Protein Structural Class Prediction Research Articles

Accurate prediction of protein structural classes by incorporating PSSS and PSSM into Chou's general PseAAC

Determination of protein structural class using a fast and suitable computational method has become a hot issue in protein science. Prediction of protein structural class for low-similarity sequences remains a challenge problem. In this study, a 111-dimensional feature vector is constructed to predict protein structural classes. Among the 111 features, 100 features based on pseudo-position specific scoring matrix (PsePSSM) are selected to reflect the evolutionary information and the sequence-order information, and the other 11 rational features based on predicted protein secondary structure sequences (PSSS) are designed in the previous works. To evaluate the performance of the proposed method (named by PSSS–PsePSSM), jackknife cross-validation tests are performed on three widely used benchmark datasets: 1189, 25PDB and 640. Our method achieves competitive performance on prediction accuracies, especially for the overall prediction accuracies for datasets 1189, 25PDB and 640, which reach 86.6%, 89.5% and 81.0%, respectively. The PSSS–PsePSSM algorithm also outperforms other existing methods, indicating that our proposed method is a cost-effective computational tool for protein structural class prediction.

Prediction of protein structural class using tri-gram probabilities of position-specific scoring matrix and recursive feature elimination.

Knowledge of structural class plays an important role in understanding protein folding patterns. As a transitional stage in recognition of three-dimensional structure of a protein, protein structural class prediction is considered to be an important and challenging task. In this study, we firstly introduce a feature extraction technique which is based on tri-grams computed directly from position-specific scoring matrix (PSSM). A total of 8,000 features are extracted to represent a protein. Then, support vector machine-recursive feature elimination (SVM-RFE) is applied for feature selection and reduced features are input to a support vector machine (SVM) classifier to predict structural class of a given protein. To examine the effectiveness of our method, jackknife tests are performed on six widely used benchmark datasets, i.e., Z277, Z498, 1189, 25PDB, D640, and D1185. The overall accuracies of 97.1, 98.6, 92.5, 93.5, 94.2, and 95.9% are achieved on these datasets, respectively. Comparison of the proposed method with other prediction methods shows that our method is very promising to perform the prediction of protein structural class.

Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM.

Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM). Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS), segmented PsePSSM, and segmented autocovariance transformation (ACT) based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640) are adopted in this paper. Then a 700-dimensional (700D) feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA). To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.

Prediction of protein structural classes for low-similarity sequences using reduced PSSM and position-based secondary structural features

Many efficient methods have been proposed to advance protein structural class prediction, but there are still some challenges where additional insight or technology is needed for low-similarity sequences. In this work, we schemed out a new prediction method for low-similarity datasets using reduced PSSM and position-based secondary structural features. We evaluated the proposed method with four experiments and compared it with the available competing prediction methods. The results indicate that the proposed method achieved the best performance among the evaluated methods, with overall accuracy 3–5% higher than the existing best-performing method. This paper also found that the reduced alphabets with size 13 simplify PSSM structures efficiently while reserving its maximal information. This understanding can be used to design more powerful prediction methods for protein structural class.

An Improved Protein Structural Classes Prediction Method by Incorporating Both Sequence and Structure Information.

Protein structural classes information is beneficial for secondary and tertiary structure prediction, protein folds prediction, and protein function analysis. Thus, predicting protein structural classes is of vital importance. In recent years, several computational methods have been developed for low-sequence-similarity (25%-40%) protein structural classes prediction. However, the reported prediction accuracies are actually not satisfactory. Aiming to further improve the prediction accuracies, we propose three different feature extraction methods and construct a comprehensive feature set that captures both sequence and structure information. By applying a random forest (RF) classifier to the feature set, we further develop a novel method for structural classes prediction. We test the proposed method on three benchmark datasets (25PDB, 640, and 1189) with low sequence similarity, and obtain the overall prediction accuracies of 93.5%, 92.6%, and 93.4%, respectively. Compared with six competing methods, the accuracies we achieved are 3.4%, 6.2%, and 8.7% higher than those achieved by the best-performing methods, showing the superiority of our method. Moreover, due to the limitation of the size of the three benchmark datasets, we further test the proposed method on three updated large-scale datasets with different sequence similarities (40%, 30%, and 25%). The proposed method achieves above 90% accuracies for all the three datasets, consistent with the accuracies on the above three benchmark datasets. Experimental results suggest our method as an effective and promising tool for structural classes prediction. Currently, a webserver that implements the proposed method is available on http://121.192.180.204:8080/RF_PSCP/Index.html.

Prediction of protein structural classes based on feature selection technique.

The prediction of protein structural classes is beneficial to understanding folding patterns, functions and interactions of proteins. In this study, we proposed a feature selection-based method to accurately predict protein structural classes. Three datasets with sequence identity lower than 25% were used to test the prediction performance of the method. Through jackknife cross-validation, we have verified that the overall accuracies of these three datasets are 92.1%, 89.7% and 84.0%, respectively. The proposed method is more efficient and accurate than other existing methods. The present study will offer an excellent alternative to other methods for predicting protein structural classes.

Protein Structural Class Prediction viak-Separated Bigrams Using Position Specific Scoring Matrix

Protein structural class prediction (SCP) is as important task in identifying protein tertiary structure and protein functions. In this study, we propose a feature extraction technique to predict secondary structures. The technique utilizes bigram (of adjacent andk-separated amino acids) information derived from Position Specific Scoring Matrix (PSSM). The technique has shown promising results when evaluated on benchmarked Ding and Dubchak dataset.

