采用核磁共振碳谱(nuclear magnetic resonance spectra,13C-NMR)对生物质的三种最主要组分纤维素、半纤维素及木质素的化学结构特性进行研究,结果表明纤维素中的脂碳与芳碳比明显高于半纤维素和木质素,分别为5.2∶1、1.9∶1和1∶1.2,...采用核磁共振碳谱(nuclear magnetic resonance spectra,13C-NMR)对生物质的三种最主要组分纤维素、半纤维素及木质素的化学结构特性进行研究,结果表明纤维素中的脂碳与芳碳比明显高于半纤维素和木质素,分别为5.2∶1、1.9∶1和1∶1.2,这直接导致了三种组分热解产物的差异。该文在前人研究的基础上建立适用于生物质组分化学结构的化学渗透脱挥发分(chemical percolation for devolatilization,CPD)模型,通过13C-NMR对生物质各组分的化学结构进行研究,得到CPD模型的4个输入参数;并预测了纤维素、半纤维素及木质素这三种组分的热解产物产量。根据各组分在生物质原样中所占比例,计算出松木屑热解的产物产量。展开更多
The three-dimensional structure of a biomolecule rather than its one-dimensionM sequence determines its biological function. At present, the most accurate structures are derived from experimental data measured mainly ...The three-dimensional structure of a biomolecule rather than its one-dimensionM sequence determines its biological function. At present, the most accurate structures are derived from experimental data measured mainly by two techniques： X-ray crystallog- raphy and nuclear magnetic resonance （NMR） spec- troscopy. Because neither X-ray crystallography nor NMR spectroscopy could directly measure the positions of atoms in a biomolecule, algorithms must be designed to compute atom coordinates from the data. One salient feature of most NMR structure computation algorithms is their reliance on stochastic search to find the lowest energy conformations that satisfy the experimentally- derived geometric restraints. However, neither the cor- rectness of the stochastic search has been established nor the errors in the output structures could be quantified. Though there exist exact algorithms to compute struc- tures from angular restraints, similar algorithms that use distance restraints remain to be developed. An important application of structures is rational drug design where protein-ligand docking plays a crit- ical role. In fact, various docking programs that place a compound into the binding site of a target protein have been used routinely by medicinal chemists for both lead identification and optimization. Unfortunately, de- spite ongoing methodological advances and some success stories, the performance of current docking algorithms is still data-dependent. These algorithms formulate the docking problem as a match of two sets of feature points. Both the selection of feature points and the search for the best poses with the minimum scores are accomplished through some stochastic search methods. Both the un- certainty in the scoring function and the limited sam- pling space attained by the stochastic search contribute to their failures. Recently, we have developed two novel docking algorithms： a data-driven docking algorithm and a general docking algorithm that does not rely on experimental data. Our algorithms search the pose space exhaustively with the pose space itself being limited to a set of hierarchical manifolds that represent, respectively, surfaces, curves and points with unique geometric and energetic properties. These algorithms promise to be es- pecially valuable for the docking of fragments and small compounds as well as for virtual screening.展开更多
文摘采用核磁共振碳谱(nuclear magnetic resonance spectra,13C-NMR)对生物质的三种最主要组分纤维素、半纤维素及木质素的化学结构特性进行研究,结果表明纤维素中的脂碳与芳碳比明显高于半纤维素和木质素,分别为5.2∶1、1.9∶1和1∶1.2,这直接导致了三种组分热解产物的差异。该文在前人研究的基础上建立适用于生物质组分化学结构的化学渗透脱挥发分(chemical percolation for devolatilization,CPD)模型,通过13C-NMR对生物质各组分的化学结构进行研究,得到CPD模型的4个输入参数;并预测了纤维素、半纤维素及木质素这三种组分的热解产物产量。根据各组分在生物质原样中所占比例,计算出松木屑热解的产物产量。
文摘The three-dimensional structure of a biomolecule rather than its one-dimensionM sequence determines its biological function. At present, the most accurate structures are derived from experimental data measured mainly by two techniques： X-ray crystallog- raphy and nuclear magnetic resonance （NMR） spec- troscopy. Because neither X-ray crystallography nor NMR spectroscopy could directly measure the positions of atoms in a biomolecule, algorithms must be designed to compute atom coordinates from the data. One salient feature of most NMR structure computation algorithms is their reliance on stochastic search to find the lowest energy conformations that satisfy the experimentally- derived geometric restraints. However, neither the cor- rectness of the stochastic search has been established nor the errors in the output structures could be quantified. Though there exist exact algorithms to compute struc- tures from angular restraints, similar algorithms that use distance restraints remain to be developed. An important application of structures is rational drug design where protein-ligand docking plays a crit- ical role. In fact, various docking programs that place a compound into the binding site of a target protein have been used routinely by medicinal chemists for both lead identification and optimization. Unfortunately, de- spite ongoing methodological advances and some success stories, the performance of current docking algorithms is still data-dependent. These algorithms formulate the docking problem as a match of two sets of feature points. Both the selection of feature points and the search for the best poses with the minimum scores are accomplished through some stochastic search methods. Both the un- certainty in the scoring function and the limited sam- pling space attained by the stochastic search contribute to their failures. Recently, we have developed two novel docking algorithms： a data-driven docking algorithm and a general docking algorithm that does not rely on experimental data. Our algorithms search the pose space exhaustively with the pose space itself being limited to a set of hierarchical manifolds that represent, respectively, surfaces, curves and points with unique geometric and energetic properties. These algorithms promise to be es- pecially valuable for the docking of fragments and small compounds as well as for virtual screening.
