用离散量的方法识别蛋白质的超二级结构

用离散量的方法,对2208个分辨率在2.5I以上的高精度的蛋白质结构中四类超二级结构进行了识别。从蛋白质一级序列出发,以氨基酸(20种氨基酸加一个空位)和其紧邻关联共同为参数,当序列模式固定长取8个氨基酸残基时,对“822”序列模式3交叉检验的平均预测精度达到78.1%,jack-knife检验的平均预测精度达到76.7%;当序列模式固定长取10个氨基酸残基时,对“1041”序列模式3交叉检验的平均预测精度达到83.1%,jack-knife检验的平均预测精度达到79.8%。     

k子串离散源结合加权KNN算法预测膜蛋白类型

膜蛋白是生物膜功能的主要体现者,是细胞执行各种功能的物质基础,在细胞中发挥着至关重要的作用.分类预测未知类型的膜蛋白对于生物学相关研究具有指导性意义,是膜蛋白结构与功能研究领域的一项重要基础性工作.针对膜蛋白分类预测问题,利用k子串离散源的方法对膜蛋白序列进行特征提取,并融合最小离散增量方法和加权K近邻算法构建一种新型的膜蛋白分类预测模型,在自检验、Jackknife检验和独立测试集检验三种典型的检验方式下,预测准确率分别为99.95%、86.16%和98.36%.实验结果表明,k子串离散源方法能够有效提取膜蛋白序列的特征信息,与现有方法相比,该分类模型具有较高的分类预测成功率.     

基于离散增量的支持向量机算法预测蛋白质中四类简单超二级结构

蛋白质超二级结构预测是三级结构预测的一个非常重要的中间步骤。本文从蛋白质的一级序列出发,对5793个蛋白质中的四类简单超二级结构进行预测,以位点氨基酸为参数,采用3种片段截取方式,分别用离散增量算法预测的结果不理想,将组合的离散增量值作为特征参数输入支持向量机,取得了较好的预测结果,5交叉检验的平均预测总精度达到83.0%,Matthew’s相关系数在0.71以上。     

基于添加模体信息和功率谱密度的组合向量预测27类蛋白质折叠子

以序列相似性低于40%的1895条蛋白质序列构建涵盖27个折叠类型的蛋白质折叠子数据库,从蛋白质序列出发,用模体频数值、低频功率谱密度值、氨基酸组分、预测的二级结构信息和自相关函数值构成组合向量表示蛋白质序列信息,采用支持向量机算法,基于整体分类策略,对27类蛋白质折叠子的折叠类型进行预测,独立检验的预测精度达到了66.67%。同时,以同样的特征参数和算法对27类折叠子的4个结构类型进行了预测,独立检验的预测精度达到了89.24%。将同样的方法用于前人使用过的27类折叠子数据库,得到了好于前人的预测结果。     

利用伪氨基酸组分预测分枝杆菌膜蛋白类型

膜蛋白是重要的药物靶位点,对膜蛋白类型的研究有助于药物的成功设计,因此正确预测膜蛋白类型对于药物研发是十分必要的。本文采用由274条分枝杆菌膜蛋白序列组成的一致性小于40%的数据集,以经过优化的伪氨基酸组分为特征,利用支持向量机分类算法预测分枝杆菌膜蛋白类型,在Jackknife检验下,得到85.4%的总体准确率和72.2%的平均准确率。结果说明,该方法可用于分枝杆菌膜蛋白类型的识别,将有助于抗分枝杆菌药物的开发。     

基于压缩氨基酸和支持向量机进行膜蛋白类型识别

膜蛋白是一类结构独特的蛋白质,是细胞执行各种功能的物质基础。根据其在细胞膜上的不同存在方式,主要分为六种类型。本文利用压缩的氨基酸对原始膜蛋白序列进行信息压缩,再对压缩序列进行氨基酸组成和顺序特征的提取,最后采用支持向量机构建分类模型。通过五叠交叉验证的结果表明,该方法对于六种膜蛋白的分类预测,准确度最高可达98％以上,平均预测准确度在85％以上,可有效实现膜蛋白六种类型的划分,为进一步分析膜蛋白的结构和功能奠定基础。     

