Improving discrimination of outer membrane proteins by fusing different forms of pseudo amino acid composition

Integral membrane proteins are central to many cellular processes and constitute approximately 50% of potential targets for novel drugs. However, the number of outer membrane proteins (OMPs) present in the public structure database is very limited due to the difficulties in determining structure with experimental methods. Therefore, discriminating OMPs from non-OMPs with computational methods is of medical importance as well as genome sequencing necessity. In this study, some sequence-derived structural and physicochemical features of proteins were incorporated with amino acid composition to discriminate OMPs from non-OMPs using support vector machines. The discrimination performance of the proposed method is evaluated on a benchmark dataset of 208 OMPs, 673 globular proteins, and 206 α-helical membrane proteins. A high overall accuracy of 97.8% was observed in the 5-fold cross-validation test. In addition, the current method distinguished OMPs from globular proteins and α-helical membrane proteins with overall accuracies of 98.2 and 96.4%, respectively. The prediction performance is superior to the state-of-the-art methods in the literature. It is anticipated that the current method might be a powerful tool for the discrimination of OMPs.     

Discrimination of outer membrane proteins using a <Emphasis Type="Italic">K</Emphasis>-nearest neighbor method

Identification of outer membrane proteins (OMPs) from genome is an important task. This paper presents a k-nearest neighbor (K-NN) method for discriminating outer membrane proteins (OMPs). The method makes predictions based on a weighted Euclidean distance that is computed from residue composition. The method achieves 89.1% accuracy with 0.668 MCC (Matthews correlation coefficient) in discriminating OMPs and non-OMPs. The performance of the method is improved by including homologous information into the calculation of residue composition. The final method achieves an accuracy of 96.1%, with 0.873 MCC, 87.5% sensitivity, and 98.2% specificity. Comparisons with multiple recently published methods show that the method proposed in this study outperforms the others.     

Discrimination of outer membrane proteins using support vector machines

MOTIVATION: Discriminating outer membrane proteins from other folding types of globular and membrane proteins is an important task both for dissecting outer membrane proteins (OMPs) from genomic sequences and for the successful prediction of their secondary and tertiary structures. RESULTS: We have developed a method based on support vector machines using amino acid composition and residue pair information. Our approach with amino acid composition has correctly predicted the OMPs with a cross-validated accuracy of 94% in a set of 208 proteins. Further, this method has successfully excluded 633 of 673 globular proteins and 191 of 206 alpha-helical membrane proteins. We obtained an overall accuracy of 92% for correctly picking up the OMPs from a dataset of 1087 proteins belonging to all different types of globular and membrane proteins. Furthermore, residue pair information improved the accuracy from 92 to 94%. This accuracy of discriminating OMPs is higher than that of other methods in the literature, which could be used for dissecting OMPs from genomic sequences. AVAILABILITY: Discrimination results are available at http://tmbeta-svm.cbrc.jp.     

Influence of amino acid properties for discriminating outer membrane proteins at better accuracy

Discriminating outer membrane proteins (OMPs) from other folding types of globular and membrane proteins is an important task both for identifying outer membrane proteins from genomic sequences and for the successful prediction of their secondary and tertiary structures. In this work, we have analyzed the influence of physico-chemical, energetic and conformational properties of amino acid residues for discriminating outer membrane proteins using different machine learning algorithms, such as, Bayes rules, Logistic functions, Neural networks, Support vector machines, Decision trees, etc. We observed that most of the properties have discriminated the OMPs with similar accuracy. The neural network method with the property, free energy change could discriminate the OMPs from other folding types of globular and membrane proteins at the 5-fold cross-validation accuracy of 94.4% in a dataset of 1,088 proteins, which is better than that obtained with amino acid composition. The accuracy of discriminating globular proteins is 94.3% and that of transmembrane helical (TMH) proteins is 91.8%. Further, the neural network method is tested with globular proteins belonging to 30 major folding types and it could successfully exclude 99.4% of the considered 1612 non-redundant proteins. These accuracy levels are comparable to or better than other methods in the literature. We suggest that this method could be effectively used to discriminate OMPs and for detecting OMPs in genomic sequences.     

