The RNase-H fold is an ancient protein fold found in diverse nucleases that is characterised by a conserved structural core (five β strands and ⍺ helices), canonical DDE/D catalytic residues and catalytic mechanism. This study focuses on the evolutionary conservation of the RNaseH-like domain in the transposase-derived...
The THAP (Thanatos-associated protein) domain is a DNA-binding domain which binds DNA via a zinc-coordinating C2CH motif. Although THAP domains share a conserved structural fold, they bind different DNA sequences in different THAP proteins which in turn perform distinct cellular functions. In this study, we investigate...
Hiral M. Sanghavi, Gautam Sah· Discover Applied Sciences· 0 citations
This study demonstrates how a single-amino-acid substitution can drive paralog specialisation in poplar paralogs, offering mechanistic insight into the evolutionary fates of duplicated genes in plants.
Haofei Wang, Ji-Fan Zhang, Ruoting Wang et al.· Plant, Cell and Environment· 0 citations
Where recombination takes place is crucial as it determines the position of genetic reshuffling, which facilitates species evolution and adaptation. In many vertebrates, PRDM9 determines the location of double-strand breaks that initiates meiotic recombination. However, this only applies for Prdm9 orthologs that posses...
Amélie Rudler, Laurent Duret, C. Grunau et al.· bioRxiv· 0 citations
The crystal structure of the Ancient domain of chicken IMPACT (GgIMPACTAncient) is determined, providing the first crystallographic view of a metazoan IMPACT Ancient domain and a structural framework for testing the biochemical function of this evolutionarily conserved module.
Mirai Ido, S. Ito, T. Nishino· Crystals· 0 citations
Gene loss is a widespread phenomenon that shapes genome evolution, yet the factors determining why certain genes are repeatedly lost while other functionally related genes are retained remain poorly understood. We addressed this question using the peptide-processing metallocarboxypeptidases carboxypeptidase E (CPE) and...
Christian Wegener, Julian C. Heitkamp, Vera S. Hunnekuhl· bioRxiv· 0 citations
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.