Identification of novel peptide inhibitors of the DR6-NAPP protein-prot...
of 1

NAPP-DR6 poster

Published on: Mar 3, 2016
Source: www.slideshare.net

Transcripts - NAPP-DR6 poster

  • 1. TEMPLATE DESIGN © 2007 www.PosterPresentations.com Identification of novel peptide inhibitors of the DR6-NAPP protein-protein interaction using a virtual screening approach Kelly Considine, Dr. Joseph Audie, and Dr. Edward Caliguri Sacred Heart University, 5151 Park Avenue, Fairfield, CT 06825 Abstract Introduction Future Work References Acknowledgements ConclusionMethods 0.33-X0.089-0.00089X- 0.86X-0.65X-X0.075+X-0.79=G torgap hbsbc/s-/+empiricalbind,   Results and Discussion (continued) Nikolaev and co-workers recently described a new apoptotic pathway that underlies neuronal development and axonal pruning. According to Nikolaev and co-workers, in response to nerve growth factor (NGF) withdrawal, the pathway is engaged by binding between the death receptor six (DR6) ectodomain and an N-terminal fragment of amyloid precursor protein (NAPP). Nikolaev and co-workers hypothesize that the DR6-NAPP apoptotic pathway might play a role in the pathophysiology of Alzheimer's Disease (AD). Thus, inhibitors of the DR6-NAPP interaction could potentially serve as a new class of drugs for the treatment of AD. Importantly, a theoretical model of the DR6-NAPP interaction has been proposed in a recent study. The model implicates a lone NAPP α-helix- loop motif as crucial to DR6 binding and recognition. Given all of this, we targeted the NAPP α-helix using structure-based peptide design. In particular, we virtually screened a focused peptide library against the NAPP α-helix. Select peptide screening hits were subsequently docked to the NAPP helix and their binding modes optimized. Final scoring and ranking was achieved using a well-validated empirical method for estimating protein-peptide binding affinities. While preliminary, our results suggest that focused peptide virtual screening, followed by peptide docking and optimization, and final empirical free energy scoring, provides an efficient work-flow for identifying novel and viable peptide inhibitors of the DR6-NAPP interaction. Alzheimer’s disease (AD) debilitates many individuals, yet there is no known treatment. A recently described apoptotic pathway involving the interaction between NAPP and DR6 may become hijacked in the AD brain and is a potential source for new therapies (3). The identification of the NAPP-DR6 interaction gives rise to a new therapeutic target (3; 4). Figure 1 shows the crystal structure of NAPP and highlights key residues (Lys66-Gln81) that are thought to mediate the interaction between NAPP and DR6 (5; 6). The residues were identified from a theoretical model of the DR6-NAPP interaction that was recently proposed by Ponomarev and Audie; Figure 2 illustrates the proposed interaction of these proteins (5). It is clear from Figure 2 that the α-helix is essential to the DR6-NAPP interaction. Thus, molecular agents designed to bind the NAPP helix and inhibit the DR6-NAPP interaction hold promise as novel treatments for AD. The goal of this work was to use a computational work-flow to quickly identify novel and promising peptides to bind the NAPP helix and prevent its interaction with DR6. The work-flow combined iterative focused peptide library design, peptide virtual screening, peptide docking, and empirical free energy scoring. During the virtual screening and docking experiments, the peptide ligands were treated as flexible and the NAPP target protein was treated as rigid. Future work will focus on molecular dynamics (MD) based refinement and rescoring of peptide- NAPP interactions and in vitro testing of select peptides. Figure 1. Secondary structure depiction of NAPP (PDB ID: 1MWP). Residues 66-81 have previously been reported as the region essential to NAPP’s interaction with DR6 (5). 1. The structure of NAPP (PDB ID: 1MWP) was obtained from the RCSB Protein Data Bank (6) and Swiss-PDB Viewer (http://spdbv.vital- it.ch/) was used to prepare it for further study (2). 