基于复杂网络的随机森林算法预测氨基酸突变对蛋白质稳定性的影响

利用机器学习方法对单个氨基酸突变引起的蛋白质稳定性变化进行精确地预测,对蛋白质的结构和功能方面的研究具有重要的价值,并且对设计新的蛋白质及蛋白质工程学具有一定的指导意义.通过对蛋白质网络拓扑特征的研究,发现网络拓扑特征对于蛋白质突变稳定性影响具有较高的准确率.基于蛋白质网络拓扑特征的随机森林算法,能较好的对蛋白质单点突...     

Prediction of T-cell epitopes based on least squares support vector machines and amino acid properties

T-lymphocyte (T-cell) is a very important component in human immune system. It possesses a receptor (TCR) that is specific for the foreign epitopes which are in a form of short peptides bound to the major histocompatibility complex (MHC). When T-cell receives the message about the peptides bound to MHC, it makes the immune system active and results in the disposal of the immunogen. The antigenic determinants recognized and bound by the T-cell receptor is known as T-cell epitope. The accurate prediction of T-cell epitopes is crucial for vaccine development and clinical immunology. For the first time we developed new models using least squares support vector machine (LSSVM) and amino acid properties for T-cell epitopes prediction. A dataset including 203 short peptides (167 non-epitopes and 36 epitopes) was used as the input dataset and it was randomly divided into a training set and a test set. The models based on LSSVM and amino acid properties were evaluated using leave-one-out cross-validation method and the predictive ability of the test set, and obtained the results of 0.9875 and 0.9734 under the ROC curves, respectively. This result is more satisfactory than that were reported before. Especially, the accuracy of true positive gets a marked enhancement.     

Peptide reagent design based on physical and chemical properties of amino acid residues

It has tremendous values for both drug discovery and basic research to develop a solid bioinformatical tool for guiding peptide reagent design. Based on the physical and chemical properties of amino acids, a new strategy for peptide reagent design, the so-called AABPD (amino acid based-peptide design), is proposed. The peptide samples in a training dataset are described by a series of HMLP (heuristic molecular lipophilicity potential) parameters and other physicochemical properties of amino acid residues that form a three-dimensional data matrix where each component is defined by three indexes: the first index refers to the peptide samples, the second to the amino acid positions, and the third to the amino acid parameters. The binding free energy between a peptide ligand and its protein receptor is calculated by a linear free energy equation through the physicochemical parameters, resulting in a set of simultaneous linear equations between the bioactivity of the peptides and the physicochemical properties of amino acids. An iterative double least square technique is developed for the solution of the three-dimensional simultaneous linear equation set to determine the amino acid position coefficients of peptide sequence and the physicochemical parameter coefficients of amino acid residues alternately. The two sets of coefficients thus obtained are used for predicting the bioactivity of other query peptide reagents. Two calculation examples, the peptide substrate specificity of the SARS coronavirus 3C-like proteinase and the affinity prediction for epitope-peptides with Class I MHC molecules are studied by using the peptide reagent design strategy.     

Exploring metabolic syndrome serum profiling based on gas chromatography mass spectrometry and random forest models

Metabolic syndrome (MetS) is a constellation of the most dangerous heart attack risk factors: diabetes and raised fasting plasma glucose, abdominal obesity, high cholesterol and high blood pressure. Analysis and representation of the variances of metabolic profiles is urgently needed for early diagnosis and treatment of MetS. In current study, we proposed a metabolomics approach for analyzing MetS based on GC–MS profiling and random forest models. The serum samples from healthy controls and MetS patients were characterized by GC–MS. Then, random forest (RF) models were used to visually discriminate the serum changes in MetS based on these GC–MS profiles. Simultaneously, some informative metabolites or potential biomarkers were successfully discovered by means of variable importance ranking in random forest models. The metabolites such as 2-hydroxybutyric acid, inositol and d-glucose, were defined as potential biomarkers to diagnose the MetS. These results obtained by proposed method showed that the combining GC–MS profiling with random forest models was a useful approach to analyze metabolites variances and further screen the potential biomarkers for MetS diagnosis.     

基于苯磺酸和2，2'-联吡啶双配体的锰配合物的结构及其催化Mannich反应性能和机理

由溶剂热法合成了 2 个锰的超分子配合物[Mn₂(2,2''-bipy)₄(H₂O)Cl₃](L₁)·6H₂O (1)和[Mn(2,2''-bipy)₂(H₂O)Cl](L₂)·3H₂O (2)(L₁^-=对甲基苯磺酸根,L₂^-=间硝基苯磺酸根,2,2''-bipy=2,2''-联吡啶),并用单晶X射线衍射、红外光谱、热重分析和氮气吸附-脱附测试对其进行了表征。以Mannich反应为探针,研究了2种配合物的催化性能,并通过对比2种配合物的扫描电镜和粉末X射线衍射表征结果,分析了配合物结构对其催化性能的影响。最后通过密度泛函理论预测了配合物的活性位点,利用X射线光电子能谱证明了活性位点的活化作用,进而阐述了配合物催化Mannich反应的机理。