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.     

Prediction of the subcellular location of prokaryotic proteins based on the hydrophobicity index of amino acids

An algorithm of predicting the subcellular location of prokaryotic proteins is proposed in this paper. In addition to the amino acid composition, the auto-correlation functions based on the hydrophobicity profile of amino acids along the primary sequence of the query protein have been used. Consequently, the best predictive accuracy to date has been achieved. Of the 997 prokaryotic proteins in the database used here, 688 cytoplasmic, 107 extracellular and 202 periplasmic proteins, the overall predictive accuracies are as high as 97.7 and 90.4% in the resubstitution and jackknife tests, respectively, using the hydrophilicity value of Hopp and Woods. The underlying mechanism of the improvement is also discussed. This work would be useful for a systematic analysis of the great amounts of prokaryotic genome sequences. The computer programs used in this paper are available on request via email.     

Prediction of protein structural class by amino acid and polypeptide composition.

A new approach of predicting structural classes of protein domain sequences is presented in this paper. Besides the amino acid composition, the composition of several dipeptides, tripeptides, tetrapeptides, pentapeptides and hexapeptides are taken into account based on the stepwise discriminant analysis. The result of jackknife test shows that this new approach can lead to higher predictive sensitivity and specificity for reduced sequence similarity datasets. Considering the dataset PDB40-B constructed by Brenner and colleagues, 75.2% protein domain sequences are correctly assigned in the jackknife test for the four structural classes: all-alpha, all-beta, alpha/beta and alpha + beta, which is improved by 19.4% in jackknife test and 25.5% in resubstitution test, in contrast with the component-coupled algorithm using amino acid composition alone (AAC approach) for the same dataset. In the cross-validation test with dataset PDB40-J constructed by Park and colleagues, more than 80% predictive accuracy is obtained. Furthermore, for the dataset constructed by Chou and Maggiona, the accuracy of 100% and 99.7% can be easily achieved, respectively, in the resubstitution test and in the jackknife test merely taking the composition of dipeptides into account. Therefore, this new method provides an effective tool to extract valuable information from protein sequences, which can be used for the systematic analysis of small or medium size protein sequences. The computer programs used in this paper are available on request.     

随机森林方法预测膜蛋白类型

膜蛋白的类型与其功能是密切相关的,因此膜蛋白类型的预测是研究其功能的重要手段,从蛋白质的氨基酸序列出发对膜蛋白的类型进行预测有重要意义。文章基于蛋白质的氨基酸序列,将组合离散增量和伪氨基酸组分信息共同作为预测参数,采用随机森林分类器,对8类膜蛋白进行了预测。在Jackknife检验下的预测精度为86.3%,独立检验的预测精度为93.8%,取得了好于前人的预测结果。     

从非同源蛋白质的一级序列预测其结构类

对基于氨基酸组成、自相关函数和自协方差函数提取特征的蛋白质结构类预测算法进行分析比较,对氨基酸组成和自相关函数相结合的方法,以及氨基酸组成和自协放差函数相结合的方法的预测算法进行了研究。结果表明：对非同源蛋白质,因氨基酸和自相关函数相结合的方法中,采用Miyazawa和Jernigan的疏水值时,训练的自检验的总精度为95．34％,其Jackknife检验的总精度为81．92％,检验加的他检验的总精工为86．61％。在氨基酸组成和自协方差函数相结合的方法中,采用Wold等的疏水值时,训练库的自检验的总精度为96．71％,其Jackknife检验的总精度为82．18％,检验加的他检验的总精工为86．88％。这说明氨基酸组成和自相关函数相结合的方法,以及氨基酸组成和自协方差函数相结合的方法可有效提高结构类预测精度,表明提取更多有效的序列信息是提高分类精度的关键。     

Prediction of protein (domain) structural classes based on amino-acid index.

A protein (domain) is usually classified into one of the following four structural classes: all-alpha, all-beta, alpha/beta and alpha + beta. In this paper, a new formulation is proposed to predict the structural class of a protein (domain) from its primary sequence. Instead of the amino-acid composition used widely in the previous structural class prediction work, the auto-correlation functions based on the profile of amino-acid index along the primary sequence of the query protein (domain) are used for the structural class prediction. Consequently, the overall predictive accuracy is remarkably improved. For the same training database consisting of 359 proteins (domains) and the same component-coupled algorithm [Chou, K.C. & Maggiora, G.M. (1998) Protein Eng. 11, 523-538], the overall predictive accuracy of the new method for the jackknife test is 5-7% higher than the accuracy based only on the amino-acid composition. The overall predictive accuracy finally obtained for the jackknife test is as high as 90.5%, implying that a significant improvement has been achieved by making full use of the information contained in the primary sequence for the class prediction. This improvement depends on the size of the training database, the auto-correlation functions selected and the amino-acid index used. We have found that the amino-acid index proposed by Oobatake and Ooi, i.e. the average nonbonded energy per residue, leads to the optimal predictive result in the case for the database sets studied in this paper. This study may be considered as an alternative step towards making the structural class prediction more practical.     