2. A complete dipeptide library was constructed using the standard amino acids (excluding glycine) and docked to a region around the NAPP α-helix (10 GA runs) using AutoDock, as implemented in the Molecular Docking Server (http://www.dockingserver.com/web) (1). 3. The 20 dipeptides with the lowest predicted binding affinities were extended into tripeptides at the C-terminus and docked using the molecular docking server (10 GA runs). 4. The 20 tripeptides with the lowest predicted binding affinities were extended into tetrapeptides at the C-terminus and docked using the molecular docking server (10 GA runs). 5. The top 60 peptides were docked to NAPP using a more exhaustive approach (100 GA runs). 6. CMDescoresm (5) was used to estimate the binding affinities for the best tetra-peptides: Table 1. Residues of NAPP involved in its interaction with DR6 that are targeted for peptide inhibition (5) Residues Lys66 Glu67 Gly68 Ile69 Leu70 Gln71 Tyr72 Cys73 Gln74 Glu75 Val76 Tyr77 Pro78 Glu79 Leu80 Gln81 Figure 3. Backbone of NAPP residues involved in its interaction with DR6 that are targeted for peptide inhibition (5) Table 2. Tetrapeptides with lowest minimum energy Tetrapeptide Auto Dock Binding Affinity (kcal/mol) CMDescoresm Binding Affinity (kcal/mol) Auto Dock Binding Constant (Ki) Trp-Asn-Trp-Trp -9.27 -3.5 161.38 nM Phe-Trp-Lys-Trp -9.02 -2.7 245.87 nM Pro-Lys-Trp-Trp -8.50 -6.0 586.72 nM Trp-Phe-Trp-Phe -8.46 -2.9 625.80 nM Pro-Lys-Trp-Phe -8.37 -2.3 729.20 nM Trp-Lys-Trp-Trp -8.33 -3.3 748.81 nM Pro-Trp-Trp-Phe -8.20 -2.3 972.88 nM Phe-Trp-Trp-Ile -8.06 -2.3 1.25 µM Trp-Phe-Trp-Trp -8.04 -3.8 1.27 µM Trp-Trp-Trp-Ile -8.02 -3.0 1.32 µM Trp-Lys-Pro-Phe -7.92 -2.8 1.57 µM Trp-Trp-Trp-Cys -7.89 -3.4 1.65 µM Trp-Lys-Phe-Asp -7.86 -1.6 1.72 µM Trp-Lys-Phe-Cys -7.83 -1.1 1.82 µM Pro-Trp-Trp-Leu -7.58 -3.7 2.76 µM Phe-Trp-Trp-Asn -7.54 -2.7 2.99 µM Phe-Trp-Lys-Lys -7.48 -2.9 3.30 µM Phe-Trp-Lys-Leu -7.44 -1.7 3.54 µM Trp-Trp-Trp-Arg -7.36 -3.2 4.00 µM Phe-Trp-Trp-Ala -7.34 -1.6 4.18 µM Figure 2. Theoretical DR6-NAPP interaction model, as taken from the recent paper by Ponomarev and Audie. NAPP is shown in blue and DR6 is shown in yellow (5). Kelly Considine acknowledges the financial support from Sacred Heart University in Fairfield, CT and would like to thank Dr. Joseph Audie for his help and guidance on this project. 1. Bikadi, Z., & Hazai, E. (2009). Application of the PM6 semi- empirical method to modeling proteins enhances docking accuracy of AutoDock. Journal of Cheminformatics, 1, 15. 2. Guex, N., & Peitsch, M.C. (1997). Swiss Model and the Swiss- PdbViewer: An environment for comparative protein modeling. Electrophoresis, 18, 2714-2723. 3. Nikolaev, A., McLaughlin, T., O’Leary, D. D. M., & Tessier-Lavigne, M. (2009). APP binds DR6 to trigger axon pruning and neuron death via distinct caspases. Nature, 457(7232), 981-989. 4. Osherovich, L., & Writer, S. (2009). Genentech’s new parADigm. Science-Business eXchange, 2(8), 1-5. 5. Ponomarev, S. Y., & Audie, J. (2011). Computational prediction and analysis of the DR6-NAPP interaction. Proteins: Structure, Function, and Bioinformatics, Early View (Articles online in advance of print). 6. Rossjohn, J. , Cappai, R., Feil, S. C., Henry, A., McKinstry, W.J., Galatis, D., et al. (1999). Crystal structure of the N-terminal, growth factor-like domain of Alzheimer amyloid precursor protein. Nature Structural and Molecular Biology, 6, 327-331. Future work will include building pentapeptides and analyzing them by the same methods described here. Furthermore, molecular dynamics (MD) simulations of the top tetrapeptides and pentapeptides that bind to 1MWP with the lowest energy will be performed. Then, the next step will involve moving from the in silico process outlined in this project to in vitro experimentation. The top 10 tetrapeptides, as predicted by the estimated minimum energy and the MD