SARS-CoV-2 Omicron Spike shows strong binding affinity and favourable interaction landscape with the TLR4/MD2 compared to other variants

Highlights

• •The study shows a molecular basis of interaction between Spike and TLR4 of wild type and significant SARS-CoV-2 variants.
• •It shows Omicron Spike strongly interact with the TLR4 compared to wild-type, Alpha, and Delta variants.
• •H-bond formation during Spike and TLR4 interaction in wild type, Alpha, Delta, and Omicron.

Abstract

Emergences of SARS-CoV-2 variants have made the pandemic more critical. Toll-like receptor 4 (TLR4) recognizes the molecular patterns of pathogens and activates the production of proinflammatory cytokines to restrain the infection. We have identified a molecular basis of interaction between the Spike and TLR4 of SARS-CoV-2 and its present and past VOCs (variant- of concern) through in silico analysis. The interaction of wild type Spike with TLR4 showed 15 number hydrogen bonds formation. Similarly, the Alpha variants’ Spike with the TLR4 has illustrated that 14 hydrogen bonds participated in the interaction. However, the Delta Spike and TLR4 interaction interface showed that 17 hydrogen bonds were formed during the interaction. Furthermore, Omicron S-glycoprotein and TLR4 interaction interface was depicted (interaction score: −170.3), and 16 hydrogen bonds were found to have been formed in the interaction. Omicron S-glycoprotein shows stronger binding affinity with the TLR4 than wild type, Alpha, and Delta variants. Similarly, the Alpha Spike shows higher binding affinity with TLR4 than the wild type and Delta variant. Now, it is an open question of the molecular basis of the interaction of Spike and TLR4 and the activated downstream signaling events of TLR4 for SARS-CoV-2 and its variants.

1. Introduction

SARS-CoV-2, an RNA virus, mutates and evolves continuously like other pathogenic microbes. Due to the continuous evolution, SARS-CoV-2 variants have emerged from time to time, making the pandemic more critical.1, 2 WHO, and CDC have labelled the variants as VOCs and VOIs due to their clinical consequences and the probable threat to the public health such as infectivity, infection severity, immune escape, and vaccine escape. First, the significant variant was reported as B.1.351 (Beta) in South Africa, May 2020. After that, we found several significant SARS-CoV-2 variants categorized as VOCs (variant of concern) and VOIs (variant of interest).3, 4 A significant B.1.1.7 (Alpha) lineage initially identified in the UK includes two significant mutations, which are N501Y and E484K mutations. The variant was first noted in Southeast England in September 2020.⁵ Madhi et al. reported that the variant has 53% augmented transmissibility⁴. It has been observed that the variant has then a nAb (neutralizing Antibodies) escape ability.⁶ Due to quick transmission in the variant in the country, Alpha (B.1.1.7) has led to more cases and generated a new wave in the United Kingdom. Davies et al. have noted this variant's 43% to 90% transmission rate⁷. The B.1.1.7 encompasses three significant mutations, which are E484K, S494P, and N501Y⁸. Subsequently, the variant spread throughout the world. Similarly, the Delta (B.1.617.2) first emerged from Maharashtra state in India. The variant has generated a second wave in India⁹. Afterward, it was spread to more than 100 countries throughout the world. The variant is efficient for immune evasion. Partial vaccine escape was noted by this variant.¹⁰ The variant harbour several mutations¹¹. Among them, four mutations are described as signature mutations which are L452R, E478K, T19T, G142D, and R158G mutations.9, 12 Mishra et al. (2021) reported the composition-sift of the SARS-CoV-2 variants in England. They observed the change of the significant variant B.1.1.7 (Alpha) to Delta (B.1.617.2) in England¹³. Afterward, researchers reported the dominancy of the Delta variant throughout the world.¹⁴.

