Systems Biology of the Structural Proteome

The complementarity of molecular-level and systems-level data types has led to the integration of protein structurally-derived data, such as melting temperature and the biological assembly of proteins, into genome-scale models. Such efforts have opened up new vistas in systems biology research and have empowered applications in evolutionary biology as well as in systems pharmacology. Using genome-scale models of metabolism (GEMs), we link metabolic enzyme activities to characteristics of observed phenotypes, whereas, using structural biology, we link molecular interaction details (e.g. protein-ligand binding affinities) to the activities of enzymes. The genome-scale models with protein structures (GEM-PRO) framework, thus, gives a direct mapping of gene to transcript, to protein structure, to biochemical reaction, to network states, and finally to phenotype. Understanding the structural properties of proteins as well as their respective ligand binding events (e.g. metabolite, drug or oncometabolite) enables the characterization of molecular-level events that trigger changes in states of an entire network. Such a multi-scale approach acts as bridge between systems biology and structural biology, which are two scientific disciplines that, when combined, become the emerging field of structural systems biology.

In recent years, the number of publicly available structures of biological macromolecules has grown to more than 105,000 entries, and continues to increase yearly by roughly 10%. The increasing availability of protein structural data brings about a number of implications for GEM-PRO models. First, to keep pace with the deluge of protein data coming from experiments, there is a developing need for pipelines that use systematic mapping and quality assurance processes to read, filter and process all deposited structures, ultimately managing relevant data in an easy-to-use knowledgebase. Second, to aid in the dissemination and further development of these resources, growing datasets and pipelines should be developed together with in silico tools that increase data accessibility and sufficient training. Finally, increasing protein structural data enhances the predictive scope of systems biology research; the more description we have of the biological components involved in complex systems, the more we can understand cellular processes that span a wide range of biological, chemical, and structural detail. Here, we demonstrate how linking protein structural data to GEMs has enabled the generation, dissemination and application of GEM-PRO for studying two contemporary organisms, Thermotoga maritima and Escherichia coli (E. coli, MG1655) as well for 55 different Escherichia coli and Shigella strains. Furthermore, we demonstrate the utility of GEM-PRO in addressing important aspects of the functional differences between species, which are not only derived from differences in genetic components but also from their molecular (proteome) landscapes.