Improving the iterative Linear Interaction Energy approach using automated recognition of configurational transitions

Recently an iterative method was proposed to enhance the accuracy and efficiency of ligand-protein binding affinity prediction through linear interaction energy (LIE) theory. For ligand binding to flexible Cytochrome P450s (CYPs), this method was shown to decrease the root-mean-square error and standard deviation of error prediction by combining interaction energies of simulations starting from different conformations. Thereby, different parts of protein-ligand conformational space are sampled in parallel simulations. The iterative LIE framework relies on the assumption that separate simulations explore different local parts of phase space, and do not show transitions to other parts of configurational space that are already covered in parallel simulations. In this work, a method is proposed to (automatically) detect such transitions during the simulations that are performed to construct LIE models and to predict binding affinities. Using noise-canceling techniques and splines to fit time series of the raw data for the interaction energies, transitions during simulation between different parts of phase space are identified. Boolean selection criteria are then applied to determine which parts of the interaction energy trajectories are to be used as input for the LIE calculations. Here we show that this filtering approach benefits the predictive quality of our previous CYP 2D6-aryloxypropanolamine LIE model. In addition, an analysis is performed of the gain in computational efficiency that can be obtained from monitoring simulations using the proposed filtering method and by prematurely terminating simulations accordingly.     

Molecular dynamics simulations of a lithium/sodium carbonate mixture

The diffusion and ionic conductivity of Li _x Na_1?x CO₃ salt mixtures were studied by means of Molecular Dynamics (MD) simulations, using the Janssen and Tissen model (Janssen and Tissen, Mol Simul 5:83–98; 1990). These salts have received particular attention due to their central role in fuel cells technology, and reliable numerical methods that could perform as important interpretative tool of experimental data are thus required but still lacking. The chosen computational model nicely reproduces the main structural behaviour of the pure Li₂CO₃, Na₂CO₃ and K₂CO₃ carbonates, but also of their Li/K and Li/Na mixtures. However, it fails to accurately describe dynamic properties such as activation energies of diffusion and conduction processes, outlining the need to develop more accurate models for the simulation of molten salt carbonates.     

Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways

Background

Cellular responses to extracellular perturbations require signaling pathways to capture and transmit the signals. However, the underlying molecular mechanisms of signal transduction are not yet fully understood, thus detailed and comprehensive models may not be available for all the signaling pathways. In particular, insufficient knowledge of parameters, which is a long-standing hindrance for quantitative kinetic modeling necessitates the use of parameter-free methods for modeling and simulation to capture dynamic properties of signaling pathways.

Results

We present a computational model that is able to simulate the graded responses to degradations, the sigmoidal biological relationships between signaling molecules and the effects of scheduled perturbations to the cells. The simulation results are validated using experimental data of protein phosphorylation, demonstrating that the proposed model is capable of capturing the main trend of protein activities during the process of signal transduction. Compared with existing simulators, our model has better performance on predicting the state transitions of signaling networks.

Conclusion

The proposed simulation tool provides a valuable resource for modeling cellular signaling pathways using a knowledge-based method.

     

Prediction of drugs having opposite effects on disease genes in a directed network

Background

Developing novel uses of approved drugs, called drug repositioning, can reduce costs and times in traditional drug development. Network-based approaches have presented promising results in this field. However, even though various types of interactions such as activation or inhibition exist in drug-target interactions and molecular pathways, most of previous network-based studies disregarded this information.

Methods

We developed a novel computational method, Prediction of Drugs having Opposite effects on Disease genes (PDOD), for identifying drugs having opposite effects on altered states of disease genes. PDOD utilized drug-drug target interactions with ‘effect type’, an integrated directed molecular network with ‘effect type’ and ‘effect direction’, and disease genes with regulated states in disease patients. With this information, we proposed a scoring function to discover drugs likely to restore altered states of disease genes using the path from a drug to a disease through the drug-drug target interactions, shortest paths from drug targets to disease genes in molecular pathways, and disease gene-disease associations.

Results

We collected drug-drug target interactions, molecular pathways, and disease genes with their regulated states in the diseases. PDOD is applied to 898 drugs with known drug-drug target interactions and nine diseases. We compared performance of PDOD for predicting known therapeutic drug-disease associations with the previous methods. PDOD outperformed other previous approaches which do not exploit directional information in molecular network. In addition, we provide a simple web service that researchers can submit genes of interest with their altered states and will obtain drugs seeming to have opposite effects on altered states of input genes at http://gto.kaist.ac.kr/pdod/index.php/main.

Conclusions

Our results showed that ‘effect type’ and ‘effect direction’ information in the network based approaches can be utilized to identify drugs having opposite effects on diseases. Our study can offer a novel insight into the field of network-based drug repositioning.