The modified Mahalanobis Discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition

The outer membrane proteins (OMPs) are β-barrel membrane proteins that performed lots of biology functions. The discriminating OMPs from other non-OMPs is a very important task for understanding some biochemical process. In this study, a method that combines increment of diversity with modified Mahalanobis Discriminant, called IDQD, is presented to predict 208 OMPs, 206 transmembrane helical proteins (TMHPs) and 673 globular proteins (GPs) by using Chou's pseudo amino acid compositions as parameters. The overall accuracy of jackknife cross-validation is 93.2% and 96.1%, respectively, for three datasets (OMPs, TMHPs and GPs) and two datasets (OMPs and non-OMPs). These predicted results suggest that the method can be effectively applied to discriminate OMPs, TMHPs and GPs. And it also indicates that the pseudo amino acid composition can better reflect the core feature of membrane proteins than the classical amino acid composition.     

Using pseudo amino acid composition to predict protein subnuclear location with improved hybrid approach

Summary. The subnuclear localization of nuclear protein is very important for in-depth understanding of the construction and function of the nucleus. Based on the amino acid and pseudo amino acid composition (PseAA) as originally introduced by K. C. Chou can incorporate much more information of a protein sequence than the classical amino acid composition so as to significantly enhance the power of using a discrete model to predict various attributes of a protein, an algorithm of increment of diversity combined with the improved quadratic discriminant analysis is proposed to predict the protein subnuclear location. The overall predictive success rates and correlation coefficient are 75.4% and 0.629 for 504 single localization proteins in jackknife test, and 80.4% for an independent set of 92 multi-localization proteins, respectively. For 406 single localization nuclear proteins with ≤25% sequence identity, the results of jackknife test show that the overall accuracy of prediction is 77.1%. Authors’ address: Qian-Zhong Li, Laboratory of Theoretical Biophysics, Department of Physics, College of Sciences and Technology, Inner Mongolia University, Hohhot 010021, China     

A graphic representation of protein sequence and predicting the subcellular locations of prokaryotic proteins

Zp curve, a three-dimensional space curve representation of protein primary sequence based on the hydrophobicity and charged properties of amino acid residues along the primary sequence is suggested. Relying on the Zp parameters extracted from the three components of the Zp curve and the Bayes discriminant algorithm, the subcellular locations of prokaryotic proteins were predicted. Consequently, an accuracy of 81.5% in the cross-validation test has been achieved using 13 parameters extracted from the curve for the database of 997 prokaryotic proteins. The result is slightly better than that of using the neural network method (80.9%) based on the amino acid composition for the same database. By jointing the amino acid composition and the Zp parameters, the overall predictive accuracy 89.6% can be achieved. It is about 3% higher than that of the Bayes discriminant algorithm based merely on the amino acid composition for the same database. The prediction is also performed with a larger dataset derived from the version 39 SWISS-PROT databank and two datasets with different sequence similarity. Even for the dataset of non-sequence similarity, the improvement can be of 4.4% in the cross-validation test. The results indicate that the Zp parameters are effective in representing the information within a protein primary sequence. The method of extracting information from the primary structure may be useful for other areas of protein studies.     

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   

集成改进KNN算法预测蛋白质亚细胞定位

基于Adaboost算法对多个相似性比对K最近邻(K-nearest neighbor,KNN)分类器集成实现蛋白质的亚细胞定位预测。相似性比对KNN算法分别以氨基酸组成、二肽、伪氨基酸组成为蛋白序列特征,在KNN的决策阶段使用Blast比对决定蛋白质的亚细胞定位。在Jackknife检验下,Adaboost集成分类算法提取3种蛋白序列特征,3种特征在数据集CH317和Gram1253的最高预测成功率分别为92.4%和93.1%。结果表明Adaboost集成改进KNN分类预测方法是一种有效的蛋白质亚细胞定位预测方法。     

Prediction of membrane protein types and subcellular locations.

