In silico panning for a non-competitive peptide inhibitor

Abstract

Background: Peptide ligands have tremendous therapeutic potential as efficacious drugs. Currently, more than 40 peptides are available in the market for a drug. However, since costly and time-consuming synthesis procedures represent a problem for high-throughput screening, novel procedures to reduce the time and labor involved in screening peptide ligands are required. We propose the novel approach of 'in silico panning' which consists of a two-stage screening, involving affinity selection by docking simulation and evolution of the peptide ligand using genetic algorithms (GAs). In silico panning was successfully applied to the selection of peptide inhibitor for water-soluble quinoprotein glucose dehydrogenase (PQQGDH).

Results: The evolution of peptide ligands for a target enzyme was achieved by combining a docking simulation with evolution of the peptide ligand using genetic algorithms (GAs), which mimic Darwinian evolution. Designation of the target area as next to the substrate-binding site of the enzyme in the docking simulation enabled the selection of a non-competitive inhibitor. In all, four rounds of selection were carried out on the computer; the distribution of the docking energy decreased gradually for each generation and improvements in the docking energy were observed over the four rounds of selection. One of the top three selected peptides with the lowest docking energy, 'SERG' showed an inhibitory effect with Ki value of 20 microM. PQQGDH activity, in terms of the Vmax value, was 3-fold lower than that of the wild-type enzyme in the presence of this peptide. The mechanism of the SERG blockage of the enzyme was identified as non-competitive inhibition. We confirmed the specific binding of the peptide, and its equilibrium dissociation constant (KD) value was calculated as 60 microM by surface plasmon resonance (SPR) analysis.

Conclusion: We demonstrate an effective methodology of in silico panning for the selection of a non-competitive peptide inhibitor from small virtual peptide library. This study is the first to demonstrate the usefulness of in silico evolution using experimental data. Our study highlights the usefulness of this strategy for structure-based screening of enzyme inhibitors.

Figure 1

A schematic diagram of the in silico peptide evolution system. The docking calculation program and genetic algorithms (GAs) were combined to evolve the peptide ligand on the computer. For the docking program, we used the MOE-Dock software, which is based on the simulated annealing method. GAs were used to evolve the peptide to produce the next generation. Four rounds of peptide evolution were performed in this study.

Figure 2

Defining a docking box next to the glucose-binding site. The 3D structure of water-soluble quinoprotein glucose dehydrogenase from Acinetobacter calcoaceticus (PQQGDH: PDB entry code 1CQ1) is shown in gray. PQQ and glucose are represented in yellow and green, respectively. The large pocket next to the glucose-binding site of PQQGDH comprises Arg148, Arg406, Arg408, Arg45 (red) and Lys28 (orange). The docking box was set as the target area for this pocket. The docking box is shown as a yellow rectangular solid. The size of the docking box was fixed as a 37 Å

Figure 3

The distribution of docking energies in each generation. The ranks of the peptide ligands in each generation are shown on the X- and Y-axes. The Z-axis shows the docking energies, which were calculated as follows: docking total energy = electrostatic energies + Van der Waals energies + the energy of the (flexible) ligand. Low docking energy means high binding ability of the ligand.

Figure 4

Peptide GEKD docked in PQQGDH. GDH, PQQ, and glucose are represented in purple, green, and pink, respectively. The GEKD peptide is displayed in red. The docking box is shown as a yellow rectangular solid.

Figure 5

PQQGDH activity for glucose in the presence of individual peptides. (A) The PQQGDH activities were measured in the presence of the synthetic peptides. The enzymatic activities for glucose are shown in the SV plot. The enzyme assay was performed with 0.57 nM PQQGDH and 20 μM of each peptide. The samples contained the following: no peptide, (●); GEKD, (○); GERD, (□); SERG, (■); DDDD, (×). (B) Linewaver-Burk plot of PQQGDH activities in the presence of 0 μM SERG (●), 2 μM SERG (△), 10 μM SERG (○), and 20 μM SERG (■). All of the correlation coefficients (R²) were > 0.98. The X-axis shows the reciprocal values of the glucose concentration, and the Y-axis indicates the reciprocal values of the kinase activity. (C) PQQGDH activities of the wild-type (●), with 0.2 μM peptide (×), with 1 μM peptide (□), with 2 μM peptide (△), with 20 μM peptide (■) were plotted against different glucose concentrations. The SERG peptide was used in the concentration range of 0.2–20 μM.