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.

Large-scale all-atom molecular dynamics alanine-scanning of IAPP octapeptides provides insights into the molecular determinants of amyloidogenicity.

In order to investigate the early phase of the amyloid formation by the short amyloidogenic octapeptide sequence ('NFGAILSS') derived from IAPP, we carried out a 100ns all-atom molecular dynamics (MD) simulations of systems that contain 27 peptides and over 30,000 water molecules. The large-scale calculations were performed for the wild type sequence and seven alanine-scanned sequences using AMBER 8.0 on RIKEN's special purpose MD-GRAPE3 supercomputer, using the all-atom point charge force field ff99, which do not favor ß-structures. Large peptide clusters (size 18-26 mers) were observed for all simulations, and our calculations indicated that isoleucine at position 5 played important role in the formation of ß-rich clusters. In the oligomeric state, the wild type and the S7A sequences had the highest ß-structure content (~14%), as calculated by DSSP, in line with experimental observations, whereas I5A and G3A had the highest helical content (~20%). Importantly, the ß-structure preferences of wild type IAPP originate from its association into clusters and are not intrinsic to its sequence. Altogether, the results of this first large-scale, multi-peptide all-atom molecular dynamics simulation appear to provide insights into the mechanism of amyloidogenic and non-amyloidogenic oligomers that mainly corroborate previous experimental observations.

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.

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.

Free-energy landscapes of protein domain movements upon ligand binding.

The conformation and functions of proteins are closely linked, and many proteins undergo conformational changes upon ligand binding. The X-ray crystallographic studies have revealed conformational differences in proteins between the liganded and unliganded states. Currently, the conformational transitions that originate in the ligand binding are explained on the basis of two representative models, the induced-fit and preexisting equilibrium dynamics models. However, the actual dynamics of the proteins remain ambiguous. Though these two models are the extreme ones, it is important to understand the difference between these two, particularly in structural biology and medicinal chemistry studies. Here, we clarified the difference in the mechanisms responsible for the conformational changes induced in two proteins upon ligand binding by examining computationally determined free-energy profiles of the apo- and holoproteins. The lysine/arginine/ornithine-binding protein and maltose/maltodextrin-binding protein were chosen as the target proteins, and the energy profiles were generated by a molecular simulation approach. Our results revealed that fluctuations in the apo state and protein-ligand interactions both play important roles in conformational transition, and the mechanism is highly influenced by the fluctuations in the apo state, which are unique to a particular structure.

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.

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.