Drug binding dynamics of the dimeric SARS-CoV-2 main protease, determined by molecular dynamics simulation

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.

A new set of atomic radii for accurate estimation of solvation free energy by Poisson-Boltzmann solvent model

The Poisson-Boltzmann implicit solvent (PB) is widely used to estimate the solvation free energies of biomolecules in molecular simulations. An optimized set of atomic radii (PB radii) is an important parameter for PB calculations, which determines the distribution of dielectric constants around the solute. We here present new PB radii for the AMBER protein force field to accurately reproduce the solvation free energies obtained from explicit solvent simulations. The presented PB radii were optimized using results from explicit solvent simulations of the large systems. In addition, we discriminated PB radii for N- and C-terminal residues from those for nonterminal residues. The performances using our PB radii showed high accuracy for the estimation of solvation free energies at the level of the molecular fragment. The obtained PB radii are effective for the detailed analysis of the solvation effects of biomolecules.

MDGRAPE-4: a special-purpose computer system for molecular dynamics simulations

We are developing the MDGRAPE-4, a special-purpose computer system for molecular dynamics (MD) simulations. MDGRAPE-4 is designed to achieve strong scalability for protein MD simulations through the integration of general-purpose cores, dedicated pipelines, memory banks and network interfaces (NIFs) to create a system on chip (SoC). Each SoC has 64 dedicated pipelines that are used for non-bonded force calculations and run at 0.8 GHz. Additionally, it has 65 Tensilica Xtensa LX cores with single-precision floating-point units that are used for other calculations and run at 0.6 GHz. At peak performance levels, each SoC can evaluate 51.2 G interactions per second. It also has 1.8 MB of embedded shared memory banks and six network units with a peak bandwidth of 7.2 GB s(-1) for the three-dimensional torus network. The system consists of 512 (8×8×8) SoCs in total, which are mounted on 64 node modules with eight SoCs. The optical transmitters/receivers are used for internode communication. The expected maximum power consumption is 50 kW. While MDGRAPE-4 software has still been improved, we plan to run MD simulations on MDGRAPE-4 in 2014. The MDGRAPE-4 system will enable long-time molecular dynamics simulations of small systems. It is also useful for multiscale molecular simulations where the particle simulation parts often become bottlenecks.

Structure basis of Leukotriene C4 Synthase and its isophtalate inhibitors

Leukotriene (LT) C4 synthase (LTC4S) is a key enzyme for the production of cysteinyl leukotrienes, LTC4, LTD4 and LTE4, which are relevance to asthma and allergy. LTC4S catalyzes the conjugation of glutathione (GSH) to LTA4. The crystal structure of LTC4S complex with GSH revealed the active sites locate at the interfaces of adjacent monomer in trans-membrane homo-trimer [1]. The unique U-shaped GSH binds the inner hydrophilic interface cavity and the amphiphilic LTA4 was proposed to bind in the hydrophobic V-shaped crevasse on the interface of the trimer hydrophobic surface. Two essential arginine residues was proved to exert as acid-base catalysis at the both sides of two substrates in highly regio- and stereo-selective manner enzymatically and crystallographically [2]. The architecture of the catalysis with Arg104 and Arg31 residues is the unique among various Glutathione-S-transferases. Arg104 activates and stabilizes the thiorate anaion of the bound GSH and Arg31 stabilize and activate epoxy of the other substrate LTA4, and each mutation causes not only reduction of activity but also each substrate binding affinity substantially. Furthermore, the putative LTA4 binding position was confirmed using the anomalous signal of selenium of the bound seleno-dodecylmaltoside (Se-DDM) at the V-shaped crevasse. To identify leads for novel therapeutics, we attempted to search competitive inhibitors against the unique shaped GSH binding site to discriminate the GSH binding sites of other GSTs that accommodate only its extending backbone conformer [3]. Hierarchical in silico screenings of 6 million compounds provided 300,000 dataset for docking, and after energy minimization based on the crystal structure of LTC4S, 111 compounds were selected as candidates for a competitive inhibitor to glutathione. 5-(5-Methylene-4-oxo-4,5-dihydrothiazol-2-ylamino) isophthalic acid moiety was identified to inhibit LTC4 formation both an enzyme assay and a whole-cell assay. Finally, 5-((Z)-5-((E)-2-methyl-3-phenylallylidene)-4-oxo-4,5-dihydrothiazol-2-ylamino) isophthalic acid was found to be the most potent inhibitor with 1.9 µM of IC50, and in the whole-cell assay to inhibit LTC4 synthesis with cell permeability in a concentration dependent manner.