The Omicron variant first identified in Botswana, South Africa has been reported to be highly mutated. The variant put an urgent public health alert globally.15, 16 Kannan et al.(2022) have observed several mutations in S-glycoprotein in Omicron from the structural study. These mutations might affect the binding affinities of antibodies with the S-glycoprotein¹⁷. Other scientists have also reported that nAb escapes phenomena by the variant.¹⁸.

Toll-like receptor (TLR) proteins recognize pathogenic microbial components and associated molecular signatures and turn the fully functional pathway. At the same time, it regulates the adaptive immune response.19, 20 Human TLRs, type I transmembrane proteins, belong to the pattern-recognition receptors (PRRs) family and are critical for host defense.. They identify components of foreign pathogens, known as PAMPs (pathogen-associated molecular patterns).21, 22, 23 Previously, we have reported from our in silico studies that TLR4 might involve recognizing the molecular patterns of S-glycoprotein of SARS-CoV-2 and further stimulating immune responses.²⁴ Zhao et al. (2021) have also revealed that the S-glycoprotein of SARS-CoV-2 might interacts with TLR4, and it further stimulating downstream immune responses.²⁵ Therefore, in this study, we undertaken in silico interaction of the S-glycoprotein of Alpha, Delta, and Omicron variants with TLR4 to provide some biological significance of such interaction.

2. Methods

First, we used docking studies of PDB files of S-glycoprotein (PDB id: 7BNN) (Fig. 1a) and TLR4 (PDB id: 4G8A) (Fig. 1b). Then, we performed the docking studies using the same PDB files to understand the interaction of S-glycoprotein of a wild strain of SARS-CoV-2 and TLR4. Again, to access the interaction of S-glycoprotein of different VOCs and TLR4, we have created different mutations in the wild type S-glycoprotein. We have also created the E484K, S494P, and N501Y mutations in the wild-type S-glycoprotein for the Alpha variant (Fig. 1c). Furthermore, we have inserted L452R, E478K, T19T, G142D, and R158G mutations in the wild type S-glycoprotein for the Delta variant (Fig. 1d). Similarly, we have considered the N440K, G446S, S477N, T478K, E484A, Q493K, G496S, Q498R, N501Y, Y505H G339D, S371L, S373P, S375F, K417N, G142D, Δ143–145, A67V, Δ69-70, T95I, del211, L212I, and T547K for Omicron variant. These mutations were introduced in the wild type S-glycoprotein structure for the Omicron variant (Fig. 1e).

Fig. 1.

The figure shows the structural conformations of the Spike protein of wild type, Alpha, Delta, Omicron, and TLR4/MD2 complex (a) Structure of wild type Spike protein. The structure is developed using a PDB file with PDB id: 7BNN (b) Structure of TLR4/MD2 complex. The structure is developed by using a PDB file with PDB id: 4G8A (c) S-glycoprotein for the Alpha variant. Here, we have created E484K, S494P, N501Y mutations in the wild type S-glycoprotein. (d) S-glycoprotein for the Delta variant. Here, we have inserted L452R, E478K, T19T, G142D, R158G mutations in the wild type S-glycoprotein (e) S-glycoprotein for the Omicron variant. Here, we have inserted N440K, G446S, S477N, T478K, E484A, Q493K, G496S, Q498R, N501Y, Y505H G339D, S371L, S373P, S375F, K417N, G142D, Δ143–145, A67V, Δ69-70, T95I, del 211, L212I, T547K.

