Bioinorganic chemistry of molybdenum and tungsten enzymes: A structural–functional modeling approach
✍ Scribed by Amit Majumdar; Sabyasachi Sarkar
- Publisher
- Elsevier Science
- Year
- 2011
- Tongue
- English
- Weight
- 783 KB
- Volume
- 255
- Category
- Article
- ISSN
- 0010-8545
No coin nor oath required. For personal study only.
✦ Synopsis
Chemical approaches toward the bioinorganic chemistry of molybdenum and tungsten enzymes had been either biomimetic (structural modeling) or bioinspired (functional modeling). Among the dithiolene type of ligands, bdt (1,2-benzene dithiolate) and related aromatic molecules as model ene-dithiolene ligands were used to react with pre-designed molybdenum complexes in organic solvents. Whereas in the alternative approach mnt (maleonitrile dithiolate) is used to mimic the ligand backbone of the central atom in the active sites of these enzymes using molybdate or tungstate as the metal source in water. Structural-functional models are known for some selected enzymes, namely, sulfite oxidase, aldehyde ferredoxin oxidoreductase, tungsten formate dehydrogenase, acetylene hydratase, polysulfide reductase and dissimilatory nitrate reductase. The protocols and methodologies adopted to achieve these model systems compared with various other model systems described in this review give testimony to chemist's ability, through chemical manipulations, to achieve the model systems which may potentially serve as structural-functional mimics of natural enzyme systems.
📜 SIMILAR VOLUMES
2-amino-5,6,7,8-tetrahydropteridin-4(3H)-one.
## Abstract ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable v
## Abstract In a significant work, Dobson and Doig (J Mol Biol 2003, 330, 771) illustrated protein prediction as enzymatic or not from spatial structure without resorting to alignments. They used 52 protein features and a nonlinear support vector machine model to classify more than 1000 proteins co