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.
Most of the currently available computational tools for protein-protein interactions are focusing on protein pairs and a comprehensible overview was published by Huang @cite_4 . Some of the existing approaches, such as ArDock @cite_29 , already combine the computational method with a basic visual representation of the predictions. There are even some solutions, such as DockingShop @cite_28 , which are enabling the user to interactively design an initial configuration for a protein docking prediction process through a molecular graphics interface.
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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. Proteinprotein docking is attracting increasing attention in drug discovery research targeting proteinprotein interactions, owing to its potential in predicting proteinprotein interactions and identifying hot spot residues at the proteinprotein interface. Given the relative lack of information about binding sites and the fact that proteins are generally larger than ligand, the search algorithms and evaluation methods for proteinprotein docking differ somewhat from those for proteinligand docking and, hence, require different research strategies. Here, we review the basic concepts, principles and advances of current search strategies and evaluation methods for proteinprotein docking. We also discuss the current challenges and limitations, as well as future directions, of established approaches. Background The development of accurate protein-protein docking programs is making this kind of simulations an effective tool to predict the 3D structure and the surface of interaction between the molecular partners in macromolecular complexes. However, correctly scoring multiple docking solutions is still an open problem. As a consequence, the accurate and tedious screening of many docking models is usually required in the analysis step.