Discrimination of outer membrane proteins using machine learning algorithms

Discriminating outer membrane proteins (OMPs) from other folding types of globular and membrane proteins is an important task both for identifying OMPs from genomic sequences and for the successful prediction of their secondary and tertiary structures. In this work, we have analyzed the performance of different methods, based on Bayes rules, logistic functions, neural networks, support vector machines, decision trees, etc. for discriminating OMPs. We found that most of the machine learning techniques discriminate OMPs with similar accuracy. The neural network-based method could discriminate the OMPs from other proteins [globular/transmembrane helical (TMH)] at the fivefold cross-validation accuracy of 91.0% in a dataset of 1,088 proteins. The accuracy of discriminating globular proteins is 88.8% and that of TMH proteins is 93.7%. Further, the neural network method is tested with globular proteins belonging to 30 different folding types and it could successfully exclude 95% of the considered proteins. The proteins with SAM domain such as knottins, rubredoxin, and thioredoxin folds are eliminated with 100% accuracy. These accuracy levels are comparable to or better than other methods in the literature. We suggest that this method could be effectively used to discriminate OMPs and for detecting OMPs in genomic sequences.     

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.     

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.     

Prediction of membrane proteins using split amino acid and ensemble classification

Knowledge of the types of membrane protein provides useful clues in deducing the functions of uncharacterized membrane proteins. An automatic method for efficiently identifying uncharacterized proteins is thus highly desirable. In this work, we have developed a novel method for predicting membrane protein types by exploiting the discrimination capability of the difference in amino acid composition at the N and C terminus through split amino acid composition (SAAC). We also show that the ensemble classification can better exploit this discriminating capability of SAAC. In this study, membrane protein types are classified using three feature extraction and several classification strategies. An ensemble classifier Mem-EnsSAAC is then developed using the best feature extraction strategy. Pseudo amino acid (PseAA) composition, discrete wavelet analysis (DWT), SAAC, and a hybrid model are employed for feature extraction. The nearest neighbor, probabilistic neural network, support vector machine, random forest, and Adaboost are used as individual classifiers. The predicted results of the individual learners are combined using genetic algorithm to form an ensemble classifier, Mem-EnsSAAC yielding an accuracy of 92.4 and 92.2% for the Jackknife and independent dataset test, respectively. Performance measures such as MCC, sensitivity, specificity, F-measure, and Q-statistics show that SAAC-based prediction yields significantly higher performance compared to PseAA- and DWT-based systems, and is also the best reported so far. The proposed Mem-EnsSAAC is able to predict the membrane protein types with high accuracy and consequently, can be very helpful in drug discovery. It can be accessed at http://111.68.99.218/membrane.     

Toward an improved discrimination of outer membrane proteins using a sequence-based approach

This article offers a novel sequence-based approach to discriminate outer membrane proteins (OMPs). The first step is to use a new representation approach, factor analysis scales of generalized amino acid information (FASGAI) representing hydrophobicity, alpha and turn propensities, bulky properties, compositional characteristics, local flexibility and electronic properties, etc., to characterize sequences of OMPs and non-OMPs. The subsequent data is then transformed into a uniform matrix by the auto cross covariance (ACC). The second step is to develop discrimination predictors of OMPs from non-OMPs using a support vector machine (SVM). The SVM predictors thus successfully produce a high Matthews correlation coefficient (MCC) of 0.916 on 208 OMPs from non-OMPs including 206 α-helical membrane proteins and 673 globular proteins by a fivefold cross validation test. Meanwhile, overall MCC values of 0.923 and 0.930 are obtained for the discrimination OMPs from the α-helical membrane proteins and the globular proteins, respectively. The results demonstrate that the FASGAI-ACC-SVM combination approach shows great prospect of application in the field of bioinformatics or proteomics studies.     

