Here we combine these two binding properties in a simple virus fitness model, using structure-based computation of all possible mutation effects averaged over 10 ACE2 complexes and 10 antibody complexes of the S-protein (∼380,000 computed mutations), and validated the approach against diverse experimental binding/escape data of ACE2 and antibodies. The ACE2-antibody selectivity change caused by mutation (i.e., the differential change in binding to ACE2 vs. immunity-inducing antibodies) is proposed to be a key metric of fitness model, enabling systematic error cancelation when evaluated. In this model, new mutations become fixated if they increase the selective binding to ACE2 relative to circulating antibodies, assuming that both are present in the host in a competitive binding situation. We use this model to categorize viral mutations that may best reach ACE2 before being captured by antibodies. Our model may aid the understanding of variant-specific vaccines and molecular mechanisms of viral evolution in the context of a human host.

Stability and expression of SARS-CoV-2 spike-protein mutations.

Protein fold stability likely plays a role in SARS-CoV-2 S-protein evolution, together with ACE2 binding and antibody evasion. While few thermodynamic stability data are available for S-protein mutants, many systematic experimental data exist for their expression. In this paper, we explore whether such expression levels relate to the thermodynamic stability of the mutants. We studied mutation-induced SARS-CoV-2 S-protein fold stability, as computed by three very distinct methods and eight different protein structures to account for method- and structure-dependencies. For all methods and structures used (24 comparisons), computed stability changes correlate significantly (99% confidence level) with experimental yeast expression from the literature, such that higher expression is associated with relatively higher fold stability. Also significant, albeit weaker, correlations were seen between stability and ACE2 binding effects. The effect of thermodynamic fold stability may be direct or a correlate of amino acid or site properties, notably the solvent exposure of the site. Correlation between computed stability and experimental expression and ACE2 binding suggests that functional properties of the SARS-CoV-2 S-protein mutant space are largely determined by a few simple features, due to underlying correlations. Our study lends promise to the development of computational tools that may ideally aid in understanding and predicting SARS-CoV-2 S-protein evolution.

Structural heterogeneity and precision of implications drawn from cryo-electron microscopy structures: SARS-CoV-2 spike-protein mutations as a test case.

Protein structures may be used to draw functional implications at the residue level, but how sensitive are these implications to the exact structure used? Calculation of the effects of SARS-CoV-2 S-protein mutations based on experimental cryo-electron microscopy structures have been abundant during the pandemic. To understand the precision of such estimates, we studied three distinct methods to estimate stability changes for all possible mutations in 23 different S-protein structures (3.69 million ΔΔG values in total) and explored how random and systematic errors can be remedied by structure-averaged mutation group comparisons. We show that computational estimates have low precision, due to method and structure heterogeneity making results for single mutations uninformative. However, structure-averaged differences in mean effects for groups of substitutions can yield significant results. Illustrating this protocol, functionally important natural mutations, despite individual variations, average to a smaller stability impact compared to other possible mutations, independent of conformational state (open, closed). In summary, we document substantial issues with precision in structure-based protein modeling and recommend sensitivity tests to quantify these effects, but also suggest partial solutions to the problem in the form of structure-averaged "ensemble" estimates for groups of residues when multiple structures are available.

Transcriptome analysis of Snow Mountain Garlic for unraveling the organosulfur metabolic pathway.

Snow Mountain Garlic grows in the high altitudes of the Himalayas under low temperature conditions. It contains various bioactive compounds whose metabolic pathways have not been worked out at genomic level. The present work is the first report on the transcriptome sequencing of this plant. >43 million paired-end reads (301 × 2) were generated using Illumina Miseq sequencing technology. Assembling of the sequencing data resulted in 326,785 transcripts. Differentially expressed genes between the clove and leaf tissues were identified and characterized. Besides, greater emphasis was laid on the genes, which were highly expressed in clove since the latter is assumed to contain high content of the bioactive compounds. Further analysis led to the identification of the genes plausibly involved in the organosulfur metabolism. We also identified several simple sequence repeats and single nucleotide polymorphism. These constitute valuable genetic resource for research and further genetic improvement of the plant.

Common mechanism of thermostability in small α- and ß-proteins studied by molecular dynamics.

