Disulfide connectivity prediction with 70% accuracy using two-level models

Disulfide bridges stabilize protein structures covalently and play an important role in protein folding. Predicting disulfide connectivity precisely helps towards the solution of protein structure prediction. Previous methods for disulfide connectivity prediction either infer the bonding potential of cysteine pairs or rank alternative disulfide bonding patterns. As a result, these methods encode data according to cysteine pairs (pair-wise) or disulfide bonding patterns (pattern-wise). However, using either encoding scheme alone cannot fully utilize the local and global information of proteins, so the accuracies of previous methods are limited. In this work, we propose a novel two-level framework to predict disulfide connectivity. With this framework, both the pair-wise and pattern-wise encoding schemes are considered. Our models were validated on the datasets derived from SWISS-PROT 39 and 43, and the results demonstrate that our models can combine both local and global information. Compared to previous methods, significant improvements were obtained by our models. Our work may also provide insights to further improvements of disulfide connectivity prediction and increase its applicability in protein structure analysis and prediction.     

Learning Biomarkers of Pluripotent Stem Cells in Mouse

Pluripotent stem cells are able to self-renew, and to differentiate into all adult cell types. Many studies report data describing these cells, and characterize them in molecular terms. Machine learning yields classifiers that can accurately identify pluripotent stem cells, but there is a lack of studies yielding minimal sets of best biomarkers (genes/features). We assembled gene expression data of pluripotent stem cells and non-pluripotent cells from the mouse. After normalization and filtering, we applied machine learning, classifying samples into pluripotent and non-pluripotent with high cross-validated accuracy. Furthermore, to identify minimal sets of best biomarkers, we used three methods: information gain, random forests and a wrapper of genetic algorithm and support vector machine (GA/SVM). We demonstrate that the GA/SVM biomarkers work best in combination with each other; pathway and enrichment analyses show that they cover the widest variety of processes implicated in pluripotency. The GA/SVM wrapper yields best biomarkers, no matter which classification method is used. The consensus best biomarker based on the three methods is Tet1, implicated in pluripotency just recently. The best biomarker based on the GA/SVM wrapper approach alone is Fam134b, possibly a missing link between pluripotency and some standard surface markers of unknown function processed by the Golgi apparatus.     

肽合成中多对二硫键的形成策略及分析方法

二硫键的正确配对是富含二硫键多肽合成的关键。本文综述了含两对二硫键以上的多肽二硫键的形成策略,优化方法、以及二硫键配对方式的测定方法。     

两种过滤特征基因选择算法的有效性研究

对基因表达谱进行特征基因选择不仅能改善疾病分类方法的效能,而且为寻找与疾病相关的特征基因提供新的途径．通过比较用调整p值的t检验、非参数评分两种特征基因选择算法后和未进行选择时支持向量机(SVM)分类器的分类性能、支持向量(SV)的吻合度、错分样本ID的吻合度和对样本均匀翻倍后的稳定性．结果发现：特征选择后线性、核函数为二阶多项式和径向基的SVM分类性能明显提高;特征选择前后的SV及错分样本ID的吻合度均较高;SVM的稳定性较好．由此得出结论：这两种特征选择算法具有一定的有效性．     

Influences of disulfide connectivity on structure and antimicrobial activity of tachyplesin I

Tachyplesin I is a potent antimicrobial peptide with broad spectrum of antimicrobial activity. It has 2 disulfide bonds and can form 3 disulfide bond isomers. In this study, the structure and antimicrobial activity of 3 tachyplesin I isomers (tachyplesin I, 3C12C, 3C7C) were investigated using molecular dynamic simulations, circular dichroism structural study, as well as antimicrobial activity and hemolysis assay. Our results suggest that in comparison to the native peptide, the 2 isomers (3C12C, 3C7C) have substantial structural and activity variations. The native peptide is in the ribbon conformation, while 3C12C and 3C7C possess remarkably different secondary structures, which are referred as “globular” and “beads” isomers, respectively. The substantially decreased hemolysis effects for these 2 isomers is accompanied by significantly decreased anti‐gram‐positive bacterial activity.     