Membrane proteins are classified according to two different schemes. In scheme 1, they are discriminated among the following five types: (1) type I single-pass transmembrane, (2) type II single-pass transmembrane, (3) multipass transmembrane, (4) lipid chain-anchored membrane, and (5) GPI-anchored membrane proteins. In scheme 2, they are discriminated among the following nine locations: (1) chloroplast, (2) endoplasmic reticulum, (3) Golgi apparatus, (4) lysosome, (5) mitochondria, (6) nucleus, (7) peroxisome, (8) plasma, and (9) vacuole. An algorithm is formulated for predicting the type or location of a given membrane protein based on its amino acid composition. The overall rates of correct prediction thus obtained by both self-consistency and jackknife tests, as well as by an independent dataset test, were around 76-81% for the classification of five types, and 66-70% for the classification of nine cellular locations. Furthermore, classification and prediction were also conducted between inner and outer membrane proteins; the corresponding rates thus obtained were 88-91%. These results imply that the types of membrane proteins, as well as their cellular locations and other attributes, are closely correlated with their amino acid composition. It is anticipated that the classification schemes and prediction algorithm can expedite the functionality determination of new proteins. The concept and method can be also useful in the prioritization of genes and proteins identified by genomics efforts as potential molecular targets for drug design.     

Prediction of Subcellular Localization of Apoptosis Protein Using Chou’s Pseudo Amino Acid Composition

Apoptosis proteins play an essential role in regulating a balance between cell proliferation and death. The successful prediction of subcellular localization of apoptosis proteins directly from primary sequence is much benefited to understand programmed cell death and drug discovery. In this paper, by use of Chou’s pseudo amino acid composition (PseAAC), a total of 317 apoptosis proteins are predicted by support vector machine (SVM). The jackknife cross-validation is applied to test predictive capability of proposed method. The predictive results show that overall prediction accuracy is 91.1% which is higher than previous methods. Furthermore, another dataset containing 98 apoptosis proteins is examined by proposed method. The overall predicted successful rate is 92.9%.     

Prediction of secondary structural content of proteins from their amino acid composition alone. I. New analytic vector decomposition methods

The predictive limits of the amino acid composition for the secondary structural content (percentage of residues in the secondary structural states helix, sheet, and coil) in proteins are assessed quantitatively. For the first time, techniques for prediction of secondary structural content are presented which rely on the amino acid composition as the only information on the query protein. In our first method, the amino acid composition of an unknown protein is represented by the best (in a least square sense) linear combination of the characteristic amino acid compositions of the three secondary structural types computed from a learning set of tertiary structures. The second technique is a generalization of the first one and takes into account also possible compositional couplings between any two sorts of amino acids. Its mathematical formulation results in an eigenvalue/eigenvector problem of the second moment matrix describing the amino acid compositional fluctuations of secondary structural types in various proteins of a learning set. Possible correlations of the principal directions of the eigenspaces with physical properties of the amino acids were also checked. For example, the first two eigenvectors of the helical eigenspace correlate with the size and hydrophobicity of the residue types respectively. As learning and test sets of tertiary structures, we utilized representative, automatically generated subsets of Protein Data Bank (PDB) consisting of non-homologous protein structures at the resolution thresholds ≤1.8Å, ≤2.0Å, ≤2.5Å, and ≤3.0Å. We show that the consideration of compositional couplings improves prediction accuracy, albeit not dramatically. Whereas in the self-consistency test (learning with the protein to be predicted), a clear decrease of prediction accuracy with worsening resolution is observed, the jackknife test (leave the predicted protein out) yielded best results for the largest dataset (≤3.0 Å, almost no difference to the self-consistency test!), i.e., only this set, with more than 400 proteins, is sufficient for stable computation of the parameters in the prediction function of the second method. The average absolute error in predicting the fraction of helix, sheet, and coil from amino acid composition of the query protein are 13.7, 12.6, and 11.4%, respectively with r.m.s. deviations in the range of 8.6 ÷ 11.8% for the 3.0 Å dataset in a jackknife test. The absolute precision of the average absolute errors is in the range of 1 ÷ 3% as measured for other representative subsets of the PDB. Secondary structural content prediction methods found in the literature have been clustered in accordance with their prediction accuracies. To our surprise, much more complex secondary structure prediction methods utilized for the same purpose of secondary structural content prediction achieve prediction accuracies very similar to those of the present analytic techniques, implying that all the information beyond the amino acid composition is, in fact, mainly utilized for positioning the secondary structural state in the sequence but not for determination of the overall number of residues in a secondary structural type. This result implies that higher prediction accuracies cannot be achieved relying solely on the amino acid composition of an unknown query protein as prediction input. Our prediction program SSCP has been made available as a World Wide Web and E-mail service. © 1996 Wiley-Liss, Inc.     

