Crystal structure of HLA-B*5801, a protective HLA allele for HIV-1 infection

Allopurinol non-covalently facilitates binding of unconventional peptides to HLA-B*58:01

Allopurinol, widely used in gout treatment, is the most common cause of severe cutaneous adverse drug reactions. The risk of developing such life-threatening reactions is increased particularly for HLA-B*58:01 positive individuals. However the mechanism of action between allopurinol and HLA remains unknown. We demonstrate here that a Lamin A/C peptide KAGQVVTI which is unable to bind HLA-B*58:01 on its own, is enabled to form a stable peptide-HLA complex only in the presence of allopurinol. Crystal structure analysis reveal that allopurinol non-covalently facilitated KAGQVVTI to adopt an unusual binding conformation, whereby the C-terminal isoleucine does not engage as a PΩ that typically fit deeply in the binding F-pocket. A similar observation, though to a lesser degree was seen with oxypurinol. Presentation of unconventional peptides by HLA-B*58:01 aided by allopurinol contributes to our fundamental understanding of drug-HLA interactions. The binding of peptides from endogenously available proteins such as self-protein lamin A/C and viral protein EBNA3B suggest that aberrant loading of unconventional peptides in the presence of allopurinol or oxypurinol may be able to trigger anti-self reactions that can lead to Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN) and Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS).

Immunoinformatic approach to design a multiepitope vaccine targeting non-mutational hotspot regions of structural and non-structural proteins of the SARS CoV2

BACKGROUND

The rapid Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV2) outbreak caused severe pandemic infection worldwide. The high mortality and morbidity rate of SARS CoV2 is due to the unavailability of vaccination and mutation in this virus. The present article aims to design a potential vaccine construct VTC3 targeting the non-mutational region of structural and non-structural proteins of SARS CoV2.

METHODS

In this study, vaccines were designed using subtractive proteomics and reverse vaccinology. To target the virus adhesion and evasion, 10 different structural and non-structural proteins have been selected. Shortlisted proteins have been screened for B cell, T cell and IFN gamma interacting epitopes. 3D structure of vaccine construct was modeled and evaluated for its physicochemical properties, immunogenicity, allergenicity, toxicity and antigenicity. The finalized construct was implemented for docking and molecular dynamics simulation (MDS) with different toll-like receptors (TLRs) and human leukocyte antigen (HLA). The binding energy and dissociation construct of the vaccine with HLA and TLR was also calculated. Mutational sensitivity profiling of the designed vaccine was performed, and mutations were reconfirmed from the experimental database. Antibody production, clonal selection, antigen processing, immune response and memory generation in host cells after injection of the vaccine was also monitored using immune simulation.

RESULTS

Subtractive proteomics identified seven (structural and non-structural) proteins of this virus that have a role in cell adhesion and infection. The different epitopes were predicted, and only extracellular epitopes were selected that do not have similarity and cross-reactivity with the host cell. Finalized epitopes of all proteins with minimum allergenicity and toxicity were joined using linkers to designed different vaccine constructs. Docking different constructs with different TLRs and HLA demonstrated a stable and reliable binding affinity of VTC3 with the TLRs and HLAs. MDS analysis further confirms the interaction of VTC3 with HLA and TLR1/2 complex. The VTC3 has a favorable binding affinity and dissociation constant with HLA and TLR. The VTC3 does not have similarities with the human microbiome, and most of the interacting residues of VTC3 do not have mutations. The immune simulation result showed that VTC3 induces a strong immune response. The present study designs a multiepitope vaccine targeting the non-mutational region of structural and non-structural proteins of the SARS CoV2 using an immunoinformatic approach, which needs to be experimentally validated.

Critical assessment of approaches for molecular docking to elucidate associations of HLA alleles with adverse drug reactions

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All software assessed could dock Abacavir back into the risk allele structure but not always predict the exact binding mode.

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Most docking software assessed can distinguish between risk and control alleles.

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Docking performance can be degraded by using a homology model.

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Receptor flexibility can negatively affect the docking performance for complex HLA examples.

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Using AutoDockFR cannot compensate for the added difficulty of docking to the unbound target.

Adverse drug reactions have been linked with genetic polymorphisms in HLA genes in numerous different studies. HLA proteins have an essential role in the presentation of self and non-self peptides, as part of the adaptive immune response. Amongst the associated drugs-allele combinations, anti-HIV drug Abacavir has been shown to be associated with the HLA-B*57:01 allele, and anti-epilepsy drug Carbamazepine with B*15:02, in both cases likely following the altered peptide repertoire model of interaction. Under this model, the drug binds directly to the antigen presentation region, causing different self peptides to be presented, which trigger an unwanted immune response. There is growing interest in searching for evidence supporting this model for other ADRs using bioinformatics techniques. In this study, in silico docking was used to assess the utility and reliability of well-known docking programs when addressing these challenging HLA-drug situations. The overall aim was to address the uncertainty of docking programs giving different results by completing a detailed comparative study of docking software, grounded in the MHC-ligand experimental structural data – for Abacavir and to a lesser extent Carbamazepine - in order to assess their performance. Four docking programs: SwissDock, ROSIE, AutoDock Vina and AutoDockFR, were used to investigate if each software could accurately dock the Abacavir back into the crystal structure for the protein arising from the known risk allele, and if they were able to distinguish between the HLA-associated and non-HLA-associated (control) alleles. The impact of using homology models on the docking performance and how using different parameters, such as including receptor flexibility, affected the docking performance were also investigated to simulate the approach where a crystal structure for a given HLA allele may be unavailable. The programs that were best able to predict the binding position of Abacavir were then used to recreate the docking seen for Carbamazepine with B*15:02 and controls alleles.

It was found that the programs investigated were sometimes able to correctly predict the binding mode of Abacavir with B*57:01 but not always. Each of the software packages that were assessed could predict the binding of Abacavir and Carbamazepine within the correct sub-pocket and, with the exception of ROSIE, was able to correctly distinguish between risk and control alleles. We found that docking to homology models could produce poorer quality predictions, especially when sequence differences impact the architecture of predicted binding pockets. Caution must therefore be used as inaccurate structures may lead to erroneous docking predictions. Incorporating receptor flexibility was found to negatively affect the docking performance for the examples investigated. Taken together, our findings help characterise the potential but also the limitations of computational prediction of drug-HLA interactions. These docking techniques should therefore always be used with care and alongside other methods of investigation, in order to be able to draw strong conclusions from the given results.