13249966
Prediction of integral membrane protein type by collocated hydrophobic amino acid pairs
A computational model, IMP-TYPE, is proposed for the classification of five types of integral membrane proteins from protein sequence. The proposed model aims not only at providing accurate predictions but most importantly it incorporates interesting and transparent biological patterns. When contrasted with the best-performing existing models, IMP-TYPE reduces the error rates of these methods by 19 and 34% for two out-of-sample tests performed on benchmark datasets. Our empirical evaluations also show that the proposed method provides even bigger improvements, i.e., 29 and 45% error rate reductions, when predictions are performed for sequences that share low (40%) identity with sequences from the training dataset. We also show that IMP-TYPE can be used in a standalone mode, i.e., it duplicates significant majority of correct predictions provided by other leading methods, while providing additional correct predictions which are incorrectly classified by the other methods. Our method computes predictions using a Support Vector Machine classifier that takes feature-based encoded sequence as its input. The input feature set includes hydrophobic AA pairs, which were selected by utilizing a consensus of three feature selection algorithms. The hydrophobic residues that build up the AA pairs used by our method are shown to be associated with the formation of transmembrane helices in a few recent studies concerning integral membrane proteins. Our study also indicates that Met and Phe display a certain degree of hydrophobicity, which may be more crucial than their polarity or aromaticity when they occur in the transmembrane segments. This conclusion is supported by a recent study on potential of mean force for membrane protein folding and a study of scales for membrane propensity of amino acids.
2074142
Prediction of protein structural class using novel evolutionary collocation-based sequence representation
Knowledge of structural classes is useful in understanding of folding patterns in proteins. Although existing structural class prediction methods applied virtually all state-of-the-art classifiers, many of them use a relatively simple protein sequence representation that often includes amino acid (AA) composition. To this end, we propose a novel sequence representation that incorporates evolutionary information encoded using PSI-BLAST profile-based collocation of AA pairs. We used six benchmark datasets and five representative classifiers to quantify and compare the quality of the structural class prediction with the proposed representation. The best, classifier support vector machine achieved 61-96% accuracy on the six datasets. These predictions were comprehensively compared with a wide range of recently proposed methods for prediction of structural classes. Our comprehensive comparison shows superiority of the proposed representation, which results in error rate reductions that range between 14% and 26% when compared with predictions of the best-performing, previously published classifiers on the considered datasets. The study also shows that, for the benchmark dataset that includes sequences characterized by low identity (i.e., 25%, 30%, and 40%), the prediction accuracies are 20-35% lower than for the other three datasets that include sequences with a higher degree of similarity. In conclusion, the proposed representation is shown to substantially improve the accuracy of the structural class prediction. A web server that implements the presented prediction method is freely available at http://biomine.ece.ualberta.ca/Structural_Class/SCEC.html.
15411856
Prediction of the Number of Helices for the Twilight Zone Proteins
Protein structure prediction is one of the core research areas in bioinformatics. This paper addresses the protein secondary structure prediction problem for the twilight zone proteins, which are characterized by low, about 25% homology to the sets of known sequences. The commonly used sequence alignment based algorithms fail to provide accurate prediction for sequences of such low homology, and thus alternative solutions should be sought. We propose a novel method that aims at the prediction of the number of helical structures based on the twilight zone protein sequences. The method is based on a custom designed and compact feature based sequences representation and applies a decision tree prediction algorithm. The performed experimental study shows superiority of the proposed method over three other prediction algorithms and the results provided by YASPIN algorithm, which is a state-of-the-art alignment based secondary structure prediction method designed using low homology sequences
193129
Sequence based residue depth prediction using evolutionary information and predicted secondary structure
BackgroundResidue depth allows determining how deeply a given residue is buried, in contrast to the solvent accessibility that differentiates between buried and solvent-exposed residues. When compared with the solvent accessibility, the depth allows studying deep-level structures and functional sites, and formation of the protein folding nucleus. Accurate prediction of residue depth would provide valuable information for fold recognition, prediction of functional sites, and protein design.ResultsA new method, RDPred, for the real-value depth prediction from protein sequence is proposed. RDPred combines information extracted from the sequence, PSI-BLAST scoring matrices, and secondary structure predicted with PSIPRED. Three-fold/ten-fold cross validation based tests performed on three independent, low-identity datasets show that the distance based depth (computed using MSMS) predicted by RDPred is characterized by 0.67/0.67, 0.66/0.67, and 0.64/0.65 correlation with the actual depth, by the mean absolute errors equal 0.56/0.56, 0.61/0.60, and 0.58/0.57, and by the mean relative errors equal 17.0%/16.9%, 18.2%/18.1%, and 17.7%/17.6%, respectively. The mean absolute and the mean relative errors are shown to be statistically significantly better when compared with a method recently proposed by Yuan and