Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins

Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.     

Prediction and Analysis of Post-Translational Pyruvoyl Residue Modification Sites from Internal Serines in Proteins

Most of pyruvoyl-dependent proteins observed in prokaryotes and eukaryotes are critical regulatory enzymes, which are primary targets of inhibitors for anti-cancer and anti-parasitic therapy. These proteins undergo an autocatalytic, intramolecular self-cleavage reaction in which a covalently bound pyruvoyl group is generated on a conserved serine residue. Traditional detections of the modified serine sites are performed by experimental approaches, which are often labor-intensive and time-consuming. In this study, we initiated in an attempt for the computational predictions of such serine sites with Feature Selection based on a Random Forest. Since only a small number of experimentally verified pyruvoyl-modified proteins are collected in the protein database at its current version, we only used a small dataset in this study. After removing proteins with sequence identities >60%, a non-redundant dataset was generated and was used, which contained only 46 proteins, with one pyruvoyl serine site for each protein. Several types of features were considered in our method including PSSM conservation scores, disorders, secondary structures, solvent accessibilities, amino acid factors and amino acid occurrence frequencies. As a result, a pretty good performance was achieved in our dataset. The best 100.00% accuracy and 1.0000 MCC value were obtained from the training dataset, and 93.75% accuracy and 0.8441 MCC value from the testing dataset. The optimal feature set contained 9 features. Analysis of the optimal feature set indicated the important roles of some specific features in determining the pyruvoyl-group-serine sites, which were consistent with several results of earlier experimental studies. These selected features may shed some light on the in-depth understanding of the mechanism of the post-translational self-maturation process, providing guidelines for experimental validation. Future work should be made as more pyruvoyl-modified proteins are found and the method should be evaluated on larger datasets. At last, the predicting software can be downloaded from http://www.nkbiox.com/sub/pyrupred/index.html.     

A new hybrid approach to predict subcellular localization of proteins by incorporating gene ontology

Based on the recent development in the gene ontology and functional domain databases, a new hybridization approach is developed for predicting protein subcellular location by combining the gene product, functional domain, and quasi-sequence-order effects. As a showcase, the same prokaryotic and eukaryotic datasets, which were studied by many previous investigators, are used for demonstration. The overall success rate by the jackknife test for the prokaryotic set is 94.7% and that for the eukaryotic set 92.9%. These are so far the highest success rates achieved for the two datasets by following a rigorous cross-validation test procedure, suggesting that such a hybrid approach may become a very useful high-throughput tool in the area of bioinformatics, proteomics, as well as molecular cell biology. The very high success rates also reflect the fact that the subcellular localization of a protein is closely correlated with: (1). the biological objective to which the gene or gene product contributes, (2). the biochemical activity of a gene product, and (3). the place in the cell where a gene product is active.     

Prediction of beta-turns with learning machines

The support vector machine approach was introduced to predict the beta-turns in proteins. The overall self-consistency rate by the re-substitution test for the training or learning dataset reached 100%. Both the training dataset and independent testing dataset were taken from Chou [J. Pept. Res. 49 (1997) 120]. The success prediction rates by the jackknife test for the beta-turn subset of 455 tetrapeptides and non-beta-turn subset of 3807 tetrapeptides in the training dataset were 58.1 and 98.4%, respectively. The success rates with the independent dataset test for the beta-turn subset of 110 tetrapeptides and non-beta-turn subset of 30,231 tetrapeptides were 69.1 and 97.3%, respectively. The results obtained from this study support the conclusion that the residue-coupled effect along a tetrapeptide is important for the formation of a beta-turn.     

Support vector machine for predicting alpha-turn types

Tight turns play an important role in globular proteins from both the structural and functional points of view. Of tight turns, beta-turns and gamma-turns have been extensively studied, but alpha-turns were little investigated. Recently, a systematic search for alpha-turns classified alpha-turns into nine different types according to their backbone trajectory features. In this paper, Support Vector Machines (SVMs), a new machine learning method, is proposed for predicting the alpha-turn types in proteins. The high rates of correct prediction imply that that the formation of different alpha-turn types is evidently correlated with the sequence of a pentapeptide, and hence can be approximately predicted based on the sequence information of the pentapeptide alone, although the incorporation of its interaction with the other part of a protein, the so-called "long distance interaction", will further improve the prediction quality.     

