海枣曲霉β-半乳糖苷酶的提纯与性质

 通过过聚乙二醇6000-磷酸钾缓冲液双相分离、Sephadex G-100凝胶过滤、DEAE-Sephadex A-50离子交换层析、羟基磷灰石层析及SephadexG-100凝胶过滤等提纯步骤,从海枣曲霉(Aspergillus phoenicis)麦麸培养物抽提液中提纯得到凝胶电泳均一的β-半乳糖苷酶。该酶的最适pH为3.5—4.0,最适温度为60℃(反应15分钟),在pH5.0—8.5之间及60℃以下稳定。在65℃和70℃保温时失活50％的时间分别为27和2分钟。用SDS凝胶电泳法和梯度凝胶电泳法分别测得该酶的分子量为115,000和118,000。薄层凝胶等电聚焦法测得其等电点为pH4.6。     

Effects of substrate limitation on product distribution and H2/CO2 ratio inKlebsiella pneumoniae during anaerobic fermentation of glycerol

Product formation during anaerobic degradation of glycerol byKlebsiella pneumoniae DSM 2026, under glycerol limitation and glycerol excess in continugius cultures, has been investigated. Major and minor products and by-products as well as gaseous products were measured. The results indicated a positive correlation between specific glycerol uptake and most product formation rates under glycerol limitation. The production of 1,3-propanediol, lactate, formate, acetate, succinate and the by-products of anaerobic glycerol degradation byK. pneumoniae, acetoin and 2,3-butanediol, was favoured by glycerol excess, while hydrogen generation and ethanol formation were best under glycerol limitation. It was also found that under glycerol limitation the rate of hydrogen evolution was generally higher than the CO₂ production rate while under excess glycerol the reverse was true. Hence, on the basis of the ratio of the specific rates of evolution of H₂ and CO₂ (q _{H 2}/q _{CO 2}), it is possible to infer the existence of glycerol limitation. On the basis of the carbon and available electron balances, which are independent of metabolic pathways, the data are consistent. The NADH₂ balance, which took into consideration the pathways of product formation, was also tested to check the validity of the assumed pathways and to check critically the consistency of the data. Good balances were also obtained.[     

EFFECTS OF HELPER PROTEINS ENCODED BY p19 AND orf1-orf2 GENES ON Cyt1Aa PROTEIN EXPRESSION IN ACRYSTALLIFEROUS STRAIN OF BACILLUS THURINGIENSIS

A series of plasmids were constructed to examine the effects of p19 and orf1‐orf2 genes from Bacillus thuringiensis on Cyt1Aa synthesis and inclusion formation. The plasmids expressed the cyt1Aa gene along with either p19 or orf1‐orf2, or each of them coordinatively with p20 in the acrystalliferous strain of B. thuringiensis subsp. israelensis 4Q7. No effect on the expression of Cyt1Aa protein was found when P19 or Orf1‐Orf2 co‐expressed with Cyt1Aa. However, when including p20 gene, the constructs with p19 or orf1‐orf2 gene produced lower yield of Cyt1Aa proteins than without p19 or orf1‐orf2 gene. Electron microscopy observation and bioassay showed that P19 and Orf1‐Orf2 have no influence on the crystal size and toxicity of Cyt1Aa protein. It is presumed that P19 and Orf1‐Orf2 might have negative effects on Cyt1Aa synthesis in B. thuringiensis.     

黄精凝集素Ⅱ分子稳定性与生物学活性研究

黄精凝集素Ⅱ分子稳定性与生物学活性研究鲍锦库,曾仲奎,周红（四川大学生物系,成都,６１００６４）本文在黄精凝集素Ⅱ纯化及性质研究的基础上,应用多种变性条件,研究其分子特性,同时对分子的巯基和色氨酸进行修饰,研究该凝集素分子保持其生物学活性与这些基团的...     

基因定点诱变删除及天然α型心钠素的分泌型表达

用基因定点诱变技术,删除了pO_1α ANF表达质粒中的33对碱基,使人α型心钠素结构基因直接与大肠杆菌分泌型表达质粒pIN-Ⅲ-OmPA中的信号肽酶切位点编码区相连,构成天然人α型心钠素的表达质粒pANF,在IPTG诱导下表达28肽的天然人α型心钠素。纯化后的表达产物具有天然心钠素的放免活性和很强的舒张血管的生物活性。     

DeepMHADTA: Prediction of Drug-Target Binding Affinity Using Multi-Head Self-Attention and Convolutional Neural Network

Drug-target interactions provide insight into the drug-side effects and drug repositioning. However, wet-lab biochemical experiments are time-consuming and labor-intensive, and are insufficient to meet the pressing demand for drug research and development. With the rapid advancement of deep learning, computational methods are increasingly applied to screen drug-target interactions. Many methods consider this problem as a binary classification task (binding or not), but ignore the quantitative binding affinity. In this paper, we propose a new end-to-end deep learning method called DeepMHADTA, which uses the multi-head self-attention mechanism in a deep residual network to predict drug-target binding affinity. On two benchmark datasets, our method outperformed several current state-of-the-art methods in terms of multiple performance measures, including mean square error (MSE), consistency index (CI), rm2, and PR curve area (AUPR). The results demonstrated that our method achieved better performance in predicting the drug–target binding affinity.