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Purpose: The first-generation ALK tyrosine kinase inhibitor (TKI) crizotinib is a standard therapy for patients with ALK-rearranged non–small cell lung cancer (NSCLC). Several next-generation ALK-TKIs have entered the clinic and have shown promising activity in crizotinib-resistant patients. As patients still relapse even on these next-generation ALK-TKIs, we examined mechanisms of resistance to the next-generation ALK-TKI alectinib and potential strategies to overcome this resistance.Experimental Design: We established a cell line model of alectinib resistance, and analyzed a resistant tumor specimen from a patient who had relapsed on alectinib. We developed Ba/F3 models harboring alectinib-resistant ALK mutations and evaluated the potency of other next-generation ALK-TKIs in these models. We tested the antitumor activity of the next-generation ALK-TKI ceritinib in the patient with acquired resistance to alectinib. To elucidate structure–activity relationships of ALK mutations, we performed computational thermodynamic simulation with MP-CAFEE.Results: We identified a novel V1180L gatekeeper mutation from the cell line model and a second novel I1171T mutation from the patient who developed resistance to alectinib. Both ALK mutations conferred resistance to alectinib as well as to crizotinib, but were sensitive to ceritinib and other next-generation ALK-TKIs. Treatment of the patient with ceritinib led to a marked response. Thermodynamics simulation suggests that both mutations lead to distinct structural alterations that decrease the binding affinity with alectinib.Conclusions: We have identified two novel ALK mutations arising after alectinib exposure that are sensitive to other next-generation ALK-TKIs. The ability of ceritinib to overcome alectinib-resistance mutations suggests a potential role for sequential therapy with multiple next-generation ALK-TKIs. Clin Cancer Res; 20(22); 5686–96. ©2014 AACR.

Supplementary Figures S1-S7. Figure S1 contains the cell survival data of H3122 cells to multiple ALK-TKIs. Figure S2 contains the comparison of experimental IC50 values and calculated free energy values corresponding to Fig 3C. Figure S3 contains the IC50 values of Ba/F3 cells following treatment with various ALK-TKIs corresponding to Fig3A and 4A. Figure S4 contains the cell survival data of Ba/F3 cells to ASP3026 and AP26113. Figure S5 contains the immunoblot data of Ba/F3 cells treated with various ALK-TKIs. Figure S6 contains the apoptosis assay of alectinib resistant H3122 CHR-A1 cells following treatment with various ALK-TKIs. Figure S7 contains the cell survival data and immunoblot data of Hsp90 inhibitor treated cells.

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.

Spatiotemporal regulation of viral capsid assembly ensures the selection of the viral genome for encapsidation. The porcine circovirus 2 is the smallest autonomously replicating pathogenic virus, yet how PCV2 capsid assembly is regulated to occur within the nucleus remains unknown. We report that pure PCV2 capsid proteins, in the absence of nucleic acids, require acidic conditions to assemble into empty capsids in vitro. By employing constant pH replica exchange molecular dynamics, we unveil the atomistic mechanism of pH-dependency for capsid assembly. The results show that an appropriate protonation configuration for a cluster of acidic amino acids is necessary to appropriately position the GH-loop for driving the capsid assembly. We demonstrate that assembly is prohibited at neutral pH because deprotonation of these residues results in their electrostatic repulsion, shifting the GH-loop to a position incompatible with capsid assembly. We propose that encapsulation of nucleic acids overcomes this repulsion to suitably position the GH-loop. Our findings provide the first atomic resolution mechanism of capsid assembly regulation. These findings are useful for the development of therapeutics that inhibit PCV2 self-assembly.

We developed MDGRAPE-4A, a special-purpose computer system for molecular dynamics simulations, consisting of 512 nodes of custom system-on-a-chip LSIs with dedicated processor cores and interconnects designed to achieve strong scalability for biomolecular simulations. To reduce the global communications required for the evaluation of Coulomb interactions, we conducted a co-design of the MDGRAPE-4A and the novel algorithm, tensor-structured multilevel Ewald summation method (TME), which produced hardware modules on the custom LSI circuit for particle-grid operations and for grid-grid separable convolutions on a 3D torus network. We implemented the convolution for the top-level grid potentials by using 3D FFTs on an FPGA, along with an FPGA-based octree network to gather grid charges. The elapsed time for the long-range part of Coulomb is 50 μs, which can mostly overlap with those for the short-range part, and the additional cost is approximately 10 μs/step, which is only a 5% performance loss.

Networks-on-Chip has become the de facto design paradigm for many-core systems. This poster presents a general purpose Networks-on-Chip (NoC) generator with optimal configurations, named EAGEN. The NoC producer module of EAGEN is written in Chisel3 language which generates both Verilog and C++ model for hardware implementation and software simulation, respectively. Once the application specification and design constrains are fed to EAGEN, the synthesizable RTL code with optimal configuration will be output.

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. Raw trajectory data analysed in this paper and movie examples are available at the zenodo repository.