Generation of two isomers with the same disulfide connectivity during disulfide bond formation of human uroguanylin

Summary In a two-step selective disulfide-bond-forming reaction of human uroguanylin, a 16-residue peptide with two intramolecular disulfide bonds, two compounds (I and II) were formed, which could be detected by RP-HPLC after the second disulfide-bond-forming reaction and were isolated as single entities. Their primary structures, molecular weights, and disulfide connectivities proved to be identical, but their optical rotation values were different, suggesting that they are topological isomers. Only compound I was found to increase the cGMP levels in cultured T84 cells significantly. The ratio of these compounds was affected by the order of the disulfide-bond-forming reactions, but not by the solvent used. The presence of a carboxyl-terminal leucine residue seems to be crucial for stabilizing the conformation of the two isomers.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, “RFE_Relief algorithm” was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, "RFE_Relief algorithm" was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Protein disulfide isomerase isomerizes non-native disulfide bonds in human proinsulin independent of its peptide-binding activity

Protein disulfide isomerase (PDI) supports proinsulin folding as chaperone and isomerase. Here, we focus on how the two PDI functions influence individual steps in the complex folding process of proinsulin. We generated a PDI mutant (PDI-aba'c) where the b' domain was partially deleted, thus abolishing peptide binding but maintaining a PDI-like redox potential. PDI-aba'c catalyzes the folding of human proinsulin by increasing the rate of formation and the final yield of native proinsulin. Importantly, PDI-aba'c isomerizes non-native disulfide bonds in completely oxidized folding intermediates, thereby accelerating the formation of native disulfide bonds. We conclude that peptide binding to PDI is not essential for disulfide isomerization in fully oxidized proinsulin folding intermediates.     

Tissue differences revealed by gene expression profiles of various cell lines

Mechanisms through which tissues are formed and maintained remain unknown but are fundamental aspects in biology. Tissue-specific gene expression is a valuable tool to study such mechanisms. But in many biomedical studies, cell lines, rather than human body tissues, are used to investigate biological mechanisms Whether or not cell lines maintain their tissue-specific characteristics after they are isolated and cultured outside the human body remains to be explored. In this study, we applied a novel computational method to identify core genes that contribute to the differentiation of cell lines from various tissues. Several advanced computational techniques, such as Monte Carlo feature selection method, incremental feature selection method, and support vector machine (SVM) algorithm, were incorporated in the proposed method, which extensively analyzed the gene expression profiles of cell lines from different tissues. As a result, we extracted a group of functional genes that can indicate the differences of cell lines in different tissues and built an optimal SVM classifier for identifying cell lines in different tissues. In addition, a set of rules for classifying cell lines were also reported, which can give a clearer picture of cell lines in different issues although its performance was not better than the optimal SVM classifier. Finally, we compared such genes with the tissue-specific genes identified by the Genotype-tissue Expression project. Results showed that most expression patterns between tissues remained in the derived cell lines despite some uniqueness that some genes show tissue specificity.     

Measuring connectivity patterns in a macro-corridor on the south coast of Western Australia

Summary   Portions of the south coast of Western Australia are recognized as environmentally valuable because of high levels of biodiversity, but are at risk due to continued anthropogenic influences, particularly in the form of fragmentation and habitat loss. Corridors of habitat, either continuous or stepping-stone, are deemed to be valuable for the maintenance and increase of biodiversity in the region. Here we apply a series of betweenness centrality analyses to quantify the connectivity in the region. The method we describe here is an extended application of the betweenness centrality measure, which is a measure of spatial connectivity that is applied to fragmented landscapes. The method is used for significant corridors between the Stirling Ranges and the Fitzgerald River National Park to identify a series of locations that are important to the connectivity in this region and thus may provide effective locations for restoration inputs.     

