Dynamics and Function of sRNA/mRNAs Under the Scrutiny of Computational Simulation Methods.

Molecular dynamics simulations have proved extremely useful in investigating the functioning of proteins with atomic-scale resolution. Many applications to the study of RNA also exist, and their number increases by the day. However, implementing MD simulations for RNA molecules in solution faces challenges that the MD practitioner must be aware of for the appropriate use of this tool. In this chapter, we present the fundamentals of MD simulations, in general, and the peculiarities of RNA simulations, in particular. We discuss the strengths and limitations of the technique and provide examples of its application to elucidate small RNA's performance.

Importance of genetic parameters and uncertainty of MANIHOT, a new mechanistic cassava simulation model.

We identified the most sensitive genotype-specific parameters (GSPs) and their contribution to the uncertainty of the MANIHOT simulation model. We applied a global sensitivity and uncertainty analysis (GSUA) of the GSPs to the simulation outputs for the cassava development, growth, and yield in contrasting environments. We compared enhanced Sampling for Uniformity, a qualitative screening method new to crop simulation modeling, and Sobol, a quantitative, variance-based method. About 80% of the GSPs contributed to most of the variation in maximum leaf area index (LAI), yield, and aboveground biomass at harvest. Relative importance of the GSPs varied between warm and cool temperatures but did not differ between rainfed and no water limitation conditions. Interactions between GSPs explained 20% of the variance in simulated outputs. Overall, the most important GSPs were individual node weight, radiation use efficiency, and maximum individual leaf area. Base temperature for leaf development was more important for cool compared to warm temperatures. Parameter uncertainty had a substantial impact on model predictions in MANIHOT simulations, with the uncertainty 2-5 times larger for warm compared to cool temperatures. Identification of important GSPs provides an objective way to determine the processes of a simulation model that are critical versus those that have little relevance.

Integrating Molecular Docking and Molecular Dynamics Simulations.

Computational methods, applied at the early stages of the drug design process, use current technology to provide valuable insights into the understanding of chemical systems in a virtual manner, complementing experimental analysis. Molecular docking is an in silico method employed to foresee binding modes of small compounds or macromolecules in contact with a receptor and to predict their molecular interactions. Moreover, the methodology opens up the possibility of ranking these compounds according to a hierarchy determined using particular scoring functions. Docking protocols assign many approximations, and most of them lack receptor flexibility. Therefore, the reliability of the resulting protein-ligand complexes is uncertain. The association with the costly but more accurate MD techniques provides significant complementary with docking. MD simulations can be used before docking since a series of "new" and broader protein conformations can be extracted from the processing of the resulting trajectory and employed as targets for docking. They also can be utilized a posteriori to optimize the structures of the final complexes from docking, calculate more detailed interaction energies, and provide information about the ligand binding mechanism. Here, we focus on protocols that offer the docking-MD combination as a logical approach to improving the drug discovery process.

Interactions between Triterpenes and a P-I Type Snake Venom Metalloproteinase: Molecular Simulations and Experiments.

Small molecule inhibitors of snake venom metalloproteinases (SVMPs) could provide a means to rapidly halt the progression of local tissue damage following viperid snake envenomations. In this study, we examine the ability of candidate compounds based on a pentacyclic triterpene skeleton to inhibit SVMPs. We leverage molecular dynamics simulations to estimate the free energies of the candidate compounds for binding to BaP1, a P-I type SVMP, and compare these results with experimental assays of proteolytic activity inhibition in a homologous enzyme (Batx-I). Both simulation and experiment suggest that betulinic acid is the most active candidate, with the simulations predicting a standard binding free energy of Δ G ∘ = - 11.0 ± 1.4 kcal/mol. The simulations also reveal the atomic interactions that underlie binding between the triterpenic acids and BaP1, most notably the electrostatic interaction between carboxylate groups of the compounds and the zinc cofactor of BaP1. Together, our simulations and experiments suggest that occlusion of the S1 ' subsite is essential for inhibition of proteolytic activity. While all active compounds make hydrophobic contacts in the S1 ' site, ß -boswellic acid, with its distinct carboxylate position, does not occlude the S1 ' site in simulation and exhibits negligible activity in experiment.