NN approach and its comparison with NN-SVM to beta-barrel prediction

This paper is concerned with applications of a dual Neural Network (NN) and Support Vector Machine (SVM) to prediction and analysis of beta barrel trans membrane proteins. The prediction and analysis of beta barrel proteins usually offer a host of challenges to the research community, because of their low presence in genomes. Current beta barrel prediction methodologies present intermittent misclassifications resulting in mismatch in the number of membrane spanning regions within amino-acid sequences. To address the problem, this research embarks upon a NN technique and its comparison with hybrid- two-level NN-SVM methodology to classify inter-class and intra-class transitions to predict the number and range of beta membrane spanning regions. The methodology utilizes a sliding-window-based feature extraction to train two different class transitions entitled symmetric and asymmetric models. In symmet- ric modelling, the NN and SVM frameworks train for sliding window over the same intra-class areas such as inner-to-inner, membrane(beta)-to-membrane and outer-to-outer. In contrast, the asymmetric transi- tion trains a NN-SVM classifier for inter-class transition such as outer-to-membrane (beta) and membrane (beta)-to-inner, inner-to-membrane and membrane-to-outer. For the NN and NN-SVM to generate robust outcomes, the prediction methodologies are analysed by jack-knife tests and single protein tests. The computer simulation results demonstrate a significant impact and a superior performance of NN-SVM tests with a 5 residue overlap for signal protein over NN with and without redundant proteins for pre- diction of trans membrane beta barrel spanning regions

Functional discrimination of membrane proteins using machine learning techniques

<p>Abstract</p> <p>Background</p> <p>Discriminating membrane proteins based on their functions is an important task in genome annotation. In this work, we have analyzed the characteristic features of amino acid residues in membrane proteins that perform major functions, such as channels/pores, electrochemical potential-driven transporters and primary active transporters.</p> <p>Results</p> <p>We observed that the residues Asp, Asn and Tyr are dominant in channels/pores whereas the composition of hydrophobic residues, Phe, Gly, Ile, Leu and Val is high in electrochemical potential-driven transporters. The composition of all the amino acids in primary active transporters lies in between other two classes of proteins. We have utilized different machine learning algorithms, such as, Bayes rule, Logistic function, Neural network, Support vector machine, Decision tree etc. for discriminating these classes of proteins. We observed that most of the algorithms have discriminated them with similar accuracy. The neural network method discriminated the channels/pores, electrochemical potential-driven transporters and active transporters with the 5-fold cross validation accuracy of 64% in a data set of 1718 membrane proteins. The application of amino acid occurrence improved the overall accuracy to 68%. In addition, we have discriminated transporters from other α-helical and β-barrel membrane proteins with the accuracy of 85% using k-nearest neighbor method. The classification of transporters and all other proteins (globular and membrane) showed the accuracy of 82%.</p> <p>Conclusion</p> <p>The performance of discrimination with amino acid occurrence is better than that with amino acid composition. We suggest that this method could be effectively used to discriminate transporters from all other globular and membrane proteins, and classify them into channels/pores, electrochemical and active transporters.</p

Ranking models of transmembrane β-barrel proteins using Z-coordinate predictions

Motivation: Transmembrane β-barrels exist in the outer membrane of gram-negative bacteria as well as in chloroplast and mitochondria. They are often involved in transport processes and are promising antimicrobial drug targets. Structures of only a few β-barrel protein families are known. Therefore, a method that could automatically generate such models would be valuable. The symmetrical arrangement of the barrels suggests that an approach based on idealized geometries may be successful