simulations, will be synthesized, and their binding with NAPP will be monitored using 2D NMR. Table 2 describes the estimated minimum binding energy of the peptide to NAPP. Moving from dipeptides to tetrapeptides, there was a decrease in the minimum free energy as expected. The data from Docking Server shows very low energies which predict strong binding between the peptides and protein. The CMDescoresm approach predicts that Pro-Lys-Trp-Trp will have the best binding with NAPP, at - 6.0 kcal/mol (5). This approach is more focused on the interactions between protein-protein and protein-peptide. The binding constants, as predicted and calculated by Docking Server are reported in Table 2, and show 7 tetrapeptides that are predicted to bind NAPP and potentially inhibit its interaction with DR6 at nanomolar concentrations. The first 2 tetrapeptides, Trp-Asn-Trp- Trp and Phe-Trp-Lys-Trp, are predicted to bind NAPP at low nanomolar concentrations, and then there is a large increase in Ki for the subsequent peptides. Therefore, in silico data predicts that either or both of these 2 tetrapeptides will bind NAPP and inhibit DR6 binding. Based on the CMDBioscience approach, the tetrapeptide with the lowest minimum binding energy (Pro-Lys-Trp-Trp), is only predicted to have a binding affinity in the micromolar range, 25.13 µM, as calculated using the Gibbs Free-Energy relationship. Results and Discussion The results here are preliminary but they do suggest the potential for in vitro/in vivo binding of peptides to NAPP. The minimum binding energies and the Ki values, predict strong binding between the peptide and protein. Although there is some conflict between the binding energies as predicted by Docking Server (1) and the CMDBioscience approach (5), utilization of in vitro methods can help compare binding/inhibition of NAPP. The CMDBioscience approach assumes vdw interactions cancel which is where the discrepancy in the energy calculation might come from. These results do give a starting point for docking a number of peptides in vitro. As of now, the results are promising, and we have a number of peptides predicted to bind NAPP. Furthermore, Ponomarev and Audie predict NAPP-DR6 hydrogen bonding at NAPP residues Gln71 and Gln74, among others (5). These 2 residues are also predicted to hydrogen bond to the tetrapeptide shown in Figure 4. Pro-Lys-Trp-Trp is the peptide with the lowest minimum binding energy, as predicted by CMDescoresm (5), and its hydrogen bonding with NAPP strongly suggests the possibility of inhibition of the NAPP-DR6 interaction. The model of NAPP used here and the selected binding site were based off of the work done by Ponomarev and Audie, whose theoretical calculations of the binding free energy of DR6-NAPP is in good agreement with experimental results (5). Therefore, there is sound reason to believe that the results of this study, which suggest binding to the residues of NAPP involved in DR6-NAPP binding, will result in inhibition of this interaction. Figure 4. Tetrapeptide of lowest estimated binding energy as predicted by CMDescoresm approach(5), Pro-Lys-Trp-Trp (blue), hydrogen bonding with NAPP (pink) As shown in Figure 4, the tetrapeptide of the lowest estimated binding energy, as predicted by the CMDBioscience approach, is stabilized by 4 hydrogen bonds, as predicted using the CMDescoresm hydrogen bond detection method. Table 3 summarizes the residues and atoms involved in the hydrogen bonding which help stabilize the interaction of peptide and protein and contribute to the strong interaction predicted between Pro-Lys-Trp-Trp and NAPP. Peptide Residue and Atom NAPP Residue and Atom Distance (Å) Angles (°) Trp4 – N Gln71 – O 3.49 119.90 119.0 Trp4 – NE1 Gln 71 – OE1 3.48 147.47 116.0 Lys2 – NZ Gln74 – OE1 2.67 129.17 160.7 Pro1 – N Pro78 – O 3.49 122.44 144.4 Lys2 – O Gln74 – OE1 3.03 117.04 141.1 Table 3. Hydrogen bonding between Pro-Lys-Trp-Trp and NAPP as predicted by CMDescoresm approach (5)

Related Documents