SARS MERS Spike S1

Basic Research Questions

• What are the key structural differences between MERS-CoV, SARS-CoV, and SARS-CoV-2 S1 proteins?

The S1 subunits of these coronaviruses exhibit distinct secondary structural characteristics despite some sequence similarities. Infrared spectroscopic studies reveal that SARS-CoV-2 S1 contains a significantly higher proportion of β-sheet structures compared to MERS-CoV and SARS-CoV. At serological pH (7.4), the percentage of β-sheet content increases progressively from MERS-CoV (20.6%) to SARS-CoV (26.8%) and SARS-CoV-2 (30.6%) . Notably, SARS-CoV-2 S1 uniquely displays an extended β-sheet absorption band at 1619 cm⁻¹, which is absent in the other coronaviruses .

This structural variation may explain differences in receptor binding affinity and viral infectivity among these coronaviruses.

• How does sequence similarity compare with structural similarity among coronavirus S1 domains?

A seemingly paradoxical relationship exists between sequence similarity and structural differences among coronavirus S1 proteins. SARS-CoV and SARS-CoV-2 S1 domains share approximately 78% sequence similarity, yet infrared spectroscopy reveals substantial differences in their secondary structures . In contrast, MERS-CoV S1 has much lower sequence similarity with either SARS-CoV S1 (36.4%) or SARS-CoV-2 S1 (33.3%) .

• What experimental methods are most effective for studying S1 protein structure?

Multiple complementary techniques are employed to elucidate the structure of coronavirus S1 proteins:

• Infrared (IR) Spectroscopy: Used to determine secondary structure composition by analyzing the amide I vibrational band (1600-1700 cm⁻¹). This technique can quantify the relative proportions of α-helices, β-sheets, and random coils . Researchers typically perform spectral component analysis by fitting component bands to the experimental spectrum, with each component corresponding to a specific secondary structure element.

• Cryo-Electron Microscopy (Cryo-EM): Enables visualization of the three-dimensional structure of S proteins at near-atomic resolution without requiring protein crystallization . This technique has been instrumental in resolving the prefusion conformations of coronavirus spike proteins.

• Affinity Purification-Mass Spectrometry (AP-MS): While not directly a structural technique, this approach helps identify protein-protein interactions of the S1 domain, providing insights into its functional structure in different cellular contexts .

For optimal results, researchers should integrate data from multiple techniques, as each provides complementary information about different structural aspects of the S1 domain.

• What is the relationship between coronavirus mortality rates and S1 protein characteristics?

The mortality rates of SARS and MERS infections show significant differences that may partially reflect S1 protein characteristics:

MERS-CoV appears significantly more lethal than SARS-CoV, with a mortality rate exceeding 30% (45 deaths among 82 diagnosed cases in early data) . While multiple factors contribute to this difference, S1 structural variations may play a role. MERS-CoV S1 has a distinct secondary structure profile compared to SARS-CoV and SARS-CoV-2, with different β-sheet content .

The relationship between mortality and S1 characteristics requires investigation at multiple levels:

• Structural analysis of receptor binding domains

• Quantification of receptor binding affinities

• Assessment of conformational stability under different conditions

• Analysis of tissue tropism based on S1 interactome studies

These approaches help elucidate how S1 variations contribute to the different clinical presentations and mortality rates of coronavirus infections.

Advanced Research Questions

• How does pH affect the secondary structure of coronavirus S1 proteins?

The SARS-CoV-2 S1 glycoprotein exhibits remarkable conformational plasticity in response to pH changes, which may contribute to its infectivity across diverse tissue environments. Research demonstrates that this protein can "rapidly adapt its secondary structure to different pH environments" .

When transitioning from serological pH (7.4) to mildly acidic or alkaline conditions, SARS-CoV-2 S1 undergoes substantial conformational changes . This adaptability could be crucial for maintaining functionality during:

• Initial attachment at the cell surface (neutral pH)

• Endocytosis (progressively acidifying environment)

• Fusion events (typically requiring specific conformational triggers)

To investigate these pH-dependent effects, researchers employ buffer-controlled infrared spectroscopy to measure the amide I absorption band under different pH conditions, followed by spectral component analysis to quantify changes in secondary structure elements . This adaptability to different pH environments may represent an evolutionary advantage for SARS-CoV-2, potentially contributing to its broader tissue tropism compared to earlier coronaviruses.

• What are the differences in S1 protein interactomes across different human cell types?

The S1 domain interacts with diverse host proteins beyond primary receptors, with significant variations across different cell types. Affinity purification-mass spectrometry (AP-MS) studies reveal:

• In human kidney cells (HK-2): 55 specific S1 interactors identified

• In normal colon cells (NCM460D): 80 interactors found

• In colorectal adenocarcinoma cells (Caco-2): 85 interactors detected

These interactomes show cell-type specificity in terms of both protein composition and subcellular localization:

• Exosomal components were enriched across all three cell types, suggesting S1 may influence exosome-mediated signaling or viral spread .