In this study, the PATCHDOCK server was initially used to find interacting residues between wild-type S-glycoprotein and TLR4. Random docking has been used to predict the interacting residues between the considered proteins. The lowest energy configuration suggests LYS113, GLN134, VAL83, and ASP138 residues of the N-terminal domain of S-glycoprotein are involved in hydrogen bond interaction with LYS224, THR284, ASP302, ASN305 of TLR4. On the other hand, GLU270, LYSS271, ASP238, and GLN188 residues of TLR4 are involved in hydrogen bond interaction with the RBD of the Spike protein. The final docking result was obtained through the docking between TLR4 and Spike proteins were obtained using the HADDOCK server. For docking, the considered residues of S-glycoprotein were LYS113, GLN134, VAL83, ASP138 from the N-terminal domain, and ASN487, LYS417, GLN493, TYR449, GLY496, GLY446, THR500, GLY502 from the RBD domain. Similarly, the considered residues of TLR4 were LYS224, THR284, ASP302, ASN305, GLU270, LYSS271, ASP238, and GLN188.

To find the best possible configuration of interaction between TLR4 and Spike protein, two possible orientations were considered for S-glycoprotein (wild type): (a) Orientation-1: Interaction of TLR4 with RBD (receptor binding domain) of B chain & N-terminal of the same chain (b) Orientation-2: Interaction of TLR4 with RBD of B chain & N-terminal of C chain. The possible binding configurations are shown in Fig. 2a. TLR4 with RBD of B chain & N-terminal of C chain shows better binding affinity than the other orientation. So, we performed docking interactions considering the orientation of TLR4 with RBD of the B-chain and N-terminal of C chain.

Fig. 2.

The figure shows the structural conformations after the interaction of Spike protein (wild type, Alpha, Delta, and Omicron) and TLR4/MD2 complex. (a) The figure shows the possible binding configurations and the conformations are: (i) Interaction of TLR4 with RBD of B chain & N-terminal of the same chain (ii) Interaction of TLR4 with RBD of B chain & N-terminal of C-chain. However, the second configuration has shown a better binding affinity than the other orientation. (b) Configuration of the interaction between wild type Spike and TLR4 (c) Configuration of the interaction between Alpha Spike and TLR4 (d) Configuration of the interaction between Delta Spike and TLR4 (e) Configuration of the interaction between Omicron Spike and TLR4.

3. Result

The docking conformation between wild type S-glycoprotein and TLR4 is depicted in Fig. 2b. The inset in the figure shows the interacting residues for the lowest energy configuration. Fifteen hydrogen bonds participated in the interaction between S-glycoprotein and TLR4. The interacting residues are noted in Table 1. The lowest energy configuration of wild type Spike protein and TLR4 docking has an interaction score of-109.2 (HADDOCK docking score).

Table 1.

Number of hydrogen bonds and interacting residues during the S-glycoprotein and TLR4 interaction in wild type SARS-CoV-2 and different variants (Alpha, Delta, and Omicron).

Spike protein	Number of hydrogen bonds formed with TLR4	Interacting residues RBD-TLR4	Interacting residues NTD-TLR4
Wild-type SARS CoV-2	15	ARG 346 - GLU 278 SER 349 - GLU 278 LYS 444 - ASP 302 ASN 448 - ASP 302 TYR 449 - ASP 302 ASN 450 - ASP 302 GLU 471 - ASN 241 TYR 351- LYS 244 ASN 450 - ASN 305	ASN 122 - GLU 169 THR 124 - GLN 200 LYS 147 - GLN 99 ASN 148 - HIS 148 ASN 149 - HIS 148
SARS CoV-2 Alpha variant	14	ARG 346 - GLU 278 SER 349 - GLU 278 TYR 351 - GLU 278 LYS 444 - ASP 302 ASN 450 - ASN 305 GLU 340 - LYS 224 GLU 471 - ASN 241	ASN 122 - GLU 169 THR 124 - GLN 200 LYS 147 - GLN 99 ASN 148 - SER 126 ASN 149 - HIS 148 PHE 157 - GLU 142
SARS CoV-2 Delta variant	17	ARG 346 - ASN 282 ARG 346 - GLU 278 SER 349 - GLU 278 TYR 351 - GLU 278 LYS 444 - ASP 302 ASN 450 - ASN 305 ARG 452 - ASP 273 ARG 452 - SER 275 GLU 340 - LYS 224 GLU 471- ASN 241	ASN 122 - GLU 169 THR 124 - GLN 200 HIS 146 - HIS 148 LYS 147 - GLN 99 ASN 148 - HIS 148 SER 161 - LYS 166
SARS CoV-2 Omicron variant	16	ARG 346 - ASN 282 ARG 346 - GLU 278 SER 349 - GLU 278 TYR 351 - LYS244 ASN 450 - LYS 274 ASN 450 - ASN 305 ASN 450 - CYS 306 GLU 471- ASN 241	ASN 122 - GLU 169 THR 124 - GLN 200 LYS 147 - GLN 99 LYS 147 - TYR 72 PHE 157 - GLU142 TYR160 - ARG 196 SER 161 - LYS 166

