Predicting disulfide connectivity patterns

Disulfide bonds play an important role in stabilizing protein structure and regulating protein function. Therefore, the ability to infer disulfide connectivity from protein sequences will be valuable in structural modeling and functional analysis. However, to predict disulfide connectivity directly from sequences presents a challenge to computational biologists due to the nonlocal nature of disulfide bonds, i.e., the close spatial proximity of the cysteine pair that forms the disulfide bond does not necessarily imply the short sequence separation of the cysteine residues. Recently, Chen and Hwang (Proteins 2005;61:507-512) treated this problem as a multiple class classification by defining each distinct disulfide pattern as a class. They used multiple support vector machines based on a variety of sequence features to predict the disulfide patterns. Their results compare favorably with those in the literature for a benchmark dataset sharing less than 30% sequence identity. However, since the number of disulfide patterns grows rapidly when the number of disulfide bonds increases, their method performs unsatisfactorily for the cases of large number of disulfide bonds. In this work, we propose a novel method to represent disulfide connectivity in terms of cysteine pairs, instead of disulfide patterns. Since the number of bonding states of the cysteine pairs is independent of that of disulfide bonds, the problem of class explosion is avoided. The bonding states of the cysteine pairs are predicted using the support vector machines together with the genetic algorithm optimization for feature selection. The complete disulfide patterns are then determined from the connectivity matrices that are constructed from the predicted bonding states of the cysteine pairs. Our approach outperforms the current approaches in the literature.     

Coding processes involved in the cortical representation of complex tactile stimuli

To understand how information is coded in the primary somatosensory cortex (S1) we need to decipher the relationship between neural activity and tactile stimuli. Such a relationship can be formally measured by mutual information. The present study was designed to determine how S1 neuronal populations code for the multidimensional kinetic features (i.e. random, time-varying patterns of force) of complex tactile stimuli, applied at different locations of the rat forepaw. More precisely, the stimulus localization and feature extraction were analyzed as two independent processes, using both rate coding and temporal coding strategies. To model the process of stimulus kinetic feature extraction, multidimensional stimuli were projected onto lower dimensional subspace and then clustered according to their similarity. Different combinations of stimuli clustering were applied to differentiate each stimulus identification process. Information analyses show that both processes are synergistic, this synergy is enhanced within the temporal coding framework. The stimulus localization process is faster than the stimulus feature extraction process. The latter provides more information quantity with rate coding strategy, whereas the localization process maximizes the mutual information within the temporal coding framework. Therefore, combining mutual information analysis with robust clustering of complex stimuli provides a framework to study neural coding mechanisms related to complex stimuli discrimination.     

基于多组学数据的肿瘤药物敏感性预测北大核心CSCD

肿瘤药物敏感性预测在指导患者临床用药方面具有重要意义。本文基于癌症药物敏感性基因组学数据库(genomics of drug sensitivity in cancer, GDSC) 198种药物的细胞系敏感性IC50数据,通过Stacking集成学习构建了包含基因表达、基因突变、拷贝数变异数据的多组学癌症药物敏感性预测模型。采用多种特征选择方法对基因特征进行降维,使用Stacking方法集成6种初级学习器和1种次级学习器进行建模,采用5折交叉进行模型验证。预测结果中AUC大于0.9的占比为36.4%,在0.8–0.9之间的占比为49.0%,最低AUC为0.682。基于Stacking构建的多组学预测模型较已有单组学和多组学模型的准确性和稳定性具有优势。多组学整合预测药物敏感性优于单一组学。特征基因功能注释和富集分析解析了肿瘤对sorafenib潜在的耐药机制,从生物学角度提供了模型可解释性及其应用于临床用药指导的价值。     

青海祁连瑞香狼毒的光谱差异特征提取

瑞香狼毒是分布在青海省高寒草甸的主要毒害草之一,近年来其迅速蔓延对当地畜牧业危害严重并使草地生态系统日趋退化.在海北州祁连县选取狼毒分布的典型退化草甸,采用2012—2014年狼毒盛花期获取的实测光谱数据,分析狼毒与牧草的光谱差异性.结果表明： 在350~900 nm的可见光 近红外波段,狼毒顶花的光谱反射特征明显异于狼毒叶片和同期牧草等绿色背景,顶花与绿色背景的光谱反射率差异主要体现在红谷和蓝谷.随着盖度的增加,狼毒群落光谱反射率整体升高,在近红外反射峰处狼毒群落与牧草群落光谱反射率具有最大差值,且不同盖度狼毒群落之间的差异性最明显.顶花与绿色背景以及狼毒群落与牧草群落的一阶导数光谱差异均体现在黄边幅值和蓝边幅值.狼毒群落盖度与光谱特征参量的线性回归分析表明,红谷与狼毒群落盖度的相关性最好(R²=0.94),反演狼毒群落盖度的精度最高.盛花期区分狼毒与牧草的主要光谱特征参量为红谷、蓝谷与近红外反射峰,其对应的红、蓝及近红外波段的组合可用于构建狼毒提取的敏感指数.     