Discrimination of outer membrane proteins with improved performance

<p>Abstract</p> <p>Background</p> <p>Outer membrane proteins (OMPs) perform diverse functional roles in Gram-negative bacteria. Identification of outer membrane proteins is an important task.</p> <p>Results</p> <p>This paper presents a method for distinguishing outer membrane proteins (OMPs) from non-OMPs (that is, globular proteins and inner membrane proteins (IMPs)). First, we calculated the average residue compositions of OMPs, globular proteins and IMPs separately using a training set. Then for each protein from the test set, its distances to the three groups were calculated based on residue composition using a weighted Euclidean distance (WED) approach. Proteins from the test set were classified into OMP versus non-OMP classes based on the least distance. The proposed method can distinguish between OMPs and non-OMPs with 91.0% accuracy and 0.639 Matthews correlation coefficient (MCC). We then improved the method by including homologous sequences into the calculation of residue composition and using a feature-selection method to select the single residue and di-peptides that were useful for OMP prediction. The final method achieves an accuracy of 96.8% with 0.859 MCC. In direct comparisons, the proposed method outperforms previously published methods.</p> <p>Conclusion</p> <p>The proposed method can identify OMPs with improved performance. It will be very helpful to the discovery of OMPs in a genome scale.</p

Investigation of transmembrane proteins using a computational approach

Background: An important subfamily of membrane proteins are the transmembrane α-helical proteins, in which the membrane-spanning regions are made up of α-helices. Given the obvious biological and medical significance of these proteins, it is of tremendous practical importance to identify the location of transmembrane segments. The difficulty of inferring the secondary or tertiary structure of transmembrane proteins using experimental techniques has led to a surge of interest in applying techniques from machine learning and bioinformatics to infer secondary structure from primary structure in these proteins. We are therefore interested in determining which physicochemical properties are most useful for discriminating transmembrane segments from non-transmembrane segments in transmembrane proteins, and for discriminating intrinsically unstructured segments from intrinsically structured segments in transmembrane proteins, and in using the results of these investigations to develop classifiers to identify transmembrane segments in transmembrane proteins. Results: We determined that the most useful properties for discriminating transmembrane segments from non-transmembrane segments and for discriminating intrinsically unstructured segments from intrinsically structured segments in transmembrane proteins were hydropathy, polarity, and flexibility, and used the results of this analysis to construct classifiers to discriminate transmembrane segments from non-transmembrane segments using four classification techniques: two variants of the Self-Organizing Global Ranking algorithm, a decision tree algorithm, and a support vector machine algorithm. All four techniques exhibited good performance, with out-of-sample accuracies of approximately 75%. Conclusions: Several interesting observations emerged from our study: intrinsically unstructured segments and transmembrane segments tend to have opposite properties; transmembrane proteins appear to be much richer in intrinsically unstructured segments than other proteins; and, in approximately 70% of transmembrane proteins that contain intrinsically unstructured segments, the intrinsically unstructured segments are close to transmembrane segments

Evaluation of methods for predicting the topology of β-barrel outer membrane proteins and a consensus prediction method

BACKGROUND: Prediction of the transmembrane strands and topology of β-barrel outer membrane proteins is of interest in current bioinformatics research. Several methods have been applied so far for this task, utilizing different algorithmic techniques and a number of freely available predictors exist. The methods can be grossly divided to those based on Hidden Markov Models (HMMs), on Neural Networks (NNs) and on Support Vector Machines (SVMs). In this work, we compare the different available methods for topology prediction of β-barrel outer membrane proteins. We evaluate their performance on a non-redundant dataset of 20 β-barrel outer membrane proteins of gram-negative bacteria, with structures known at atomic resolution. Also, we describe, for the first time, an effective way to combine the individual predictors, at will, to a single consensus prediction method. RESULTS: We assess the statistical significance of the performance of each prediction scheme and conclude that Hidden Markov Model based methods, HMM-B2TMR, ProfTMB and PRED-TMBB, are currently the best predictors, according to either the per-residue accuracy, the segments overlap measure (SOV) or the total number of proteins with correctly predicted topologies in the test set. Furthermore, we show that the available predictors perform better when only transmembrane β-barrel domains are used for prediction, rather than the precursor full-length sequences, even though the HMM-based predictors are not influenced significantly. The consensus prediction method performs significantly better than each individual available predictor, since it increases the accuracy up to 4% regarding SOV and up to 15% in correctly predicted topologies. CONCLUSIONS: The consensus prediction method described in this work, optimizes the predicted topology with a dynamic programming algorithm and is implemented in a web-based application freely available to non-commercial users at

