Evolution leads to considerable changes in the sequence of biomolecules, while their overall structure and function remain quite conserved. The wealth of genomic sequences, the ‘Biological Big Data’, modern sequencing techniques provide allows us to investigate biomolecular evolution with unprecedented detail. Sophisticated statistical models can infer residue pair mutations resulting from spatial proximity. The introduction of predicted spatial adjacencies as constraints in biomolecular structure prediction workflows has transformed the field of protein and RNA structure prediction toward accuracies approaching the experimental resolution limit. Going beyond structure prediction, the same mathematical framework allows mimicking evolutionary fitness landscapes to infer signaling interactions, epistasis, or mutational landscapes.
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Cover Image
Cover Image
Cryo-electron microscopy density map of a Cowpea Mosaic virus (CPMV) empty virus-like particle (eVLP) at 2.7 Å resolution (EMD-3952). The large (L) subunit is displayed in green and the small (S) subunit in blue. Five S subunits interact to form pronounced turrets at the 5-fold axis. Here we show a view down a two-fold axis. The eVLP was produced by transient co-expression in plants of the precursor of the L and S subunits (VP60) and the virus-encoded protease (24K) required for its processing. For further details, please see article by Lomonossoff et al, pages 1263–1269
Biomolecular coevolution and its applications: Going from structure prediction toward signaling, epistasis, and function
Mehari B. Zerihun, Alexander Schug; Biomolecular coevolution and its applications: Going from structure prediction toward signaling, epistasis, and function. Biochem Soc Trans 15 December 2017; 45 (6): 1253–1261. doi: https://doi.org/10.1042/BST20170063
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