Wang [Proteins 2008; 70:509–516]. The results show that three-fold cross validation underestimates the variability of the prediction quality when compared with the results based on the ten-fold cross validation. We also show that the hydrophilic and flexible residues are predicted more accurately than hydrophobic and rigid residues. Similarly, the charged residues that include Lys, Glu, Asp, and Arg are the most accurately predicted. Our analysis reveals that evolutionary information encoded using PSSM is characterized by stronger correlation with the depth for hydrophilic amino acids (AAs) and aliphatic AAs when compared with hydrophobic AAs and aromatic AAs. Finally, we show that the secondary structure of coils and strands is useful in depth prediction, in contrast to helices that have relatively uniform distribution over the protein depth. Application of the predicted residue depth to prediction of buried/exposed residues shows consistent improvements in detection rates of both buried and exposed residues when compared with the competing method. Finally, we contrasted the prediction performance among distance based (MSMS and DPX) and volume based (SADIC) depth definitions. We found that the distance based indices are harder to predict due to the more complex nature of the corresponding depth profiles.ConclusionThe proposed method, RDPred, provides statistically significantly better predictions of residue depth when compared with the competing method. The predicted depth can be used to provide improved prediction of both buried and exposed residues. The prediction of exposed residues has implications in characterization/prediction of interactions with ligands and other proteins, while the prediction of buried residues could be used in the context of folding predictions and simulations.
3658125
PFRES: protein fold classification by using evolutionary information and predicted secondary structure
MOTIVATION
The number of protein families has been estimated to be as small as 1000. Recent study shows that the growth in discovery of novel structures that are deposited into PDB and the related rate of increase of SCOP categories are slowing down. This indicates that the protein structure space will be soon covered and thus we may be able to derive most of remaining structures by using the known folding patterns. Present tertiary structure prediction methods behave well when a homologous structure is predicted, but give poorer results when no homologous templates are available. At the same time, some proteins that share twilight-zone sequence identity can form similar folds. Therefore, determination of structural similarity without sequence similarity would be beneficial for prediction of tertiary structures.


RESULTS
The proposed PFRES method for automated protein fold classification from low identity (<35%) sequences obtains 66.4% and 68.4% accuracy for two test sets, respectively. PFRES obtains 6.3-12.4% higher accuracy than the existing methods. The prediction accuracy of PFRES is shown to be statistically significantly better than the accuracy of competing methods. Our method adopts a carefully designed, ensemble-based classifier, and a novel, compact and custom-designed feature representation that includes nearly 90% less features than the representation of the most accurate competing method (36 versus 283). The proposed representation combines evolutionary information by using the PSI-BLAST profile-based composition vector and information extracted from the secondary structure predicted with PSI-PRED.


AVAILABILITY
The method is freely available from the authors upon request.
1589839
Optimization of the Sliding Window Size for Protein Structure Prediction
Sliding window based methods are relatively often applied in prediction of various aspects related to protein structure. Despite their wide spread use, researchers did not establish a standard related to the size of the window, i.e., window sizes ranging between 7 and 17 residues were used in the past. To this end, this paper performs a computational study based on a probabilistic approach that aims at finding an optimal sliding window size. The results shows that formation of helical structure can be affected by amino acids (AAs) that are up to 9 positions away in the sequence, while the formation of coils and strands can be affected by AAs that are up to 3 and 6 positions away, respectively. Overall, our results suggest that a sliding window with 19 residues is optimal for secondary structure prediction, while for a specific prediction tasks, such as prediction of p-strands, a smaller window size is sufficient. Finally, the 20 AAs are categorized into five groups based on their influence of formation of the secondary structure. The finding related to the optimal window size was confirmed based on an independent experimental study related to the prediction of secondary protein structure
11395736
Repetitive motion of redundant robots planned by three kinds of recurrent neural networks and illustrated with a four-link planar manipulator's straight-line example
In this paper, a dual neural network, LVI (linear variational inequalities)-based primal-dual neural network and simplified LVI-based primal-dual neural network are presented for online repetitive motion planning (RMP) of redundant robot manipulators (with a four-link planar manipulator as an example). To do this, a drift-free criterion is exploited in the form of a quadratic performance index. In addition, the repetitive-motion-planning scheme could incorporate the joint physical limits such as joint limits and joint velocity limits simultaneously. Such a scheme is finally reformulated as a quadratic program (QP). As QP real-time solvers, the aforementioned three kinds of neural networks all have piecewise-linear dynamics and could globally exponentially converge to the optimal solution of strictly-convex quadratic-programs. Furthermore, the neural-network based RMP scheme is simulated based on a four-link planar robot manipulator. Computer-simulation results substantiate the theoretical analysis and also show the effective remedy of the joint angle drift problem of robot manipulators.
