Mito-GSAAC: mitochondria prediction using genetic ensemble classifier and split amino acid composition

Mitochondria are all-important organelles of eukaryotic cells since they are involved in processes associated with cellular mortality and human diseases. Therefore, trustworthy techniques are highly required for the identification of new mitochondrial proteins. We propose Mito-GSAAC system for prediction of mitochondrial proteins. The aim of this work is to investigate an effective feature extraction strategy and to develop an ensemble approach that can better exploit the advantages of this feature extraction strategy for mitochondria classification. We investigate four kinds of protein representations for prediction of mitochondrial proteins: amino acid composition, dipeptide composition, pseudo amino acid composition, and split amino acid composition (SAAC). Individual classifiers such as support vector machine (SVM), k-nearest neighbor, multilayer perceptron, random forest, AdaBoost, and bagging are first trained. An ensemble classifier is then built using genetic programming (GP) for evolving a complex but effective decision space from the individual decision spaces of the trained classifiers. The highest prediction performance for Jackknife test is 92.62% using GP-based ensemble classifier on SAAC features, which is the highest accuracy, reported so far on the Mitochondria dataset being used. While on the Malaria Parasite Mitochondria dataset, the highest accuracy is obtained by SVM using SAAC and it is further enhanced to 93.21% using GP-based ensemble. It is observed that SAAC has better discrimination power for mitochondria prediction over the rest of the feature extraction strategies. Thus, the improved prediction performance is largely due to the better capability of SAAC for discriminating between mitochondria and non-mitochondria proteins at the N and C terminus and the effective combination capability of GP. Mito-GSAAC can be accessed at . It is expected that the novel approach and the accompanied predictor will have a major impact to Molecular Cell Biology, Proteomics, Bioinformatics, System Biology, and Drug Development.     

Using ensemble classifier to identify membrane protein types

Predicting membrane protein type is both an important and challenging topic in current molecular and cellular biology. This is because knowledge of membrane protein type often provides useful clues for determining, or sheds light upon, the function of an uncharacterized membrane protein. With the explosion of newly-found protein sequences in the post-genomic era, it is in a great demand to develop a computational method for fast and reliably identifying the types of membrane proteins according to their primary sequences. In this paper, a novel classifier, the so-called "ensemble classifier", was introduced. It is formed by fusing a set of nearest neighbor (NN) classifiers, each of which is defined in a different pseudo amino acid composition space. The type for a query protein is determined by the outcome of voting among these constituent individual classifiers. It was demonstrated through the self-consistency test, jackknife test, and independent dataset test that the ensemble classifier outperformed other existing classifiers widely used in biological literatures. It is anticipated that the idea of ensemble classifier can also be used to improve the prediction quality in classifying other attributes of proteins according to their sequences.     

Prediction of protein subcellular multi-localization based on the general form of Chou's pseudo amino acid composition

Many proteins bear multi-locational characteristics, and this phenomenon is closely related to biological function. However, most of the existing methods can only deal with single-location proteins. Therefore, an automatic and reliable ensemble classifier for protein subcellular multi-localization is needed. We propose a new ensemble classifier combining the KNN (K-nearest neighbour) and SVM (support vector machine) algorithms to predict the subcellular localization of eukaryotic, Gram-negative bacterial and viral proteins based on the general form of Chou's pseudo amino acid composition, i.e., GO (gene ontology) annotations, dipeptide composition and AmPseAAC (Amphiphilic pseudo amino acid composition). This ensemble classifier was developed by fusing many basic individual classifiers through a voting system. The overall prediction accuracies obtained by the KNN-SVM ensemble classifier are 95.22, 93.47 and 80.72% for the eukaryotic, Gram-negative bacterial and viral proteins, respectively. Our prediction accuracies are significantly higher than those by previous methods and reveal that our strategy better predicts subcellular locations of multi-location proteins.     