We illustrated the docking conformation between the Alpha variant S-glycoprotein and TLR4, which is shown in Fig. 2c. Here, we found 14 number hydrogen bonds that participated between the interacting Alpha Spike and TLR4. At the same time, the interacting residues are recorded in Table 1. The lowest energy configuration of Alpha Spike protein and TLR4 docking was observed as −164.6 (HADDOCK docking score).

The docking conformation between the Delta S-glycoprotein and TLR4 was illustrated in Fig. 2d. In this case, we found 17 hydrogen bonds were formed during the interaction between S-glycoprotein and TLR4, and the interacting residues are noted in Table 1. The lowest energy configuration of Delta variant S-glycoprotein and TLR4 docking was recorded as −161.3 (the HADDOCK docking score). L452R mutation might help to increase the binding affinity of the RBD region of S-glycoprotein with the TLR4 through hydrogen bonding. Tchesnokova et al. found that gaining the L452R mutation in the S-glycoprotein binding interface of the ACE2 triggers binding affinity with the variants SARS-CoV-2.²⁶ However, our study shows a similar result regarding the binding S-glycoprotein and TLR4. The binding relationship of the Delta variant is much higher than the S-glycoprotein of wild type SARS-CoV-2. However, we noted the interaction between the Omicron S-glycoprotein and TLR4. Here, we considered 23 mutations for the generation of Omicron S-glycoprotein before the interaction study. The interaction configuration between the Omicron S-glycoprotein and TLR4 was depicted in Fig. 2e. The 16 hydrogen bond formations are observed in minimum energy binding conformation. The interaction score is recorded as −170.3 (HADDOCK score). We found that the Omicron variant has the lowest energy interaction with TLR4 than other variants. The deletion event of residues 143 to 145 in the N-terminal domain makes the interacting chain more flexible to bind with TLR4. Finally, the flexible binding helps to form of more hydrogen bonds between the N-terminal domain of S-glycoprotein and TLR4. The lowest energy conformation interactions between the Omicron variant and TLR4 are depicted in Fig. 2e.

4. Discussions

TLR4 has a role in the inflammatory responses during acute viral infections. Several studies documented that viral glycoprotein or other viral proteins activate TLR4, and subsequently, downstream inflammatory signaling has been activated²⁷.At the same time, it was noted that the SARS-CoV-2 Spike trimer interacts with the TLR4 dimer. Zhao et al.(2021) have also developed two models for the interaction of SARS-CoV-2 Spike trimer with TLR4 dimer with two different conformations from their study²⁵; our studies confirm these findings (Fig. 2a).

4.1. Strong binding affinity and favourable interaction landscape with the TLR4/MD2

Our study shows 16 hydrogen bond formations among S-glycoprotein and TLR4/MD2 complex. At the same time, the study shows the Omicron variant has the lowest energy interaction S-glycoprotein with TLR4 compared to other variants. However, it creates a strong binding affinity and favorable energy landscape for the interaction of Omicron S-glycoprotein with the TLR4/MD2.