Protein thermostability is important to evolution, diseases, and industrial applications. Proteins use diverse molecular strategies to achieve stability at high temperature, yet reducing the entropy of unfolding seems required. We investigated five small α-proteins and five ß-proteins with known, distinct structures and thermostability (Tm ) using multi-seed molecular dynamics simulations at 300, 350, and 400 K. The proteins displayed diverse changes in hydrogen bonding, solvent exposure, and secondary structure with no simple relationship to Tm . Our dynamics were in good agreement with experimental B-factors at 300 K and insensitive to force-field choice. Despite the very distinct structures, the native-state (300 + 350 K) free-energy landscapes (FELs) were significantly broader for the two most thermostable proteins and smallest for the three least stable proteins in both the α- and ß-group and with both force fields studied independently (tailed t-test, 95% confidence level). Our results suggest that entropic ensembles stabilize proteins at high temperature due to reduced entropy of unfolding, viz., ΔG = ΔH - TΔS. Supporting this mechanism, the most thermostable proteins were also the least kinetically stable, consistent with broader FELs, typified by villin headpiece and confirmed by specific comparison to a mesophilic ortholog of Thermus thermophilus apo-pyrophosphate phosphohydrolase. We propose that molecular strategies of protein thermostabilization, although diverse, tend to converge toward highest possible entropy in the native state consistent with the functional requirements. We speculate that this tendency may explain why many proteins are not optimally structured and why molten-globule states resemble native proteins so much.

Systematic Investigation of the Data Set Dependency of Protein Stability Predictors.

Prediction of protein stability changes caused by mutation is of major importance to protein engineering and for understanding protein misfolding diseases and protein evolution. The major limitation to these applications is the fact that different prediction methods vary substantially in terms of performance for specific proteins; i.e., performance is not transferable from one type of mutation or protein to another. In this study, we investigated the performance and transferability of eight widely used methods. We first constructed a new data set composed of 2647 mutations using strict selection criteria for the experimental data and then defined a variety of subdata sets that are unbiased with respect to various aspects such as mutation type, stabilization extent, structure type, and solvent exposure. Benchmarking the methods against these subdata sets enabled us to systematically investigate how data set biases affect predictor performance. In particular, we use a reduced amino acid alphabet to quantify the bias toward mutation type, which we identify as the major bias in current approaches. Our results show that all prediction methods exhibit large biases, stemming not from failures of the models applied but mostly from the selection biases of experimental data used for training or parametrization. Our identification of these biases and the construction of new mutation-type-balanced data should lead to the development of more balanced and transferable prediction methods in the future.

Cryo-temperature effects on membrane protein structure and dynamics.

Innovations in cryogenic electron microscopy (Cryo-EM) have led to high-quality structures of important proteins such as the ribosome and γ-secretase, the membrane protease that produces Aß involved in Alzheimer's disease. However, freezing may change protein structure and dynamics relative to the physiologically relevant "hot" state. To explore this, we studied substrate-bound γ-secretase (6IYC) by molecular dynamics as a hot, cold, and quickly cooled state in both membrane and water systems. We show that the experimental structure resembles the simulated cooled state, structurally between the hot and cold states and membrane and water systems, but with cold dynamics. We observe "cryo-contraction" in the membrane from 303 to 85 K, reducing radius of gyration (Rg) by 1% from 4.01 to 3.97 nm (6IYC = 3.95 nm). The hot state features an unwound C83-substrate with 10-14 α-helix residues (6IYC: 11) in equilibrium with an intact state with 16 helix residues not previously reported. The ß-sheet is weakened with temperature. Multiple hot conformations probably control the Aß42/Aß40 ratio. We thus propose that MD simulation protocols of hot, cold, and cooled states as applied here can correct cryo-EM coordinates. However, important frozen-out fast modes require specific supplementary hot simulations or experiments.

Computational analysis of Alzheimer-causing mutations in amyloid precursor protein and presenilin 1.