     

The D Domain of LRRC4 anchors ERK1/2 in the cytoplasm and competitively inhibits MEK/ERK activation in glioma cells

Background

As a well-characterized key player in various signal transduction networks, extracellular-signal-regulated kinase (ERK1/2) has been widely implicated in the development of many malignancies. We previously found that Leucine-rich repeat containing 4 (LRRC4) was a tumor suppressor and a negative regulator of the ERK/MAPK pathway in glioma tumorigenesis. However, the precise molecular role of LRRC4 in ERK signal transmission is unclear.

Methods

The interaction between LRRC4 and ERK1/2 was assessed by co-immunoprecipitation and GST pull-down assays in vivo and in vitro. We also investigated the interaction of LRRC4 and ERK1/2 and the role of the D domain in ERK activation in glioma cells.

Results

Here, we showed that LRRC4 and ERK1/2 interact via the D domain and CD domain, respectively. Following EGF stimuli, the D domain of LRRC4 anchors ERK1/2 in the cytoplasm and abrogates ERK1/2 activation and nuclear translocation. In glioblastoma cells, ectopic LRRC4 expression competitively inhibited the interaction of endogenous mitogen-activated protein kinase (MEK) and ERK1/2. Mutation of the D domain decreased the LRRC4-mediated inhibition of MAPK signaling and its anti-proliferation and anti-invasion roles.

Conclusions

Our results demonstrated that the D domain of LRRC4 anchors ERK1/2 in the cytoplasm and competitively inhibits MEK/ERK activation in glioma cells. These findings identify a new mechanism underlying glioblastoma progression and suggest a novel therapeutic strategy by restoring the activity of LRRC4 to decrease MAPK cascade activation.

     

Identification of CR1 retroposons in Arborophila rufipectus and their application to Phasianidae phylogeny

Chicken repeat 1 (CR1), a member of non‐LTR retroposon, is an important phylogenetic marker in avian systematics. In this study, we reported several characteristics of CR1 elements in a draft genome of Arborophila rufipectus (Sichuan partridge). According to the analyses of RepeatMasker, approximately 254 966 CR1 elements were identified in A. rufipectus, covering 6.7% of the genome. Subsequently, we selected eighteen novel CR1 elements by comparing the chicken genome, turkey genome and assembled A. rufipectus scaffolds. Here, a combined data set comprising of 22 CR1 loci, mitochondrial genomes and eight unlinked introns was analysed to infer the evolutionary relationships of twelve Phasianidae species. The applicability of CR1 sequences for inferring avian phylogeny relative to mtDNA and intron sequences was investigated as well. Our results elucidated the position of A. rufipectus in Phasianidae with robust supports that it presented a sister clade to Arborophila ardens/Arborophila brunneopectus, and implied that genus Arborophila was in a basal phylogenetic position within Phasianidae and a phylogenetic affinity between Meleagris gallopavo and Pucrasia macrolopha. Therefore, this work not only resolved some of the confounding relationships among Phasianidae, but also suggested CR1 sequences could provide powerful complementary data for phylogeny reconstruction.     

Seasonal change in xylem growth of <Emphasis Type="Italic">Pinus densiflora</Emphasis> in central Japan

This study investigated the seasonal change in xylem growth of Japanese red pine (Pinus densiflora). Wood cores were sampled at 2-week intervals from April to November in 2012 using the microcoring method. Daily increment rates of tracheid number and tree-ring width were compared with seasonal changes in daily mean temperature and photoperiod. Xylem growth started in early to late May and stopped in late October to early November. The maximum daily increment rates of tracheid number and tree-ring width were in early July. The 95 % confidence intervals of the timing of the maximum daily increment rates included the summer solstice (23 June) with the longest photoperiod, but not the warmest day (30 July). The maximum daily increment rate of xylem growth is thought to be controlled by the photoperiod rather than by temperature. The daily mean temperature exceeded 20 °C after the summer solstice, indicating that temperature is not a limiting factor for xylem growth. This study suggests that the timing of maximum daily increment rates of xylem growth of P. densiflora is controlled by the photoperiod.     

Enhanced sampling simulations to construct free-energy landscape of protein–partner substrate interaction

Molecular dynamics (MD) simulations using all-atom and explicit solvent models provide valuable information on the detailed behavior of protein–partner substrate binding at the atomic level. As the power of computational resources increase, MD simulations are being used more widely and easily. However, it is still difficult to investigate the thermodynamic properties of protein–partner substrate binding and protein folding with conventional MD simulations. Enhanced sampling methods have been developed to sample conformations that reflect equilibrium conditions in a more efficient manner than conventional MD simulations, thereby allowing the construction of accurate free-energy landscapes. In this review, we discuss these enhanced sampling methods using a series of case-by-case examples. In particular, we review enhanced sampling methods conforming to trivial trajectory parallelization, virtual-system coupled multicanonical MD, and adaptive lambda square dynamics. These methods have been recently developed based on the existing method of multicanonical MD simulation. Their applications are reviewed with an emphasis on describing their practical implementation. In our concluding remarks we explore extensions of the enhanced sampling methods that may allow for even more efficient sampling.