Asymmetric Periodic Boundary Conditions for All-Atom Molecular Dynamics and Coarse-Grained Simulations of Nucleic Acids.

Periodic boundary conditions are commonly applied in molecular dynamics simulations in the microcanonical (NVE), canonical (NVT), and isothermal-isobaric (NpT) ensembles. In their simplest application, a biological system of interest is placed in the middle of a solvation box, which is chosen 'sufficiently large' to minimize any numerical artifacts associated with the periodic boundary conditions. This practical approach brings limitations to the size of biological systems that can be simulated. Here, we study simulations of effectively infinitely long nucleic acids, which are solvated in the directions perpendicular to the polymer chain, while periodic boundary conditions are also applied along the polymer chain. We study the effects of these asymmetric periodic boundary conditions (APBC) on the simulated results, including the mechanical properties of biopolymers and the properties of the surrounding solvent. To get some further insights into the advantages of using the APBC, a coarse-grained worm-like chain model is first studied, illustrating how the persistence length can be extracted from the local properties of the polymer chain, which are less affected by the APBC than some global averages. This is followed by all-atom molecular dynamics simulations of DNA in ionic solutions, where we use the APBC to investigate sequence-dependent properties of DNA molecules and properties of the surrounding solvent.

Molecular dynamics analysis of biomolecular systems including nucleic acids.

With the recent progress in structural biology and genome biology, structural dynamics of molecular systems that include nucleic acids has attracted attention in the context of gene regulation. The structure-function relationship is an important topic that highlights the importance of the physicochemical properties of nucleotides, as well as that of amino acids in proteins. Simulations are a useful tool for the detailed analysis of molecular dynamics that complement experiments in molecular biology; however, molecular simulation of nucleic acids is less well developed than that of proteins partly due to the physical nature of nucleic acids. In this review, we briefly describe the current status and future directions of the field as a guide to promote collaboration between experimentalists and computational biologists.

Translational recoding by chemical modification of non-AUG start codon ribonucleotide bases.

In contrast to prokaryotes wherein GUG and UUG are permissive start codons, initiation frequencies from non-AUG codons are generally low in eukaryotes, with CUG being considered as strongest. Here, we report that combined 5-cytosine methylation (5mC) and pseudouridylation (Ψ) of near-cognate non-AUG start codons convert GUG and UUG initiation strongly favored over CUG initiation in eukaryotic translation under a certain context. This prokaryotic-like preference is attributed to enhanced NUG initiation by Ψ in the second base and reduced CUG initiation by 5mC in the first base. Molecular dynamics simulation analysis of tRNAiMet anticodon base pairing to the modified codons demonstrates that Ψ universally raises the affinity of codon:anticodon pairing within the ribosomal preinitiation complex through partially mitigating discrimination against non-AUG codons imposed by eukaryotic initiation factor 1. We propose that translational control by chemical modifications of start codon bases can offer a new layer of proteome diversity regulation and therapeutic mRNA technology.

The synergistic antitumor effect of combination therapy with a MEK inhibitor and YAP inhibitor on pERK-positive neuroblastoma.