Single-point mutations in the genes coding for amyloid precursor protein (APP) and presenilin 1 (PS1), the active subunit of γ-secretase that cleaves APP to produce Aß, are the main causes of rare but severe familial Alzheimer's disease (fAD). Recent structures of the transmembrane parts of APP and γ-secretase with a fragment of APP bound enable us to study the origins of the pathogenicity of the single amino acid changes in the context of the actual enzyme-substrate complex, which has not previously been possible. We used the new structures as input for several state-of-the-art computational methods that predict the folding stability effect of mutations. We find that pathogenic mutations almost exclusively reduce the stability of the proteins. Since most "random" mutations of an evolutionarily optimized protein tend to destabilize, we also show that the APP mutations destabilize the complex-bound substrate more than the free substrate, indicating reduced affinity of APP to γ-secretase. We confirmed this using two other methods, BEATMUSIC and mCSM PPI, specifically developed for calculating binding affinities of mutants. Although pathogenic PS1 mutations destabilize the complex and substrate-free form to the same extent, they significantly destabilize the protein more than the control set of random mutations. We conclude that fAD mutations most likely reduce the stability of the protein-substrate complex and thus retention time of APP-C99, leading to premature release of longer toxic Aß42 in accordance with the FIST model of Aß production, whereas the observed general destabilization of the protein may reduce activity towards other substrates.

Contribution of substrate reorganization energies of electron transfer to laccase activity.

Electron transfer is the most fundamental reaction in chemistry, yet its exact mechanistic details are often complex. Laccases are important electron-transfer enzymes of substantial utility in bleaching, bioremediation, catalytic synthesis, and enzymatic fuel cells. These multi-copper oxidases catalyze the one-electron oxidation of substrates by outer-sphere electron transfer to a copper T1 site, and subsequent intramolecular electron transfer to a tri-nuclear copper site where O2 is reduced to water. Understanding the molecular mechanism of the first, supposedly rate-determining pure electron transfer step is of major fundamental and technological interest. It is widely thought that the difference in the half potentials of the substrate and the T1 copper enables the powerful electron abstraction from nearby substrates. However, the reorganization energy during electron transfer could also contribute to catalytic turnover. To explore this, we computed the self-exchange reorganization energies of 54 substrates with experimentally known activity or kcat data using density functional theory. We show that the energy costs of changing the substrate geometries during electron removal correlate significantly with experimental activity data with a physically meaningful direction of correlation. This means that substrate electronic reorganization, rather than only potential differences, plays a role in the activity of electron transfer proteins such as laccases. This finding is consistent with the Marcus theory and suggests that the first electron transfer step from substrate to T1 is rate-determining in the real enzymes; the electronic reorganization energies can rationalize "good" vs. "bad" laccase substrates, which has not previously been possible.

Cell size effects in the molecular dynamics of the intrinsically disordered Aß peptide.

Periodic molecular dynamics simulations of proteins may suffer from image interactions. Similarly, the hydrophobic effect required to keep a protein folded may not be enforced by small simulation cells. Accordingly, errors may arise both from the water concentration per se and the image interactions. Intrinsically disordered proteins are particularly sensitive, providing a worst-case estimate of the errors. Following this reasoning, we studied Aß40 (Aß), a disordered peptide central to Alzheimer's disease, by 100 different simulations with variable cell size from very large (20 Å) to very small (3 Å). Even for this very disordered peptide, most properties are not cell-size dependent, justifying the common use of modest-sized (10 Å) cells for simulating proteins. The radius of gyration, secondary structure, intrapeptide, and peptide-water hydrogen bonds are similar relative to standard deviations at any cell size. However, hydrophobic surface area increases significantly in small cells (confidence 95%, two-tailed t-test), as does the standard deviation in exposure and backbone conformations (>40% and >27%). Similar results were obtained for the force fields OPLS3e, Ambersb99-ILDN, and Charmm22*. The similar prevalence of structures and α-ß transitions in long and short simulations indicate small diffusion barriers, which we suggest is a defining hallmark of intrinsically disordered proteins. Whereas hydrophilic exposure dominates in large cells, hydrophobic exposure dominates in small cells, suggesting a weakening of the hydrophobic effect by image interactions and the few water layers available to keep the protein compact, with a critical limit of 2-3 water layers required to enforce the hydrophobic effect.

Benzothiazole Derivative as a Novel Mycobacterium tuberculosis Shikimate Kinase Inhibitor: Identification and Elucidation of Its Allosteric Mode of Inhibition.

