Quantum Simulations of Materials and Biological Systems

X, 198 p.online resource

Successful computational prediction of the structure-activity relationship of a potent JAK2 inhibitor

JAK2 is a member of the JAK family of protein tyrosine kinases (PTK). JAK2 is an important intracellular mediator of cytokine signaling and its mutants are involved in haematological cancers and other chronic diseases. The specificity of inhibitors targeting the JAK2 PTK domain, particularly over other JAK kinases, is a critical design element of clinically relevant drugs. Due to the considerable structural similarity amongst the over 500 kinases in the human genome, designing a highly specific inhibitor of just one kinase is a nontrivial task. We have performed molecular docking studies of a high-affinity pan-JAK inhibitor (tetracyclic pyridone 2-tert-butyl-9-fluoro-3,6-dihydro-7H-benz[h]-imidaz[4,5-f]isoquinoline-7-one) into a JAK2 PTK structure using our in-house software ChemaPhore™. Lacking an X-ray structure, the studies were based initially on a homology model of JAK2. In order to improve the specificity and potency of the inhibitors, a series of modifications to the pan-JAK inhibitor were suggested and the new compounds were then synthesized and biologically tested. The results of the structure-activity relationship (SAR) obtained from wet screening are consistent with the proposed binding mode of the pan-JAK inhibitor. More importantly, recent X-ray crystal structures of JAK2 inhibitor complexes are consistent with our JAK2 homology model and confirm our predictions. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved

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

Antibody Recognition of Shiga Toxins (Stxs): Computational Identification of the Epitopes of Stx2 Subunit A to the Antibodies 11E10 and S2C4

<div><p>We have recently developed a new method to predict the epitopes of the antigens that are recognized by a specific antibody. In this work, we applied the method to identify the epitopes of the Shiga toxin (Stx2 subunit A) that were bound by two specific antibodies 11E10 and S2C4. The predicted epitopes of Stx2 binding to the antibody 11E10 resembles the recognition surface constructed by the regions of Stx2 identified experimentally. For the S2C4, our results indicate that the antibody recognizes the Stx2 at two different regions on the protein surface. The first region (residues 246-254: ARSVRAVNE) is similar to the recognition region of the 11E10, while the second region is formed by two epitopes. The second region is particularly significant because it includes the amino acid sequence region that is diverse between Stx2 and other Stx (residues 176-188: QREFRQALSETAPV). This new recognition region is believed to play an important role in the experimentally observed selectivity of S2C4 to the Stx2.</p></div

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

Sequence alignment of the VL and VH domains of the 11E10 and S2C4 antibodies and their templates.

<p>A) 11E10 aligned to antibody of the Murine T-cell receptor vairable domain/FAB complex (PDB 1KB5) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088191#pone.0088191-Housset1" target="_blank">[24]</a>, UCHT1 single-chain antibody fragment (PDB 1XIW) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088191#pone.0088191-Arnett1" target="_blank">[25]</a> and a Fab fragment of mAb107 complexed to the low- and high-affinity states of CD11bA (PDB 3Q3G) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088191#pone.0088191-Mahalingam1" target="_blank">[26]</a>; B) S2C4 aligned to a Murine unglycosylated IgG Fc fragment (PDB 2HKF) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088191#pone.0088191-Feige1" target="_blank">[27]</a> and the anti-VEGF receptor antibody IMC-1121B (PDB 3S35) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088191#pone.0088191-Franklin1" target="_blank">[28]</a>.</p

Selected MCSS minima of functional groups on the surface of antibody S2C4 against subunit A of Stx2.

<p>A) ACEM; B) MEOH; C) IMIA; D) ACET; E) MAMM; F) MGUA. Figures were prepared using PyMOL <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088191#pone.0088191-The1" target="_blank">[38]</a>.</p

Selected MCSS minima of functional groups on the surface of antibody 11E10.

<p>A) ACEM; B) MEOH; C) IMIA; D) INDO; E) MGUA; F) ACET. Figures were prepared using PyMOL<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088191#pone.0088191-The1" target="_blank">[38]</a>.</p

Distribution of key minima and the derived sequence pattern for the binding epitope peptides to the antibody S2C4.

<p>A sequence pattern of “XZ—J”(X = R or K, and Z = R, Q or N, H, S or T, and J = D or E) was obtained.</p

The predicted epitopes of Stx2 to antibody 11E10.

<p>A) The predicted epitopes of Stx2 to antibody 11E10 are highlighted in lower case and colored orange in the protein sequence. The recognition regions identified previously (Smith et al 2009) are underlined. B) Backbone presentation of the antigen subunits A and B showing the predicted epitopes in orange and identified regions <b>A-C</b> colored in blue. The antibody binds to subunit A only. Subunit B is shown in green. C) Surface presentation of the antigen subunits A and B showing the predicted epitopes in orange and recognition regions <b>A, B</b> and <b>C</b> colored in blue. Note that the region <b>C</b> is only partially shown as the region is missing in the crystal structure of Stx2 (PDB 1R4P) (Fraser et al, 2004). Figures were prepared using PyMOL <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088191#pone.0088191-The1" target="_blank">[38]</a>.</p