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  • The cpm corresponding to bound substrate in the sample well

    2019-10-09

    The cpm corresponding to bound substrate in the sample well can be expressed as the difference between the total cpm in the sample well (3Hswtotal; 14Cswtotal) and the cpm for the unbound substrate in the sample well, as illustrated by Eq. (2): By combining Eqs. (1), (2), cpm for the bound substrate in the sample well can be expressed as a relation of the cpm for substrate in the buffer well: BIEs can be expressed as a relation of equilibrium constants for the “light” substrate (e.g., [8-14C]SAM, where the remote 14C reports on 1H at the position on interest) and the “heavy” substrate (e.g., [Me-3H3]SAM or [5′-3H2]SAM). Alternatively, BIEs can be defined as a relation of the 14C/3H ratio for the total bound substrate and the 14C/3H ratio for the total unbound substrate, as illustrated in Eq. (4). Substituting Eqs. (1), (3) into Eq. (4) allows for the Methylcobalamin of Eq. (5): If volumes vsw and vbw are removed from the sample well and buffer well (respectively), then Eq. (5) can be modified to give Eq. (6) Scintillation counting for BIEs measurements is carried out using equivolume aliquots from the sample and buffer wells (vide infra). As such, vsw=vbw and Eq. (6) can be reduced to give Eq. (7):
    BIE Interpretation and Examples Equilibrium BIE values can be interpreted using quantum chemical calculations to investigate the molecular-scale bonding environment of a ligand when bound to its protein target (Lewis and Schramm, 2003a, Lewis and Schramm, 2003b, Murkin et al., 2007, Poulin et al., 2016, Schramm, 2007, Stratton et al., 2015, Zhang and Schramm, 2011). The nature of the calculations required to interpret BIE values depends largely on the protein–ligand system under investigation. In general, density functional theory (DFT) calculations as implemented in Gaussian 09 (Frisch et al., 2009) are used to model conformations of the ligand when free in solution and when bound in the ES complex. Then, predicted equilibrium BIEs are calculated by comparing the scaled vibrational frequencies for the free and bound ligands using the ISOEFF98 program (Anisimov & Paneth, 1999). The structure of the free ligand is generally optimized in water at physiological pH. Conformations of the protein-bound ligand will vary depending on the specific binding interactions under evaluation, but using input coordinates from cocrystal structures of the protein and ligand, if available, is a reasonable starting point. Though detailed methods for the computational modeling of BIE data is beyond the scope of this chapter, the conceptual aspects of a few investigations are outlined below. For example, the influence of hydrogen bonding interactions on equilibrium BIEs has been modeled by modulating the distance between the hydrogen atom of interest in the substrate and hydrogen bond donor residues in the enzyme active site. This approach has been used to model equilibrium BIEs for glucose binding to human brain hexokinase (Lewis & Schramm, 2003a), as well as NSD2 (Poulin, Schneck, Matico, Hou, et al., 2016). Protein-mediated atomic distortions are another important factor impacting the expression of equilibrium BIEs on protein–ligand association. Geometric distortions can be modeled by freezing specific dihedral angles in the substrate when optimizing the structure of the bound ligand. In addition, steric compaction has been modeled by modulating the distance between the atom of interest in the substrate and specific active site residues (Lewis & Schramm, 2003a), or by constraining specific bond lengths in the substrate (Poulin et al., 2016, Stratton et al., 2015). Specific examples of measured BIEs and interpretations based on quantum chemical calculations are presented in Table 9 and the references cited therein.
    Conclusions
    Acknowledgments
    Introduction The most abundant hemicellulose on the Earth is β-1,4-xylan. Its depolymerization is performed by endo-β-1,4-xylanases (xylanases; EC 3.2.1.8) that are crucial enzymes in the xylan degradation. They are produced by various microorganisms, including bacteria, fungi and yeasts [1,2]. During the evolution the microorganisms have developed different strategies for xylan degradation what is reflected in a production of several types of xylanases. Glucuronoxylan xylanohydrolases (EC 3.2.1.136) are highly specialized xylanases which require for their action D-glucuronic or 4-O-methyl-d-glucuronic acid (MeGlcA) attached to the xylan backbone [3]. They hydrolyze the glucuronoxylan main chain at the second glycosidic linkage from the MeGlcA residue towards the reducing end and they do not attack, or attack extremely slowly, xylan or xylooligosaccharides without MeGlcA substitution [[4], [5], [6], [7], [8]]. The glucuronoxylanases are classified in glycoside hydrolase (GH) family 30, subfamily 8 (http://www.cazy.org/) [9,10].