Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network

It is a critical challenge to develop automated methods for fast and accurately determining the structures of proteins because of the increasingly widening gap between the number of sequence-known proteins and that of structure-known proteins in the post-genomic age. The knowledge of protein structural class can provide useful information towards the determination of protein structure. Thus, it is highly desirable to develop computational methods for identifying the structural classes of newly found proteins based on their primary sequence. In this study, according to the concept of Chou's pseudo amino acid composition (PseAA), eight PseAA vectors are used to represent protein samples. Each of the PseAA vectors is a 40-D (dimensional) vector, which is constructed by the conventional amino acid composition (AA) and a series of sequence-order correlation factors as original introduced by Chou. The difference among the eight PseAA representations is that different physicochemical properties are used to incorporate the sequence-order effects for the protein samples. Based on such a framework, a dual-layer fuzzy support vector machine (FSVM) network is proposed to predict protein structural classes. In the first layer of the FSVM network, eight FSVM classifiers trained by different PseAA vectors are established. The 2nd layer FSVM classifier is applied to reclassify the outputs of the first layer. The results thus obtained are quite promising, indicating that the new method may become a useful tool for predicting not only the structural classification of proteins but also their other attributes.     

Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition

As more and more genomes have been discovered in recent years, there is an urgent need to develop a reliable method to predict the subcellular localization for the explosion of newly found proteins. However, many well-known prediction methods based on amino acid composition have problems utilizing the sequence-order information. Here, based on the concept of Chou's pseudo amino acid composition (PseAA), a new feature extraction method, the multi-scale energy (MSE) approach, is introduced to incorporate the sequence-order information. First, a protein sequence was mapped to a digital signal using the amino acid index. Then, by wavelet transform, the mapped signal was broken down into several scales in which the energy factors were calculated and further formed into an MSE feature vector. Following this, combining this MSE feature vector with amino acid composition (AA), we constructed a series of MSEPseAA feature vectors to represent the protein subcellular localization sequences. Finally, according to a new kind of normalization approach, the MSEPseAA feature vectors were normalized to form the improved MSEPseAA vectors, named as IEPseAA. Using the technique of IEPseAA, C-support vector machine (C-SVM) and three multi-class SVMs strategies, quite promising results were obtained, indicating that MSE is quite effective in reflecting the sequence-order effects and might become a useful tool for predicting the other attributes of proteins as well.     

基于近似熵的伪氨基酸组成预测蛋白质亚核定位

了解真核细胞中细胞核内蛋白质的定位情况对于新发现蛋白质的功能注释具有重要意义.随着蛋白质数据库中蛋白质序列数量的急速增加,采用计算方法来预测蛋白质亚核定位已经成为蛋白质科学领域研究的热点.根据Chou提出的伪氨基酸组成离散模型,提出了一种新的蛋白质亚核定位预测方法.计算蛋白质序列的近似熵作为附加特征构建伪氨基酸组成,表示蛋白质序列特征,AdaBoost分类算法作为预测工具.与已报道的亚核定位预测方法的性能相比,这种方法具有更高的准确率.     

Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network

Because a priori knowledge of a protein structural class can provide useful information about its overall structure, the determination of protein structural class is a quite meaningful topic in protein science. However, with the rapid increase in newly found protein sequences entering into databanks, it is both time-consuming and expensive to do so based solely on experimental techniques. Therefore, it is vitally important to develop a computational method for predicting the protein structural class quickly and accurately. To deal with the challenge, this article presents a dual-layer support vector machine (SVM) fusion network that is featured by using a different pseudo-amino acid composition (PseAA). The PseAA here contains much information that is related to the sequence order of a protein and the distribution of the hydrophobic amino acids along its chain. As a showcase, the rigorous jackknife cross-validation test was performed on the two benchmark data sets constructed by Zhou. A significant enhancement in success rates was observed, indicating that the current approach may serve as a powerful complementary tool to other existing methods in this area.     

Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy

The function of protein is closely correlated with it subcellular location. Prediction of subcellular location of apoptosis proteins is an important research area in post-genetic era because the knowledge of apoptosis proteins is useful to understand the mechanism of programmed cell death. Compared with the conventional amino acid composition (AAC), the Pseudo Amino Acid composition (PseAA) as originally introduced by Chou can incorporate much more information of a protein sequence so as to remarkably enhance the power of using a discrete model to predict various attributes of a protein. In this study, a novel approach is presented to predict apoptosis protein solely from sequence based on the concept of Chou's PseAA composition. The concept of approximate entropy (ApEn), which is a parameter denoting complexity of time series, is used to construct PseAA composition as additional features. Fuzzy K-nearest neighbor (FKNN) classifier is selected as prediction engine. Particle swarm optimization (PSO) algorithm is adopted for optimizing the weight factors which are important in PseAA composition. Two datasets are used to validate the performance of the proposed approach, which incorporate six subcellular location and four subcellular locations, respectively. The results obtained by jackknife test are quite encouraging. It indicates that the ApEn of protein sequence could represent effectively the information of apoptosis proteins subcellular locations. It can at least play a complimentary role to many of the existing methods, and might become potentially useful tool for protein function prediction. The software in Matlab is available freely by contacting the corresponding author.     

PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition

The pseudo amino acid (PseAA) composition can represent a protein sequence in a discrete model without completely losing its sequence-order information, and hence has been widely applied for improving the prediction quality for various protein attributes. However, dealing with different problems may need different kinds of PseAA composition. Here, we present a web-server called PseAAC at http://chou.med.harvard.edu/bioinf/PseAA/, by which users can generate various kinds of PseAA composition to best fit their need.     

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.     

Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition

Cell membranes are vitally important to the life of a cell. Although the basic structure of biological membrane is provided by the lipid bilayer, membrane proteins perform most of the specific functions. Membrane proteins are putatively classified into five different types. Identification of their types is currently an important topic in bioinformatics and proteomics. In this paper, based on the concept of representing protein samples in terms of their pseudo-amino acid composition (Chou, K.C., 2001. Prediction of protein cellular attributes using pseudo amino acid composition. Proteins: Struct. Funct. Genet. 43, 246-255), the fuzzy K-nearest neighbors (KNN) algorithm has been introduced to predict membrane protein types, and high success rates were observed. It is anticipated that, the current approach, which is based on a branch of fuzzy mathematics and represents a new strategy, may play an important complementary role to the existing methods in this area. The novel approach may also have notable impact on prediction of the other attributes, such as protein structural class, protein subcellular localization, and enzyme family class, among many others.     

How good is prediction of protein structural class by the component-coupled method?

Proteins of known structures are usually classified into four structural classes: all-alpha, all-beta, alpha+beta, and alpha/beta type of proteins. A number of methods to predicting the structural class of a protein based on its amino acid composition have been developed during the past few years. Recently, a component-coupled method was developed for predicting protein structural class according to amino acid composition. This method is based on the least Mahalanobis distance principle, and yields much better predicted results in comparison with the previous methods. However, the success rates reported for structural class prediction by different investigators are contradictory. The highest reported accuracies by this method are near 100%, but the lowest one is only about 60%. The goal of this study is to resolve this paradox and to determine the possible upper limit of prediction rate for structural classes. In this paper, based on the normality assumption and the Bayes decision rule for minimum error, a new method is proposed for predicting the structural class of a protein according to its amino acid composition. The detailed theoretical analysis indicates that if the four protein folding classes are governed by the normal distributions, the present method will yield the optimum predictive result in a statistical sense. A non-redundant data set of 1,189 protein domains is used to evaluate the performance of the new method. Our results demonstrate that 60% correctness is the upper limit for a 4-type class prediction from amino acid composition alone for an unknown query protein. The apparent relatively high accuracy level (more than 90%) attained in the previous studies was due to the preselection of test sets, which may not be adequately representative of all unrelated proteins.     