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.

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 β-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 β-rich clusters. In the oligomeric state, the wild type and the S7A sequences had the highest β-structure content (~14%), as calculated by DSSP, in line with experimental observations, whereas I5A and G3A had the highest helical content (~20%). Importantly, the β-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.

We present the results of Molecular Dynamics simulations of a viral capsid with the aim to analyse ion distribution on the capsid's surface that defines its stability. Two systems were modelled, a stable capsid with neutralising number of ions and an unstable capsid with low number of ions. For the ion distribution analysis the capsid's structure was identical and fixed in both simulations. It was then released for the stability analysis. The ion distribution demonstrated two types of the local regions on the inner surface of the capsid's wall: highly occupied with chloride ions in both systems despite a largely uniform electrostatic potential everywhere on the surface, and the regions that loose almost all chloride ions in the unstable capsid. The latter regions are located close to the cracks that are formed when the capsid is destabilised and thus could initiate the collapse of the capsid.

Conjugation of small molecular inhibitor with a site recognition peptide would generate highly potent peptide–drug conjugate inhibitors of protein kinases. A peptide selected by ribosome display wi...

Present experimental methods do not have sufficient resolution to investigate all processes in virus particles at atomistic details. We report the results of molecular dynamics simulations and analyze the connection between the number of ions inside an empty capsid of PCV2 virus and its stability. We compare the crystallographic structures of the capsids with unresolved N-termini and without them in realistic conditions (room temperature and aqueous solution) and show that the structure is preserved. We find that the chloride ions play a key role in the stability of the capsid. A low number of chloride ions results in loss of the native icosahedral symmetry, while an optimal number of chloride ions create a neutralizing layer next to the positively charged inner surface of the capsid. Understanding the dependence of the capsid stability on the distribution of the ions will help clarify the details of the viral life cycle that is ultimately connected to the role of packaged viral genome inside the capsid.

Purpose: The first-generation ALK tyrosine kinase inhibitor (TKI) crizotinib is a standard therapy for patients with ALK-rearranged non–small cell lung cancer (NSCLC). Several next-generation ALK-TKIs have entered the clinic and have shown promising activity in crizotinib-resistant patients. As patients still relapse even on these next-generation ALK-TKIs, we examined mechanisms of resistance to the next-generation ALK-TKI alectinib and potential strategies to overcome this resistance. Experimental Design: We established a cell line model of alectinib resistance, and analyzed a resistant tumor specimen from a patient who had relapsed on alectinib. We developed Ba/F3 models harboring alectinib-resistant ALK mutations and evaluated the potency of other next-generation ALK-TKIs in these models. We tested the antitumor activity of the next-generation ALK-TKI ceritinib in the patient with acquired resistance to alectinib. To elucidate structure–activity relationships of ALK mutations, we performed computational thermodynamic simulation with MP-CAFEE. Results: We identified a novel V1180L gatekeeper mutation from the cell line model and a second novel I1171T mutation from the patient who developed resistance to alectinib. Both ALK mutations conferred resistance to alectinib as well as to crizotinib, but were sensitive to ceritinib and other next-generation ALK-TKIs. Treatment of the patient with ceritinib led to a marked response. Thermodynamics simulation suggests that both mutations lead to distinct structural alterations that decrease the binding affinity with alectinib. Conclusions: We have identified two novel ALK mutations arising after alectinib exposure that are sensitive to other next-generation ALK-TKIs. The ability of ceritinib to overcome alectinib-resistance mutations suggests a potential role for sequential therapy with multiple next-generation ALK-TKIs. Clin Cancer Res; 20(22); 5686–96. ©2014 AACR.

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. © 2014 The Authors Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

To increase the inhibitory activity of purvalanol against cyclin-dependent kinase 2, we increased the extent of interaction between the inhibitor and the target by coupling a peptide aptamer to purvalanol. The peptide–purvalanol conjugate, selected using a ribosome display, had a significantly enhanced inhibitory effect compared with purvalanol alone. The technique is useful as another type of fragment-based drug design tool.

We performed a quantum chemical study on evaluating chemical reactivity for drug molecules in Cytochrome P450 (CYP) metabolic reaction. In this study, we focused on two insights for analysing the chemical reactivity: one is the Fukui reaction indices for a molecule, and the other is the minimum energy paths of hydrogen transition reaction between drug molecule and FeO-Porphyrin ring part on the CYP metabolic reaction. The Fukui indices are obtained numerically by using the familiar population analysis method in quantum chemical calculations. We performed the assessments of some population analyses to clarify the numerical behaviour clearly and then evaluated the potential of the reactivity of drug molecule. On the other hand, in the analysis of the minimum energy path for the CYP metabolic reaction, we performed the nudged elastic band (NEB) calculations for the hydrogen transition reaction in the first step of the CYP metabolic reaction. We report the findings for the reactivity from the Fukui indices method and also show the NEB results for the reaction paths.