Predicting Membrane Protein Types Research Articles

Multi-View Kernel Sparse Representation for Identification of Membrane Protein Types.

Membrane proteins are the main undertaker of biomembrane functions and play a vital role in many biological activities of organisms. Prediction of membrane protein types has a great help in determining the function of proteins and understanding the interactions of membrane proteins. However, the biochemical experiment is expensive and not suitable for the large-scale identification of membrane protein types. Therefore, computational methods were used to improve the efficiency of biological experiments. Most existing computational methods only use a single feature of protein, or use multiple features but do not integrate these well. In our study, the protein sequence is described via three different views (features), including amino acid composition, evolutionary information and physicochemical properties of amino acids. To exploit information among all views (features), we introduce a coupling strategy for Kernel Sparse Representation based Classification (KSRC) and construct a new model called Multi-view KSRC (MvKSRC). We implement our method on 4 benchmark data sets of membrane proteins. The comparison results indicate that our method is much superior to all existing methods.

Sequence based Prediction of Membrane Protein Types using Machine Learning methods

Membrane proteins perform an essential protein function, which are cell receptors, channels, and energy transducers. These functions work to keep the survival of the cell. The membrane protein is an integral part of body structure. Therefore, it is necessary to build up machine learning methods to correct identifying membrane protein types to recognize protein function for finding cell life cycle, gene interaction, cell signals, disease occurrence, drug design, and many more. Detection of a membrane protein from sequence length encoding based feature is a key task. The predictor GBDT and Ensemble Adaboost classifiers used to predict a membrane protein, with sequence length, encodes single descriptor value to encodes multilabel feature types. This proposed method has identified the types of the membrane without sequence information loss, and machine Learning classifiers handle the protein datasets well.

2Gram Features Based Prediction of Membrane Protein Types Using Ensemble Classifiers Methods

Membrane proteins are an essential type of protein used in a cell as receptors, channels, and energy transducers. Moreover, they do many of the functions imperative to the survival of the cell. Therefore, it is necessary to build up machine learning methods to correctly identify membrane protein types to recognize protein function, disease occurrence, and drug therapy design. So the function prediction of membrane protein performs a key task. In this paper, membrane protein types are detected by using 2-gram and sequence length feature. The predictor ensemble classifiers used to predict a membrane protein, with sequence length encodes single descriptor value and 2-gram exchange group, encodes multilabel feature types. Were, 2-gram special kinds of feature type focus on its different modes of protein information to emulate the fundamental functions that are enormously unknown in complex protein sequences and find a relation between residues. The proposed method avoids biasing among the differentiation between types of membrane proteins. Ensemble classifiers well handle the imbalanced protein datasets.

Prediction of membrane protein types by fusing protein-protein interaction and protein sequence information

Membrane proteins are gatekeepers to the cell and essential for determination of the function of cells. Identification of the types of membrane proteins is an essential problem in cell biology. It is time-consuming and expensive to identify the type of membrane proteins with traditional experimental methods. The alternative way is to design effective computational methods, which can provide quick and reliable predictions. To date, several computational methods have been proposed in this regard. Several of them used the features extracted from the sequence information of individual proteins. Recently, networks are more and more popular to tackle different protein-related problems, which can organize proteins in a system level and give an overview of all proteins. However, such form weakens the essential properties of proteins, such as their sequence information. In this study, a novel feature fusion scheme was proposed, which integrated the information of protein sequences and protein-protein interaction network. The fused features of a protein were defined as the linear combination of sequence features of all proteins in the network, where the combination coefficients were the probabilities yielded by the random walk with restart algorithm with the protein as the seed node. Several models with such fused features and different classification algorithms were built and evaluated. Their performance for predicting the type of membrane proteins was improved compared with the models only with the sequence features or network information.

Accurate classification of membrane protein types based on sequence and evolutionary information using deep learning

BackgroundMembrane proteins play an important role in the life activities of organisms. Knowing membrane protein types provides clues for understanding the structure and function of proteins. Though various computational methods for predicting membrane protein types have been developed, the results still do not meet the expectations of researchers.ResultsWe propose two deep learning models to process sequence information and evolutionary information, respectively. Both models obtained better results than traditional machine learning models. Furthermore, to improve the performance of the sequence information model, we also provide a new vector representation method to replace the one-hot encoding, whose overall success rate improved by 3.81% and 6.55% on two datasets. Finally, a more effective model is obtained by fusing the above two models, whose overall success rate reached 95.68% and 92.98% on two datasets.ConclusionThe final experimental results show that our method is more effective than existing methods for predicting membrane protein types, which can help laboratory researchers to identify the type of novel membrane proteins.