Mycobacterium tuberculosis shikimate kinase (Mtb-SK) is a key enzyme involved in the biosynthesis of aromatic amino acids through the shikimate pathway. Since it is proven to be essential for the survival of the microbe and is absent from mammals, it is a promising target for anti-TB drug discovery. In this study, a combined approach of in silico similarity search and pharmacophore building using already reported inhibitors was used to screen a procured library of 20,000 compounds of the commercially available ChemBridge database. From the in silico screening, 15 hits were identified, and these hits were evaluated in vitro for Mtb-SK enzyme inhibition. Two compounds presented significant enzyme inhibition with IC50 values of 10.69 ± 0.9 and 46.22 ± 1.2 µM. The best hit was then evaluated for the in vitro mode of inhibition where it came out to be an uncompetitive and noncompetitive inhibitor with respect to shikimate (SKM) and ATP, respectively, suggesting its binding at an allosteric site. Potential binding sites of Mtb-SK were identified which confirmed the presence of an allosteric binding pocket apart from the ATP and SKM binding sites. The docking simulations were performed at this pocket in order to find the mode of binding of the best hit in the presence of substrates and the products of the enzymatic reaction. Molecular dynamics (MD) simulations elucidated the probability of inhibitor binding at the allosteric site in the presence of ADP and shikimate-3-phosphate (S-3-P), that is, after the formation of products of the reaction. The inhibitor binding may prevent the release of the product from Mtb-SK, thereby inhibiting its activity. The binding stability and the key residue interactions of the inhibitor to this product complex were also revealed by the MD simulations. Residues ARG43, ILE45, and PHE57 were identified as crucial that were involved in interactions with the best hit. This is the first report of an allosteric binding site of Mtb-SK, which could largely address the selectivity issue associated with kinase inhibitors.

Screening of antitubercular compound library identifies novel shikimate kinase inhibitors of Mycobacterium tuberculosis.

Shikimate kinase of Mycobacterium tuberculosis is involved in the biosynthesis of aromatic amino acids through shikimate pathway. The enzyme is essential for the survival of M. tuberculosis and is absent from mammals, thus providing an excellent opportunity for identifying new chemical entities to combat tuberculosis with a novel mechanism of action. In this study, an antitubercular library of 1000 compounds was screened against M. tuberculosis shikimate kinase (MtSK). This effort led to the identification of 20 inhibitors, among which five promising leads exhibited half maximal inhibitory concentration (IC50) values below 10 µM. The most potent inhibitor ("5631296") showed an IC50 value of 5.10 µM ± 0.6. The leads were further evaluated for the activity against multidrug-resistant (MDR)-TB, Gram-positive and Gram-negative bacterial strains, mode of action, docking simulations, and combinatorial study with three frontline anti-TB drugs. Compound "5491210" displayed a nearly synergistic activity with rifampicin, isoniazid, and ethambutol while compound "5631296" was synergistic with rifampicin. In vitro cytotoxicity against HepG2 cell line was evaluated and barring one compound; all were found to be non-toxic (SI > 10). In order to rule out mitochondrial toxicity, the promising inhibitors were also evaluated for cell cytotoxicity using galactose medium where compounds "5631296" and "5122752" appeared non-toxic. Upon comprehensive analysis, compound "5631296" was found to be the most promising MtSK inhibitor that was safe, synergistic with rifampicin, and bactericidal against M. tuberculosis.

Identification and characterization of novel small molecule inhibitors of the acetyltransferase activity of Escherichia coli N-acetylglucosamine-1-phosphate-uridyltransferase/glucosamine-1-phosphate-acetyltransferase (GlmU).

