When studying multi-body protein complexes, biochemists use computational tools that can suggest hundreds or thousands of their possible spatial configurations. However, it is not feasible to experimentally verify more than only a very small subset of them. In this paper, we propose a novel multiscale visual drilldown approach that was designed in tight collaboration with proteomic experts, enabling a systematic exploration of the configuration space. Our approach takes advantage of the hierarchical structure of the data -- from the whole ensemble of protein complex configurations to the individual configurations, their contact interfaces, and the interacting amino acids. Our new solution is based on interactively linked 2D and 3D views for individual hierarchy levels and at each level, we offer a set of selection and filtering operations enabling the user to narrow down the number of configurations that need to be manually scrutinized. Furthermore, we offer a dedicated filter interface, which provides the users with an overview of the applied filtering operations and enables them to examine their impact on the explored ensemble. This way, we maintain the history of the exploration process and thus enable the user to return to an earlier point of the exploration. We demonstrate the effectiveness of our approach on two case studies conducted by collaborating proteomic experts.
Although these schematic representations are conveying the information about a single configuration, they do not support the comparison and interactive filtering of entire ensembles of configurations. This issue is addressed in the CoCoMaps @cite_22 and COZOID @cite_25 tools. Both tools come with linked visualizations, aiding the users in analyzing and comparing interactions between protein pairs. CoCoMaps and its successor CONS-COCOMAPS @cite_1 enable to measure and visualize the consensus in multiple docking solutions and display the conservation of residue contacts using intermolecular contact maps. The COZOID tool uses a set of linked views for the interactive exploration of large ensembles of protein pairs, supporting a visual drilldown approach for narrowing down the set of possibly relevant configurations. The main limitation of these approaches is that they are operating only on protein pairs (i.e., single ) and cannot be directly applied to multi-body complexes. The multiscale aspect in molecular visualization can be explored on different granularity levels, as shown in the recent survey of @cite_6 .
@cite_1 @cite_25 @cite_22 @cite_6
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Summary: Herein we present COCOMAPS, a novel tool for analyzing, visualizing and comparing the interface in protein-protein and protein-nucleic acids complexes. COCOMAPS combines traditional analyses and 3D visualization of the interface with the effectiveness of intermolecular contact maps. Availability: COCOMAPS is accessible as a public web tool at http: www.molnac.unisa.it BioTools cocomaps Systems biologists and cancer researchers require interactive visualization tools that enable them to more easily navigate and discover patterns at different levels of the biological hierarchy of signaling pathways. Furthermore, biologists are often interested in understanding and exploring the causal biochemical links between processes. When exploring the literature of particular biological pathways or specific proteins within those pathways, biologists find it useful to know the contexts in which biochemical links are active and, importantly, to be aware of potential conflicts when different experiments introduce alternative interpretations of the function of a pathway or biochemical reaction. We introduce BioLinker, a interactive visualization system that helps users to perform bottom-up exploration of complex protein interaction networks. Five interconnected views provide the user with a range of ways to explore pathway data, including views that show potential conflicts within pathway databases and publications and that highlight contextual information about individual proteins. Additionally, we discuss system details to show how our system manages the large amount of protein interactions extracted from the literature of biological pathways. The exploration and analysis of scientific literature collections is an important task for effective knowledge management. Past interest in such document sets has spurred the development of numerous visualization approaches for their interactive analysis. They either focus on the textual content of publications, or on document metadata including authors and citations. Previously presented approaches for citation analysis aim primarily at the visualization of the structure of citation networks and their exploration. We extend the state-of-the-art by presenting an approach for the interactive visual analysis of the contents of scientific documents, and combine it with a new and flexible technique to analyze their citations. This technique facilitates user-steered aggregation of citations which are linked to the content of the citing publications using a highly interactive visualization approach. Through enriching the approach with additional interactive views of other important aspects of the data, we support the exploration of the dataset over time and enable users to analyze citation patterns, spot trends, and track long-term developments. We demonstrate the strengths of our approach through a use case and discuss it based on expert user feedback. The molecular docking problem is to determine how molecules interact with other molecules and plays a key role in understanding how cells function. DockingShop is an integrated environment far interactively steering molecular docking by navigating a ligand or protein to the receptor's estimated binding site. This tool provides a graphical interface for molecular modeling featuring real-time visual guides, interactive manipulation, navigation, optimization, and dynamic visualization enabling users to apply their biological knowledge to steer the docking process.