Interactive exploration of ligand transportation through protein tunnels

From complex data to clear insights: visualizing molecular dynamics trajectories

Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.

sMolBoxes: Dataflow Model for Molecular Dynamics Exploration

We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case studies illustrate that even with relatively few sMolBoxes, it is possible to express complex analytical tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.

Engineering of Cyclodextrin Glycosyltransferase through a Size/Polarity Guided Triple-Code Strategy with Enhanced α-Glycosyl Hesperidin Synthesis Ability

Hesperidin, a flavonoid enriched in citrus peel, can be enzymatically glycosylated using CGTase with significantly improved water solubility. However, the reaction catalyzed by wild-type CGTase is rather inefficient, reflected in the poor production rate and yield. By focusing on the aglycon attacking step, seven residues were selected for mutagenesis in order to improve the transglycosylation efficiency. Due to the lack of high-throughput screening technology regarding to the studied reaction, we developed a size/polarity guided triple-code strategy in order to reduce the library size. The selected residues were replaced by three rationally chosen amino acids with either changed size or polarity, leading to an extremely condensed library with only 32 mutants to be screened. Twenty-five percent of the constructed mutants were proved to be positive, suggesting the high quality of the constructed library. Specific transglycosylation activity of the best mutant Y217F was assayed to be 935.7 U/g, and its k_cat/K_mA is 6.43 times greater than that of the wild type. Homology modeling and docking computation suggest the source of notably enhanced catalytic efficiency is resulted from the combination of ligand transfer and binding effect. IMPORTANCE Size/polarity guided triple-code strategy, a novel semirational mutagenesis strategy, was developed in this study and employed to engineer the aglycon attacking site of CGTase. Screening pressure was set as improved hesperidin glucoside synthesis ability, and eight positive mutants were obtained by screening only 32 mutants. The high quality of the designed library confirms the effectiveness of the developed strategy is potentially valuable to future mutagenesis studies. Mechanisms of positive effect were explained. The best mutant exhibits 6.43 times enhanced k_cat/K_mA value and confirmed to be a superior whole-cell catalyst with potential application value in synthesizing hesperidin glucosides.

Visualization of Large Molecular Trajectories

The analysis of protein-ligand interactions is a time-intensive task. Researchers have to analyze multiple physico-chemical properties of the protein at once and combine them to derive conclusions about the protein-ligand interplay. Typically, several charts are inspected, and 3D animations can be played side-by-side to obtain a deeper understanding of the data. With the advances in simulation techniques, larger and larger datasets are available, with up to hundreds of thousands of steps. Unfortunately, such large trajectories are very difficult to investigate with traditional approaches. Therefore, the need for special tools that facilitate inspection of these large trajectories becomes substantial. In this paper, we present a novel system for visual exploration of very large trajectories in an interactive and user-friendly way. Several visualization motifs are automatically derived from the data to give the user the information about interactions between protein and ligand. Our system offers specialized widgets to ease and accelerate data inspection and navigation to interesting parts of the simulation. The system is suitable also for simulations where multiple ligands are involved. We have tested the usefulness of our tool on a set of datasets obtained from protein engineers, and we describe the expert feedback.