BACKGROUND: We previously reported the in vitro and in vivo antitumor effects of trametinib, a MEK inhibitor, on neuroblastoma with MAPK pathway mutations. As we observed eventual resistance to trametinib in our previous study, we evaluated the combination therapy of CA3, a YAP inhibitor, with trametinib, based on a recent report suggesting the potential involvement of YAP in the mechanism underlying the resistance to trametinib in neuroblastoma. METHODS: SK-N-AS cells (a neuroblastoma cell line harboring RAS mutation) were treated with CA3 in vitro and subjected to a viability assay, immunocytochemistry and flow cytometry. Next, we analyzed the in vitro combination effect of CA3 and trametinib using the CompuSyn software program. Finally, we administered CA3, trametinib or both to SK-N-AS xenograft mice for 10 weeks to analyze the combination effect. RESULTS: CA3 inhibited cell proliferation by both cell cycle arrest and apoptosis in vitro. Combination of CA3 and trametinib induced a significant synergistic effect in vitro (Combination Index <1). Regarding the in vivo experiment, combination therapy suppressed tumor growth, and 100% of mice in the combination therapy group survived, whereas the survival rates were 0% in the CA3 group and 33% in the trametinib group. However, despite this promising survival rate in the combination group, the tumors gradually grew after seven weeks with MAPK reactivation. CONCLUSION: Our results indicated that CA3 and trametinib exerted synergistic antitumor effects on neuroblastoma in vitro and in vivo, and CA3 may be a viable option for concomitant drug therapy with trametinib, since it suppressed the resistance to trametinib. However, this combination effect was not sufficient to achieve complete remission. Therefore, we need to adjust the protocol to obtain a better outcome by determining the mechanism underlying regrowth in the future.

Free energy landscape of RNA binding dynamics in start codon recognition by eukaryotic ribosomal pre-initiation complex.

Specific interaction between the start codon, 5'-AUG-3', and the anticodon, 5'-CAU-3', ensures accurate initiation of translation. Recent studies show that several near-cognate start codons (e.g. GUG and CUG) can play a role in initiating translation in eukaryotes. However, the mechanism allowing initiation through mismatched base-pairs at the ribosomal decoding site is still unclear at an atomic level. In this work, we propose an extended simulation-based method to evaluate free energy profiles, through computing the distance between each base-pair of the triplet interactions involved in recognition of start codons in eukaryotic translation pre-initiation complex. Our method provides not only the free energy penalty for mismatched start codons relative to the AUG start codon, but also the preferred pathways of transitions between bound and unbound states, which has not been described by previous studies. To verify the method, the binding dynamics of cognate (AUG) and near-cognate start codons (CUG and GUG) were simulated. Evaluated free energy profiles agree with experimentally observed changes in initiation frequencies from respective codons. This work proposes for the first time how a G:U mismatch at the first position of codon (GUG)-anticodon base-pairs destabilizes the accommodation in the initiating eukaryotic ribosome and how initiation at a CUG codon is nearly as strong as, or sometimes stronger than, that at a GUG codon. Our method is expected to be applied to study the affinity changes for various mismatched base-pairs.

Anti-relapse effect of trametinib on a local minimal residual disease neuroblastoma mouse model.

PURPOSE: We reported the in vitro and in vivo anti-tumor effects of trametinib, an MEK inhibitor, on neuroblastoma. However, long-term trametinib administration for bulky tumors failed to prevent local relapse. In this study, we established a local minimal residual disease (L-MRD) model to develop an optimal clinical protocol. METHODS: We prepared an l-MRD model by implanting neuroblastoma cells (SK-N-AS) into the renal capsule of nude mice with total tumorectomy or sham operation 14 days later. These mice received post-operative administration of trametinib or vehicle for eight weeks. Relapse was measured once weekly. Flow cytometry was performed with SK-N-AS cells treated by trametinib. RESULTS: Tumorectomy+trametinib dramatically suppressed relapse, and all mice survived during trametinib administration, while other treatments failed to suppress relapse. The survival rates for other groups were 20% in sham+trametinib, 17% in tumorectomy+vehicle, and 0% in sham+vehicle. Relapse occurred in the tumorectomy+trametinib group after withdrawal of trametinib administration. Flow cytometry revealed G1 arrest in SK-N-AS cells treated with trametinib. CONCLUSION: These findings suggested that trametinib was able to suppress relapse from minimal residual tumor cells. Therefore, we propose that trametinib be administered as an option for maintenance therapy after surgical and chemotherapeutic treatments for neuroblastoma in future clinical protocols.

Structural dynamics of DNA depending on methylation pattern.

