Published papers

Asparagine synthetase (ASNS) is a crucial enzyme for the de novo biosynthesis of endogenous asparagine (Asn), and ASNS shows the positive relationship with the growth of several solid tumors. Most of ASNS inhibitors are analogs of transition-state in ASNS reaction, but their low cell permeability hinders their anticancer activity. Therefore, novel ASNS inhibitors with a new pharmacophore urgently need to be developed. In this study, we established and applied a system for in vitro screening of ASNS inhibitors, and found a promising unique bisabolane-type meroterpenoid molecule, bisabosqual A (Bis A), able to covalently modify K556 site of ASNS protein. Bis A targeted ASNS to suppress cell proliferation of human non-small cell lung cancer A549 cells and exhibited a synergistic effect with L-asparaginase (L-ASNase). Mechanistically, Bis A promoted oxidative stress and apoptosis, while inhibiting autophagy, cell migration and epithelial-mesenchymal transition (EMT), impeding cancer cell development. Moreover, Bis A induced negative feedback pathways containing the GCN2-eIF2α-ATF4, PI3K-AKT-mTORC1 and RAF-MEK-ERK axes, but combination treatment of Bis A and rapamycin/torin-1 overcame the potential drug resistance triggered by mTOR pathways. Our study demonstrates that ASNS inhibition is promising for cancer chemotherapy, and Bis A is a potential lead ASNS inhibitor for anticancer development.

Prediction of protein–ligand binding affinity is a major goal in drug discovery. Generally, free energy gap is calculated between two states (e.g., ligand binding and unbinding). The energy gap implicitly includes the effects of changes in protein dynamics induced by ligand binding. However, the relationship between protein dynamics and binding affinity remains unclear. Here, we propose a method that represents ligand-binding-induced protein behavioral change with a simple feature that can be used to predict protein–ligand affinity. From unbiased molecular simulation data, an unsupervised deep learning method measures the differences in protein dynamics at a ligand-binding site depending on the bound ligands. A dimension reduction method extracts a dynamic feature that strongly correlates to the binding affinities. Moreover, the residues that play important roles in protein–ligand interactions are specified based on their contribution to the differences. These results indicate the potential for binding dynamics-based drug discovery.

Cyclodextrins (CDs) are suitable drug carriers because of their doughnut-shaped cavities with hydrophilic outer and hydrophobic inner surfaces. Temperature-responsive CD-based drug carriers are expected to be one of the most promising candidates for drug delivery systems. In this study, we performed molecular dynamics simulations of the inclusion complex of β-CD with cyclophosphamide (CP) at temperatures from 300 K to 400 K to investigate the temperature dependency of the release behaviour of CP and structural changes of β-CD in an aqueous solution. We analysed the distance between the centres of mass of β-CD and CP and the radius of gyration of β-CD. The CP molecule was released from the β-CD cavity at 400 K, whereas two different inclusion complexes, partially and completely, were observed at T < 400 K. β-CD encapsulating a CP molecule had a more spherical shape and rigidity than β-CD without a CP, and the rigidity of their inclusion complex decreased with increasing temperature. Our findings provide fundamental insights into the behaviours of the β-CD/CP complex and drug release at the molecular level and can facilitate the development of new temperature-responsive drug delivery systems with CD nanocarriers triggered by localised temperature increases using focused ultrasound.

We propose a water pump that actively transports water molecules through nanochannels. Spatially asymmetric noise fluctuations imposed on the channel radius cause unidirectional water flow without osmotic pressure, which can be attributed to hysteresis in the cyclic transition between the wetting/drying states. We show that the water transport depends on fluctuations, such as white, Brownian, and pink noises. Because of the high-frequency components in white noise, fast switching of open and closed states inhibits channel wetting. Conversely, pink and Brownian noises generate high-pass filtered net flow. Brownian fluctuation leads to a faster water transport rate, whereas pink noise has a higher capability to overcome pressure differences in the opposite direction. A trade-off relationship exists between the resonant frequency of the fluctuation and the flow amplification. The proposed pump can be considered as an analogy for the reversed Carnot cycle, which is the upper limit of the energy conversion efficiency.

The interactions between proteins and ligands are involved in various biological functions. While experimental structures provide key static structural information of ligand-unbound and ligand-bound proteins, dynamic information is often insufficient for understanding the detailed mechanism of protein-ligand binding. Here, we studied the conformational changes of the tankyrase 2 binding pocket upon ligand binding using molecular dynamics simulations of the ligand-unbound and ligand-bound proteins. The ligand-binding pocket has two subsites: The nicotinamide and adenosine subsite. Comparative analysis of these molecular dynamics trajectories revealed that the conformational change of the ligand-binding pocket was characterized by four distinct conformations of the ligand-binding pocket. Two of the four conformations were observed only in molecular dynamics simulations. We found that the pocket conformational change on ligand binding was based on the connection between the nicotinamide and adenosine subsites that are located adjacently in the pocket. From the analysis, we proposed the protein-ligand binding mechanism of tankyrase 2. Finally, we discussed the computational prediction of the ligand binding pose using the tankyrase 2 structures obtained from the molecular dynamics simulations.

We performed molecular dynamics simulation of the dimeric SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) main protease (Mpro) to examine the binding dynamics of small molecular ligands. Seven HIV inhibitors, darunavir, indinavir, lopinavir, nelfinavir, ritonavir, saquinavir, and tipranavir, were used as the potential lead drugs to investigate access to the drug binding sites in Mpro. The frequently accessed sites on Mpro were classified based on contacts between the ligands and the protein, and the differences in site distributions of the encounter complex were observed among the ligands. All seven ligands showed binding to the active site at least twice in 28 simulations of 200 ns each. We further investigated the variations in the complex structure of the active site with the ligands, using microsecond order simulations. Results revealed a wide variation in the shapes of the binding sites and binding poses of the ligands. Additionally, the C-terminal region of the other chain often interacted with the ligands and the active site. Collectively, these findings indicate the importance of dynamic sampling of protein–ligand complexes and suggest the possibilities of further drug optimisations.

In computational drug discovery, ranking a series of compound analogues in the order that is consistent with the experimental binding affinities remains a challenge. Many of the computational methods available for evaluating binding affinities have adopted molecular mechanics (MM)-based force fields, although they cannot completely describe protein-ligand interactions. By contrast, quantum mechanics (QM) calculations play an important role in understanding the protein-ligand interactions; however, their huge computational costs hinder their application in drug discovery. In this study, we have evaluated the ability to rank the binding affinities of tankyrase 2 ligands by combining both MM and QM calculations. Our computational approach uses the protein-ligand binding energies obtained from a cost-effective multilayer fragment molecular orbital (MFMO) method combined with the solvation energy obtained from the MM-Poisson-Boltzmann/surface area (MM-PB/SA) method to predict the binding affinity. This approach enabled us to rank tankyrase 2 inhibitor analogues, outperforming several MM-based methods, including rescoring by molecular docking and the MM-PB/SA method alone. Our results show that this computational approach using the MFMO method is a promising tool for predicting the rank order of the binding affinities of inhibitor analogues.