基于上转换纳米材料的免疫检测技术应用

免疫分析法具有简便、快速、准确等特点，广泛应用于医学、食品、环境等领域检测，将免疫分析方法与纳米材料相结合可以提高免疫分析的性能。与传统纳米材料相比，上转换纳米颗粒（upconversion nanoparticles，UCNPs）具有光稳定性好、发光寿命长和狭窄及可调整的发射带等优秀的光学性质，与免疫分析相结合可显著降低背景噪声，提高分析灵敏度。本文简要介绍了UCNPs的发光机制，对UCNPs的合成和表面修饰方法进行了总结，并详细论述荧光共振能量转移、内滤效应、磁分离技术、上转化连接免疫吸附技术和上转换免疫层析技术五种基于UCNPs的免疫检测技术，最后对该技术所面临的挑战和前景进行总结和展望，以期为UCNPs免疫检测技术的发展提供理论指导。     

Development and application of RAPD-SCAR markers to identify intra-species hybrids of industrial Saccharomyces cerevisiae

To overcome the drawbacks of protoplast fusion in industrial breeding, strain-specific molecular markers were applied to select hybrids of industrial Saccharomyces cerevisiae strains. Random Amplified Polymorphic DNA (RAPD) analysis was used to generate strain-specific RAPD markers for two industrial yeast strains, Z8 and Z9. For industrial and technical controls, two RAPD markers with non-coding regions were converted into stable Sequence Characterized Amplified Region (SCAR) markers. Hybrids of Z8 and Z9 were obtained by protoplast fusion in combination with SCAR markers and were found to increase ethanol production by 4.3–8.1%. Results suggested that protoplast fusion could be combined with RAPD-SCAR molecular markers and applied in industrial breeding instead of auxotrophic markers.     

冠状病毒PLpro蛋白酶对泛素样分子的DUB活性

SARS冠状病毒基因组中非结构基因nsp3编码的木瓜样蛋白酶 (PLpro) 在病毒基因组复制及逃避宿主天然免疫中发挥重要作用,是研发抗病毒药物的重要靶标．SARS冠状病毒PLpro是一种病毒编码的去泛素化酶 (DUB)．为深入研究SARS冠状病毒 PLpro对泛素样分子 (ubiquitin-like protein,UBL) 的DUB特性,本研究构建缺失 PLpro N末端泛素样结构域 (Ubl) 和下游跨膜结构域 (TM) 的PLpro构建体（constructs）,并构建3种缺失蛋白酶催化活性的突变体,检测PLpro对泛素样分子干扰素刺激基因15 (ISG15)及SUMO-1的作用．实验结果表明,PLpro和PLpro-TM 在细胞内具有很强的去ISG(DeISGylation) 活性;缺失PLpro N末端泛素样结构域(Ubl) 对PLpro 的去ISG15 活性没有影响;对PLpro蛋白酶活性位点C1651 和 H1812 突变后,PLpro-TM的去ISG15活性消失,而对D1826位点突变后不影响此活性．PLpro 不具有去SUMO (DeSUMOylation)活性,而PLpro-TM具有一定的去SUMO活性;PLpro催化活性相关的3个关键氨基酸残基 Cys-His-Asp突变后对去SUMO活性有一定的影响．研究结果提示,SARS PLpro除了具有DUB的活性,还具有体内去ISG活性和去SUMO活性;PLpro蛋白酶活性与其去ISG活性之间有一定相关性;PLpro去SUMO-1 活性具有TM 依赖性．SARS冠状病毒PLpro 对泛素样分子作用特性的研究为阐明病毒逃避宿主天然免疫机制和开发新型抗病毒药物提供重要的理论依据．