A leukotriene C4 synthase inhibitor with the backbone of 5-(5-methylene-4-oxo-4,5-dihydrothiazol-2-ylamino) isophthalic acid

The cysteinyl leukotrienes (cys-LTs), leukotriene C4 (LTC4) and its metabolites, LTD4 and LTE4, are proinflammatory lipid mediators in asthma and other inflammatory diseases. They are generated through the 5-lipoxygenase/LTC4 synthase (LTC4S) pathway and act via at least two distinct G protein-coupled receptors. The inhibition of human LTC4S will make a simple way to treat the cys-LT relevant inflammatory diseases. Here, we show that compounds having 5-(5-methylene-4-oxo-4,5-dihydrothiazol-2-ylamino) isophthalic acid moiety suppress LTC4 synthesis, glutathione conjugation to the precursor LTA4, in both an enzyme assay and a whole-cell assay. Hierarchical in silico screenings of 6 million compounds provided 300,000 dataset for docking, and after energy minimization based on the crystal structure of LTC4S, 111 compounds were selected as candidates for a competitive inhibitor to glutathione. One of those compounds showed significant inhibitory activity, and subsequently, its derivative 5-((Z)-5-((E)-2-methyl-3-phenylallylidene)-4-oxo-4,5-dihydrothiazol-2-ylamino) isophthalic acid (compound 1) was found to be the most potent inhibitor. The enzyme assay showed the IC50 was 1.9 µM and the corresponding 95% confidence interval was from 1.7 to 2.2 µM. The whole-cell assay showed that compound 1 was cell permeable and inhibited LTC4 synthesis in a concentration dependent manner.

High-performance drug discovery: computational screening by combining docking and molecular dynamics simulations

Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, this method is not completely reliable and therefore unsatisfactory. In this study, we used massive molecular dynamics simulations of protein-ligand conformations obtained by molecular docking in order to improve the enrichment performance of molecular docking. Our screening approach employed the molecular mechanics/Poisson-Boltzmann and surface area method to estimate the binding free energies. For the top-ranking 1,000 compounds obtained by docking to a target protein, approximately 6,000 molecular dynamics simulations were performed using multiple docking poses in about a week. As a result, the enrichment performance of the top 100 compounds by our approach was improved by 1.6–4.0 times that of the enrichment performance of molecular dockings. This result indicates that the application of molecular dynamics simulations to virtual screening for lead discovery is both effective and practical. However, further optimization of the computational protocols is required for screening various target proteins.

Lead discovery is one of the most important processes in rational drug design. To improve the rate of the detection of lead compounds, various technologies such as high-throughput screening and combinatorial chemistry have been introduced into the pharmaceutical industry. However, since these technologies alone may not improve lead productivity, computational screening has become important. A central method for computational screening is molecular docking. This method generally docks many flexible ligands to a rigid protein and predicts the binding affinity for each ligand in a practical time. However, its ability to detect lead compounds is less reliable. In contrast, molecular dynamics simulations can treat both proteins and ligands in a flexible manner, directly estimate the effect of explicit water molecules, and provide more accurate binding affinity, although their computational costs and times are significantly greater than those of molecular docking. Therefore, we developed a special purpose computer “MDGRAPE-3” for molecular dynamics simulations and applied it to computational screening. In this paper, we report an effective method for computational screening; this method is a combination of molecular docking and massive-scale molecular dynamics simulations. The proposed method showed a higher and more stable enrichment performance than the molecular docking method used alone.

Chaotic itinerancy in coupled dynamical recognizers

We argue that chaotic itinerancy in interaction between humans originates in the fluctuation of predictions provided by the nonconvergent nature of learning dynamics. A simple simulation model called the coupled dynamical recognizer is proposed to study this phenomenon. Daily cognitive phenomena provide many examples of chaotic itinerancy, such as turn taking in conversation. It is therefore an interesting problem to bridge two chaotic itinerant phenomena. A clue to solving this is the fluctuation of prediction, which can be translated as "hot prediction" in the context of cognitive theory. Hot prediction is simply defined as a prediction based on an unstable model. If this approach is correct, the present simulation will reveal some dynamic characteristics of cognitive interactions.