Prediction of membrane protein types by exploring local discriminative information from evolutionary profiles

Membrane protein is a pivotal constituent of a cell that exerts a crucial influence on diverse biological processes. The accurate identification of membrane protein types is deeply essential for revealing molecular mechanisms and drug development. Primarily, several traditional methods were exploited to classify these types. However, experimental methods are laborious, time-consuming, and costly due to rapid exploration of uncharacterized protein sequences generated in the postgenomic era. Hence, machine learning-based methods are more indispensable for reliable and fast identification of membrane protein types. A variety of state-of-the-art investigations have been elucidated to improve prediction performance, but predictive validity is still insufficient. Motivated by this, we designed a promising sequential support vector machine based predictor called TargetHMP to predict types of membrane proteins. We captured the local informative features by exploring evolutionary profiles through a novel method called the segmentation-based pseudo position-specific scoring matrix (Seg-PsePSSM). TargetHMP attained high accuracy of 94.99%, 93.48%, and 90.36% on the S1, S2, and S3 datasets, respectively, using a vigorous leave-one-out-cross-validation test. The results indicate that the performance of the proposed method outperformed prior predictors. We expect that the proposed approach will help research academia in general and pharmaceutical drug discovery in particular.

Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC

Membrane proteins are vital type of proteins that serve as channels, receptors and energy transducers in a cell. They perform various important functions, which are mainly associated with their types. They are also attractive targets of drug discovery for various diseases. So predicting membrane protein types is a crucial and challenging research area in bioinformatics and proteomics. Because of vast investigation of uncharacterized protein sequences in databases, customary biophysical techniques are extremely tedious, costly and vulnerable to mistakes. Subsequently, it is very attractive to build a vigorous, solid, proficient technique to predict membrane protein types. In this work, a novel feature set Exchange Group Based Protein Sequence Representation (EGBPSR) is proposed for classification of membrane proteins with two new feature extraction strategies known as Exchange Group Local Pattern (EGLP) and Amino acid Interval Pattern (AIP). Imbalanced dataset and large dataset are often handled well by decision tree classifiers. Since imbalanced dataset are taken, the performance of various decision tree classifiers such as Decision Tree (DT), Classification and Regression Tree (CART), ensemble methods such as Adaboost, Random Under Sampling (RUS) boost, Rotation forest and Random forest are analyzed. The overall accuracy achieved in predicting membrane protein types is 96.45%.

Prediction for Membrane Protein Types Based on Effective Fusion Representation and MIC-GA Feature Selection

Membrane proteins occupy an important position in the life activities of humans and other species. The elucidation of membrane protein types provides clues for understanding the structure and function of proteins. With the fusion of various protein information including amino acid classification, physicochemical property, and evolutionary information, this paper proposes a system for predicting membrane protein types. In this system, a new feature selection method called MIC-GA is proposed to deal with the curse of high-dimensional features. The findings show that this approach is effective in reducing feature dimensions and improves prediction accuracy. Ensemble method based on stacked generalization is also used to solve the problem of feature heterogeneity. The performance of the present method is evaluated on two benchmark datasets. The overall prediction accuracies of eight types are 89.23% and 93.49% using jackknife test and independent test, respectively. The final experimental results show that our method is more effective than the existing methods for prediction of membrane protein types.

Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets

Predicting membrane protein types is an important and challenging research area in bioinformatics and proteomics. Traditional biophysical methods are used to classify membrane protein types. Due to large exploration of uncharacterized protein sequences in databases, traditional methods are very time consuming, expensive and susceptible to errors. Hence, it is highly desirable to develop a robust, reliable, and efficient method to predict membrane protein types. Imbalanced datasets and large datasets are often handled well by decision tree classifiers. Since imbalanced datasets are taken, the performance of various decision tree classifiers such as Decision Tree (DT), Classification And Regression Tree (CART), C4.5, Random tree, REP (Reduced Error Pruning) tree, ensemble methods such as Adaboost, RUS (Random Under Sampling) boost, Rotation forest and Random forest are analysed. Among the various decision tree classifiers Random forest performs well in less time with good accuracy of 96.35%. Another inference is RUS boost decision tree classifier is able to classify one or two samples in the class with very less samples while the other classifiers such as DT, Adaboost, Rotation forest and Random forest are not sensitive for the classes with fewer samples. Also the performance of decision tree classifiers is compared with SVM (Support Vector Machine) and Naive Bayes classifier.

