Antibody recognition of Shiga toxins (Stxs): computational identification of the epitopes of Stx2 subunit A to the antibodies 11E10 and S2C4

Computational Prediction of the Epitopes of HA1 Protein of Influenza Viruses to its Neutralizing Antibodies

In this work, we have used a new method to predict the epitopes of HA1 protein of influenza virus to several antibodies HC19, CR9114, BH151 and 4F5. While our results reproduced the binding epitopes of H3N2 or H5N1 for the neutralizing antibodies HC19, CR9114, and BH151 as revealed from the available crystal structures, additional epitopes for these antibodies were also suggested. Moreover, the predicted epitopes of H5N1 HA1 for the newly developed antibody 4F5 are located at the receptor binding domain, while previous study identified a region 76-WLLGNP-81 as the epitope. The possibility of antibody recognition of influenza virus via different mechanism by binding to different epitopes of an antigen is also discussed.

Development of monoclonal anti-PDGF-CC antibodies as tools for investigating human tissue expression and for blocking PDGF-CC induced PDGFRα signalling in vivo

PDGF-CC is a member of the platelet-derived growth factor (PDGF) family that stimulates PDGFRα phosphorylation and thereby activates intracellular signalling events essential for development but also in cancer, fibrosis and neuropathologies involving blood-brain barrier (BBB) disruption. In order to elucidate the biological and pathological role(s) of PDGF-CC signalling, we have generated high affinity neutralizing monoclonal antibodies (mAbs) recognizing human PDGF-CC. We determined the complementarity determining regions (CDRs) of the selected clones, and mapped the binding epitope for clone 6B3. Using the monoclonal 6B3, we determined the expression pattern for PDGF-CC in different human primary tumours and control tissues, and explored its ability to neutralize PDGF-CC-induced phosphorylation of PDGFRα. In addition, we showed that PDGF-CC induced disruption of the blood-retinal barrier (BRB) was significantly reduced upon intraperitoneal administration of a chimeric anti-PDGF-CC antibody. In summary, we report on high affinity monoclonal antibodies against PDGF-CC that have therapeutic efficacy in vivo.

Computational identification of antibody epitopes of human papillomavirus 16 (HPV16) L1 proteins

Previously, we developed a method to predict epitopes on a protein recognized by specific antibodies. In this study, we have applied this method to identify the epitopes of the human papillomavirus 16 (HPV16) L1 capsomer that is bound by monoclonal antibodies U4, AE3 and AG7. Initially, the method was validated by the identification of epitopes of HPV16 L1 capsomer that bind to antibody U4. Our predicted epitopes were in agreement with the cryto-electron microscopy (cryto-EM) structure of the complex. The method was then used to predict the epitopes of HPV16 L1 binding of antibodies AE3 and AG7. Our calculations indicated that antibody AE3 binds to the HPV16 L1 capsomer at two different regions. Firstly, the region recognized by antibody U4 and secondly, the region recognized by antibody V5, which have been shown in the cryto-EM structure of the V5 and HPV16 L1 complex. In comparison, the antibody AG7 binds to the capsomer only at the epitopes bound by antibody U4. Therefore, antibody AE3 is predicted to have higher affinity than antibody AG7 and could be used for developing highly efficient anti-HPV monoclonal antibodies in the clinical treatment of HPV infections.

Epitope Sequences in Dengue Virus NS1 Protein Identified by Monoclonal Antibodies

Dengue nonstructural protein 1 (NS1) is a multi-functional glycoprotein with essential functions both in viral replication and modulation of host innate immune responses. NS1 has been established as a good surrogate marker for infection. In the present study, we generated four anti-NS1 monoclonal antibodies against recombinant NS1 protein from dengue virus serotype 2 (DENV2), which were used to map three NS1 epitopes. The sequence ¹⁹³AVHADMGYWIESALNDT²⁰⁹ was recognized by monoclonal antibodies 2H5 and 4H1BC, which also cross-reacted with Zika virus (ZIKV) protein. On the other hand, the sequence ²⁵VHTWTEQYKFQPES³⁸ was recognized by mAb 4F6 that did not cross react with ZIKV. Lastly, a previously unidentified DENV2 NS1-specific epitope, represented by the sequence ¹²⁷ELHNQTFLIDGPETAEC¹⁴³, is described in the present study after reaction with mAb 4H2, which also did not cross react with ZIKV. The selection and characterization of the epitope, specificity of anti-NS1 mAbs, may contribute to the development of diagnostic tools able to differentiate DENV and ZIKV infections.

Design of peptide inhibitors of human papillomavirus 16 (HPV16) transcriptional regulator E1–E2 formation

Here, we have proposed a new scheme of the computational combinatorial design approach to identify potential inhibitor peptides. It consists of four steps: (i) using “multiple copy simultaneous search” (MCSS) procedure to locate specific functional groups on the protein surface; (ii) the peptide main chain is constructed based on the location of favored N-methylacetamide (NMA) groups; (iii) molecular dynamics simulations of the complex formed between the constructed peptides with the target protein in explicit water molecules are carried to select the peptides with strong binding to the protein and (iv) the sequences of the stable peptides selected from (iii) are aligned and the frequencies of the amino acids at each position of peptide are calculated. Sequence patterns of potential inhibitors are determined based on the frequency of amino acids at each position. It was applied to design peptide inhibitors that bind to the E2 protein of HPV16 so as to disrupt its transcriptional regulator of E1–E2 complex formation. The sequence pattern of these potential inhibitors is in agreement with known inhibitors obtained from phage display, and the MCSS calculations indicate that a hydrophobic pocket on HPV16 E2 plays a significant role in E1–E2 formation and inhibitor-E2 binding.

Computational Identification of Antibody Epitopes on the Dengue Virus NS1 Protein

We have previously described a method to predict antigenic epitopes on proteins recognized by specific antibodies. Here we have applied this method to identify epitopes on the NS1 proteins of the four Dengue virus serotypes (DENV1-4) that are bound by a small panel of monoclonal antibodies 1H7.4, 1G5.3 and Gus2. Several epitope regions were predicted for these antibodies and these were found to reflect the experimentally observed reactivities. The known binding epitopes on DENV2 for the antibodies 1H7.4 and 1G5.3 were identified, revealing the reasons for the serotype specificity of 1H7.4 and 1G5.3, and the non-selectivity of Gus2. As DENV NS1 is critical for virus replication and a key vaccine candidate, epitope prediction will be valuable in designing appropriate vaccine control strategies. The ability to predict potential epitopes by computational methods significantly reduces the amount of experimental work required to screen peptide libraries for epitope mapping.