The Structure of Physarum polycephalum hemagglutinin I suggests a minimal carbohydrate recognition domain of legume lectin fold

Physarum polycephalum hemagglutinin I (HA1) is a 104-residue protein that is secreted to extracellular space. The crystal structure of HA1 has a β-sandwich fold found among lectin structures, such as legume lectins and galectins. Interestingly, the β-sandwich of HA1 lacks a jelly roll motif and is essentially composed of two simple up-and-down β-sheets. This up-and-down β-sheet motif is well conserved in other legume lectin-like proteins derived from animals, plants, bacteria, and viruses. It is more noteworthy that the up-and-down β-sheet motif includes many residues that make contact with the target carbohydrates. Our NMR data demonstrate that HA1 lacking a jelly roll motif also binds to its target glycopeptide. Taken together, these data show that the up-and-down β-sheet motif provides a fundamental scaffold for the binding of legume lectin-like proteins to the target carbohydrates, and the structure of HA1 suggests a minimal carbohydrate recognition domain.     

阿维菌素起始酰基转移酶的底物特异性

[背景]阿维菌素起始酰基转移酶(AveAT0)能够以2-甲基丁酰-辅酶A (coenzyme A,CoA)和异丁酰-CoA作为起始单元分别合成"a"系列或"b"系列的阿维菌素。[目的]探究AveAT0对两种底物的偏好性并进行改造。[方法]通过与识别不同底物的起始酰基转移酶(loading acyltransferases,AT0s)进行序列比对,找到AveAT0底物结合重要的氨基酸,利用活性位点定点突变的方法得到对底物偏好性改变的特定突变体。以2-甲基丁酰-CoA、异丁酰-CoA的类似物2-甲基丁酰-N-乙酰半胱氨(N-acetylcysteamine,SNAC)和异丁酰-SNAC为底物,用Ellman测试法检测释放SNAC的游离巯基(sulfhydryl,SH),测定AveAT0及其突变体的动力学常数,以此表征AveAT0及其突变体的底物偏好性。[结果]AveAT0对2-甲基丁酰SNAC的Km值为0.4 mmol/L,kcat值为14.1 min^-1,kcat/Km为32.1 L/(mmol·min);对异丁酰-SNAC的Km值为0.8 mmol/L,kcat值为6.4 min^-1,kcat/Km为7.5 L/(mmol·min)。选定的突变位点为V224M、Q149L、L121M。按顺序累积突变后发现三突变株AveAT0 V224M/Q149L/L121M对两个底物的偏好性区别最大,对2-甲基丁酰SNAC的Km值为0.8 mmol/L,kcat值为5.4 min^-1,kcat/Km为6.9 L/(mmol·min);对异丁酰-SNAC的kcat/Km为0.1 L/(mmol·min)。[结论]研究发现了AveAT0识别底物过程中的关键氨基酸,为改造阿维菌素聚酮合酶酰基转移酶提供了依据。     

Linking co-expression modules with phenotypes

The method for quantifying the association between co-expression module and clinical trait of interest requires application of dimensionality reduction to summaries modules as one dimensional (1D) vector. However, these methods are often linked with information loss. The amount of information lost depends upon the percentage of variance captured by the reduced 1D vector. Therefore, it is of interest to describe a method using analysis of rank (AOR) to assess the association between module and clinical trait of interest. This method works with clinical traits represented as binary class labels and can be adopted for clinical traits measured in continuous scale by dividing samples in two groups around median value. Application of the AOR method on test data for muscle gene expression profiles identifies modules significantly associated with diabetes status.     

Structure dissection of zebrafish progranulins identifies a well‐folded granulin/epithelin module protein with pro‐cell survival activities

The ancient and pluripotent progranulins contain multiple repeats of a cysteine‐rich sequence motif of ∼60 amino acids, called the granulin/epithelin module (GEM) with a prototypic structure of four β‐hairpins zipped together by six inter‐hairpin disulfide bonds. Prevalence of this disulfide‐enforced structure is assessed here by an expression screening of 19 unique GEM sequences of the four progranulins in the zebrafish genome, progranulins 1, 2, A and B. While a majority of the expressed GEM peptides did not exhibit uniquely folded conformations, module AaE from progranulin A and AbB from progranulin B were found to fold into the protopypic 4‐hairpin structure along with disulfide formation. Module AaE has the most‐rigid three‐dimensional structure with all four β‐hairpins defined using high‐resolution (H–¹⁵N) NMR spectroscopy, including 492 inter‐proton nuclear Overhauser effects, 23 ³J(HN,H_α) coupling constants, 22 hydrogen bonds as well as 45 residual dipolar coupling constants. Three‐dimensional structure of AaE and the partially folded AbB re‐iterate the conformational stability of the N‐terminal stack of two beta‐hairpins and varying degrees of structural flexibility for the C‐terminal half of the 4‐hairpin global fold of the GEM repeat. A cell‐based assay demonstrated a functional activity for the zebrafish granulin AaE in promoting the survival of neuronal cells, similarly to what has been found for the corresponding granulin E module in human progranulin. Finally, this work highlights the remaining challenges in structure‐activity studies of proteins containing the GEM repeats, due to the apparent prevalence of structural disorder in GEM motifs despite potentially a high density of intramolecular disulfide bonds.     

Expression of functional Bacillus SpoIISAB toxin–antitoxin modules in Escherichia coli

SpoIISA and SpoIISB proteins from Bacillus subtilis belong to a recently described bacterial programmed-cell death system. The current work demonstrates that the toxin–antitoxin module is also functional in Escherichia coli cells, where the expression of SpoIISA toxin leads to transient growth arrest coupled with cell lysis, and SpoIISA-induced death can be prevented by coexpression of its cognate antitoxin, SpoIISB. Escherichia coli cells appear to be able to escape the SpoIISA killing by activation of a specific, as yet unidentified protease that cleaves out the cytosolic part of the protein. Analysis of the toxic effects of the transmembrane and cytosolic portions of SpoIISA showed that neither of them separately can function as a toxin; therefore, both parts of the protein have to act in concert to exert the killing. This work also identifies genes encoding putative homologues of SpoIISA and SpoIISB proteins on chromosomes of other Bacilli species. The SpoIISA-like proteins from Bacillus anthracis and Bacillus cereus were shown to manifest the same effect on the viability of E. coli as their homologue from B. subtilis . Moreover, expression of the proposed spoIISB -like gene rescues E. coli cells from death induced by the SpoIISA homologue.     

面向鸟鸣声识别任务的深度学习技术

在生态系统中,鸟类是重要的组成部分,对调节生态环境和监测生物多样性至关重要,甚至可以通过监测鸟群动向与监听鸟群异常鸣声对地震、海啸等自然灾害进行辅助预测和防范,为此,鸟鸣声识别和异常鸣声监测成为热门的研究方向。然而,由于传统鸟鸣声识别方法存在特征提取不充分等问题,导致识别率不高。本文采用融合特征的方法结合深度学习技术提取鸟鸣声特征,融合特征选择改良后的对数梅尔谱差分参数同原始信号参数拼接所得的特征;深度学习方法是基于Dense Net121网络结构,并融入自注意力模块与中心损失函数进行鸟鸣声识别。自注意力模块部分提高了关键通道的特征表达能力;中心损失函数可解决类内特征不紧凑问题。我们通过消融实验对比验证,对在Xeno-Canto世界野生鸟类声音公开数据集上选取的10种鸟类声音进行识别,准确率达到96.9%。代码已开源至Github:https://github.com/Carrie X6/-Xeno-Canto-.git。     

Identifying responsive functional modules from protein-protein interaction network

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.