Atsushi Tomonaga

The Feasibility of an Efficient Drug Design Method with High-Performance Computers

In this study, we propose a supercomputer-assisted drug design approach involving all-atom molecular dynamics (MD)-based binding free energy prediction after the traditional design/selection step. Because this prediction is more accurate than the empirical binding affinity scoring of the traditional approach, the compounds selected by the MD-based prediction should be better drug candidates. In this study, we discuss the applicability of the new approach using two examples. Although the MD-based binding free energy prediction has a huge computational cost, it is feasible with the latest 10 petaflop-scale computer. The supercomputer-assisted drug design approach also involves two important feedback procedures: The first feedback is generated from the MD-based binding free energy prediction step to the drug design step. While the experimental feedback usually provides binding affinities of tens of compounds at one time, the supercomputer allows us to simultaneously obtain the binding free energies of hundreds of compounds. Because the number of calculated binding free energies is sufficiently large, the compounds can be classified into different categories whose properties will aid in the design of the next generation of drug candidates. The second feedback, which occurs from the experiments to the MD simulations, is important to validate the simulation parameters. To demonstrate this, we compare the binding free energies calculated with various force fields to the experimental ones. The results indicate that the prediction will not be very successful, if we use an inaccurate force field. By improving/validating such simulation parameters, the next prediction can be made more accurate.

Knowledge‐Based Identification of the ERK2/STAT3 Signal Pathway as a Therapeutic Target for Type 2 Diabetes and Drug Discovery

Many existing agents for diabetes therapy are unable to restore or maintain normal glucose homeostasis or prevent the eventual emergence of hyperglycemia‐related complication. Therefore, agents based on novel mechanisms are sought to complement and extend the current therapeutic approaches. Based on the initial paper research, we focused on active STAT3 as an attractive pharmacological target for type 2 diabetes. The subsequent text mining with a unique query to identify suppressors but not activators of STAT3 revealed the ERK2/STAT3 pathway as a novel diabetes target. The description of ERK2 inhibitors as diabetes target had not been found in our text mining research at present. The mechanism‐based peptide inhibitor for ERK2 was identified using the knowledge of the KIM sequence, which has an important role in the recognition of cognate kinases, phosphatases, scaffold proteins, and substrates. The peptide inhibitor was confirmed to exert effects in vitro and in vivo. The peptide inhibitor conferred a significant decrease in HOMA‐IR levels on Day 28 compared with that in the vehicle group. Besides lowering the fasting blood glucose level, the peptide inhibitor also attenuated the blood glucose increment in the fed state, as compared with the vehicle group.

Identification of allosteric ERK2 inhibitors through in silico biased screening and competitive binding assay.

Extracellular signal-regulated kinase 2 (ERK2) is a drug target for type 2 diabetes mellitus. A peptide-type ERK2 inhibitor (PEP) was discovered in the previous study through the knowledge-based method and showed physiological effects on the db/db mice model of type 2 diabetes. Here, the crystal structure showed that PEP bound to the allosteric site without the interruption of the ATP competitive inhibitor binding to ERK2. An in silico biased-screening using the focused library rendered three compounds with inhibitory activity of IC50 <100 μM. Among them, two compounds revealed the concentration-dependent competition with PEP and could be lead compounds for antidiabetic medicine.