Cellular processes often depend on interactions between proteins and the formation of macromolecular complexes. The impairment of such interactions can lead to deregulation of pathways resulting in disease states, and it is hence crucial to gain insights into the nature of the macromolecular assemblies. Detailed structural knowledge about complexes and protein-protein interactions is growing, but experimentally determined three-dimensional multimeric assemblies are outnumbered by complexes supported by non-structural experimental evidence.
In this thesis, we aim to fill this gap by modeling multimeric structures by homology, and we ask which properties of proteins within a family can assist in the prediction of the correct quaternary structure. Specifically, we introduce a description of protein-protein interface conservation as a function of evolutionary distance. This enables us to reduce the noise in deep multiple sequence alignments where sequences of proteins organized in different oligomeric states are interspersed. We also define a distance measure to structurally compare homologous multimeric protein complexes. This allows us to hierarchically cluster protein structures and quantify the diversity of alternative biological assemblies known today in the Protein Data Bank (PDB). We find that a combination of conservation scores, structural clustering, and classical interface descriptors, is able to improve the selection of homologous protein templates leading to reliable models of protein complexes
