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.
Original language | English |
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Article number | 64 |
Number of pages | 9 |
Journal | Scientific data |
Volume | 6 |
Issue number | 1 |
DOIs | |
Publication status | Published - 17 May 2019 |
Funding
This work was supported by the Israel Science Foundation and the I-CORE Program of the Planning and Budgeting Committee (Grant Nos 1775/12, 2179/14, and 1452/18), by the Marie Curie CIG Program (Project No. 711715), by the Human Frontier Science Program Career Development Award (Number CDA00077/2015), by a research grant from A.-M. Boucher, the Estelle Funk Foundation, the Estate of Fannie Sherr, the Estate of Albert Delighter, the Merle S. Cahn Foundation, Mrs Mildred S. Gosden, the Estate of Elizabeth Wachsman, and the Arnold Bortman Family Foundation. H.G.S. and S.D. received support from the Koshland Foundation and H.S.G. from a McDonald-Leapman Grant. E.D.L. is incumbent of the Recanati Career Development Chair of Cancer Research.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Information Systems
- Education
- Computer Science Applications
- Statistics, Probability and Uncertainty
- Library and Information Sciences