asakawa@rsi.co.jp

AutoGPA: Visual estimation of shape and features of the structure of the interaction site from ligand-based analysis

Naoyuki ASAKAWA*1, Seiichi KOBAYASHI1, Junichi GOTO1, Noriaki HIRAYAMA2

1) Computational Science Department, Science and Technology Systems Division, Ryoka Systems, Inc., 1-28-38 Shinkawa, Chuo-ku, Tokyo 104-0033, JAPAN

2) Basic Medical Science and Molecular Medicine, Tokai University School of Medicine, 143 Shimokasuya, Isehara, Kanagawa 259-1143, JAPAN

1. Introduction 3D-QSAR models derived from grid potential analysis provides spatial distributions of important potentials in active ligands. Since the distributions usually vary widely depending on superimposed conformations of the active ligands, it is not easy to find the most appropriate model that represents the actual binding site. A novel 3D-QSAR method, AutoGPA1 implemented in MOE,2 whereby a series of conformations of the active ligands are aligned based on the pharmacophores and grid potentials around them are calculated, allows for objective identification of the most reasonable 3D-QSAR model. In this paper, we report the results of an AutoGPA study on a series of inhibitors of phosphoinositide-dependent kinase-1 (PDK1), a protein kinase in insulin and growth factor signaling. 2. Method 2.1 AutoGPA: Generating the models

70 inhibitors of PDK1 reported by Islam et al.3 were divided into a training set of 20 and a test set of 50. 437 conformations were obtained for the molecules in the training set by use of Conformation Import function implemented in MOE and the MMFF94x forcefield. 31 models initially obtained by AutoGPA were evaluated by cross-validation methods and the 22 models with q2=1.0 were selected for pIC50 prediction. 2.2 Evaluation of the models by screening test Although all the 22 models were generated from very similar conformational alignments, the pharmacophore models and the grid potentials were significantly different. These models were assessed using the test set. The screening performance is shown in Fig. 1. The model No. 16 gave the best correlation coefficient of r and the model No. 22 the best hit rate. 3. Results

In Fig. 2, the pharmacophores assigned to the best model of No. 6 are shown on the left and the actual binding site determined by X-ray analysis is shown on the right. The aromatic ring regions, hydrogen bond donor and acceptor positions in the pharmacophore model correspond very well to the aromatic rings of the bound ligand, Ala162, and Ser160, respectively. A superposition between the pharmacophores and the corresponding actual ligand scaffold is illustrated in Fig. 3a. The aligned inhibitors are properly placed at the binding site. In addition, the positive and negative steric potentials are assigned to the binding site and the inaccessible interior of the enzyme, respectively. Fig. 3b shows the distributions of electrostatic potentials. It is noteworthy that a distinctly negative

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Fig. 1 Screening performance of the 22 models

potential is located close to Lys111 that is actually involved in a hydrogen bond with the ligand as shown in Fig. 2.

4. Discussion This study has demonstrated successfully that AutoGPA is a very powerful method to derive a reliable 3D-QSAR model automatically from the chemical structures and the biological activities of a set of biologically active molecules. Although AutoGPA is ligand-based in principle, it can give us a trustworthy image of the structure of the binding site comparable to that determined by X-ray analysis. Therefore it is highly expected that AutoGPA will play a significant role in drug discovery process. 5. References 1) AutoGPA, oral presentation KO-10. 2) MOE (Molecular Operating Environment), Version 2008.1002; Chemical Computing Group Inc.: Montreal, Qubec, Canada 2008. 3) Islam, I. et al. Bioorg. Med. Chem. Lett. 2007, 17, 3814-3818.

Fig. 2 Top view of the best model (left) and an interaction map from the actual complex (right)

(b)

Fig. 3 Side view of the best model and the actual binding site of 2PE13 superposed by the ligand scaffold: (a) the surface of the actual binding site and distributions of steric potentials (b) electrostatic potentials and the corresponding amino acid residues at the binding site

Steric positive favored

Steric negative favored (a) Electrostatic negative favored