Carbohydrate-binding module O-mannosylation alters binding selectivity to cellulose and lignin

Improved understanding of the effect of protein glycosylation is expected to provide the foundation for the design of protein glycoengineering strategies. In this study, we examine the impact of O-glycosylation on the binding selectivity of a model Family 1 carbohydrate-binding module (CBM), which has been shown to be one of the primary sub-domains responsible for non-productive lignin binding in multi-modular cellulases. Specifically, we examine the relationship between glycan structure and the binding specificity of the CBM to cellulose and lignin substrates. We find that the glycosylation pattern of the CBM exhibits a strong influence on the binding affinity and the selectivity between both cellulose and lignin. In addition, the large set of binding data collected allows us to examine the relationship between binding affinity and the correlation in motion between pairs of glycosylation sites. Our results suggest that glycoforms displaying highly correlated motion in their glycosylation sites tend to bind cellulose with high affinity and lignin with low affinity. Taken together, this work helps lay the groundwork for future exploitation of glycoengineering as a tool to improve the performance of industrial enzymes.

Improved understanding of the effect of protein glycosylation is expected to provide the foundation for the design of protein glycoengineering strategies.

The cell walls of terrestrial plants primarily comprise the polysaccharides cellulose, hemicellulose, and pectin, as well as the heterogeneous aromatic polymer, lignin. In nature, carbohydrates derived from plant polysaccharides provide a massive carbon and energy source for biomass-degrading fungi, bacteria, and archaea, which together are the primary organisms that recycle plant matter and are a critical component of the global carbon cycle. Across the various environments in which these microbes break down lignocellulose, a few known enzymatic and chemical systems have evolved to deconstruct polysaccharides to soluble sugars.^1–6 These natural systems are, in several cases, being evaluated for industrial use to produce sugars for further conversion into renewable biofuels and chemicals.From an industrial perspective, overcoming biomass recalcitrance to cost-effectively produce soluble intermediates, including sugars for further upgrading remains the main challenge in biomass conversion. Lignin, the evolution of which in planta provided a significant advantage for terrestrial plants to mitigate microbial attack, is now widely recognized as a primary cause of biomass recalcitrance.⁷ Chemical and/or biological processing scenarios of lignocellulose have been evaluated⁸ and several approaches have been scaled to industrial biorefineries to date. Many biomass conversion technologies overcome recalcitrance by partially or wholly removing lignin from biomass using thermochemical pretreatment or fractionation. This approach enables easier polysaccharide access for carbohydrate-active enzymes and/or microbes. There are however, several biomass deconstruction approaches that employ enzymes or microbes with whole, unpretreated biomass.^9,10 In most realistic biomass conversion scenarios wherein enzymes or microbes are used to depolymerize polysaccharides, native or residual lignin remains.^11,12 It is important to note that lignin can bind and sequester carbohydrate-active enzymes, which in turn can affect conversion performance.¹³Therefore, efforts aimed at improving cellulose binding selectivity relative to lignin have emerged as major thrusts in cellulase studies.^14–25 Multiple reports in the past a few years have made exciting new contributions to our collective understanding of how fungal glycoside hydrolases, which are among the most well-characterized cellulolytic enzymes given their importance to cellulosic biofuels production, bind to lignin from various pretreatments.^15,17 Taken together, these studies have demonstrated that the Family 1 carbohydrate-binding modules (CBMs) often found in fungal cellulases are the most relevant sub-domains for non-productive binding to lignin,^15,17,20,26 likely due to the hydrophobic face of these CBMs that is known to be also responsible for cellulose binding (Fig. 1).²⁷Open in a separate windowFig. 1Model of glycosylated CBM binding the surface of a cellulose crystal. Glycans are shown in green with oxygen atoms in red, tyrosines known to be critical to binding shown in purple, and disulfide bonds Cys8–Cys25 and Cys19–Cys35 in yellow.Furthermore, several studies have been published recently using protein engineering of Family 1 CBMs to improve CBM binding selectivity to cellulose with respect to lignin. Of particular note, Strobel et al. screened a large library of point mutations in both the Family 1 CBM and the linker connecting the catalytic domain (CD) and CBM.^21,22 These studies demonstrated that several mutations in the CBM and one in the linker led to improved cellulose binding selectivity compared to lignin. The emerging picture is that the CBM-cellulose interaction, which occurs mainly as a result of stacking between the flat, hydrophobic CBM face (which is decorated with aromatic residues) and the hydrophobic crystal face of cellulose I, is also likely the main driving force in the CBM-lignin interaction given the strong potential for aromatic–aromatic and hydrophobic interactions.Alongside amino acid changes, modification of O-glycosylation has recently emerged as a potential tool in engineering fungal CBMs, which Harrison et al. demonstrated to be O-glycosylated.^28–31 In particular, we have revealed that the O-mannosylation of a Family 1 CBM of Trichoderma reesei cellobiohydrolase I (TrCel7A) can lead to significant enhancements in the binding affinity towards bacterial microcrystalline cellulose (BMCC).^30,32,33 This observation, together with the fact that glycans have the potential to form both hydrophilic and hydrophobic interactions with other molecules, led us to hypothesize that glycosylation may have a unique role in the binding selectivity of Family 1 CBMs to cellulose relative to lignin and as such, glycoengineering may be exploited to improve the industrial performance of these enzymes. To test this hypothesis, in the present study, we systematically probed the effects of glycosylation on CBM binding affinity for a variety of lignocellulose-derived cellulose and lignin substrates and investigated routes to computationally predict the binding properties of different glycosylated CBMs.     

