Improved protein-ligand docking using GOLD

The Chemscore function was implemented as a scoring function for the protein-ligand docking program GOLD, and its performance compared to the original Goldscore function and two consensus docking protocols, "Goldscore-CS" and "Chemscore-GS," in terms of docking accuracy, prediction of binding affinities, and speed. In the "Goldscore-CS" protocol, dockings produced with the Goldscore function are scored and ranked with the Chemscore function; in the "Chemscore-GS" protocol, dockings produced with the Chemscore function are scored and ranked with the Goldscore function. Comparisons were made for a "clean" set of 224 protein-ligand complexes, and for two subsets of this set, one for which the ligands are "drug-like," the other for which they are "fragment-like." For "drug-like" and "fragment-like" ligands, the docking accuracies obtained with Chemscore and Goldscore functions are similar. For larger ligands, Goldscore gives superior results. Docking with the Chemscore function is up to three times faster than docking with the Goldscore function. Both combined docking protocols give significant improvements in docking accuracy over the use of the Goldscore or Chemscore function alone. "Goldscore-CS" gives success rates of up to 81% (top-ranked GOLD solution within 2.0 A of the experimental binding mode) for the "clean list," but at the cost of long search times. For most virtual screening applications, "Chemscore-GS" seems optimal; search settings that give docking speeds of around 0.25-1.3 min/compound have success rates of about 78% for "drug-like" compounds and 85% for "fragment-like" compounds. In terms of producing binding energy estimates, the Goldscore function appears to perform better than the Chemscore function and the two consensus protocols, particularly for faster search settings. Even at docking speeds of around 1-2 min/compound, the Goldscore function predicts binding energies with a standard deviation of approximately 10.5 kJ/mol.     

气囊在细菌中的生理功能、生物合成及应用研究进展

气囊是在水生细菌中广泛存在的一种具有刚性中空蛋白结构的特殊细胞器,不仅为水生细菌提供浮力,还对其在不利环境或应激条件下的生存至关重要.近期研究发现在其他非水生细菌如沙雷氏菌和链霉菌中也存在气囊结构,而且表现出不同的生理功能.来源于不同种属细菌的气囊生物合成基因簇具有各自鲜明的特征,其生物合成和调控机制也有所不同.本综述...     

纤细裸藻培养及其产物功能的研究与展望

纤细裸藻(Euglena gracilis)是一类介于动物和植物之间的单细胞真核生物,在自然界中分布广泛。纤细裸藻兼具动植物细胞的特征,可进行光合自养、混养和异养培养,细胞内含有丰富的营养物质。近年来随着对纤细裸藻生物特性和胞内组分的深入研究,发现其在医药、食品以及饲料方面具有重要的应用价值,在提高免疫力、抗炎以及降低尿酸等方面的功效非常值得开发,特别是纤细裸藻特有的裸藻多糖等在生物医药领域具有广阔的应用前景。纤细裸藻作为食品原料、食品添加剂、饲料添加剂以及化妆品原料在国内外已经获得相关机构的认证,国外尤其是日本已经开发出系列产品。然而,国内对纤细裸藻的研究及开发还处于起步阶段,发展空间巨大,且目前关于纤细裸藻培养与功能研究及潜在的应用的综述报道较少。本文系统地介绍了纤细裸藻培养和生产的国内外研究现状,以及纤细裸藻粉与裸藻多糖活性功能的国内外研究结果,并对纤细裸藻应用开发存在的问题以及未来可能的发展方向进行了展望,为进一步建立和优化大规模且高效异养培养工艺、开发具有特定功能的纤细裸藻相关产品等提供科学依据。     

普氏蹄蝠下丘谐波内外神经元处理多普勒频移补偿信息的差异

为了探讨普氏蹄蝠下丘神经元在处理多普勒频移补偿后回声定位信号中的作用,实验采用双声刺激模式模拟蝙蝠不同飞行状态下产生多普勒频移补偿后的脉冲-回声对,即发声频率改变,回声频率维持恒定的情况下,研究下丘神经元对不同补偿值下的回声反应恢复率.结果发现:根据神经元在某一补偿值下对回声信号反应的恢复率是否超过70%,可将其分为具有选择性(S)和无选择性(NS)的两类神经元.且谐波内S神经元所占比例(68%)远超过非谐波内S神经元(39%).分析神经元的发放模式发现谐波内S神经元中相位型发放模式比例(44.3%)明显高于其他三种类型神经元.另外,虽然S和NS神经元的强度-潜伏期函数类型均以饱和型为主,但谐波内S神经元强度-潜伏期函数的最佳强度(best amplitude,BA)(95.3±14.0)dB SPL低于NS神经元的BA(104.1±10.2)d B SPL(P0.01),同时也低于非谐波内S神经元的BA(109.7±7.9)dB SPL(P0.01).以上实验结果表明,在下丘水平,神经元就已对多普勒频移补偿后回声定位信号的处理有了分工,集中在谐波内的S神经元通过提高对某一补偿值下回声信号反应的恢复率实现,对回声信息的精确编码,避免其他杂波干扰信息.同时,谐波内S神经元的发放模式和强度-潜伏期函数特点也满足其在复杂环境中精确声学成像的需求.     

On the accuracy of analytical potentials: comment on ‘Accurate ab initio calculation of the Ar–CF4 intermolecular potential energy surface’

In a recent study of the Ar–CF₄ intermolecular interaction potential [Shen C-C, Chang R-Y. Accurate ab initio calculation of the Ar–CF₄ intermolecular potential energy surface. Mol Sim. 2010;36:1111–1122], Shen and Chang (SC) illustrated how the use of bond functions can improve the accuracy and basis-set saturation of electronic structure calculations employing perturbation and coupled-cluster theory. SC then used these ab initio data to derive analytic potential energy functions for use in chemical dynamics simulations. We critically examine these analytic potentials and comment on their usage in such simulations. Our analysis highlights the need for care and global validation when deriving analytic potential energy functions.