19202214
A self-generating modular neural network architecture for supervised learning
Abstract In this paper, we present a self-generating modular neural network architecture for supervised learning. In the architecture, any kind of feedforward neural networks can be employed as component nets. For a given task, a tree-structured modular neural network is automatically generated with a growing algorithm by partitioning input space recursively to avoid the problem of pre-determined structure. Due to the principle of divide-and-conquer used in the proposed architecture, the modular neural network can yield both good performance and significantly faster training. The proposed architecture has been applied to several supervised learning tasks and has achieved satisfactory results.
15381679
A Modular Neural Network Architecture for Pattern Classification Based on Different Feature Sets
We propose a novel connectionist method for the use of different feature sets in pattern classification. Unlike traditional methods, e.g., combination of multiple classifiers and use of a composite feature set, our method copes with the problem based on an idea of soft competition on different feature sets developed in our earlier work. An alternative modular neural network architecture is proposed to provide a more effective implementation of soft competition on different feature sets. The proposed architecture is interpreted as a generalized finite mixture model and, therefore, parameter estimation is treated as a maximum likelihood problem. An EM algorithm is derived for parameter estimation and, moreover, a model selection method is proposed to fit the proposed architecture to a specific problem. Comparative results are presented for the real world problem of speaker identification.
6083596
Improved learning algorithms for mixture of experts in multiclass classification
Mixture of experts (ME) is a modular neural network architecture for supervised learning. A double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME architecture for adjusting the parameters and the iteratively reweighted least squares (IRLS) algorithm is used to perform maximization in the inner loop [Jordan, M.I., Jacobs, R.A. (1994). Hierarchical mixture of experts and the EM algorithm, Neural Computation, 6(2), 181-214]. However, it is reported in literature that the IRLS algorithm is of instability and the ME architecture trained by the EM algorithm, where IRLS algorithm is used in the inner loop, often produces the poor performance in multiclass classification. In this paper, the reason of this instability is explored. We find out that due to an implicitly imposed incorrect assumption on parameter independence in multiclass classification, an incomplete Hessian matrix is used in that IRLS algorithm. Based on this finding, we apply the Newton-Raphson method to the inner loop of the EM algorithm in the case of multiclass classification, where the exact Hessian matrix is adopted. To tackle the expensive computation of the Hessian matrix and its inverse, we propose an approximation to the Newton-Raphson algorithm based on a so-called generalized Bernoulli density. The Newton-Raphson algorithm and its approximation have been applied to synthetic data, benchmark, and real-world multiclass classification tasks. For comparison, the IRLS algorithm and a quasi-Newton algorithm called BFGS have also been applied to the same tasks. Simulation results have shown that the use of the proposed learning algorithms avoids the instability problem and makes the ME architecture produce good performance in multiclass classification. In particular, our approximation algorithm leads to fast learning. In addition, the limitation of our approximation algorithm is also empirically investigated in this paper.
3182057
A method of combining multiple probabilistic classifiers through soft competition on different feature sets
Abstract A novel method is proposed for combining multiple probabilistic classifiers on different feature sets. In order to achieve the improved classification performance, a generalized finite mixture model is proposed as a linear combination scheme and implemented based on radial basis function networks. In the linear combination scheme, soft competition on different feature sets is adopted as an automatic feature rank mechanism so that different feature sets can be always simultaneously used in an optimal way to determine linear combination weights. For training the linear combination scheme, a learning algorithm is developed based on Expectation–Maximization (EM) algorithm. The proposed method has been applied to a typical real-world problem, viz., speaker identification, in which different feature sets often need consideration simultaneously for robustness. Simulation results show that the proposed method yields good performance in speaker identification.