Core elements of cell regulation are made up of protein-protein interaction (PPI) networks. However, many
parts of the cell regulatory systems include unknown PPIs. To approach this problem, we have developed a computational
method of high-throughput PPI network prediction based on all-to-all rigid-body docking of protein tertiary structures.
The prediction system accepts a set of data comprising protein tertiary structures as input and generates a list of possible
interacting pairs from all the combinations as output. A crucial advantage of this docking based method is in providing
predictions of protein pairs that increases our understanding of biological pathways by analyzing the structures of candidate
complex structures, which gives insight into novel interaction mechanisms. Although such exhaustive docking calculation
requires massive computational resources, recent advancements in the computational sciences have made such
large-scale calculations feasible.
In this study we applied our prediction method to a pathway reconstruction problem of bacterial chemotaxis by using two
different rigid-body docking tools with different scoring models. We found that the predicted interactions were different
between the results from the two tools. When the positive predictions from both of the docking tools were combined, all
the core signaling interactions were correctly predicted with the exception of interactions activated by protein phosphorylation.
Large-scale PPI prediction using tertiary structures is an effective approach that has a wide range of potential applications.
This method is especially useful for identifying novel PPIs of new pathways that control cellular behavior.