固定化酶的微环境效应—载体离子基团性质的影响

作者合成了阴离子型和阳离子型葡聚糖,以此为载体,用CNBr活化其剩余羟基,固定化了葡萄糖淀粉酶和葡萄糖异构酶。就离子型载体对固定化酶的蛋白载量、最适pH和热稳定性等的影响做了考察。发现固定化酶的蛋白载量不仅与载体的电性质有关,也与酶分子自身的电性质有关。当载体电性质与酶蛋白电性质相反时,固定化酶的蛋白载量增加,热稳定性提高、载体电性质与酶蛋白电性质相同时,固定化酶的蛋白载量不变或下降,其热稳定性不变。作者还发现当离子型载体孔度和体系缓冲液浓度一定时,酶分子能否进入多孔性载体内部,对其最适pH是否变化影响极大。若酶分子仅被连接在载体的外表层,其最适pH不发生变化,反之亦然。作者还观察到当多糖类载体引入氨基或羧基后,大大增强了其抵抗微生物侵蚀的能力。     

Influence of the nature of the aromatic side-chain on the conductance of the channel of linear gramicidin: study of a series of 9,11,13,15-Tyr(O-protected) derivatives

This paper describes the single channel properties of a series of synthetic analogues of gramicidin A, where all four tryptophans are replaced either by tyrosine or by several O-protected (benzyl, methyl, ethyl or t-butyl) derivatives. It is shown that, although all analogues bear similar dipole moment on their side-chains, the conductance depends on the hydrophobicity of these protecting groups. An analysis of the conductance data suggests that the conductance is governed by the binding process and a possible explanation, based on conformational considerations, is proposed.Abbreviations GA X=tryptophane - GM X=phenylalanine - GN X=naphthylalanine - GQ8 X=8-quinolylalanine - GQ4 X=4-quinolylalanine - GT X=tyrosine - GTBzl X=O-benzyltyrosine - GTMe X=O-methyltyrosine - GTEt X=O-ethyltyrosine - GTBu X=O-t-butyltyrosine     

A novel protein structural classes prediction method based on predicted secondary structure

Knowledge of structural classes plays an important role in understanding protein folding patterns. In this paper, features based on the predicted secondary structure sequence and the corresponding E–H sequence are extracted. Then, an 11-dimensional feature vector is selected based on a wrapper feature selection algorithm and a support vector machine (SVM). Among the 11 selected features, 4 novel features are newly designed to model the differences between α/β class and α + β class, and other 7 rational features are proposed by previous researchers. To examine the performance of our method, a total of 5 datasets are used to design and test the proposed method. The results show that competitive prediction accuracies can be achieved by the proposed method compared to existing methods (SCPRED, RKS-PPSC and MODAS), and 4 new features are demonstrated essential to differentiate α/β and α + β classes. Standalone version of the proposed method is written in JAVA language and it can be downloaded from http://web.xidian.edu.cn/slzhang/paper.html.     

Active and machine learning-based approaches to rapidly enhance microbial chemical production

In order to make renewable fuels and chemicals from microbes, new methods are required to engineer microbes more intelligently. Computational approaches, to engineer strains for enhanced chemical production typically rely on detailed mechanistic models (e.g., kinetic/stoichiometric models of metabolism)—requiring many experimental datasets for their parameterization—while experimental methods may require screening large mutant libraries to explore the design space for the few mutants with desired behaviors. To address these limitations, we developed an active and machine learning approach (ActiveOpt) to intelligently guide experiments to arrive at an optimal phenotype with minimal measured datasets. ActiveOpt was applied to two separate case studies to evaluate its potential to increase valine yields and neurosporene productivity in Escherichia coli. In both the cases, ActiveOpt identified the best performing strain in fewer experiments than the case studies used. This work demonstrates that machine and active learning approaches have the potential to greatly facilitate metabolic engineering efforts to rapidly achieve its objectives.     

用支持向量机预测人类基因5′/3′选择性剪切位点

选择性剪切是调解基因表达的重要机制。识别选择性剪切位点是后基因组时代的一个重要工作。本文从最新的EBI人类基因选择性剪切数据库中,选取5′/3′选择性剪切位点作为正集,选取在剪切位点附近的假剪切位点作为负集,并把所有的选择性剪切位点和假剪切位点随机分成训练集和测试集。本文选用的预测选择性剪切位点的方法是基于位置权重矩阵和离散增量的支持向量机方法。此方法仅基于训练集,以不同位点的单碱基概率和序列片断的三联体频数作为信息参数,利用位置权重矩阵和离散增量算法结合支持向量机,得到了选择性供体位点和受体位点的分类器,并用此分类器对测试集中的选择性供体位点和受体位点进行预测。对独立测试集中的选择性供体位点和选择性受体位点的预测成功率分别为88．74%和90．86%,特异性分别为85．62%和81．19%。本文预测选择性剪切位点的方法成功率高于其它选择性剪切位点预测方法预测成功率,此预测方法进一步提高了对选择性剪切位点的理论预测能力。     