基于蛋白序列的IDQD算法预测蛋白质相互作用

基于蛋白质序列组分信息,提出一个离散增量结合二次判别分析法(IDQD)预测蛋白质相互作用的模型,对人类蛋白质相互作用进行预测.自洽检验的识别精度达到75.89%,3-fold交叉检验的敏感性和特异性分别为64.22%和64.68%.结果表明IDQD算法可以用于蛋白质相互作用的预测.     

基于K-mer扭转角偏好的蛋白质结构类型预测

蛋白质的序列、结构和功能多种多样。大量研究表明蛋白质的结构与其氨基酸序列的排序有关,并且局部的氨基酸序列环境对蛋白质的结构具有一定的影响。本文提出一种新的基于5-mer氨基酸扭转角统计偏好的蛋白质结构类型预测方法,该方法通过PDB数据库中5-mer中间氨基酸的扭转角统计偏好来进行结构类型的预测。新方法可以通过计算机仿真实现对新蛋白质序列结构类型的快速预测,并通过两组随机抽取的CATH数据验证了新方法的有效性。     

基于模糊支持向量机的膜蛋白折叠类型预测

现有的基于支持向量机（support vector machine,SVM）来预测膜蛋白折叠类型的方法．利用的蛋白质序列特征并不充分．并且在处理多类蛋白质分类问题时存在不可分区域,针对这两类问题．提取蛋白质序列的氨基酸和二肽组成特征,并计算加权的多阶氨基酸残基指数相关系数特征,将3类特征融和作为分类器的输入特征矢量．并采用模糊SVM（fuzzy SVM,FSVM）算法解决对传统SVM不可分数据的分类．在无冗余的数据集上测试结果显示．改进的特征提取方法在相同分类算法下预测性能优于已有的特征提取方法：FSVM在相同特征提取方法下性能优于传统的SVM．二者相结合的分类策略在独立性数据集测试下的预测精度达到96．6％．优于现有的多种预测方法．能够作为预测膜蛋白和其它蛋白质折叠类型的有效工具．     

基于最优分割位点的蛋白质亚细胞位点预测方法

蛋白质的亚细胞位点信息有助于我们了解蛋白质的功能以及它们之间的相互作用,同时还可以为新药物的研发提供帮助。目前普遍采用的亚细胞位点预测方法主要是基于N端分选信号或氨基酸组分特征,但研究表明,单纯基于N端分选信号或氨基酸组分的方法都会丢失序列的序信息。为了克服此缺陷,本文提出了一种基于最优分割位点的蛋白质亚细胞位点预测方法。首先,把每条蛋白质序列分割为N端、中间和C端三部分,然后在每个子序列和整条序列中分别提取氨基酸组分、双肽组分和物理化学性质,最后我们把这些特征融合起来作为整条序列的特征。通过夹克刀检验,该方法在NNPSL数据集上得到的总体精度分别是87.8%和92.1%。     

Prediction of membrane protein types based on the hydrophobic index of amino acids

A new algorithm to predict the types of membrane proteins is proposed. Besides the amino acid composition of the query protein, the information within the amino acid sequence is taken into account. A formulation of the autocorrelation functions based on the hydrophobicity index of the 20 amino acids is adopted. The overall predictive accuracy is remarkably increased for the database of 2054 membrane proteins studied here. An improvement of about 13% in the resubstitution test and 8% in the jackknife test is achieved compared with those of algorithms based merely on the amino acid composition. Consequently, overall predictive accuracy is as high as 94% and 82% for the resubstitution and jackknife tests, respectively, for the prediction of the five types. Since the proposed algorithm is based on more parameters than those in the amino acid composition approach, the predictive accuracy would be further increased for a larger and more class-balanced database. The present algorithm should be useful in the determination of the types and functions of new membrane proteins. The computer program is available on request.     