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。     

基于分段氨基酸组成成分的蛋白质相互作用预测

蛋白质相互作用研究有助于揭示生命过程的许多本质问题,也有助于疾病预防、诊断,对药物研制具有重要的参考价值。文章首先构建出蛋白质作用数据库,提出分段氨基酸组成成分特征提取方法来预测蛋白质相互作用。10CV检验下,基于支持向量机的3段氨基酸组成成分特征提取方法的预测总精度为86.2%,比传统的氨基酸组成成分方法提高2.31个百分点;采用Guo的数据库和检验方法,3段氨基酸组成成分特征提取方法的预测总精度为90.11%,比Guo的自相关函数特征提取方法提高2.75个百分点,从而表明分段氨基酸组成成分特征提取方法可有效地应用于蛋白质相互作用预测。     

GPCR-MPredictor: multi-level prediction of G protein-coupled receptors using genetic ensemble

G protein-coupled receptors (GPCRs) are transmembrane proteins, which transduce signals from extracellular ligands to intracellular G protein. Automatic classification of GPCRs can provide important information for the development of novel drugs in pharmaceutical industry. In this paper, we propose an evolutionary approach, GPCR-MPredictor, which combines individual classifiers for predicting GPCRs. GPCR-MPredictor is a web predictor that can efficiently predict GPCRs at five levels. The first level determines whether a protein sequence is a GPCR or a non-GPCR. If the predicted sequence is a GPCR, then it is further classified into family, subfamily, sub-subfamily, and subtype levels. In this work, our aim is to analyze the discriminative power of different feature extraction and classification strategies in case of GPCRs prediction and then to use an evolutionary ensemble approach for enhanced prediction performance. Features are extracted using amino acid composition, pseudo amino acid composition, and dipeptide composition of protein sequences. Different classification approaches, such as k-nearest neighbor (KNN), support vector machine (SVM), probabilistic neural networks (PNN), J48, Adaboost, and Naives Bayes, have been used to classify GPCRs. The proposed hierarchical GA-based ensemble classifier exploits the prediction results of SVM, KNN, PNN, and J48 at each level. The GA-based ensemble yields an accuracy of 99.75, 92.45, 87.80, 83.57, and 96.17% at the five levels, on the first dataset. We further perform predictions on a dataset consisting of 8,000 GPCRs at the family, subfamily, and sub-subfamily level, and on two other datasets of 365 and 167 GPCRs at the second and fourth levels, respectively. In comparison with the existing methods, the results demonstrate the effectiveness of our proposed GPCR-MPredictor in classifying GPCRs families. It is accessible at .     

Predicting protein subcellular location by fusing multiple classifiers

One of the fundamental goals in cell biology and proteomics is to identify the functions of proteins in the context of compartments that organize them in the cellular environment. Knowledge of subcellular locations of proteins can provide key hints for revealing their functions and understanding how they interact with each other in cellular networking. Unfortunately, it is both time-consuming and expensive to determine the localization of an uncharacterized protein in a living cell purely based on experiments. With the avalanche of newly found protein sequences emerging in the post genomic era, we are facing a critical challenge, that is, how to develop an automated method to fast and reliably identify their subcellular locations so as to be able to timely use them for basic research and drug discovery. In view of this, an ensemble classifier was developed by the approach of fusing many basic individual classifiers through a voting system. Each of these basic classifiers was trained in a different dimension of the amphiphilic pseudo amino acid composition (Chou [2005] Bioinformatics 21: 10-19). As a demonstration, predictions were performed with the fusion classifier for proteins among the following 14 localizations: (1) cell wall, (2) centriole, (3) chloroplast, (4) cytoplasm, (5) cytoskeleton, (6) endoplasmic reticulum, (7) extracellular, (8) Golgi apparatus, (9) lysosome, (10) mitochondria, (11) nucleus, (12) peroxisome, (13) plasma membrane, and (14) vacuole. The overall success rates thus obtained via the resubstitution test, jackknife test, and independent dataset test were all significantly higher than those by the existing classifiers. It is anticipated that the novel ensemble classifier may also become a very useful vehicle in classifying other attributes of proteins according to their sequences, such as membrane protein type, enzyme family/sub-family, G-protein coupled receptor (GPCR) type, and structural class, among many others. The fusion ensemble classifier will be available at www.pami.sjtu.edu.cn/people/hbshen.     

