Please use this identifier to cite or link to this item:
http://doi.org/10.25358/openscience-8709
Authors: | Brems, Maarten A. Runkel, Robert Yeates, Todd O. Virnau, Peter |
Title: | AlphaFold predicts the most complex protein knot and composite protein knots |
Online publication date: | 2-Feb-2023 |
Year of first publication: | 2022 |
Language: | english |
Abstract: | The computer artificial intelligence system AlphaFold has recently predicted previously unknown three-dimensional structures of thousands of proteins. Focusing on the subset with high-confidence scores, we algorithmically analyze these predictions for cases where the protein backbone exhibits rare topological complexity, that is, knotting. Amongst others, we discovered a 71-knot, the most topologically complex knot ever found in a protein, as well several six-crossing composite knots comprised of two methyltransferase or carbonic anhydrase domains, each containing a simple trefoil knot. These deeply embedded composite knots occur evidently by gene duplication and interconnection of knotted dimers. Finally, we report two new five-crossing knots including the first 51-knot. Our list of analyzed structures forms the basis for future experimental studies to confirm these novel-knotted topologies and to explore their complex folding mechanisms. |
DDC: | 530 Physik 530 Physics |
Institution: | Johannes Gutenberg-Universität Mainz |
Department: | FB 08 Physik, Mathematik u. Informatik |
Place: | Mainz |
ROR: | https://ror.org/023b0x485 |
DOI: | http://doi.org/10.25358/openscience-8709 |
Version: | Published version |
Publication type: | Zeitschriftenaufsatz |
Document type specification: | Scientific article |
License: | CC BY |
Information on rights of use: | https://creativecommons.org/licenses/by/4.0/ |
Journal: | Protein science 31 8 |
Publisher: | John Wiley & Sons, Ltd |
Publisher place: | Hoboken, NJ |
Issue date: | 2022 |
ISSN: | 1469-896X |
Publisher DOI: | 10.1002/pro.4380 |
Appears in collections: | DFG-491381577-H |
Files in This Item:
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![]() | alphafold_predicts_the_most_c-20230130153447798.pdf | 1.15 MB | Adobe PDF | View/Open |