Predicting membrane protein types using residue-pair models based on reduced similarity dataset

An algorithm to predict the membrane protein types based on the multi-residue-pair effect in the Markov model is proposed. For a newly constructed dataset of 835 membrane proteins with very low sequence similarity, the overall prediction accuracy has been achieved as high as 81.1% and 71.7% in the resubstitution and jackknife test, respectively, for a prediction of type I single-pass, type II single-pass, multi-pass membrane proteins, lipid chain-anchored and GPI-anchored membrane proteins. The improvement of about 11% in the jackknife test can be achieved compared with the component-coupled algorithm merely based on the amino acid composition (AAC approach). The improvement is also confirmed on a high similarity dataset and the other extrapolating test. The result implies that designing more incisive analysis tools, one should develop algorithms based on the representative dataset with lower sequence similarity. The present algorithm is useful to expedite the determination of the types and functions of new membrane proteins and may be useful for the systematic analysis of functional genome data in a large scale. The computer program is available on request.     

Using GO-PseAA predictor to identify membrane proteins and their types

Cell membranes are crucial to the life of a cell. Although the basic structure of biological membrane is provided by the lipid bilayer, most of the specific functions are carried out by membrane proteins. Knowledge of membrane protein type often offers important clues toward determining the function of an uncharacterized protein. Therefore, predicting the type of a membrane protein from its primary sequence, or even just identifying whether the uncharacterized protein belongs to a membrane protein or not, is an important and challenging problem in bioinformatics and proteomics. To deal with these problems, the GO-PseAA predictor is introduced that is operated in a hybridization space by combining the gene ontology and pseudo amino acid composition. Meanwhile, to test the prediction quality, a dataset was constructed that contains 6476 non-membrane proteins and 5122 membrane proteins classified into five different types. To avoid redundancy and bias, none of the proteins included has > or = 40% sequence identity to any other. It has been observed that the overall success rate by the jackknife cross-validation test in identifying non-membrane proteins and membrane proteins was 94.76%, and that in identifying the five membrane protein types was 95.84%. The high success rates suggest that the GO-PseAA predictor can catch the core feature of the statistical samples concerned and may become an automated high throughput toll in molecular and cell biology.     

Using Fourier Spectrum Analysis and Pseudo Amino Acid Composition for Prediction of Membrane Protein Types

Membrane proteins are generally classified into the following five types: (1) type I membrane protein, (2) type II membrane protein, (3) multipass transmembrane proteins, (4) lipid chain-anchored membrane proteins, and (5) GPI-anchored membrane proteins. Given the sequence of an uncharacterized membrane protein, how can we identify which one of the above five types it belongs to? This is important because the biological function of a membrane protein is closely correlated with its type. Particularly, with the explosion of protein sequences entering into databanks, it is in high demand to develop an automated method to address this problem. To realize this, the key is to catch the statistical characteristics for each of the five types. However, it is not easy because they are buried in a pile of long and complicated sequences. In this paper, based on the concept of the pseudo amino acid composition (Chou, K. C. (2001). PROTEINS: Structure, Function, and Genetics 43: 246–255), the technique of Fourier spectrum analysis is introduced. By doing so, the sample of a protein is represented by a set of discrete components that can incorporate a considerable amount of the sequence order effects as well as its amino acid composition information. On the basis of such a statistical frame, the support vector machine (SVM) is introduced to perform predictions. High success rates were yielded by the self-consistency test, jackknife test, and independent dataset test, suggesting that the current approach holds a promising potential to become a high throughput tool for membrane protein type prediction as well as other related areas.     

