A comparison of representative structures extracted from 10 × 500-ns molecular dynamics (MD) simulations of an ancestral glycosidase both (A) in the absence and (B) in the presence of heme, as well as (C) its corresponding extant counterpart (in this case a glycosidase from Halothermothrix orenii). Also shown here are (D) the absolute and (E) the relative (heme-bound vs non-heme-bound ancestral glycosidase) root mean square fluctuations (RMSFs) (Å) of the backbone C α -atoms of each relevant system. It can be seen from these simulations that there are clear differences in flexibility in the region spanning residues 227-334, when comparing both the ancestral and extant proteins and the ancestral glycosidase with and without heme bound. This region corresponds to missing residues in the electron density of the ancestral protein, an effect that is particularly pronounced in the absence of heme. The corresponding rigidification of the ancestral protein by bound heme was shown, in turn, to lead to differences in catalytic activity. This figure was originally published in [36]. Copyright 2018, Springer Nature. Published under a CC-BY license (http://creativecommons.org/licenses/by/4.0).

A comparison of representative structures extracted from 10 × 500-ns molecular dynamics (MD) simulations of an ancestral glycosidase both (A) in the absence and (B) in the presence of heme, as well as (C) its corresponding extant counterpart (in this case a glycosidase from Halothermothrix orenii). Also shown here are (D) the absolute and (E) the relative (heme-bound vs non-heme-bound ancestral glycosidase) root mean square fluctuations (RMSFs) (Å) of the backbone C α -atoms of each relevant system. It can be seen from these simulations that there are clear differences in flexibility in the region spanning residues 227-334, when comparing both the ancestral and extant proteins and the ancestral glycosidase with and without heme bound. This region corresponds to missing residues in the electron density of the ancestral protein, an effect that is particularly pronounced in the absence of heme. The corresponding rigidification of the ancestral protein by bound heme was shown, in turn, to lead to differences in catalytic activity. This figure was originally published in [36]. Copyright 2018, Springer Nature. Published under a CC-BY license (http://creativecommons.org/licenses/by/4.0).

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Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to...

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... are discussed in more detail in the subsequent section. However, as some (of many) other examples of recent success stories, ASR has been used to understand allosteric communication in a multienzyme complex of a key metabolic enzyme, tryptophan synthase [32,33], to obtain a high-redox-potential laccase [34], to modulate the catalytic adaptability of an extremophile kinase [35], and to identify novel heme binding that modulates the allosteric regulation of an ancestral glycosidase (Figure 1) [36]. This latter study is notable as heme binding was not observed in any of ~5500 crystal structures of ~1400 modern glycosidases. ...

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... ; https://doi.org/10. 1101/2021 One challenge in answering these questions stems from the lack of a resource that stores easy-to-use information about the optimal growth conditions of living organisms, together with their genomic data. Currently, there are more than 14,400 genome sequences from representative bacterial species publicly available. ...
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Despite the rapidly increasing number of organisms with sequenced genomes, there is no existing resource that simultaneously contains information about genome sequences and the optimal growth conditions for a given species. In the absence of such a resource, we cannot immediately sort genomic sequences by growth conditions, making it difficult to study how organisms and biological molecules adapt to distinct environments. To address this problem, we have created a database called GSHC (Genome Sequences: Hot, Cold, and everything in between). This database, available at http://melnikovlab.com/gshc, brings together information about the genomic sequences and optimal growth temperatures for 25,324 species, including ~89% of the bacterial species with known genome sequences. Using this database, it is now possible to readily compare genomic sequences from thousands of species and correlate variations in genes and genomes with optimal growth temperatures, at the scale of the entire tree of life. The database interface allows users to retrieve protein sequences sorted by optimal growth temperature for their corresponding species, providing a tool to explore how organisms, genomes, and individual proteins and nucleic acids adapt to certain temperatures. We hope that this database will contribute to medicine and biotechnology by helping to create a better understanding of molecular adaptations to heat and cold, leading to new ways to preserve biological samples, engineer useful enzymes, and develop new biological materials and organisms with the desired tolerance to heat and cold.
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