Motifs in outer membrane protein sequences: applications for discrimination

Discriminating outer membrane proteins (OMPs) from other folding types of globular and membrane proteins is an important problem for predicting their secondary and tertiary structures and detecting outer membrane proteins from genomic sequences as well. In this work, we have systematically analyzed the distribution of amino acid residues in the sequences of globular and outer membrane proteins with several motifs, such as A*B, A**B, etc. We observed that the motifs E*L, A*K and L*E occur frequently in globular proteins while S*S, N*S and R*D predominantly occur in OMPs. We have devised a statistical method based on frequently occurring motifs in globular and OMPs and obtained an accuracy of 96% and 82% for correctly identifying OMPs and excluding globular proteins, respectively. Further, we noticed that the motifs of transmembrane helical (TMH) proteins are different from that of OMPs. While I*A, I*L and L*I prefer in TMH proteins S*S, N*S and N*N predominantly occur in OMPs. The information about the occurrence of A*B motifs in TMH and OMPs could discriminate them with an accuracy of 80% for excluding OMPs and 100% for identifying OMPs. The influence of protein size and structural class for discrimination is discussed.     

使用多特征联合变量的支持向量机方法预测外膜蛋白

外膜蛋白(Outer Membrane Proteins, OMPs)是一类具有重要生物功能的蛋白质, 通过生物信息学方法来预测OMPs能够为预测OMPs的二级和三级结构以及在基因组发现新的OMPs提供帮助。文中提出计算蛋白质序列的氨基酸含量特征、二肽含量特征和加权多阶氨基酸残基指数相关系数特征, 将三类特征组合, 采用支持向量机(Support Vector Machine, SVM)算法来识别OMPs。计算了包括四种残基指数的多种组合特征的识别结果, 并且讨论了相关系数的阶次和权值对预测性能的影响。在数据集上的十倍交叉验证测试和独立性测试结果显示, 组合特征识别方法对OMPs和非OMPs的识别精度最高分别达到96.96%和97.33%, 优于现有的多种方法。在五种细菌基因组内识别OMPs的结果显示, 组合特征方法具有很高的特异性, 并且对PDB数据库中已知结构的OMPs识别准确度超过99%。表明该方法能够作为基因组内筛选OMPs的有效工具。     

使用多特征联合变量的支持向量机方法预测外膜蛋白

外膜蛋白(Outer Membrane Proteins, OMPs)是一类具有重要生物功能的蛋白质, 通过生物信息学方法来预测OMPs能够为预测OMPs的二级和三级结构以及在基因组发现新的OMPs提供帮助。文中提出计算蛋白质序列的氨基酸含量特征、二肽含量特征和加权多阶氨基酸残基指数相关系数特征, 将三类特征组合, 采用支持向量机(Support Vector Machine, SVM)算法来识别OMPs。计算了包括四种残基指数的多种组合特征的识别结果, 并且讨论了相关系数的阶次和权值对预测性能的影响。在数据集上的十倍交叉验证测试和独立性测试结果显示, 组合特征识别方法对OMPs和非OMPs的识别精度最高分别达到96.96%和97.33%, 优于现有的多种方法。在五种细菌基因组内识别OMPs的结果显示, 组合特征方法具有很高的特异性, 并且对PDB数据库中已知结构的OMPs识别准确度超过99%。表明该方法能够作为基因组内筛选OMPs的有效工具。     

A simple statistical method for discriminating outer membrane proteins with better accuracy

MOTIVATION: Discriminating outer membrane proteins from other folding types of globular and membrane proteins is an important task both for identifying outer membrane proteins from genomic sequences and for the successful prediction of their secondary and tertiary structures. RESULTS: We have systematically analyzed the amino acid composition of globular proteins from different structural classes and outer membrane proteins. We found that the residues, Glu, His, Ile, Cys, Gln, Asn and Ser, show a significant difference between globular and outer membrane proteins. Based on this information, we have devised a statistical method for discriminating outer membrane proteins from other globular and membrane proteins. Our approach correctly picked up the outer membrane proteins with an accuracy of 89% for the training set of 337 proteins. On the other hand, our method has correctly excluded the globular proteins at an accuracy of 79% in a non-redundant dataset of 674 proteins. Furthermore, the present method is able to correctly exclude alpha-helical membrane proteins up to an accuracy of 80%. These accuracy levels are comparable to other methods in the literature, and this is a simple method, which could be used for dissecting outer membrane proteins from genomic sequences. The influence of protein size, structural class and specific residues for discrimination is discussed.     