Investigating the form-function interface in African apes: Relationships between principal moments of area and positional behaviors in femoral and humeral diaphyses

Investigations of cross-sectional geometry in nonhuman primate limb bones typically attribute shape ratios to qualitative behavioral characterizations, e.g., leaper, slow climber, brachiator, or terrestrial vs. arboreal quadruped. Quantitative positional behavioral data, however, have yet to be used in a rigorous evaluation of such shape-behavior connections. African apes represent an ideal population for such an investigation because their relatedness minimizes phylogenetic inertia, they exhibit diverse behavioral repertoires, and their locomotor behaviors are known from multiple studies. Cross-sectional data from femoral and humeral diaphyses were collected for 222 wild-shot specimens, encompassing Pan paniscus and all commonly recognized African ape subspecies. Digital representations of diaphyseal cross sections were acquired via computed tomography at three locations per diaphysis. Locomotor behaviors were pooled broadly into arboreal and terrestrial categories, then partitioned into quadrupedal walking, quadrumanous climbing, scrambling, and suspensory categories. Sex-specific taxonomic differences in ratios of principal moments of area (PMA) were statistically significant more often in the femoral diaphysis than the humeral diaphysis. While it appears difficult to relate a measure of shape (e.g., PMA ratio) to individual locomotor modes, general locomotor differences (e.g., percentage arboreal vs. terrestrial locomotion) are discerned more easily. As percentage of arboreal locomotion for a group increases, average cross sections appear more circular. Associations between PMA ratio and specific locomotor behaviors are less straightforward. Individual behaviors that integrate eccentric limb positions (e.g., arboreal scrambling) may not engender more circular cross sections than behaviors that incorporate repetitive sagittal movements (e.g., quadrupedal walking) in a straightforward manner.     

Using pseudo-amino acid composition and support vector machine to predict protein structural class

As a result of genome and other sequencing projects, the gap between the number of known protein sequences and the number of known protein structural classes is widening rapidly. In order to narrow this gap, it is vitally important to develop a computational prediction method for fast and accurately determining the protein structural class. In this paper, a novel predictor is developed for predicting protein structural class. It is featured by employing a support vector machine learning system and using a different pseudo-amino acid composition (PseAA), which was introduced to, to some extent, take into account the sequence-order effects to represent protein samples. As a demonstration, the jackknife cross-validation test was performed on a working dataset that contains 204 non-homologous proteins. The predicted results are very encouraging, indicating that the current predictor featured with the PseAA may play an important complementary role to the elegant covariant discriminant predictor and other existing algorithms.     

Helix packing moments reveal diversity and conservation in membrane protein structure

Helical membrane proteins are more tightly packed and the packing interactions are more diverse than those found in helical soluble proteins. Based on a linear correlation between amino acid packing values and interhelical propensity, we propose the concept of a helix packing moment to predict the orientation of helices in helical membrane proteins and membrane protein complexes. We show that the helix packing moment correlates with the helix interfaces of helix dimers of single pass membrane proteins of known structure. Helix packing moments are also shown to help identify the packing interfaces in membrane proteins with multiple transmembrane helices, where a single helix can have multiple contact surfaces. Analyses are described on class A G protein-coupled receptors (GPCRs) with seven transmembrane helices. We show that the helix packing moments are conserved across the class A family of GPCRs and correspond to key structural contacts in rhodopsin. These contacts are distinct from the highly conserved signature motifs of GPCRs and have not previously been recognized. The specific amino acid types involved in these contacts, however, are not necessarily conserved between subfamilies of GPCRs, indicating that the same protein architecture can be supported by a diverse set of interactions. In GPCRs, as well as membrane channels and transporters, amino acid residues with small side-chains (Gly, Ala, Ser, Cys) allow tight helix packing by mediating strong van der Waals interactions between helices. Closely packed helices, in turn, facilitate interhelical hydrogen bonding of both weakly polar (Ser, Thr, Cys) and strongly polar (Asn, Gln, Glu, Asp, His, Arg, Lys) amino acid residues. We propose the use of the helix packing moment as a complementary tool to the helical hydrophobic moment in the analysis of transmembrane sequences.     

A Simple Method for Measuring the Changeable Mechanical Action of Unloader Knee Braces for Osteoarthritis

Purpose

Today's orthotics should be designed to apply the external orthosis moment to the knee joint solely during the stance phase instead of the entire gait cycle. The aim of this study was to validate the reliability of a simple device for measuring forces at the leg–orthosis interface and describe the behavior of an innovating dynamic unloader knee brace built to interrupt its mechanical action during large knee flexion (swing phase of gait).

The C-Domain of the NAC Transcription Factor ANAC019 Is Necessary for pH-Tuned DNA Binding through a Histidine Switch in the N-Domain

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