Ultrasonic velocity assay of extracellular invertase in living yeasts

Being the largest family of cell surface receptors, G-protein-coupled receptors (GPCRs) are among the most frequent targets. The functions of many GPCRs are unknown, and it is both time-consuming and expensive to determine their ligands and signaling pathways by experimental methods. It is of great practical significance to develop an automated and reliable method for classification of GPCRs. In this study, a novel method based on the concept of Chou’s pseudo amino acid composition has been developed for predicting and recognizing GPCRs. The discrete wavelet transform was used to extract feature vectors from the hydrophobicity scales of amino acid to construct pseudo amino acid (PseAA) composition for training support vector machine. The prediction accuracies by the current method among the major families of GPCRs, subfamilies of class A, and types of amine receptors were 99.72%, 97.64%, and 99.20%, respectively, showing 9.4% to 18.0% improvement over other existing methods and indicating that the proposed method is a useful automated tool in identifying GPCRs.     

Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image

A novel approach was developed for predicting the structural classes of proteins based on their sequences. It was assumed that proteins belonging to the same structural class must bear some sort of similar texture on the images generated by the cellular automaton evolving rule [Wolfram, S., 1984. Cellular automation as models of complexity. Nature 311, 419-424]. Based on this, two geometric invariant moment factors derived from the image functions were used as the pseudo amino acid components [Chou, K.C., 2001. Prediction of protein cellular attributes using pseudo amino acid composition. Proteins: Struct., Funct., Genet. (Erratum: ibid., 2001, vol. 44, 60) 43, 246-255] to formulate the protein samples for statistical prediction. The success rates thus obtained on a previously constructed benchmark dataset are quite promising, implying that the cellular automaton image can help to reveal some inherent and subtle features deeply hidden in a pile of long and complicated amino acid sequences.     

Using pseudo amino acid composition to predict protein subcellular location: approached with amino acid composition distribution

In the Post Genome Age, there is an urgent need to develop the reliable and effective computational methods to predict the subcellular localization for the explosion of newly found proteins. Here, a novel method of pseudo amino acid (PseAA) composition, the so-called “amino acid composition distribution” (AACD), is introduced. First, a protein sequence is divided equally into multiple segments. Then, amino acid composition of each segment is calculated in series. After that, each protein sequence can be represented by a feature vector. Finally, the feature vectors of all sequences thus obtained are further input into the multi-class support vector machines to predict the subcellular localization. The results show that AACD is quite effective in representing protein sequences for the purpose of predicting protein subcellular localization.     

Predicting membrane protein types with bragging learner

The membrane protein type is an important feature in characterizing the overall topological folding type of a protein or its domains therein. Many investigators have put their efforts to the prediction of membrane protein type. Here, we propose a new approach, the bootstrap aggregating method or bragging learner, to address this problem based on the protein amino acid composition. As a demonstration, the benchmark dataset constructed by K.C. Chou and D.W. Elrod was used to test the new method. The overall success rate thus obtained by jackknife cross-validation was over 84%, indicating that the bragging learner as presented in this paper holds a quite high potential in predicting the attributes of proteins, or at least can play a complementary role to many existing algorithms in this area. It is anticipated that the prediction quality can be further enhanced if the pseudo amino acid composition can be effectively incorporated into the current predictor. An online membrane protein type prediction web server developed in our lab is available at http://chemdata.shu.edu.cn/protein/protein.jsp.     

Prediction of protein structural classes by Chou's pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis

A prior knowledge of protein structural classes can provide useful information about its overall structure, so it is very important for quick and accurate determination of protein structural class with computation method in protein science. One of the key for computation method is accurate protein sample representation. Here, based on the concept of Chou’s pseudo-amino acid composition (AAC, Chou, Proteins: structure, function, and genetics, 43:246–255, 2001), a novel method of feature extraction that combined continuous wavelet transform (CWT) with principal component analysis (PCA) was introduced for the prediction of protein structural classes. Firstly, the digital signal was obtained by mapping each amino acid according to various physicochemical properties. Secondly, CWT was utilized to extract new feature vector based on wavelet power spectrum (WPS), which contains more abundant information of sequence order in frequency domain and time domain, and PCA was then used to reorganize the feature vector to decrease information redundancy and computational complexity. Finally, a pseudo-amino acid composition feature vector was further formed to represent primary sequence by coupling AAC vector with a set of new feature vector of WPS in an orthogonal space by PCA. As a showcase, the rigorous jackknife cross-validation test was performed on the working datasets. The results indicated that prediction quality has been improved, and the current approach of protein representation may serve as a useful complementary vehicle in classifying other attributes of proteins, such as enzyme family class, subcellular localization, membrane protein types and protein secondary structure, etc.     

