基于疏水性小波分析的膜蛋白结构预测

膜蛋白在细胞膜上具有重要的生理功能,大部分膜蛋白在药物设计、转运蛋白和免疫识别等方面起着关键的作用,从分子水平上预测这类蛋白质的结构具有非常重要的意义。本文提出一种基于氨基酸疏水性小波变换技术预测膜蛋白跨膜区段数目和位置的新方法。以代码为upkb_bovin的膜蛋白为例,对跨膜螺旋区数目和位置的预测分析进行了描述。从膜蛋白数据库中随机抽取36个蛋白质(含跨膜螺旋区232)作为测试集检验小波分析的预测方法,其中226个跨膜螺旋区能被准确预测,准确率为96．8％。结果表明,这种预测方法具有较高的准确性。     

膜蛋白跨膜区段的预测分析

将连续小波变换技术的时频局部化特点和氨基酸的疏水特性相结合,提出了一种用于预测膜蛋白跨膜区段数目和位置的新方法,以代码为1YST的膜蛋白为例,对小波尺度和疏水值的种类进行了选择,同时描述了该法对跨膜螺旋区数目和位置的预测分析过程.从膜蛋白数据库中随机抽取36个蛋白质(含跨膜螺旋区232)作为测试集,采用该方法对其跨膜螺旋区进行预测,其中222个跨膜螺旋区能被准确预测,准确率为96.1%.结果表明,该法具有较高的预测准确性.     

A Novel Approach to the Prediction of Transmembrane Proteins

A novel method based on continuous, wavelet transform (CWT) for predicting the number and location of helices in membrane proteins is presented. The PDB code of lyst is chosen as an example to describe the prediction of transmembrane helices (HTM) by using CWT. The results indicate that CWT is a promising approach for the prediction of HTM.     

应用连续小波变换预测蛋白质的二级结构

将代码为lgca蛋白质的氨基酸序列映射为疏水值序列，在合适的尺度下，通过 连续小波变换法分别对其α螺旋,α螺旋和β折叠之间的连接多肽(即部分规则和无 规则二级结构)进行预测，准确率分别为76．5％和85．7％．从PDBsum数据库中随 机抽取100个蛋白质作为测试对象，其中全α螺旋、全β折叠、α／β以及α+β蛋 白质各25个．在100个蛋白质中共有1618个连接多肽和747个α螺旋.本法预测到的 连接多肽共有1536个，其中1308个与实际结构一致，平均预测准确率为85.2％；预 测到的α螺旋有770个，其中581个与实际结构一致，平均预测准确率为75．5％． 结果表明：该法可较好地预测蛋白质的α螺旋、连接多肽，具有极大的发展前景．     

用外推容量因子法测定药物的疏水性

本文利用不同体积分数的甲醇-水作流动相,用外推容量因子法测定20种不同结构类型药物的疏水性,该法操作简便、快速、测定疏水性范围宽,相关性好。     

基于氨基酸模糊聚类分析的跨膜区域预测

跨膜蛋白在进化过程中,序列保守性较差,即使是同源蛋白序列的一致性程度也较低,因而在跨膜区预测算法中,通过序列的一致性程度来选取训练集并不能有效地消除预测结果对训练集的过度适应性．本文提出了一种基于氨基酸模糊聚类分析的预测算法,通过氨基酸在各个区域分布的相似性程度进行模糊聚类,从而根据一类氨基酸的分布特性而不是各个氨基酸的分布特性进行跨膜区预测．结果表明,该方法能在一定程度上消除训练集的选取对测试结果的影响,提高跨膜蛋白拓扑结构预测的准确度,特别是提高对目前知之甚少的跨膜蛋白的预测准确度．     

室温固化疏水性聚合物涂层研究

结霜问题广泛存在于制冷和低温领域并降低制冷系统的运行效率及运行稳定 .为防止在材料表面附着小水滴并结霜 ,通常的表面改性方法有 :(1 )涂敷亲水性聚合物[1] ,通过提高表面的亲水性来使水滴铺张 ,但附着的水滴易使表面膨胀、强度降低并破坏与基材的粘结力 ;(2 )涂敷疏水性聚合物 ,它们一般含有大量低表面能的硅、氟等原子基团 ,通过提高表面的疏水性来提高表面对水滴的接触角 ,并使水滴滑落 ,但通常认为涂层对基材的粘接力低且强度及耐久性差[2 ] .氟树脂涂料具有优良的耐候性、耐腐蚀性、耐沾污性、耐热性、耐化学品性、斥水斥油性、绝…     