Improved prediction of subcellular location for apoptosis proteins by the dual-layer support vector machine

Apoptosis proteins play an important role in the development and homeostasis of an organism. The accurate prediction of subcellular location for apoptosis proteins is very helpful for understanding the mechanism of apoptosis and their biological functions. However, most of the existing predictive methods are designed by utilizing a single classifier, which would limit the further improvement of their performances. In this paper, a novel predictive method, which is essentially a multi-classifier system, has been proposed by combing a dual-layer support vector machine (SVM) with multiple compositions including amino acid composition (AAC), dipeptide composition (DPC) and amphiphilic pseudo amino acid composition (Am-Pse-AAC). As a demonstration, the predictive performance of our method was evaluated on two datasets of apoptosis proteins, involving the standard dataset ZD98 generated by Zhou and Doctor, and a larger dataset ZW225 generated by Zhang et al. With the jackknife test, the overall accuracies of our method on the two datasets reach 94.90% and 88.44%, respectively. The promising results indicate that our method can be a complementary tool for the prediction of subcellular location.     

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/.     

A new approach to predict the helix/strand content of globular proteins

An improved multiple linear regression method has been proposed to predict the content of alpha-helix and beta-strand of a globular protein based on its primary sequence and structural class. The amino acid composition and the auto-correlation functions derived from the hydrophobicity profile of the primary sequence have been taken into account. However, only the compositions of a part of the amino acids and a part of the auto-correlation functions are selected as the regression terms, which lead to the least prediction error. The resubstitution test shows that the average absolute errors are 0.052 and 0.047 with the standard deviations 0.050 and 0.047 for the prediction of helix/strand content, respectively. A rigorous cross-validation test, the jackknife test shows that the average absolute errors are 0.058 and 0.053 with the standard deviations 0.057 and 0.053 for the prediction of helix/strand content, respectively. Both tests indicate the self-consistency and the extrapolating effectiveness of the new method. The high prediction accuracy means that the method is suitable for practical applications.     

Predicting conotoxin superfamily and family by using pseudo amino acid composition and modified Mahalanobis discriminant

The conotoxin proteins are disulfide rich small peptides that target ion channels and G protein coupled receptors. And they provide promising application in treating some chronic pain, epilepsy, cardiovascular diseases, and so on. Conotoxins may be classified into 11 superfamilies: A, D, I1, I2, J, L, M, O, P, S, and T according to the disulfide connectivity, highly conserved N-terminal precursor sequence and similar mode of actions. Successful prediction mature conotoxin superfamily peptide has important signification for the biological and pharmacological functions of the toxins. In this study, a new algorithm of increment of diversity combined with modified Mahalanobis discriminant is presented to predict five superfamilies by using the pseudo amino acid composition. The results of jackknife cross-validation test show that the overall prediction sensitivity and specificity are 88% and 91%, respectively. The predictive algorithm is also used to predict three O-conotoxin families. The 72% sensitivity and 78% specificity are obtained. These results indicate that the conotoxin superfamily peptides correlate with their amino acid compositions.     

用BP神经网络基于氨基酸特性预测非同源蛋白质二级结构含量

依据蛋白质氨基酸特性,以氨基酸组成和有偏自协方差函数为特征矢量,用BP神经网络提出了一种预测非同源蛋白质中α螺旋和β折叠二级结构含量的计算方法。采用相互独立的非同源蛋白质数据库对该方法进行了检验。用Ponnuswamy值时,对二级结构α螺旋和β折叠含量的预测结果是;自检验平均绝对误差分别为0.069和0.065,相应标准偏差分别为0.044和0.047;他检验平均绝对误差分别为0.077和0.070,相应标准偏差分别为0.051和0.049。与仅以氨基酸组成为特征矢量的BP神经网络方法比较,相应的他检验平均绝对误差分别减小了0.024和0.016,标准偏差分别减小了0.031和0.018;与改进的多元线性回归方法比较,相应的他检验平均绝对误差分别减小了0.018和0.011,准偏差分别减小了0.020和0.012。表明：基于氨基酸组成和有偏自协方差函数为特征矢量的BP神经网络预测蛋白质二级结构含量的方法可有效提高预测精度。