Molecular interactions are at the core of all biological processes. Despite the existence of powerful methods such as the yeast two-hybrid and its variants, comprehensive quantitative surveys of molecular interactions remain low-throughput, costly, labor-intensive, and suffer from biases that limit coverage to only a few percent of all possible interactions. A major challenge in biology is to develop novel methods that allow rapid, near- comprehensive coverage of all bi-molecular interactions. If they were available, such ultra-deep interactome maps will revolutionize biology by providing a rich knowledge scaffold for a systems-level understanding of biological processes and their high-level organization. We propose to develop a revolutionary ultra-high- throughput technology to easily and comprehensively map protein-protein, protein-DNA, and protein-RNA interactions in any organism of interest. The technology will be: (1) ultra-high-throughput, allowing a single investigator to conduct a deep and comprehensive survey of all pair-wise interactions (e.g. ~109 for all human proteins) in a single tube; (2) It will have the sensitivity and dynamic-range to provide a quantitative readout of interaction-strengths; (3) It will be extremely fast, enabling a single investigator to conduct a global survey on the timescale of a few days; (4) It will enable the monitoring of interactome dynamics-as a function of cellular- state or other perturbations; (5) it will capture the native in vivo physiological state of proteins; and (6) It will be extremely lo-cost and not require the use of specialized robotics or large laboratory real estate. The comprehensive and quantitative nature of these maps will allow us to go beyond the current low-hanging-fruit limits, and for the first time, measure the entire distribution of interaction strengths. This capacity will reveal an unbiased view of connectivity and modularity, potentially revamping our fundamental understanding of molecular network evolution and function. The unprecedented scale of these observations will present unique opportunities for extracting novel insights that are not possible with the low coverage and sparsity of existing technologies. In the second major aim, with the development of computational tools, we aim to achieve a predictive understanding of the observed molecular interactions in terms of nucleic-acid and peptide sequence- motifs that mediate interactions. The astronomical scale of these observations may enable a new plateau in understanding and modeling molecular recognition rules, paving the way for ab initio engineering of molecular network architecture and dynamics.
The proposed research is a systems biology approach for discovering and quantifying interactions between the major biomolecules in the cell. This knowledge is of fundamental importance to the work of the entire biomedical community and in our better understanding of processes that are affected in disease states.