4.2. S-glycoprotein and TLR4 interaction and feedback loop generation

It has been noted that the interaction between Spike protein and TLR4 works in two directions: Firstly, it activates downstream signaling cascades. Secondly, it enhances ACE2 expression. Aboudounya and Heads (2021) described that S-glycoprotein binds with TLR4, which activating TLR4-associated signaling and augmentingACE2 expression²⁸. Therefore, due to the stronger omicron Spike-TLR4 interaction, there might be a possibility of enhancing the ACE2 expression in the case of an Omicron-infected host, which might cause increased interaction events for S-glycoprotein with ACE2. Several studies documented an increased interaction event of Omicron S-glycoprotein with ACE2.29, 30 Therefore, we hypothesize that the molecular basis of increased infectivity and transmissibility in Omicron. From this study, we have generated a hypothesis illustrating that due to more vital interaction of Omicron Spike with the TLR4/MD2, ACE2 expression might increase in Omicron infected host, which helps to interact Omicron S-glycoprotein with ACE2 through a positive feedback loop. After the Spike-TLR4 interaction, a positive feedback loop might be generated, which helps overexpression of ACE2 in the host (Fig. 3a). However, this hypothesis needs to be clarified with more experimental models in the future.

Fig. 3.

A hypothetical schematic diagram shows S-glycoprotein and TLR4 interaction and feedback loop generation. It also shows activation of downstream signaling event by the interaction Omicron Spike via TLR4. (a) The hypothetical figure demonstrates the increased ACE2 expression and S-protein and ACE2 interaction through positive feedback loop generation. The figure also shows the activation of downstream signaling event by Omicron Spike via TLR4. (b) The figure depicts the interaction of Omicron Spike via TLR4, which further activates the downstream signaling for the immune response generation, cytokine signaling, T-cell and B-cell activation.

However, studies have suggested a link between TLR4 activation and increased ACE2 expression.³¹ Increased ACE2 expression could counteract some of angiotensin II's vasoconstrictive and hypertensive effects. Still, the overall outcome might depend on various factors, including the context of TLR4 activation and the balance between the various components of the renin-angiotensin system.32, 33 In some conditions, increased ACE2 expression might be protective against hypertension, as it can lead to increased levels of angiotensin, which has vasodilatory effects.³² However, the relationship is not straightforward, and the role of TLR4 and ACE2 in hypertension is still an active area of research.

It has been well known that the Omicron variant has acquired several mutations. The mutations uniquely positioned S-glycoprotein, and these mutational events help the Omicron variant with increased binding of S-glycoprotein and TLR4 compared to other variants. However, we have noted that the mutations in NTD (N-terminal domain), especially deletion of the residues 143 to 145 in the domain (NTD), provide the flexibility of S-glycoprotein. It makes the chain more interactive, which assists in binding strongly with TLR4. At the same time, we also noted that the Alpha variant shows higher binding affinity than both wild type and Delta variant of SARS-CoV-2.

Finally, our study provides crucial information on the interaction landscape for TLR4 and Spike of wild type of SARS-CoV-2 and different significant variants (Alpha, Delta, Omicron). The study provides a molecular basis of interaction, such as several hydrogen bond formations, interacting residues, etc. Here, we hypothesize through our in silico study of Omicron Spike interaction with TLR4. It might open a new direction, not only about the interaction of Omicron Spike-TLR4 and the subsequent activation of TLR4-associated downstream signaling along with hACE2 (human ACE2) overexpression. At the same time, our study also provides a hypothesis about a positive feedback loop generation, which might be one of the factors for the molecular basis of infectivity and transmissibility of the Omicron variant. Finally, we urge future researchers to perform more in vivo studies for a deeper understanding of TLR4-mediated signaling activation and the molecular basis of the immune response for SARS-CoV-2 and its variants.