Genetic variants in root architecture-related genes in a Glycine soja accession,a potential resource to improve cultivated soybean

Background

Root system architecture is important for water acquisition and nutrient acquisition for all crops. In soybean breeding programs, wild soybean alleles have been used successfully to enhance yield and seed composition traits, but have never been investigated to improve root system architecture. Therefore, in this study, high-density single-feature polymorphic markers and simple sequence repeats were used to map quantitative trait loci (QTLs) governing root system architecture in an inter-specific soybean mapping population developed from a cross between Glycine max and Glycine soja.

Results

Wild and cultivated soybean both contributed alleles towards significant additive large effect QTLs on chromosome 6 and 7 for a longer total root length and root distribution, respectively. Epistatic effect QTLs were also identified for taproot length, average diameter, and root distribution. These root traits will influence the water and nutrient uptake in soybean. Two cell division-related genes (D type cyclin and auxin efflux carrier protein) with insertion/deletion variations might contribute to the shorter root phenotypes observed in G. soja compared with cultivated soybean. Based on the location of the QTLs and sequence information from a second G. soja accession, three genes (slow anion channel associated 1 like, Auxin responsive NEDD8-activating complex and peroxidase), each with a non-synonymous single nucleotide polymorphism mutation were identified, which may also contribute to changes in root architecture in the cultivated soybean. In addition, Apoptosis inhibitor 5-like on chromosome 7 and slow anion channel associated 1-like on chromosome 15 had epistatic interactions for taproot length QTLs in soybean.

Conclusion

Rare alleles from a G. soja accession are expected to enhance our understanding of the genetic components involved in root architecture traits, and could be combined to improve root system and drought adaptation in soybean.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1334-6) contains supplementary material, which is available to authorized users.     

Structural and thermal characterization of hemicelluloses isolated by organic solvents and alkaline solutions from Tamarix austromongolica

Three organosolv and three alkaline hemicellulosic fractions were prepared from lignocellulosic biomass of the fast-growing shrub Tamarix austromongolica (Tamarix Linn.). Sugar analysis revealed that the organosolv-soluble fractions contained a higher content of glucose (33.7-6.5%) and arabinose (14.8-5.6%), and a lower content of xylose (62.2-54.8%) than the hemicellulosic fractions isolated with aqueous alkali solutions. A relatively high concentration of alkali resulted in a decreasing trend of the xylose/4-O-methyl-d-glucuronic acid ratio in the alkali-soluble fractions. The results of NMR analysis supported a major substituted structure based on a linear polymer of β-(1 → 4)-linked d-xylopyranosyl residues, having ramifications of α-l-arabinofuranose and 4-O-methyl-d-glucuronic acid residues monosubstituted at O-3 and O-2, respectively. Thermogravimetric analysis revealed that one step of major mass loss occurred between 200-400 °C, as hemicelluloses devolatilized with total volatile yield of about 55%. It was found that organosolv-soluble fractions are more highly ramified, and showed a higher thermal stability than the alkali-soluble fractions.     

An accurate feature-based method for identifying DNA-binding residues on protein surfaces

Proteins that interact with DNA play vital roles in all mechanisms of gene expression and regulation. In order to understand these activities, it is crucial to analyze and identify DNA-binding residues on DNA-binding protein surfaces. Here, we proposed two novel features B-factor and packing density in combination with several conventional features to characterize the DNA-binding residues in a well-constructed representative dataset of 119 protein-DNA complexes from the Protein Data Bank (PDB). Based on the selected features, a prediction model for DNA-binding residues was constructed using support vector machine (SVM). The predictor was evaluated using a 5-fold cross validation on above dataset of 123 DNA-binding proteins. Moreover, two independent datasets of 83 DNA-bound protein structures and their corresponding DNA-free forms were compiled. The B-factor and packing density features were statistically analyzed on these 83 pairs of holo-apo proteins structures. Finally, we developed the SVM model to accurately predict DNA-binding residues on protein surface, given the DNA-free structure of a protein. Results showed here indicate that our method represents a significant improvement of previously existing approaches such as DISPLAR. The observation suggests that our method will be useful in studying protein-DNA interactions to guide consequent works such as site-directed mutagenesis and protein-DNA docking.     

Gamma神经振荡产生机制及其功能研究进展

Gamma神经振荡的频率在30～100 Hz之间,存在于动物和人类大脑的多个区域,如丘脑、体感皮层以及海马等部位,在各个尺度水平上都可被检测到.抑制性中间神经元组成的神经网络是产生此高频节律性活动的主要条件之一.皮层的gamma神经振荡与丘脑-皮层系统有关.Gamma神经振荡具有易化突触可塑性和调节神经网络的作用,主要参与感觉特征绑定、选择性注意以及记忆等高级功能.     

基于邻接矩阵分解的肿瘤亚型特征提取方法

基于肿瘤基因表达谱的肿瘤分类是生物信息学的一个重要研究内容。传统的肿瘤信息特征提取方法大多基于信息基因选择方法,但是在筛选基因时,不可避免的会造成分类信息的流失。提出了一种基于邻接矩阵分解的肿瘤亚型特征提取方法,首先对肿瘤基因表达谱数据构造高斯权邻接矩阵,接着对邻接矩阵进行奇异值分解,最后将分解得到的正交矩阵特征行向量作为分类特征输入支持向量机进行分类识别。采用留一法对白血病两个亚型的基因表达谱数据集进行实验,实验结果证明了该方法的可行性和有效性。