Gene expression data classification using consensus independent component analysis

We propose a new method for tumor classification from gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are modeled by independent component analysis （ICA）. Secondly, the most discriminant eigenassays extracted by ICA are selected by the sequential floating forward selection technique. Finally, support vector machine is used to classify the modeling data. To show the validity of the proposed method, we applied it to classify three DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.     

Support Vector Machines for Predicting Apoptosis Proteins Types

Apoptosis proteins have a central role in the development and homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death, and their function is related to their types. According to the classification scheme by Zhou and Doctor (2003), the apoptosis proteins are categorized into the following four types: (1) cytoplasmic protein; (2) plasma membrane-bound protein; (3) mitochondrial inner and outer proteins; (4) other proteins. A powerful learning machine, the Support Vector Machine, is applied for predicting the type of a given apoptosis protein by incorporating the sqrt-amino acid composition effect. High success rates were obtained by the re-substitute test (98/98 = 100 %) and the jackknife test (89/98 = 90.8%).     

Novel disulfide engineering in human carbonic anhydrase II using the PAIRWISE side-chain geometry database

An analysis of the pairwise side-chain packing geometries of cysteine residues observed in high-resolution protein crystal structures indicates that cysteine pairs have pronounced orientational preferences due to the geometric constraints of disulfide bond formation. A potential function was generated from these observations and used to evaluate models for novel disulfide bonds in human carbonic anhydrase II (HCAII). Three double-cysteine variants of HCAII were purified and the effective concentrations of their thiol groups were determined by titrations with glutathione and dithiothreitol. The effects of the cysteine mutations on the native state structure and stability were characterized by circular dichroism, enzymatic activity, sulfonamide binding, and guanidine hydrochloride titration. These analyses indicate that the PAIRWISE potential is a good predictor of the strength of the disulfide bond itself, but the overall structural and thermodynamic effects on the protein are complicated by additional factors. In particular, the effects of cysteine substitutions on the native state and the stabilization of compact nonnative states by the disulfide can override any stabilizing effect of the cross-link.     

层析辅助体外复性含有多对二硫键的融合蛋白质

为建立一种基于阴离子交换介质辅助的含多对二硫键的抗凝溶栓双功能水蛭素12肽-瑞替普酶融合蛋白质 (HV12p-rPA) 的复性方法,采用Q Sepharose XL作为层析复性介质,通过正交实验考察蛋白质上样量、流速、脲梯度、洗脱液中精氨酸浓度、脲浓度、pH、还原型及氧化型谷胱甘肽等因素对复性过程的影响,探索最佳层析复性条件。结果表明：脲梯度、上样量及精氨酸浓度是影响复性的3个主要因素。脲梯度是复性成功的关键,上样量增大时复性蛋白质比活降低,精氨酸辅助HV12p-rPA复性的最佳浓度为1 mol/L。创建了脲、pH双梯度下的阴离子交换层析辅助HV12p-rPA的复性方法,复性后蛋白质的溶栓比活达到46 520 IU/mg,抗凝比活达到9 980 ATU/mg,与稀释复性方法相比,该方法能使复性蛋白质的溶栓比活提高14~15倍,抗凝比活提高7~8倍。     

mr1e, a conotoxin from Conus marmoreus with a novel disulfide pattern

Conotoxins are well known for their highly variable structures and functions. Here we report the identification of a novel conotoxin named mr1e from Conus marmoreus . mr1e is composed of 11 amino acid residues cross-linked by two disulfide bonds (CCHSSWCKHLC). The spacing of intercysteine loops in mr1e is exactly the same as that in α4/3 conotoxins. However, the native mr1e peptide co-eluted on reverse-phase HPLC with the regioselectively synthesized ribbon disulfide linkage isomer (C¹-C⁴, C²-C³) but not the globular linkage isomer (C¹-C³, C²-C⁴). Although this peptide has the same disulfide connectivity as the χ-conotoxins, their sequences do not share significant homology. Thus, mr1e could be defined as a novel conotoxin family. By intracranial injection into mice, mr1e showed an excitatory effect. The characterization of mr1e certainly enriches our understanding of conotoxins, and also opens an avenue for further structural and functional investigation.