DNA methylation is associated with a number of biological phenomena, and plays crucial roles in epigenetic regulation of eukaryotic gene expression. It is also suggested that DNA methylation alters the mechanical properties of DNA molecules, which is likely to affect epigenetic regulation. However, it has not been systematically investigated how methylation changes the structural and dynamic features of DNA. In this research, to elucidate the effects of methylation on DNA mechanics, a fully atomic molecular dynamics simulation of double-stranded DNA with several methylation patterns was performed. Through the analysis of the relative positioning of the nucleotides (base-step variables), characteristic changes in terms of local flexibility were observed, which further affected the overall DNA geometry and stiffness. These findings may serve as a basis for a discussion on methylation-dependent DNA dynamics in physiological conditions.

Immunohistochemical staining of phosphorylated-ERK in post-chemotherapeutic samples is a potential predictor of the prognosis of neuroblastoma.

PURPOSE: The majority of relapsed neuroblastomas have mitogen-activated protein kinase (MAPK) pathway activating mutations. We previously showed the in vitro and in vivo anti-tumor effects of MAPK/ERK kinase (MEK) inhibitors in MAPK-activated neuroblastoma. We herein assessed the correlation between MAPK activation and the prognosis in neuroblastoma patients using phosphorylated extra-cellular signal-regulated kinase (pERK) immunohistochemistry to establish the protocol for the clinical administration of MEK inhibitors. METHODS: Neuroblastoma samples from patients treated in our hospital were immunostained with pERK. The clinical outcomes were retrospectively collected from medical records. The correlation between pERK positivity and the prognosis was analyzed. RESULTS: Regarding pre-chemotherapeutic specimens, there were no differences in the pERK status between tumors with a good and bad prognosis in both the nuclei and cytoplasm. Regarding post-chemotherapeutic specimens, one of eight tumors with a good prognosis and four of six tumors with a poor prognosis showed pERK-positive nuclear staining (p = 0.0909) and five of eight tumors with a good prognosis and four of six tumors with a poor prognosis showed pERK-positive cytoplasmic staining (p > 0.9999). CONCLUSION: These findings suggested post-chemotherapeutic-not pre-chemotherapeutic-nuclear pERK-positive neuroblastoma tends to be associated with a poor prognosis and may be a potential therapeutic target for MEK inhibitor treatment.

PHi-C: deciphering Hi-C data into polymer dynamics.

Genomes are spatiotemporally organized within the cell nucleus. Genome-wide chromosome conformation capture (Hi-C) technologies have uncovered the 3D genome organization. Furthermore, live-cell imaging experiments have revealed that genomes are functional in 4D. Although computational modeling methods can convert 2D Hi-C data into population-averaged static 3D genome models, exploring 4D genome nature based on 2D Hi-C data remains lacking. Here, we describe a 4D simulation method, PHi-C (polymer dynamics deciphered from Hi-C data), that depicts 4D genome features from 2D Hi-C data by polymer modeling. PHi-C allows users to interpret 2D Hi-C data as physical interaction parameters within single chromosomes. The physical interaction parameters can then be used in the simulations and analyses to demonstrate dynamic characteristics of genomic loci and chromosomes as observed in live-cell imaging experiments. PHi-C is available at https://github.com/soyashinkai/PHi-C.

Histone Tail Dynamics in Partially Disassembled Nucleosomes During Chromatin Remodeling.

Nucleosomes are structural units of the chromosome consisting of DNA wrapped around histone proteins, and play important roles in compaction and regulation of the chromatin structure. While the structure and dynamics of canonical nucleosomes have been studied extensively, those of nucleosomes in intermediate states, that occur when their structure or positioning is modulated, have been less understood. In particular, the dynamic features of partially disassembled nucleosomes have not been discussed in previous studies. Using all-atom molecular dynamics simulations, in this study, we investigated the dynamics and stability of nucleosome structures lacking a histone-dimer. DNA in nucleosomes lacking a histone H2A/H2B dimer was drastically deformed due to loss of local interactions between DNA and histones. In contrast, conformation of DNA in nucleosomes lacking H3/H4 was similar to the canonical nucleosome, as the H2A C-terminal domain infiltrated the space originally occupied by the dissociated H3/H4 histones and restricted DNA dynamics in close proximity. Our results suggest that, besides histone chaperones, the intrinsic dynamics of nucleosomes support the exchange of H2A/H2B, which is significantly more frequent than that of H3/H4.