Prediction of protein homo-oligomer types by pseudo amino acid composition: Approached with an improved feature extraction and Naive Bayes Feature Fusion

Summary. The interaction of non-covalently bound monomeric protein subunits forms oligomers. The oligomeric proteins are superior to the monomers within the scope of functional evolution of biomacromolecules. Such complexes are involved in various biological processes, and play an important role. It is highly desirable to predict oligomer types automatically from their sequence. Here, based on the concept of pseudo amino acid composition, an improved feature extraction method of weighted auto-correlation function of amino acid residue index and Naive Bayes multi-feature fusion algorithm is proposed and applied to predict protein homo-oligomer types. We used the support vector machine (SVM) as base classifiers, in order to obtain better results. For example, the total accuracies of A, B, C, D and E sets based on this improved feature extraction method are 77.63, 77.16, 76.46, 76.70 and 75.06% respectively in the jackknife test, which are 6.39, 5.92, 5.22, 5.46 and 3.82% higher than that of G set based on conventional amino acid composition method with the same SVM. Comparing with Chou’s feature extraction method of incorporating quasi-sequence-order effect, our method can increase the total accuracy at a level of 3.51 to 1.01%. The total accuracy improves from 79.66 to 80.83% by using the Naive Bayes Feature Fusion algorithm. These results show: 1) The improved feature extraction method is effective and feasible, and the feature vectors based on this method may contain more protein quaternary structure information and appear to capture essential information about the composition and hydrophobicity of residues in the surface patches that buried in the interfaces of associated subunits; 2) Naive Bayes Feature Fusion algorithm and SVM can be referred as a powerful computational tool for predicting protein homo-oligomer types.     

Discriminating lysosomal membrane protein types using dynamic neural network

This work presents a dynamic artificial neural network methodology, which classifies the proteins into their classes from their sequences alone: the lysosomal membrane protein classes and the various other membranes protein classes. In this paper, neural networks-based lysosomal-associated membrane protein type prediction system is proposed. Different protein sequence representations are fused to extract the features of a protein sequence, which includes seven feature sets; amino acid (AA) composition, sequence length, hydrophobic group, electronic group, sum of hydrophobicity, R-group, and dipeptide composition. To reduce the dimensionality of the large feature vector, we applied the principal component analysis. The probabilistic neural network, generalized regression neural network, and Elman regression neural network (RNN) are used as classifiers and compared with layer recurrent network (LRN), a dynamic network. The dynamic networks have memory, i.e. its output depends not only on the input but the previous outputs also. Thus, the accuracy of LRN classifier among all other artificial neural networks comes out to be the highest. The overall accuracy of jackknife cross-validation is 93.2% for the data-set. These predicted results suggest that the method can be effectively applied to discriminate lysosomal associated membrane proteins from other membrane proteins (Type-I, Outer membrane proteins, GPI-Anchored) and Globular proteins, and it also indicates that the protein sequence representation can better reflect the core feature of membrane proteins than the classical AA composition.     

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.     

Prediction of thermophilic protein with pseudo amino Acid composition: an approach from combined feature selection and reduction

Prediction of thermophilic and mesophilic protein plays a crucial role in both biochemistry and bioengineering. In this study, a different mode of pseudo amino acid composition (PseAAC) was proposed to formulate the protein samples by integrating the amino acid composition, the physic chemical features, as well as the composition transition and distribution features, where each of the protein samples was represented by a numerical vector through the sequence-based approach. Using the support vector machine algorithm, an accurate and reliable classifier was constructed to predict the thermophilic and mesophilic proteins. Moreover, three feature reduction algorithms were obtained for locating the most vital features and reducing the size of feature space. Among the three feature reduction algorithms, the genetic algorithm performed best. Finally, with the reduced features extracted from the genetic algorithm, it was observed that for the selected dataset the new classifier achieved a high accuracy of 95.93% with the Matthews correlation coefficient of 0.9187.     

