Abstract
Proteins can self-associate with copies of themselves to form symmetric complexes called homomers. Homomers are widespread in all kingdoms of life and allow for unique geometric and functional properties, as reflected in viral capsids or allostery. Once a protein forms a homomer, however, its internal symmetry can compound the effect of point mutations and trigger uncontrolled self-assembly into high-order structures. We identified mutation hot spots for supramolecular assembly, which are predictable by geometry. Here, we present a dataset of descriptors that characterize these hot spot positions both geometrically and chemically, as well as computer scripts allowing the calculation and visualization of these properties for homomers of choice. Since the biological relevance of homomers is not readily available from their X-ray crystallographic structure, we also provide reliability estimates obtained by methods we recently developed. These data have implications in the study of disease-causing mutations, protein evolution and can be exploited in the design of biomaterials.
Highlights
Background & SummaryThe controlled association of proteins into functional complexes is central to the myriad of biochemical processes required to maintain cellular functions[1,2]
Previous work showed that the chemical composition of protein interfaces, distinct from surfaces, is relatively close
We present a dataset of descriptors that characterize these geometric hot spot positions and buffering effects on 165,916 proposed biological assemblies from the Protein Data Bank (PDB)[13,14], together with the workflow and computer scripts used to compute these descriptors[15]. These data serve multiple uses: (i) they will be important to consider in future studies predicting the molecular consequences of mutations, including single nucleotide polymorphisms, (ii) from an evolutionary standpoint, they describe molecular phenotypes that may constrain amino acid changes and thereby, could be considered in phylogenetic models of sequence evolution, and (iii) in the field of bio-materials design, these data facilitate the application of our simple strategy to program protein self-assembly at length scales up to several micrometers either in vitro or in vivo, using the PDB as a source of natural “building blocks”
Summary
The controlled association of proteins into functional complexes is central to the myriad of biochemical processes required to maintain cellular functions[1,2]. In our previous work[3], we introduced point mutations solely designed to increase surface hydrophobicity into 12 dihedral homomers from Escherichia coli These mutations triggered new self-interactions resulting in all complexes forming high-order supramolecular assemblies both in vitro and in vivo upon heterologous expression in Saccharomyces cerevisiae. We present a dataset of descriptors that characterize these geometric hot spot positions and buffering effects on 165,916 proposed biological assemblies from the Protein Data Bank (PDB)[13,14], together with the workflow and computer scripts used to compute these descriptors[15] These data serve multiple uses: (i) they will be important to consider in future studies predicting the molecular consequences of mutations, including single nucleotide polymorphisms, (ii) from an evolutionary standpoint, they describe molecular phenotypes that may constrain amino acid changes and thereby, could be considered in phylogenetic models of sequence evolution, and (iii) in the field of bio-materials design, these data facilitate the application of our simple strategy to program protein self-assembly at length scales up to several micrometers either in vitro or in vivo, using the PDB as a source of natural “building blocks”
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