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。     

Geometry preserving projections algorithm for predicting membrane protein types

Given a new uncharacterized protein sequence, a biologist may want to know whether it is a membrane protein or not? If it is, which membrane protein type it belongs to? Knowing the type of an uncharacterized membrane protein often provides useful clues for finding the biological function of the query protein, developing the computational methods to address these questions can be really helpful. In this study, a sequence encoding scheme based on combing pseudo position-specific score matrix (PsePSSM) and dipeptide composition (DC) is introduced to represent protein samples. However, this sequence encoding scheme would correspond to a very high dimensional feature vector. A dimensionality reduction algorithm, the so-called geometry preserving projections (GPP) is introduced to extract the key features from the high-dimensional space and reduce the original high-dimensional vector to a lower-dimensional one. Finally, the K-nearest neighbor (K-NN) and support vector machine (SVM) classifiers are employed to identify the types of membrane proteins based on their reduced low-dimensional features. Our jackknife and independent dataset test results thus obtained are quite encouraging, which indicate that the above methods are used effectively to deal with this complicated problem of predicting the membrane protein type.     

Outer membrane proteins can be simply identified using secondary structure element alignment

Background  

Outer membrane proteins (OMPs) are frequently found in the outer membranes of gram-negative bacteria, mitochondria and chloroplasts and have been found to play diverse functional roles. Computational discrimination of OMPs from globular proteins and other types of membrane proteins is helpful to accelerate new genome annotation and drug discovery.     

Prediction of protein subcellular location using a combined feature of sequence

To understand the structure and function of a protein, an important task is to know where it occurs in the cell. Thus, a computational method for properly predicting the subcellular location of proteins would be significant in interpreting the original data produced by the large-scale genome sequencing projects. The present work tries to explore an effective method for extracting features from protein primary sequence and find a novel measurement of similarity among proteins for classifying a protein to its proper subcellular location. We considered four locations in eukaryotic cells and three locations in prokaryotic cells, which have been investigated by several groups in the past. A combined feature of primary sequence defined as a 430D (dimensional) vector was utilized to represent a protein, including 20 amino acid compositions, 400 dipeptide compositions and 10 physicochemical properties. To evaluate the prediction performance of this encoding scheme, a jackknife test based on nearest neighbor algorithm was employed. The prediction accuracies for cytoplasmic, extracellular, mitochondrial, and nuclear proteins in the former dataset were 86.3%, 89.2%, 73.5% and 89.4%, respectively, and the total prediction accuracy reached 86.3%. As for the prediction accuracies of cytoplasmic, extracellular, and periplasmic proteins in the latter dataset, the prediction accuracies were 97.4%, 86.0%, and 79.7, respectively, and the total prediction accuracy of 92.5% was achieved. The results indicate that this method outperforms some existing approaches based on amino acid composition or amino acid composition and dipeptide composition.     

Discrimination of outer membrane proteins by a new measure of information discrepancy

Discriminating outer membrane proteins for globular proteins (GPs) and other types of membrane proteins from genomic sequences is an important and hot topic. In this paper, a measure based on information discrepancy is proposed and applied to the discrimination of outer membrane proteins. It differs from previous methods which are based on amino acid composition. Our approach focuses on the comparison of subsequence distributions and takes into account the effect of residue order in protein primary structures. As a result, the new approach outperforms all previous methods on the same benchmark datasets. In particular, we show that the proposed approach has correctly identified the outer membrane proteins at an accuracy of 99% for the training set of 337 proteins and has correctly excluded the GPs at an accuracy of 86% in a non-redundant dataset of 668 proteins. Furthermore, this method is able to correctly exclude alpha-helical membrane proteins at an accuracy of 100%.