RiboVision is a visualization and analysis tool for the simultaneous display of multiple layers of diverse information on primary (1D), secondary (2D), and three-dimensional (3D) structures of ribosomes. The ribosome is a macromolecular complex containing ribosomal RNA and ribosomal proteins and is a key component of life responsible for the synthesis of proteins in all living organisms. RiboVision is intended for rapid retrieval, analysis, filtering, and display of a variety of ribosomal data. Preloaded information includes 1D, 2D, and 3D structures augmented by base-pairing, base-stacking, and other molecular interactions. RiboVision is preloaded with rRNA secondary structures, rRNA domains and helical structures, phylogeny, crystallographic thermal factors, etc. RiboVision contains structures of ribosomal proteins and a database of their molecular interactions with rRNA. RiboVision contains preloaded structures and data for two bacterial ribosomes (Thermus thermophilus and Escherichia coli), one archaeal ribosome (Haloarcula marismortui), and three eukaryotic ribosomes (Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens). RiboVision revealed several major discrepancies between the 2D and 3D structures of the rRNAs of the small and large subunits (SSU and LSU). Revised structures mapped with a variety of data are available in RiboVision as well as in a public gallery (). RiboVision is designed to allow users to distill complex data quickly and to easily generate publication-quality images of data mapped onto secondary structures. Users can readily import and analyze their own data in the context of other work. This package allows users to import and map data from CSV files directly onto 1D, 2D, and 3D levels of structure. RiboVision has features in rough analogy with web-based map services capable of seamlessly switching the type of data displayed and the resolution or magnification of the display. RiboVision is available at .

In recent years, various virtual screening (VS) tools have been developed, and many successful screening campaigns have been showcased. However, whether by conventional molecular docking or pharmacophore screening, the selection of virtual hits is based on the ranking of compounds by scoring functions or fit values, which remains the bottleneck of VS due to insufficient accuracy. As the limitations of individual methods persist, a comprehensive comparison and integration of different methods may provide insights into selecting suitable methods for VS. Here, we evaluated the performance of molecular docking, fingerprint-based 2D similarity and multicomplex pharmacophore in an individual and a combined manner, through a retrospective VS study on VEGFR-2 inhibitors. An integrated two-layer workflow was developed and validated through VS of VEGFR-2 inhibitors against the DUD-E database, which demonstrated improved VS performance through a ligand-based method ECFP_4, followed by molecular docking, and then a strict multicomplex pharmacophore. Through a retrospective comparison with six published papers, this integrated approach outperformed 43 out of 45 methods, indicating a great effectiveness. This kind of integrated VS approach can be extended to other targets for the screening and discovery of inhibitors.

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c-Met has been considered as an attractive target for developing antitumor agents. The highly selective c-Met inhibitors provide invaluable opportunities for the combination with other therapies safely to achieve the optimal efficacy. In this work, a series of triazolopyrazine c-Met inhibitors with exquisitely selectivity were investigated using a combination of molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR), and molecular dynamics simulation. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models were developed to reveal the structural determinants for c-Met inhibition. Both models were validated to have high reliability and predictability, and contour map analysis suggested feature requirements for different substituents on the scaffold. It is worth noting that an important hydrogen bond rich region was identified in the unique narrow channel, which is distinct from other kinases. Molecular dynamics simulations and binding free energy calculations provided further support that suitable groups in this hydrogen bond rich region made great contributions to the binding of ligands. Moreover, hydrogen bonds with residues of the narrow channel were also indicated to be essential to improve the activity and selectivity. This study will facilitate the discovery and optimization of novel c-Met inhibitors with higher activity and selectivity.