疏水硅沸石Silicalite-I)结构性质的表征

用粉末XRD, FT-IR, 29^Si MAS NMR, 对高硅HZSM-5沸石及疏水硅沸石Silicalite I进行结构性质表征。在室温下, 疏水硅沸石具有ZSM-5的单斜对称性。它的红外骨架振动谱及高分辨29^Si固体核磁共振谱均显示出高的分辨率。在红外光谱中, 3700和3500cm^-1左右的表面Si-OH基振动消失。表明疏水硅沸石晶格中的[SiO~4]四面体排列完美。由-Si-O-Si-构成的微也表面, 具有优良的疏水性。     

含氮硅烷对聚二甲基硅氧烷涂膜表面疏水性的影响

将含氮硅烷与正硅酸乙酯进行对比，研究了它们对端羟基聚二甲基硅氧烷（ＨＴＰＤＭＳ）缩合固化形成的涂膜表面疏水性的影响，发现固化剂ＮＨ２（ＣＨ２）３Ｓｉ（ＯＣ２Ｈ）３使涂膜疏水性明显下降．用ＥＳＣＡ和相差显微镜等方法证明了这种现象是由于ＮＨ２（ＣＨ２）３Ｓｉ（ＯＣ２Ｈ５）３自身固化产生的分相性引起的．     

Prediction of protein secondary structure based on continuous wavelet transform

In this paper, continuous wavelet transform (CWT) is used to extract the number and the relevant positions of the α-helices and short peptides connecting α-helices and β-strands (connecting peptides) from the amino acid sequences of proteins. The amino acid sequence is first mapped into hydrophobicity sequence, and then transformed into CWT value of sequence domain in appropriate scale via CWT. The number and the relevant positions of the α-helices and connecting peptides can be extracted easily and accurately according to the minima of wavelet coefficient in corresponding CWT plot of hydrophobic value sequences with appropriate scale. The analytical results demonstrate that α-helices and connecting peptides can be predicted conveniently and rapidly when this method is used in the processing of 100 non-homologous sequences. However, this method is not suitable for predicting the length of α-helices and connecting peptides.     

Prediction of transmembrane proteins based on the continuous wavelet transform

A novel method based on continuous wavelet transform (CWT) for predicting the number and location of helices in membrane proteins is presented. Two bacteria proteins are chosen as examples to describe the prediction of transmembrane helices (HTM) by using this method. Selections of an appropriate dilation and hydrophobicity data types are discussed in the text. The results indicate that CWT is a promising approach for the prediction of HTM.     

Site‐directed analysis on protein hydrophobicity

Hydrophobicity of a protein is considered to be one of the major intrinsic factors dictating the protein aggregation propensity. Understanding how protein hydrophobicity is determined is, therefore, of central importance in preventing protein aggregation diseases and in the biotechnological production of human therapeutics. Traditionally, protein hydrophobicity is estimated based on hydrophobicity scales determined for individual free amino acids, assuming that those scales are unaltered when amino acids are embedded in a protein. Here, we investigate how the hydrophobicity of constituent amino acid residues depends on the protein context. To this end, we analyze the hydration free energy—free energy change on hydration quantifying the hydrophobicity—of the wild‐type and 21 mutants of amyloid‐beta protein associated with Alzheimer's disease by performing molecular dynamics simulations and integral‐equation calculations. From detailed analysis of mutation effects on the protein hydrophobicity, we elucidate how the protein global factor such as the total charge as well as underlying protein conformations influence the hydrophobicity of amino acid residues. Our results provide a unique insight into the protein hydrophobicity for rationalizing and predicting the protein aggregation propensity on mutation, and open a new avenue to design aggregation‐resistant proteins as biotherapeutics. © 2014 Wiley Periodicals, Inc.     