Prediction of Multi-Type Membrane Proteins in Human by an Integrated Approach

Membrane proteins were found to be involved in various cellular processes performing various important functions, which are mainly associated to their types. However, it is very time-consuming and expensive for traditional biophysical methods to identify membrane protein types. Although some computational tools predicting membrane protein types have been developed, most of them can only recognize one kind of type. Therefore, they are not as effective as one membrane protein can have several types at the same time. To our knowledge, few methods handling multiple types of membrane proteins were reported. In this study, we proposed an integrated approach to predict multiple types of membrane proteins by employing sequence homology and protein-protein interaction network. As a result, the prediction accuracies reached 87.65%, 81.39% and 70.79%, respectively, by the leave-one-out test on three datasets. It outperformed the nearest neighbor algorithm adopting pseudo amino acid composition. The method is anticipated to be an alternative tool for identifying membrane protein types. New metrics for evaluating performances of methods dealing with multi-label problems were also presented. The program of the method is available upon request.

A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC

Membrane proteins play important roles in many biochemical processes and are also attractive targets of drug discovery for various diseases. The elucidation of membrane protein types provides clues for understanding the structure and function of proteins. Recently we developed a novel system for predicting protein subnuclear localizations. In this paper, we propose a simplified version of our system for predicting membrane protein types directly from primary protein structures, which incorporates amino acid classifications and physicochemical properties into a general form of pseudo-amino acid composition. In this simplified system, we will design a two-stage multi-class support vector machine combined with a two-step optimal feature selection process, which proves very effective in our experiments. The performance of the present method is evaluated on two benchmark datasets consisting of five types of membrane proteins. The overall accuracies of prediction for five types are 93.25% and 96.61% via the jackknife test and independent dataset test, respectively. These results indicate that our method is effective and valuable for predicting membrane protein types. A web server for the proposed method is available at http://www.juemengt.com/jcc/memty_page.php

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.

BinMemPredict: a Web Server and Software for Predicting Membrane Protein Types

Membrane protein, which is closely related to cellular biological functions, is a vital component of cell membranes. Predicting membrane protein and its types is a challenging task that offers promising results. This paper proposes a novel membrane detection method that is more efficient and accurate than traditional technologies. Two methods were used to extract features from protein sequences. The 20-D feature was extracted from the position-specific scoring matrix of proteins, and the 188-D feature was extracted based on protein composition and physical-chemical properties. These features, together with a novel ensemble voting strategy that was derived from the theorem of minimal false classified samples set, were employed to improve classification performance. The proposed method offers efficient memory usage and accurate predictions. By using the jackknife test on the 20-D feature, the proposed method obtained 91.2% accuracy in distinguishing membrane proteins and 86.1% accuracy in predicting membrane protein types. Two interesting discoveries are presented: 1) approximately 12% of total enzymes are membrane proteins, and 2) membrane proteins occupy a higher proportion in alternative splicing peptides than normal proteins. A new membrane protein dataset which contains 7388 membrane protein sequences is built by using the latest Swiss-Prot database. Furthermore, a Web server and software called BinMemPredict is developed, which is freely accessible to the public at http://datamining.xmu.- edu.cn/software/bmp.

BinMemPredict: a Web Server and Software for Predicting Membrane Protein Types

BinMemPredict: a Web Server and Software for Predicting Membrane Protein Types

Prediction of Membrane Protein Types Using Pseudo-Amino Acid Composition and Ensemble Classification

Prediction of Membrane Protein Types Using Pseudo-Amino Acid Composition and Ensemble Classification

Prediction of membrane protein types by using dipeptide and pseudo amino acid composition-based composite features

Membrane proteins are fundamental elements of a cell that play essential roles nearly in all the cellular processes. Prediction of membrane protein types using biological experiments are often complicated and time consuming. Therefore it is highly desirable to develop a robust, reliable and high-throughput silico method to predict membrane protein types. In this study, the authors have used two feature extraction strategies known as dipeptide and pseudo amino acid (PseAA) compositions for classification of membrane proteins types. In addition, a composite model is also developed by concatenating dipeptide and PseAA composition based features. Further, two feature selection methods such as neighbourhood preserving embedding and locally linear embedding (LLE) are applied to reduce the dimensionality of the composite model. The performance of these feature extraction strategies is evaluated using four different classifiers: K-nearest neighbour, probabilistic neural network (PNN), support vector machine (SVM) and grey incidence degree. The highest success rates have been observed using the LLE-based reduced features. SVM has yielded the best accuracy of 88.2% in case of jackknife test. Although in case of independent dataset test, PNN has obtained the highest accuracy of 98.4%. Performance measures other than accuracy are also used such as ‘Mathew correlation coefficient’, sensitivity and precision. The authors simulated results show that the composite model has significantly discriminated the types of membrane protein and might be useful for future research and drug discovery.

Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou’s pseudo amino acid composition

The type information of un-annotated membrane proteins provides an important hint for their biological functions. The experimental determination of membrane protein types, despite being more accurate and reliable, is not always feasible due to the costly laboratory procedures, thereby creating a need for the development of bioinformatics methods. This article describes a novel computational classifier for the prediction of membrane protein types using proteins’ sequences. The classifier, comprising a collection of one-versus-one support vector machines, makes use of the following sequence attributes: (1) the cationic patch sizes, the orientation, and the topology of transmembrane segments; (2) the amino acid physicochemical properties; (3) the presence of signal peptides or anchors; and (4) the specific protein motifs. A new voting scheme was implemented to cope with the multi-class prediction. Both the training and the testing sequences were collected from SwissProt. Homologous proteins were removed such that there is no pair of sequences left in the datasets with a sequence identity higher than 40%. The performance of the classifier was evaluated by a Jackknife cross-validation and an independent testing experiments. Results show that the proposed classifier outperforms earlier predictors in prediction accuracy in seven of the eight membrane protein types. The overall accuracy was increased from 78.3% to 88.2%. Unlike earlier approaches which largely depend on position-specific substitution matrices and amino acid compositions, most of the sequence attributes implemented in the proposed classifier have supported literature evidences. The classifier has been deployed as a web server and can be accessed at http://bsaltools.ym.edu.tw/predmpt.

ProClusEnsem: Predicting membrane protein types by fusing different modes of pseudo amino acid composition

Knowing the type of an uncharacterized membrane protein often provides a useful clue in both basic research and drug discovery. With the explosion of protein sequences generated in the post genomic era, determination of membrane protein types by experimental methods is expensive and time consuming. It therefore becomes important to develop an automated method to find the possible types of membrane proteins. In view of this, various computational membrane protein prediction methods have been proposed. They extract protein feature vectors, such as PseAAC (pseudo amino acid composition) and PsePSSM (pseudo position-specific scoring matrix) for representation of protein sequence, and then learn a distance metric for the KNN (K nearest neighbor) or NN (nearest neighbor) classifier to predicate the final type. Most of the metrics are learned using linear dimensionality reduction algorithms like Principle Components Analysis (PCA) and Linear Discriminant Analysis (LDA). Such metrics are common to all the proteins in the dataset. In fact, they assume that the proteins lie on a uniform distribution, which can be captured by the linear dimensionality reduction algorithm. We doubt this assumption, and learn local metrics which are optimized for local subset of the whole proteins. The learning procedure is iterated with the protein clustering. Then a novel ensemble distance metric is given by combining the local metrics through Tikhonov regularization. The experimental results on a benchmark dataset demonstrate the feasibility and effectiveness of the proposed algorithm named ProClusEnsem.

MemHyb: Predicting membrane protein types by hybridizing SAAC and PSSM

About 50% of available drugs are targeted against membrane proteins. Knowledge of membrane protein's structure and function has great importance in biological and pharmacological research. Therefore, an automated method is exceedingly advantageous, which can help in identifying the new membrane protein types based on their primary sequence. In this paper, we tackle the interesting problem of classifying membrane protein types using their sequence information. We consider both evolutionary and physicochemical features and provide them to our classification system based on support vector machine (SVM) with error correction code. We employ a powerful sequence encoding scheme by fusing position specific scoring matrix and split amino acid composition to effectively discriminate membrane protein types. Linear, polynomial, and RBF based-SVM with Bose, Chaudhuri, Hocquenghem coding are trained and tested. The highest success rate of 91.1% and 93.4% on two datasets is obtained by RBF-SVM using leave-one-out cross-validation. Thus, our proposed approach is an effective tool for the discrimination of membrane protein types and might be helpful to researchers/academicians working in the field of Drug Discovery, Cell Biology, and Bioinformatics. The web server for the proposed MemHyb-SVM is accessible at http://111.68.99.218/MemHyb-SVM.

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.