Computational 3D imaging to quantify structural components and assembly of protein networks

Abstract

Traditionally, protein structures have been described by the secondary structure architecture and fold arrangement. However, the relatively novel method of 3D confocal microscopy of fluorescent-protein-tagged networks in living cells allows resolving the detailed spatial organization of these networks. This provides new possibilities to predict network functionality, as structure and function seem to be linked at various scales. Here, we propose a quantitative approach using 3D confocal microscopy image data to describe protein networks based on their nano-structural characteristics. This analysis is constructed in four steps: (i) Segmentation of the microscopic raw data into a volume model and extraction of a spatial graph representing the protein network. (ii) Quantifying protein network gross morphology using the volume model. (iii) Quantifying protein network components using the spatial graph. (iv) Linking these two scales to obtain insights into network assembly. Here, we quantitatively describe the filamentous temperature sensitive Z protein network of the moss Physcomitrella patens and elucidate relations between network size and assembly details. Future applications will link network structure and functionality by tracking dynamic structural changes over time and comparing different states or types of networks, possibly allowing more precise identification of (mal) functions or the design of protein-engineered biomaterials for applications in regenerative medicine. Statement of SignificanceProtein networks are highly complex and dynamic structures that play various roles in biological environments. Analyzing the detailed spatial structure of these networks may lead to new insight into biological functions and malfunctions. Here, we propose a tool set that extracts structural information at two scales of the protein network and allows therefore to address questions such as “how is the network built?” or “how networks grow?”.