4.3. TLR4/MD2 complex and different potential therapeutics targeting the complex

Targeting TLR4/MD2, several therapeutics has been developed for various inflammatory and immune-related conditions. Therefore, drug development has immense potential to target TLR4/MD2 for various inflammatory and immune-related conditions. Several therapeutics have been developed for developing drugs targeting TLR4/MD2, including small molecules, antibodies, and peptides. Eritoran is a synthetic TLR4 antagonist that has been investigated as a potential treatment for sepsis and other inflammatory conditions. It competitively inhibits the binding of lipopolysaccharide (LPS) to TLR4.³⁴ Tocilizumab is an FDA-approved monoclonal antibody that targets the IL-6 receptor but can indirectly modulate TLR4-mediated inflammation by affecting downstream signaling pathways.³⁵ NI-0101 is an experimental monoclonal antibody designed to target TLR4 and reduce inflammation. It has shown promise in preclinical studies.³⁶ RNA-based oligonucleotides, such as small interfering RNA (siRNA) or antisense oligonucleotides, can silence TLR4 or MD2 gene expression to reduce their activity.³⁷ Peptide inhibitors (Pep19-2.5) is a synthetic peptides that inhibits TLR4-mediated inflammation by interfering with the TLR4/MD2 signaling complex.³⁸ There is an increased number of COVID-19 patients with excessive inflammation. Therefore, our study is significant for the therapeutic development of COVID-19 patients with excessive inflammations.

5. Future prospect

Understanding the interaction of the SARS-CoV-2 S-glycoprotein with the TLR4/MD2 complex is a significant area to research. It might open a new direction of downstream signaling of the adaptive immune response during the SARS-CoV-2 interaction. Several studies have tried to understand the interaction of SARS-CoV-2 S-glycoprotein with TLR4/MD-2 complex and its consequences.39, 40, 41 In the present in silico study, we provide an overview landscape of Omicron S-glycoprotein interaction with TLR4, and it might provide a new direction for signaling the adaptive immune response (Fig. 3b) and help to understand the pattern of cytokine signalling, and T-Cell and B-Cell activation, which has immense clinical significance. Shirato and Kizaki suggested that the S1 subunit of SARS-CoV-2 S-glycoprotein stimulates pro-inflammatory responses through the TLR4 signaling in human and murine macrophages. It might be a therapeutic target for increased inflammation in patients with COVID-19, providing a therapeutic discovery for treating those COVID-19 patients ⁴². At the same time, the study provides a hypothesis indicating the feedback loop activation of TLR4-associated downstream signaling and hACE2 (human ACE2) overexpression. Therefore, the study opened a new direction of the basic understanding of the landscape of signalling of the adaptive immune response to drug discovery increased inflammation in patients with COVID-19. Simultaneously, it creates open questions to unfold the molecular basis of the interaction of the feedback loop. We urge future researchers to conduct more in vitro and in vivo studies to solve these open questions.

6. Conclusion

The study shows significant hydrogen bond formations and a favorable energy landscape among S-protein and TLR4/MD2 complex. The study provides an understanding of the molecular interaction landscape of the S-glycoprotein and TLR4/MD2 complex. The study’s primary ground of the three probable ways: i) S-glycoprotein and TLR4 interaction and feedback loop generation; ii) the basic understanding of the landscape of the signaling of an adaptive immune response, and iii) the evaluating the therapeutic target for increased inflammation in patients with COVID-19. Therefore, the study has high public importance and will help in the therapeutics discovery of excessive inflammation in patients with COVID-19.

Declarations

Ethics approval and consent to participate

This research paper does not contain any studies with human participants or animals performed by any of the authors.

Availability of data and material

All data and materials included within the manuscript file.

Funding

There is not any financial support for this study.

CRediT authorship contribution statement

Chiranjib Chakraborty: Conceptualization, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing. Bidyut Mallick: Data curation, Formal analysis, Investigation, Methodology. Manojit Bhattacharya: Validation, Visualization, editing. Siddappa N.Byrareddy: Formal analysis, Validation, Visualization.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.