Analyzing Fluctuation Properties in Protein Elastic Networks with Sequence-Specific and Distance-Dependent Interactions.

Simple protein elastic networks which neglect amino-acid information often yield reasonable predictions of conformational dynamics and are broadly used. Recently, model variants which incorporate sequence-specific and distance-dependent interactions of residue pairs have been constructed and demonstrated to improve agreement with experimental data. We have applied the new variants in a systematic study of protein fluctuation properties and compared their predictions with those of conventional anisotropic network models. We find that the quality of predictions is frequently linked to poor estimations in highly flexible protein regions. An analysis of a large set of protein structures shows that fluctuations of very weakly connected network residues are intrinsically prone to be significantly overestimated by all models. This problem persists in the new models and is not resolved by taking into account sequence information. The effect becomes even enhanced in the model variant which takes into account very soft long-ranged residue interactions. Beyond these shortcomings, we find that model predictions are largely insensitive to the integration of chemical information, at least regarding the fluctuation properties of individual residues. One can furthermore conclude that the inherent drawbacks may present a serious hindrance when improvement of elastic network models are attempted.

Enhanced metastatic growth after local tumor resection in the presence of synchronous metastasis in a mouse allograft model of neuroblastoma.

PURPOSE: We investigated how local tumor resection affects metastatic lesions in neuroblastoma. METHODS: MYCN Tg tumor-derived cells were injected subcutaneously into 129+Ter/SvJcl wild-type mice. First, the frequency of metastasis-bearing mice was investigated immunohistochemically (metastatic ratio) at endpoint or post-injection day (PID) 90. Second, the threshold volume of local tumor in mice bearing microscopic lymph node metastasis (mLNM) was investigated at PID 30. Finally, local tumors were resected after exceeding the threshold. Mice were divided into local tumor resection (Resection) and observation (Observation) groups, and the metastatic ratio and volume of LNM were compared between the groups at endpoint or PID 74. RESULTS: The metastatic ratio without local resection was 88% at PID 78-90. The threshold local tumor volume in the mice with mLNM was 745 mm3 at PID 30, so local tumors were resected after exceeding 700 mm3. The metastatic ratio and LNM volume were significantly greater in the Resection group (n = 16) than in the Observation group (n = 16) (94% vs. 38%, p < 0.001; 2092 ± 2310 vs. 275 ± 218 mm3, p < 0.01; respectively) at PID 50-74. CONCLUSION: Local tumor resection might augment the growth of synchronous microscopic metastases. Our results provide insights into the appropriate timing of local resection for high-risk neuroblastoma.

Modeling of Nanomachine/Micromachine Crowds: Interplay between the Internal State and Surroundings.

The activity of biological cells is primarily based on chemical reactions and typically modeled as a reaction-diffusion system. Cells are, however, highly crowded with macromolecules, including a variety of molecular machines such as enzymes. The working cycles of these machines are often coupled with their internal motion (conformational changes). In the crowded environment of a cell, motion interference between neighboring molecules is not negligible, and this interference can affect the reaction dynamics through machine operation. To simulate such a situation, we propose a reaction-diffusion model consisting of particles whose shape depends on an internal state variable, for crowds of nano- to micromachines. The interference between nearby particles is naturally introduced through excluded volume repulsion. In the simulations, we observed segregation and flow-like patterns enhanced by crowding out of relevant molecules, as well as molecular synchronization waves and phase transitions. The presented model is simple and extensible for diverse molecular machinery and may serve as a framework to study the interplay between the mechanical stress/strain network and the chemical reaction network in the cell. Applications to more macroscopic systems, e.g., crowds of cells, are also discussed.