Predicting protein subcellular location using Chou's pseudo amino acid composition and improved hybrid approach

The location of a protein in a cell is closely correlated with its biological function. Based on the concept that the protein subcellular location is mainly determined by its amino acid and pseudo amino acid composition (PseAA), a new algorithm of increment of diversity combined with support vector machine is proposed to predict the protein subcellular location. The subcellular locations of plant and non-plant proteins are investigated by our method. The overall prediction accuracies in jackknife test are 88.3% for the eukaryotic plant proteins and 92.4% for the eukaryotic non-plant proteins, respectively. In order to estimate the effect of the sequence identity on predictive result, the proteins with sequence identity 相似文献   

A Multilabel Model Based on Chou’s Pseudo–Amino Acid Composition for Identifying Membrane Proteins with Both Single and Multiple Functional Types

Predicting membrane protein type is a meaningful task because this kind of information is very useful to explain the function of membrane proteins. Due to the explosion of new protein sequences discovered, it is highly desired to develop efficient computation tools for quickly and accurately predicting the membrane type for a given protein sequence. Even though several membrane predictors have been developed, they can only deal with the membrane proteins which belong to the single membrane type. The fact is that there are membrane proteins belonging to two or more than two types. To solve this problem, a system for predicting membrane protein sequences with single or multiple types is proposed. Pseudo–amino acid composition, which has proven to be a very efficient tool in representing protein sequences, and a multilabel KNN algorithm are used to compose this prediction engine. The results of this initial study are encouraging.     

Identify Golgi protein types with modified Mahalanobis discriminant algorithm and pseudo amino acid composition

The Golgi apparatus is an important eukaryotic organelle. Successful prediction of Golgi protein types can provide valuable information for elucidating protein functions involved in various biological processes. In this work, a method is proposed by combining a special mode of pseudo amino acid composition (increment of diversity) with the modified Mahalanobis discriminant for predicting Golgi protein types. The benchmark dataset used to train the predictor thus formed contains 95 Golgi proteins in which none of proteins included has ≥40% pairwise sequence identity to any other. The accuracy obtained by the jackknife test was 74.7%, with the ROC curve of 0.772 in identifying cis-Golgi proteins and trans-Golgi proteins. Subsequently, the method was extended to discriminate cis-Golgi network proteins from cis-Golgi network membrane proteins and trans-Golgi network proteins from trans-Golgi network membrane proteins, respectively. The accuracies thus obtained were 76.1% and 83.7%, respectively. These results indicate that our method may become a useful tool in the relevant areas. As a user-friendly web-server, the predictor is freely accessible at http://immunet.cn/SubGolgi/.     

Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition

Membrane proteins are vital type of proteins that serve as channels, receptors, and energy transducers in a cell. Prediction of membrane protein types is an important research area in bioinformatics. Knowledge of membrane protein types provides some valuable information for predicting novel example of the membrane protein types. However, classification of membrane protein types can be both time consuming and susceptible to errors due to the inherent similarity of membrane protein types. In this paper, neural networks based membrane protein type prediction system is proposed. Composite protein sequence representation (CPSR) is used to extract the features of a protein sequence, which includes seven feature sets; amino acid composition, sequence length, 2 gram exchange group frequency, hydrophobic group, electronic group, sum of hydrophobicity, and R-group. Principal component analysis is then employed to reduce the dimensionality of the feature vector. The probabilistic neural network (PNN), generalized regression neural network, and support vector machine (SVM) are used as classifiers. A high success rate of 86.01% is obtained using SVM for the jackknife test. In case of independent dataset test, PNN yields the highest accuracy of 95.73%. These classifiers exhibit improved performance using other performance measures such as sensitivity, specificity, Mathew's correlation coefficient, and F-measure. The experimental results show that the prediction performance of the proposed scheme for classifying membrane protein types is the best reported, so far. This performance improvement may largely be credited to the learning capabilities of neural networks and the composite feature extraction strategy, which exploits seven different properties of protein sequences. The proposed Mem-Predictor can be accessed at http://111.68.99.218/Mem-Predictor.