Cascading classifier application for topology prediction of TMB proteins

This paper is concerned with the use of a cascading classifier for trans-membrane beta-barrel topology prediction analysis. Most of novel drug design requires the use of membrane proteins. Trans-membrane proteins have key roles such as active transport across the membrane and signal transduction among other functions. Given their key roles, understanding their structures mechanisms and regulation at the level of molecules with the use of computational modeling is essential. In the field of bioinformatics, many years have been spent on the trans-membrane protein structure prediction focusing on the alpha-helix membrane proteins. Technological developments have been increasingly utilized in order to understand in more details membrane protein function and structure. Various methodologies have been developed for the prediction of TMB proteins topology however the use of cascading classifier has not been fully explored. This research presents a novel approach for TMB topology prediction. The MATLAB computer simulation results show that the proposed methodology predicts transmembrane topologies with high accuracy for randomly selected proteins

Protein subcellular localization prediction based on compartment-specific features and structure conservation

BACKGROUND: Protein subcellular localization is crucial for genome annotation, protein function prediction, and drug discovery. Determination of subcellular localization using experimental approaches is time-consuming; thus, computational approaches become highly desirable. Extensive studies of localization prediction have led to the development of several methods including composition-based and homology-based methods. However, their performance might be significantly degraded if homologous sequences are not detected. Moreover, methods that integrate various features could suffer from the problem of low coverage in high-throughput proteomic analyses due to the lack of information to characterize unknown proteins. RESULTS: We propose a hybrid prediction method for Gram-negative bacteria that combines a one-versus-one support vector machines (SVM) model and a structural homology approach. The SVM model comprises a number of binary classifiers, in which biological features derived from Gram-negative bacteria translocation pathways are incorporated. In the structural homology approach, we employ secondary structure alignment for structural similarity comparison and assign the known localization of the top-ranked protein as the predicted localization of a query protein. The hybrid method achieves overall accuracy of 93.7% and 93.2% using ten-fold cross-validation on the benchmark data sets. In the assessment of the evaluation data sets, our method also attains accurate prediction accuracy of 84.0%, especially when testing on sequences with a low level of homology to the training data. A three-way data split procedure is also incorporated to prevent overestimation of the predictive performance. In addition, we show that the prediction accuracy should be approximately 85% for non-redundant data sets of sequence identity less than 30%. CONCLUSION: Our results demonstrate that biological features derived from Gram-negative bacteria translocation pathways yield a significant improvement. The biological features are interpretable and can be applied in advanced analyses and experimental designs. Moreover, the overall accuracy of combining the structural homology approach is further improved, which suggests that structural conservation could be a useful indicator for inferring localization in addition to sequence homology. The proposed method can be used in large-scale analyses of proteomes

TranCEP: Predicting the substrate class of transmembrane transport proteins using compositional, evolutionary, and positional information

Transporters mediate the movement of compounds across the membranes that separate the cell from its environment and across the inner membranes surrounding cellular compartments. It is estimated that one third of a proteome consists of membrane proteins, and many of these are transport proteins. Given the increase in the number of genomes being sequenced, there is a need for computational tools that predict the substrates that are transported by the transmembrane transport proteins. In this paper, we present TranCEP, a predictor of the type of substrate transported by a transmembrane transport protein. TranCEP combines the traditional use of the amino acid composition of the protein, with evolutionary information captured in a multiple sequence alignment (MSA), and restriction to important positions of the alignment that play a role in determining the specificity of the protein. Our experimental results show that TranCEP significantly outperforms the state-of-the-art predictors. The results quantify the contribution made by each type of information used