基于氨基酸组成分布的蛋白质同源寡聚体分类研究

基于一种新的特征提取方法——氨基酸组成分布,使用支持向量机作为成员分类器,采用“一对一”的多类分类策略,从蛋白质一级序列对四类同源寡聚体进行分类研究。结果表明,在10-CV检验下,基于氨基酸组成分布,其总分类精度和精度指数分别达到了86.22%和67.12%,比基于氨基酸组成成分的传统特征提取方法分别提高了5.74和10.03个百分点,比二肽组成成分特征提取方法分别提高了3.12和5.63个百分点,说明氨基酸组成分布对于蛋白质同源寡聚体分类是一种非常有效的特征提取方法;将氨基酸组成分布和蛋白质序列长度特征组合,其总分类精度和精度指数分别达到了86.35%和67.23%,说明蛋白质序列长度特征含有一定的空间结构信息。     

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 stacked generalization to predict membrane protein types based on pseudo-amino acid composition

Membrane proteins are vitally important for many biological processes and have become an attractive target for both basic research and drug design. Knowledge of membrane protein types often provides useful clues in deducing the functions of uncharacterized membrane proteins. With the unprecedented increasing of newly found protein sequences in the post-genomic era, it is highly demanded to develop an automated method for fast and accurately identifying the types of membrane proteins according to their amino acid sequences. Although quite a few identifiers have been developed in this regard through various approaches, such as covariant discriminant (CD), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN), classifier the way they operate the identification is basically individual. As is well known, wise persons usually take into account the opinions from several experts rather than rely on only one when they are making critical decisions. Likewise, a sophisticated identifier should be trained by several different modes. In view of this, based on the frame of pseudo-amino acid that can incorporate a considerable amount of sequence-order effects, a novel approach called "stacked generalization" or "stacking" has been introduced. Unlike the "bagging" and "boosting" approaches which only combine the classifiers of a same type, the stacking approach can combine several different types of classifiers through a meta-classifier to maximize the generalization accuracy. The results thus obtained were very encouraging. It is anticipated that the stacking approach may also hold a high potential to improve the identification quality for, among many other protein attributes, subcellular location, enzyme family class, protease type, and protein-protein interaction type. The stacked generalization classifier is available as a web-server named "SG-MPt_Pred" at: http://202.120.37.186/bioinf/wangsq/service.htm.     

Discriminating acidic and alkaline enzymes using a random forest model with secondary structure amino acid composition

Understating the adaptation mechanism of enzymes to pH extremes and discriminating them is a challenging task and would help to design stable enzymes. In this work, we have systematically analyzed the secondary structure amino acid compositions of 105 acidic and 111 alkaline enzymes, respectively. We found that the propensity of the individual residues to participate in different secondary structures might be a general stability mechanism for their adaptation to pH extremes. Based on it, we present a secondary structure amino acid composition method for extracting useful features from sequence, and a novel ensemble classifier named random forest was used. The overall prediction accuracy evaluated by the 10-fold cross-validation reached 90.7%. Comparing our method with other feature extraction methods, the improvement of the overall prediction accuracy ranged from 5.5% to 21.2%. The random forests algorithm also outperformed other machine learning techniques with an improvement ranging from 3.2% to 19.9%.     

Discriminating outer membrane proteins with Fuzzy K-nearest Neighbor algorithms based on the general form of Chou's PseAAC

Outer membrane proteins (OMPs) play important roles in cell biology. In addition, OMPs are targeted by multiple drugs. The identification of OMPs from genomic sequences and successful prediction of their secondary and tertiary structures is a challenging task due to short membrane-spanning regions with high variation in properties. Therefore, an effective and accurate silico method for discrimination of OMPs from their primary sequences is needed. In this paper, we have analyzed the performance of various machine learning mechanisms for discriminating OMPs such as: Genetic Programming, K-nearest Neighbor, and Fuzzy K-nearest Neighbor (Fuzzy K-NN) in conjunction with discrete methods such as: Amino acid composition, Amphiphilic Pseudo amino acid composition, Split amino acid composition (SAAC), and hybrid versions of these methods. The performance of the classifiers is evaluated by two datasets using 5-fold crossvalidation. After the simulation, we have observed that Fuzzy K-NN using SAAC based-features makes it quite effective in discriminating OMPs. Fuzzy K-NN achieves the highest success rates of 99.00% accuracy for discriminating OMPs from non-OMPs and 98.77% and 98.28% accuracies from α-helix membrane and globular proteins, respectively on dataset1. While on dataset2, Fuzzy K-NN achieves 99.55%, 99.90%, and 99.81% accuracies for discriminating OMPs from non- OMPs, α-helix membrane, and globular proteins, respectively. It is observed that the classification performance of our proposed method is satisfactory and is better than the existing methods. Thus, it might be an effective tool for high throughput innovation of OMPs.     

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。