Low-temperature growth and photoluminescence property of ZnS nanoribbons

At a low temperature of 450 degrees C, ZnS nanoribbons have been synthesized on Si and KCl substrates by a simple chemical vapor deposition (CVD) method with a two-temperature-zone furnace. Zinc and sulfur powders are used as sources in the different temperature zones. X-ray diffraction (XRD), selected area electron diffraction (SEAD), and transmission electron microscopy (TEM) analysis show that the ZnS nanoribbons are the wurtzite structure, and there are two types-single-crystal and bicrystal nanoribbons. Photoluminescence (PL) spectrum shows that the spectrum mainly includes two parts: a purple emission band centering at about 390 nm and a blue emission band centering at about 445 nm with a weak green shoulder around 510 nm.     

Solutions of the generalized Dirichlet problem for the iterated slice Dirac equation

Applying the method of normalized systems of functions we construct solutions of the generalized Dirichlet problem for the iterated slice Dirac operator in Clifford analysis. This problem is a natural generalization of the Dirichlet problem.

     

聚氨酯／聚甲基丙烯酸甲酯互穿聚合物网络的研究

本文对(蓖麻油-聚乙二醇)聚氨酯/聚甲基丙烯酸甲酯(PU/PMMA)互穿聚合物网络(IPN)体系进行了研究.实验结果表明:IPN的力学性能受网络化学构型控制.在PU/PMMA为25/75时,应力-应变曲线出现屈服点,产生类似皮革行为,并在此点抗张强度最高,表现明显的协同效应.动态力学性能及电镜均证明该体系相分离十分严重,通过加入扩链剂及网间交联剂能显著改善两组分相容性,其中网间交联剂效果最好。     

On the Sixth Power Mean of the Two-term Exponential Sums

Acta Mathematica Sinica, English Series - The main purpose of this article is to study the calculating problem of the sixth power mean of the two-term exponential sums, and give an interesting...     

A comparison of two dissimilarity functions for mixed-type predictor variables in the $$\delta $$ -machine

Advances in Data Analysis and Classification - The $$\delta $$ -machine is a statistical learning tool for classification based on dissimilarities or distances between profiles of the observations...