A correlation-coefficient method to predicting protein-structural classes from amino acid compositions.

A protein is usually classified into one of the following four structural classes: all alpha, all beta, (alpha + beta) and alpha/beta. In this paper, based on the maximum correlation-coefficient principle, a new formulation is proposed for predicting the structural class of a protein according to its amino acid composition. Calculations have been made for a development set of proteins from which the amino acid compositions for the standard structural classes were derived, and an independent set of proteins which are outside the development set. The former can test the self consistency of a method and the latter can test its extrapolating effectiveness. In both cases, the results showed that the new method gave a considerably higher rate of correct prediction than any of the previous methods, implying that a significant improvement has been achieved by implementing the maximum-correlation-coefficient principle in the new method.     

Cross-validation of protein structural class prediction using statistical clustering and neural networks.

We present an approach to predicting protein structural class that uses amino acid composition and hydrophobic pattern frequency information as input to two types of neural networks: (1) a three-layer back-propagation network and (2) a learning vector quantization network. The results of these methods are compared to those obtained from a modified Euclidean statistical clustering algorithm. The protein sequence data used to drive these algorithms consist of the normalized frequency of up to 20 amino acid types and six hydrophobic amino acid patterns. From these frequency values the structural class predictions for each protein (all-alpha, all-beta, or alpha-beta classes) are derived. Examples consisting of 64 previously classified proteins were randomly divided into multiple training (56 proteins) and test (8 proteins) sets. The best performing algorithm on the test sets was the learning vector quantization network using 17 inputs, obtaining a prediction accuracy of 80.2%. The Matthews correlation coefficients are statistically significant for all algorithms and all structural classes. The differences between algorithms are in general not statistically significant. These results show that information exists in protein primary sequences that is easily obtainable and useful for the prediction of protein structural class by neural networks as well as by standard statistical clustering algorithms.     

系统生物学视野下的功能氨基酸组学

传统营养学将氨基酸作为蛋白质的构建单元来研究蛋白质和氨基酸。近年来的研究表明,氨基酸在物质代谢和免疫功能调控等方面亦发挥重要作用,并提出功能性氨基酸的概念。随着各种组学技术的不断发展,通过系统生物学的理念与方法整合组学数据,系统地分析功能氨基酸的分子作用机制、药效学、体内动态过程成为可能。为此,本文提出功能氨基酸组学的概念,指出功能氨基酸组学领域的科学问题,提出功能氨基酸组学的研究内容。研究结果可用于朝向特定目标的氨基酸组合设计。     

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     

Predicting protein subcellular location using digital signal processing

The biological functions of a protein are closely related to its attributes in a cell. With the rapid accumulation of newly found protein sequence data in databanks, it is highly desirable to develop an automated method for predicting the subcellular location of proteins. The establishment of such a predictor will expedite the functional determination of newly found proteins and the process of prioritizing genes and proteins identified by genomic efforts as potential molecular targets for drug design. The traditional algorithms for predicting these attributes were based solely on amino acid composition in which no sequence order effect was taken into account. To improve the prediction quality, it is necessary to incorporate such an effect. However, the number of possible patterns in protein sequences is extremely large, posing a formidable difficulty for realizing this goal. To deal with such difficulty, a well-developed tool in digital signal processing named digital Fourier transform (DFT) [1] was introduced. After being translated to a digital signal according to the hydrophobicity of each amino acid, a protein was analyzed by DFT within the frequency domain. A set of frequency spectrum parameters, thus obtained, were regarded as the factors to represent the sequence order effect. A significant improvement in prediction quality was observed by incorporating the frequency spectrum parameters with the conventional amino acid composition. One of the crucial merits of this approach is that many existing tools in mathematics and engineering can be easily applied in the predicting process. It is anticipated that digital signal processing may serve as a useful vehicle for many other protein science areas.