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.     

How strong is the edge effect in the adsorption of anticancer drugs on a graphene cluster?

The adsorption of nucleobase-analog anticancer drugs (fluorouracil, thioguanine, and mercaptopurine) on a graphene flake (C₅₄H₁₈) was investigated by shifting the site at which adsorption occurs from one end of the sheet to the other end. The counterpoise-corrected M06-2X/cc-pVDZ binding energies revealed that the binding stability decreases in the sequence thioguanine?>?mercaptopurine?>?fluorouracil. We found that adsorption near the middle of the sheet is more favorable than adsorption near the edge due to the edge effect. This edge effect is stronger for the adsorption of thioguanine or mercaptopurine than for fluorouracil adsorption. However, the edge effect reduces the binding energy of the drug to the flake by only a small amount, <5 kcal/mol, depending on the adsorption site and the alignment of the drug at this site.     

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.

     

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.     

Strategies for Genomic and Proteomic Profiling of Cancers

Omics-based technology platforms have made new kinds of cancer profiling tests feasible. There are several valuable examples in clinical practice, and many more under development. A concerted, transparent process of discovery with lock-down of candidate assays and classifiers and clear specification of intended clinical use is essential. The Institute of Medicine has now proposed a three-stage scheme of confirming and validating analytical findings, validating performance on clinical specimens, and demonstrating explicit clinical utility for an approvable test (Micheel et al., Evolution of translational omics: lessons learned and path forward, 2012).     

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.