This study aims at identifying novel chemical scaffolds as inhibitors specific to the acetyltransferase domain of a bifunctional enzyme, Escherichia coli GlmU, involved in the cell wall biosynthesis of Gram-negative organisms. A two-pronged approach was used to screen a 50,000 small-molecule library. Using the first approach, the library was in silico screened by docking the library against acetyltransferase domain of E. coli GlmU studies. In the second approach, complete library was screened against Escherichia coli ATCC 25922 to identify the whole cell active compounds. Active compounds from both the screens were screened in a colorimetric absorbance-based assay to identify inhibitors of acetyltransferase domain of E. coli GlmU which resulted in the identification of 1 inhibitor out of 56 hits identified by in silico screening and 4 inhibitors out of 35 whole cell active compounds on Gram-negative bacteria with the most potent inhibitor showing IC50 of 1.40 ± 0.69 µM. Mode of inhibition studies revealed these inhibitors to be competitive with AcCoA and uncompetitive with GlcN-1-P. These selected inhibitors were also tested for their antibacterial and cytotoxic activities. Compounds 5175178 and 5215319 exhibited antibacterial activity that co-related with GlmU inhibition. These compounds, therefore, represent novel chemical scaffolds targeting acetyltransferase activity of E. coli GlmU.

Discovery of new Mycobacterium tuberculosis proteasome inhibitors using a knowledge-based computational screening approach.

Mycobacterium tuberculosis bacteria cause deadly infections in patients [Corrected]. The rise of multidrug resistance associated with tuberculosis further makes the situation worse in treating the disease. M. tuberculosis proteasome is necessary for the pathogenesis of the bacterium validated as an anti-tubercular target, thus making it an attractive enzyme for designing Mtb inhibitors. In this study, a computational screening approach was applied to identify new proteasome inhibitor candidates from a library of 50,000 compounds. This chemical library was procured from the ChemBridge (20,000 compounds) and the ChemDiv (30,000 compounds) databases. After a detailed analysis of the computational screening results, 50 in silico hits were retrieved and tested in vitro finding 15 compounds with IC50 values ranging from 35.32 to 64.15 µM on lysate. A structural analysis of these hits revealed that 14 of these compounds probably have non-covalent mode of binding to the target and have not reported for anti-tubercular or anti-proteasome activity. The binding interactions of all the 14 protein-inhibitor complexes were analyzed using molecular docking studies. Further, molecular dynamics simulations of the protein in complex with the two most promising hits were carried out so as to identify the key interactions and validate the structural stability.

Computational Insight into Substrate-Induced Conformational Changes in Methionyl-tRNA Synthetase of Mycobacterium Tuberculosis.

Tuberculosis caused by Mycobacterium tuberculosis (M.tb) has killed millions worldwide. Antibiotic resistance leads to the ineffectiveness of the current therapies. Aminoacyl tRNA synthetase (aaRS) class of proteins involved in protein synthesis are promising bacterial targets for developing new therapies. Here, we carried out a systematic comparative study on the aaRS sequences from M.tb and human. We listed important M.tb aaRS that could be explored as potential M.tb targets alongside the detailed conformational space analysis of methionyl-tRNA synthetase (MetRS) in apo- and substrate-bound form, which is among the proposed targets. Understanding the conformational dynamics is central to the mechanistic understanding of MetRS, as the substrate binding leads to the conformational changes causing the reaction to proceed. We performed the most complete simulation study of M.tb MetRS for 6 microseconds (2 systems × 3 runs × 1 microsecond) in the apo and substrate-bound states. Interestingly, we observed differential features, showing comparatively large dynamics for the holo simulations, whereas the apo structures became slightly compact with reduced solvent exposed area. In contrast, the ligand size decreased significantly in holo structures possibly to relax ligand conformation. Our findings correlate with experimental studies, thus validating our protocol. Adenosine monophosphate moiety of the substrate exhibited quite higher fluctuations than the methionine. His21 and Lys54 were found to be the important residues forming prominent hydrogen bond and salt-bridge interactions with the ligand. The ligand-protein affinity decreased during simulations as computed by MMGBSA analysis over the last 500 ns trajectories, which indicates the conformational changes upon ligand binding. These differential features could be further explored for designing new M.tb inhibitors.

Modelling SARS-CoV-2 spike-protein mutation effects on ACE2 binding.