Prediction of protein structural class based on symmetrical recurrence quantification analysis

Protein structural class prediction for low similarity sequences is a significant challenge and one of the deeply explored subjects. This plays an important role in drug design, folding recognition of protein, functional analysis and several other biology applications. In this paper, we worked with two benchmark databases existing in the literature (1) 25PDB and (2) 1189 to apply our proposed method for predicting protein structural class. Initially, we transformed protein sequences into DNA sequences and then into binary sequences. Furthermore, we applied symmetrical recurrence quantification analysis (the new approach), where we got 8 features from each symmetry plot computation. Moreover, the machine learning algorithms such as Linear Discriminant Analysis (LDA), Random Forest (RF) and Support Vector Machine (SVM) are used. In addition, comparison was made to find the best classifier for protein structural class prediction. Results show that symmetrical recurrence quantification as feature extraction method with RF classifier outperformed existing methods with an overall accuracy of 100% without overfitting.     

Prediction of peptides retention behavior in reversed-phase liquid chromatography based on their hydrophobicity

Hydrophobicity is an important physicochemical property of peptides and proteins. It is responsible for their conformational changes, stability, as well as various chemical intramolecular and intermolecular interactions. Enormous efforts have been invested to study the extent of hydrophobicity and how it could influence various biological processes, in addition to its crucial role in the separation and purification endeavor as well. Here, we have reviewed various studies that were carried out to determine the hydrophobicity starting from (i) simple amino acids solubility behavior, (ii) experimental approach that was undertaken in the reversed-phase liquid chromatography mode, and ending with (iii) some examples of more advanced computational and machine learning models.     

Feature analysis of protein structure by using discrete Fourier transform and continuous wavelet transform

In this paper, discrete Fourier transform (DFT) and continuous wavelet transform (CWT) are used to predict the protein structure. Hydrophobicity plays a key role in the form of protein structure. The amino acid sequence is first mapped into hydrophobicity sequence, and then process it by DFT and CWT so that power spectral density is gained. The results show that continuous wavelet transform can extract the features of protein structure effectively and availably and has a tremendous development foreground.     

Prediction of protein secondary structure content by artificial neural network

The neural network method was applied to the prediction of the content of protein secondary structure elements, including alpha-helix, beta-strand, beta-bridge, 3(10)-helix, pi-helix, H-bonded turn, bend, and random coil. The "pair-coupled amino acid composition" originally proposed by K. C. Chou [J Protein Chem 1999, 18, 473] was adopted as the input. Self-consistency and independent-dataset tests were used to appraise the performance of the neural network. Results of both tests indicated high performance of the method.     

Prediction of protein secondary structure content using support vector machine

In this paper, the support vector machine was trained to grasp the relationship between the pair-coupled amino acid composition and the content of protein secondary structural elements, including -helix, 3₁₀-helix, π-helix, β-strand, β-bridge, turn, bend and the rest random coil. Self-consistency and cross validation tests were made to assess the performance of our method. Results superior to or competitive with the popular theoretical and experimental methods have been obtained.     

Inherent structure analysis of protein folding

An analysis in terms of the inherent structures (IS, local minima) of the multidimensional potential energy landscape is applied to proteins. Detailed calculations are performed for the 46 bead BLN model, which folds into a four-stranded beta-barrel. Enhanced sampling has allowed determination of 239 199 IS states, believed to encompass nearly all the compact, low-energy states, and of well-averaged thermodynamic quantities at low temperature. The density of states shows distinct lobes for compact and extended states, and entropic barriers for the collapse and local ordering transitions. A two-dimensional scatterplot or density of states clearly shows the multifunnel structure of the energy landscape. The anharmonic vibrational free energy is found to play a crucial role in protein folding. The problem of determining the folding transition in a multifunnel system is discussed, and novel indicators of folding are introduced. A particularly clear picture is obtained through the occupation probabilities, pi, of individual low-lying IS, which become finite below the collapse temperature; it is suggested that poor foldability corresponds to a large "misfolding interval" where the excited state pi>0 exceeds that of the native state p0.