Coarse-Grained Protein Dynamics Studies Using Elastic Network Models.

Elastic networks have been used as simple models of proteins to study their slow structural dynamics. They consist of point-like particles connected by linear Hookean springs and hence are convenient for linear normal mode analysis around a given reference structure. Furthermore, dynamic simulations using these models can provide new insights. As the computational cost associated with these models is considerably lower compared to that of all-atom models, they are also convenient for comparative studies between multiple protein structures. In this review, we introduce examples of coarse-grained molecular dynamics studies using elastic network models and their derivatives, focusing on the nonlinear phenomena, and discuss their applicability to large-scale macromolecular assemblies.

Designed Elastic Networks: Models of Complex Protein Machinery.

Recently, the design of mechanical networks with protein-inspired responses has become increasingly popular. Here, we review contributions which were motivated by studies of protein dynamics employing coarse-grained elastic network models. First, the concept of evolutionary optimization that we developed to design network structures which execute prescribed tasks is explained. We then review what presumably marks the origin of the idea to design complex functional networks which encode protein-inspired behavior, namely the design of an elastic network structure which emulates the cycles of ATP-powered conformational motion in protein machines. Two recent applications are reviewed. First, the construction of a model molecular motor, whose operation incorporates both the tight coupling power stroke as well as the loose coupling Brownian ratchet mechanism, is discussed. Second, the evolutionary design of network structures which encode optimal long-range communication between remote sites and represent mechanical models of allosteric proteins is presented. We discuss the prospects of designed protein-mimicking elastic networks as model systems to elucidate the design principles and functional signatures underlying the operation of complex protein machinery.

Single-molecule diffusion-based estimation of ligand effects on G protein-coupled receptors.

G protein-coupled receptors (GPCRs) are major drug targets. Developing a method to measure the activities of GPCRs is essential for pharmacology and drug screening. However, it is difficult to measure the effects of a drug by monitoring the receptor on the cell surface; thus, changes in the concentrations of downstream signaling molecules, which depend on the signaling pathway selectivity of the receptor, are often used as an index of receptor activity. We show that single-molecule imaging analysis provides an alternative method for assessing the effects of ligands on GPCRs. Using total internal reflection fluorescence microscopy (TIRFM), we monitored the dynamics of the diffusion of metabotropic glutamate receptor 3 (mGluR3), a class C GPCR, under various ligand conditions. Our single-molecule tracking analysis demonstrated that increases and decreases in the average diffusion coefficient of mGluR3 quantitatively reflected the ligand-dependent inactivation and activation of receptors, respectively. Through experiments with inhibitors and dual-color single-molecule imaging analysis, we found that the diffusion of receptor molecules was altered by common physiological events associated with GPCRs, including G protein binding, and receptor accumulation in clathrin-coated pits. We also confirmed that agonist also decreased the average diffusion coefficient for class A and B GPCRs, demonstrating that this parameter is a good index for estimating ligand effects on many GPCRs regardless of their phylogenetic groups, the chemical properties of the ligands, or G protein-coupling selectivity.

The 1-Particle-per-k-Nucleotides (1PkN) Elastic Network Model of DNA Dynamics with Sequence-Dependent Geometry.

Coarse-grained models of DNA have made important contributions to the determination of the physical properties of genomic DNA, working as a molecular machine for gene regulation. In this study, to analyze the global dynamics of long DNA sequences with consideration of sequence-dependent geometry, we propose elastic network models of DNA where each particle represents k nucleotides (1-particle-per-k-nucleotides, 1PkN). The models were adjusted according to profiles of the anisotropic fluctuations obtained from our previous 1-particle-per-1-nucleotide (1P1N) model, which was proven to reproduce such profiles of all-atom models. We confirmed that the 1P3N and 1P4N models are suitable for the analysis of detailed dynamics such as local twisting motion. The models are intended for the analysis of large structures, e.g., 10-nm fibers in the nucleus, and nucleoids of mitochondrial or phage DNA at low computational costs. As an example, we surveyed the physical characteristics of the whole mitochondrial human and Plasmodium falciparum genomes.

