UCSF

To understand the workings of a living cell, we need to identify its molecular components and determine how they associate with each other. To date, studies that identify new macromolecular assemblies have been mainly limited to low-throughput biochemistry assays. Structures of macromolecular assemblies also have been difficult to obtain, and are mostly available for a small subset of individual components or their assemblies amenable to conventional approaches, such as X-ray crystallography and nuclear magnetic resonance spectroscopy. In this dissertation, I describe my contributions to the development of four novel pipelines that utilize new technologies and datasets to facilitate the identification of macromolecular assemblies and the determination of their structures. First, we designed an algorithm to identify genes coding for biosynthetic macromolecular assemblies. Second, we developed a platform to identify previously unknown HIV-human protein assemblies based on affinity purification, mass spectrometry, and computational scoring. Third, to aid the structure determination of macromolecular assemblies that are challenging to isolate and purify, we proposed a new strategy based on in vivo measurements of genetic interaction and integrative modeling. Finally, to facilitate rational discovery of small molecule modulators of macromolecular assemblies and their components, we presented a new approach based on computational identification of putative ligand-binding sites that are not detectable in ligand-free structures, due to insufficient structure resolutions or flatness in the absence of a ligand.