Biology subjects

Samejima, M.

Publications and source records attributed to Samejima, M..

3 recordsLinked to original sources

bioRxiv2020

Crystal structure of GH43 exo-β-1,3-galactanase from the basidiomycete Phanerochaete chrysosporium provides insights into the mechanism of bypassing side chains

Arabinogalactan proteins (AGPs) are functional plant proteoglycans, but their functions are largely unexplored, mainly because of the complexity of the sugar moieties, which are generally analyzed with the aid of glycoside hydrolases. In this study, we solved the apo and liganded structures of exo-{beta}-1,3-galactanase from the basidiomycete Phanerochaete chrysosporium (Pc1,3Gal43A), which specifically cleaves AGPs. It is composed of a glycoside hydrolase family 43 subfamily 24 (GH43_sub24) catalytic domain together with a carbohydrate-binding module family (CBM) 35 binding domain. GH43_sub24 lacks the catalytic base Asp that is conserved among other GH43 subfamilies. Crystal structure and kinetic analyses indicated that the tautomerized imidic acid function of Gln263 serves instead as the catalytic base residue. Pc1,3Gal43A has three subsites that continue from the bottom of the catalytic pocket to the solvent. Subsite -1 contains a space that can accommodate the C-6 methylol of Gal, enabling the enzyme to bypass the {beta}-1,6-linked galactan side chains of AGPs. Furthermore, the galactan-binding domain in CBM35 has a different ligand interaction mechanism from other sugar-binding CBM35s. Some of the residues involved in ligand recognition differ from those of galactomannan-binding CBM35, including substitution of Trp for Gly, which affects pyranose stacking, and substitution of Asn for Asp in the lower part of the binding pocket. Pc1,3Gal43A WT and its mutants at residues involved in substrate recognition are expected to be useful tools for structural analysis of AGPs. Our findings should also be helpful in engineering designer enzymes for efficient utilization of various types of biomass.

molecular biology↗

bioRxiv2020

Thermostable mutants of glycoside hydrolase family 6 cellobiohydrolase from the basidiomycete Phanerochaete chrysosporium

Thermal inactivation of saccharifying enzymes is a crucial issue for the efficient utilization of cellulosic biomass as a renewable resource. Cellobiohydrolases (CBHs) is a kind of cellulase. In general, CBHs belonging to glycoside hydrolase (GH) family 6 (Cel6) act synergistically with CBHs of GH family 7 (Cel7) and other carbohydrate-active enzymes during the degradation of cellulosic biomass. However, while the catalytic rate of enzymes generally becomes faster at higher temperatures, Cel6 CBHs are inactivated at lower temperatures than Cel7 CBHs, and this represents a limiting factor for industrial utilization. In this study, we produced a series of mutants of the glycoside hydrolase family 6 cellobiohydrolase PcCel6A from the fungus Phanerochaete chrysosporium, and compared their thermal stability. Eight mutants from a random mutagenesis library and one rationally designed mutant were selected as candidate thermostable mutants and produced by heterologous expression in the yeast Pichia pastoris. Comparison of the hydrolytic activities at 50 and 60 {degrees}C indicated that the thermal stability of PcCel6A is influenced by the number and position of cysteine residues that are not involved in disulfide bonds.

biochemistry↗

bioRxiv2019

AI on animals: AI-assisted animal-borne logger never misses the moments that biologists want

Animal-borne data loggers, i.e., biologgers, allow researchers to record a variety of sensor data from animals in their natural environments (Hussey et al. 2015; Kays et al. 2015). This data allows biologists to observe many aspects of the animals lives, including their behavior, physiology, social interactions, and external environment. However, the need to limit the size of these devices to a small fraction of the animals size imposes strict limits on the devices hardware and battery capacities (Kays et al. 2015). Here we show how AI can be leveraged on board these devices to intelligently control their activation of costly sensors, e.g., video cameras, allowing them to make the most of their limited resources during long deployment periods. Our method goes beyond previous works that have proposed controlling such costly sensors using simple threshold-based triggers, e.g., depth-based (Watanuki et al. 2007; Volpov et al. 2015) and acceleration-based (Nishiumi et al. 2018; Brown et al. 2012) triggers. Using AI-assisted biologgers, biologists can focus their data collection on specific complex target behaviors such as foraging activities, allowing them to automatically record video that captures only the moments they want to see. By doing so, the biologger can reserve its battery power for recording only those target activities. We anticipate our work will provide motivation for more widespread adoption of AI techniques on biologgers, both for intelligent sensor control and intelligent onboard data processing. Such techniques can not only be used to control what is collected by such devices, but also what is transmitted off the devices, such as is done by satellite relay tags (Cox et al. 2018).

ecology↗