Bridging the dynamics and organization of chromatin domains by mathematical modeling.

The genome is 3-dimensionally organized in the cell, and the mammalian genome DNA is partitioned into submegabase-sized chromatin domains. Genome functions are regulated within and across the domains according to their organization, whereas the chromatin itself is highly dynamic. However, the details of such dynamic organization of chromatin domains in living cells remain unclear. To unify chromatin dynamics and organization, we recently demonstrated that structural information of chromatin domains in living human cells can be extracted from analyses of the subdiffusive nucleosome movement using mathematical modeling. Our mathematical analysis suggested that as the chromatin domain becomes smaller and more compact, nucleosome movement becomes increasingly restricted. Here, we show the implication of these results for bridging the gap between chromatin dynamics and organization, and provide physical insight into chromatin domains as efficient units to conduct genome functions in the thermal noisy environment of the cell.

Non-RVD mutations that enhance the dynamics of the TAL repeat array along the superhelical axis improve TALEN genome editing efficacy.

Transcription activator-like effector (TALE) nuclease (TALEN) is widely used as a tool in genome editing. The DNA binding part of TALEN consists of a tandem array of TAL-repeats that form a right-handed superhelix. Each TAL-repeat recognises a specific base by the repeat variable diresidue (RVD) at positions 12 and 13. TALEN comprising the TAL-repeats with periodic mutations to residues at positions 4 and 32 (non-RVD sites) in each repeat (VT-TALE) exhibits increased efficacy in genome editing compared with a counterpart without the mutations (CT-TALE). The molecular basis for the elevated efficacy is unknown. In this report, comparison of the physicochemical properties between CT- and VT-TALEs revealed that VT-TALE has a larger amplitude motion along the superhelical axis (superhelical motion) compared with CT-TALE. The greater superhelical motion in VT-TALE enabled more TAL-repeats to engage in the target sequence recognition compared with CT-TALE. The extended sequence recognition by the TAL-repeats improves site specificity with limiting the spatial distribution of FokI domains to facilitate their dimerization at the desired site. Molecular dynamics simulations revealed that the non-RVD mutations alter inter-repeat hydrogen bonding to amplify the superhelical motion of VT-TALE. The TALEN activity is associated with the inter-repeat hydrogen bonding among the TAL repeats.

Dynamic Nucleosome Movement Provides Structural Information of Topological Chromatin Domains in Living Human Cells.

The mammalian genome is organized into submegabase-sized chromatin domains (CDs) including topologically associating domains, which have been identified using chromosome conformation capture-based methods. Single-nucleosome imaging in living mammalian cells has revealed subdiffusively dynamic nucleosome movement. It is unclear how single nucleosomes within CDs fluctuate and how the CD structure reflects the nucleosome movement. Here, we present a polymer model wherein CDs are characterized by fractal dimensions and the nucleosome fibers fluctuate in a viscoelastic medium with memory. We analytically show that the mean-squared displacement (MSD) of nucleosome fluctuations within CDs is subdiffusive. The diffusion coefficient and the subdiffusive exponent depend on the structural information of CDs. This analytical result enabled us to extract information from the single-nucleosome imaging data for HeLa cells. Our observation that the MSD is lower at the nuclear periphery region than the interior region indicates that CDs in the heterochromatin-rich nuclear periphery region are more compact than those in the euchromatin-rich interior region with respect to the fractal dimensions as well as the size. Finally, we evaluated that the average size of CDs is in the range of 100-500 nm and that the relaxation time of nucleosome movement within CDs is a few seconds. Our results provide physical and dynamic insights into the genome architecture in living cells.