The binding affinity of the SARS-CoV-2 spike (S)-protein to the human membrane protein ACE2 is critical for virus function. Computational structure-based screening of new S-protein mutations for ACE2 binding lends promise to rationalize virus function directly from protein structure and ideally aid early detection of potentially concerning variants. We used a computational protocol based on cryo-electron microscopy structures of the S-protein to estimate the change in ACE2-affinity due to S-protein mutation (ΔΔGbind) in good trend agreement with experimental ACE2 affinities. We then expanded predictions to all possible S-protein mutations in 21 different S-protein-ACE2 complexes (400,000 ΔΔGbind data points in total), using mutation group comparisons to reduce systematic errors. The results suggest that mutations that have arisen in major variants as a group maintain ACE2 affinity significantly more than random mutations in the total protein, at the interface, and at evolvable sites. Omicron mutations as a group had a modest change in binding affinity compared to mutations in other major variants. The single-mutation effects seem consistent with ACE2 binding being optimized and maintained in omicron, despite increased importance of other selection pressures (antigenic drift), however, epistasis, glycosylation and in vivo conditions will modulate these effects. Computational prediction of SARS-CoV-2 evolution remains far from achieved, but the feasibility of large-scale computation is substantially aided by using many structures and mutation groups rather than single mutation effects, which are very uncertain. Our results demonstrate substantial challenges but indicate ways forward to improve the quality of computer models for assessing SARS-CoV-2 mutation effects.

Capsaicin, a novel inhibitor of the NorA efflux pump, reduces the intracellular invasion of Staphylococcus aureus.

OBJECTIVES: To delineate the role of capsaicin (8-methyl-N-vanillyl-6-nonenamide) as an inhibitor of the NorA efflux pump and its impact on invasion of macrophages by Staphylococcus aureus. METHODS: Capsaicin in combination with ciprofloxacin was tested for activity against S. aureus SA-1199B (NorA overproducing), SA-1199 (wild-type) and SA-K1758 (norA knockout). The role of NorA in the intracellular invasion of S. aureus and the ability of capsaicin to inhibit this invasion was established in J774 macrophage cell lines. The three-dimensional structure of NorA was predicted using an in silico approach and docking studies of capsaicin were performed. RESULTS: Capsaicin significantly reduced the MIC of ciprofloxacin for S. aureus SA-1199 and SA-1199B. Furthermore, capsaicin also extended the post-antibiotic effect of ciprofloxacin by 1.1 h at MIC concentration. There was a decrease in mutation prevention concentration of ciprofloxacin when combined with capsaicin. Inhibition of ethidium bromide efflux by NorA-overproducing S. aureus SA-1199B confirmed the role of capsaicin as a NorA efflux pump inhibitor (EPI). The most significant finding of this study was the ability of capsaicin to reduce the intracellular invasion of S. aureus SA-1199B (NorA overproducing) in J774 macrophage cell lines by 2 log(10). CONCLUSIONS: This study, for the first time, has shown that capsaicin, a novel EPI, not only inhibits the NorA efflux pump of S. aureus but also reduces the invasiveness of S. aureus, thereby reducing its virulence.

Structure and Mutations of SARS-CoV-2 Spike Protein: A Focused Overview.

The spike protein (S-protein) of SARS-CoV-2, the protein that enables the virus to infect human cells, is the basis for many vaccines and a hotspot of concerning virus evolution. Here, we discuss the outstanding progress in structural characterization of the S-protein and how these structures facilitate analysis of virus function and evolution. We emphasize the differences in reported structures and that analysis of structure-function relationships is sensitive to the structure used. We show that the average residue solvent exposure in nearly complete structures is a good descriptor of open vs closed conformation states. Because of structural heterogeneity of functionally important surface-exposed residues, we recommend using averages of a group of high-quality protein structures rather than a single structure before reaching conclusions on specific structure-function relationships. To illustrate these points, we analyze some significant chemical tendencies of prominent S-protein mutations in the context of the available structures. In the discussion of new variants, we emphasize the selectivity of binding to ACE2 vs prominent antibodies rather than simply the antibody escape or ACE2 affinity separately. We note that larger chemical changes, in particular increased electrostatic charge or side-chain volume of exposed surface residues, are recurring in mutations of concern, plausibly related to adaptation to the negative surface potential of human ACE2. We also find indications that the fixated mutations of the S-protein in the main variants are less destabilizing than would be expected on average, possibly pointing toward a selection pressure on the S-protein. The richness of available structures for all of these situations provides an enormously valuable basis for future research into these structure-function relationships.