• Mitochondrial components showed cell-type-specific patterns, with mitochondrial matrix proteins more enriched in NCM460D cells, while mitochondrial inner membrane proteins were more prominent in HK-2 cells .

The large number of interactors suggests that S1 may influence multiple cellular processes beyond receptor binding, potentially explaining the diverse pathological manifestations of coronavirus infections. This challenges the view that S1 merely functions in receptor recognition.

• How do researchers differentiate between specific S1 protein interactions and contaminants in pulldown experiments?

Distinguishing genuine protein interactions from background contaminants in affinity purification experiments requires robust experimental design and computational filtering. The methodological approach includes:

• Experimental Controls:

  • Pre-incubation of membrane protein extracts with solid supports alone serves as a negative control

  • Parallel processing of samples and controls under identical conditions

• Computational Filtering Pipeline:

  • Application of the SAINT (Significance Analysis of INTeractome) algorithm to compare sample and control datasets

  • Retention of only proteins never occurring in any control samples

  • Further filtering using the Contaminant Repository for Affinity Purification (CRAPome) 2.0 database

  • Elimination of proteins reported as contaminants in >50% of similar experiments

This stringent filtering process typically reduces the initial list of identified proteins substantially, resulting in higher confidence interactome datasets. For the S1 interactomes, this approach yielded 55, 80, and 85 high-confidence interactors in HK-2, NCM460D, and Caco-2 cells, respectively .

• What protease inhibitors are effective against MERS-CoV and SARS-CoV-2, and how is their efficacy determined?

Protease inhibitors represent an important class of antiviral compounds targeting coronavirus main proteases (Mpro). While not directly targeting S1 proteins, they provide insights into coronavirus inhibition strategies:

Nirmatrelvir (formerly PF-07321332) has demonstrated inhibitory activity against several coronavirus proteases, including those of SARS-CoV-2 and MERS-CoV . In contrast, ensitrelvir shows a narrower spectrum of activity .

Research methodology for evaluating protease inhibitors includes:

• Established protease assays to quantify inhibition across different coronavirus proteases

• Surrogate virus-based systems to simulate clinical use and resistance development

• Selection experiments to identify resistance mutations (e.g., T21I, M49L, S144A, E166A/K/V, L167F for SARS-CoV-2 Mpro)

• Structural modeling to understand the steric effects of catalytic site mutations (e.g., S142G, S142R, S147Y, A171S for MERS-CoV Mpro)

These approaches provide crucial data for developing effective coronavirus therapeutics with high barriers to resistance.

• What cellular components are significantly enriched in S1 interactomes?

Functional enrichment analysis of S1 interactomes reveals several overrepresented cellular components, suggesting specific roles for S1 beyond primary receptor binding:

• Exosomal Components: Significantly enriched across all three cell types (HK-2, NCM460D, and Caco-2), with fold enrichments of 5-7 depending on the cell line . This suggests S1 may influence exosome-mediated signaling or viral spread, potentially manipulating host exosomal vesicles to deliver pro-inflammatory molecules.

• Mitochondrial Components: Enriched with fold enrichments of approximately 7 in both HK-2 and NCM460D cells, but with cell-type specific patterns:

  • "Mitochondrial matrix" more enriched in NCM460D cells

  • "Mitochondrial inner membrane" more prominent in HK-2 cells

To identify these enriched components, researchers perform over-representation analysis using tools like FunRich 3.1.3, querying Gene Ontology databases for subcellular localization terms and applying statistical filters (typically p-value < 0.01 with Benjamini-Hochberg correction) . Components with fold enrichment > 3 are considered significantly overrepresented.

The enrichment of mitochondrial components is particularly intriguing, as it suggests potential viral interference with energy metabolism or mitochondrial-associated immune signaling pathways.

• How can interdisciplinary research approaches enhance our understanding of coronavirus S1 proteins?

Interdisciplinary research frameworks are essential for comprehensive understanding of coronavirus S1 proteins and developing effective countermeasures. The Centre for Emerging Infectious Diseases (CEID) has established a platform for such interdisciplinary research on SARS-CoV and MERS-CoV, focusing on prevention and control applications .

Effective interdisciplinary approaches should integrate:

• Structural Biology: Determining S1 conformations through techniques like IR spectroscopy and cryo-EM

• Molecular Biology: Analyzing sequence-structure relationships and key mutations

• Cell Biology: Mapping interactomes across different cell types

• Biochemistry: Characterizing pH-dependent conformational changes

• Epidemiology: Correlating molecular properties with disease characteristics

• Computational Biology: Modeling protein structures and predicting binding interactions

This integrated approach can address complex questions that single disciplines cannot answer alone. For example, combining structural analysis of S1 proteins with interactome studies can reveal how specific structural features enable interactions with different host proteins across various tissue types.