Prediction of protein structure classes by incorporating different protein descriptors into general Chou’s pseudo amino acid composition

Successful protein structure identification enables researchers to estimate the biological functions of proteins, yet it remains a challenging problem. The most common method for determining an unknown protein’s structural class is to perform expensive and time-consuming manual experiments. Because of the availability of amino acid sequences generated in the post-genomic age, it is possible to predict an unknown protein’s structural class using machine learning methods given a protein’s amino-acid sequence and/or its secondary structural elements. Following recent research in this area, we propose a new machine learning system that is based on combining several protein descriptors extracted from different protein representations, such as position specific scoring matrix (PSSM), the amino-acid sequence, and secondary structural sequences. The prediction engine of our system is operated by an ensemble of support vector machines (SVMs), where each SVM is trained on a different descriptor. The results of each SVM are combined by sum rule. Our final ensemble produces a success rate that is substantially better than previously reported results on three well-established datasets. The MATLAB code and datasets used in our experiments are freely available for future comparison at http://www.dei.unipd.it/node/2357.

Secondary Structure Element Alignment Kernel Method for Prediction of Protein Structural Classes

In this paper, we aim at predicting protein structural classes for low-homology data sets based on predicted secondary structures. We propose a new and simple kernel method, named as SSEAKSVM, to predict protein structural classes. The secondary structures of all protein sequences are obtained by using the tool PSIPRED and then a linear kernel on the basis of secondary structure element alignment scores is constructed for training a support vector machine classifier without parameter adjusting. Our method SSEAKSVM was evaluated on two low-homology datasets 25PDB and 1189 with sequence homology being 25% and 40%, respectively. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies on these two data sets are 86.3% and 84.5%, respectively, which are higher than those obtained by other existing methods. Especially, our method achieves higher accuracies (88.1% and 88.5%) for differentiating the α + β class and the α/β class compared to other methods. This suggests that our method is valuable to predict protein structural classes particularly for low-homology protein sequences. The source code of the method in this paper can be downloaded at http://math.xtu.edu.cn/myphp/math/research/source/SSEAK_source_code.rar.

A novel predictor for protein structural class based on integrated information of the secondary structure sequence.

The structural class has become one of the most important features for characterizing the overall folding type of a protein and played important roles in many aspects of protein research. At present, it is still a challenging problem to accurately predict protein structural class for low-similarity sequences. In this study, an 18-dimensional integrated feature vector is proposed by fusing the information about content and position of the predicted secondary structure elements. The consistently high accuracies of jackknife and 10-fold cross-validation tests on different low-similarity benchmark datasets show that the proposed method is reliable and stable. Comparison of our results with other methods demonstrates that our method is an effective computational tool for protein structural class prediction, especially for low-similarity sequences.

Predict protein structural class for low-similarity sequences by evolutionary difference information into the general form of Chou׳s pseudo amino acid composition

Knowledge of protein structural class plays an important role in characterizing the overall folding type of a given protein. At present, it is still a challenge to extract sequence information solely using protein sequence for protein structural class prediction with low similarity sequence in the current computational biology. In this study, a novel sequence representation method is proposed based on position specific scoring matrix for protein structural class prediction. By defined evolutionary difference formula, varying length proteins are expressed as uniform dimensional vectors, which can represent evolutionary difference information between the adjacent residues of a given protein. To perform and evaluate the proposed method, support vector machine and jackknife tests are employed on three widely used datasets, 25PDB, 1189 and 640 datasets with sequence similarity lower than 25%, 40% and 25%, respectively. Comparison of our results with the previous methods shows that our method may provide a promising method to predict protein structural class especially for low-similarity sequences.

Novel structure-driven features for accurate prediction of protein structural class

Prediction of protein structural class plays an important role in inferring tertiary structure and function of a protein. Extracting good representation from protein sequence is fundamental for this prediction task. In this paper, a novel computational method is proposed to predict protein structural class solely from the predicted secondary structure information. A total of 27 features rationally divided into 3 different groups are extracted to characterize general contents and spatial arrangements of the predicted secondary structural elements. Then, a multi-class nonlinear support vector machine classifier is used to implement prediction. Various prediction accuracies evaluated by the jackknife cross-validation test are reported on four widely-used low-homology benchmark datasets. Comparing with the state-of-the-art in protein structural class prediction, the proposed method achieves the highest overall accuracies on all the four datasets. The experimental results confirm that the proposed structure-driven features are very useful for accurate prediction of protein structural class.

PSSP-RFE: Accurate Prediction of Protein Structural Class by Recursive Feature Extraction from PSI-BLAST Profile, Physical-Chemical Property and Functional Annotations

Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

A protein structural classes prediction method based on PSI-BLAST profile

Knowledge of protein structural classes plays an important role in understanding protein folding patterns. Prediction of protein structural class based solely on sequence data remains to be a challenging problem. In this study, we extract the long-range correlation information and linear correlation information from position-specific score matrix (PSSM). A total of 3600 features are extracted, then, 278 features are selected by a filter feature selection method based on 1189 dataset. To verify the performance of our method (named by LCC–PSSM), jackknife tests are performed on three widely used low similarity benchmark datasets. Comparison of our results with the existing methods shows that our method provides the favorable performance for protein structural class prediction. Stand-alone version of the proposed method (LCC–PSSM) is written in MATLAB language and it can be downloaded from http://bioinfo.zstu.edu.cn/LCC-PSSM/.

High-accuracy prediction of protein structural classes using PseAA structural properties and secondary structural patterns

Since introduction of PseAAs and functional domains, promising results have been achieved in protein structural class predication, but some challenges still exist in the representation of the PseAA structural correlation and structural domains. This paper proposed a high-accuracy prediction method using novel PseAA structural properties and secondary structural patterns, reflecting the long-range and local structural properties of the PseAAs and certain compact structural domains. The proposed prediction method was tested against the competing prediction methods with four experiments. The experiment results indicate that the proposed method achieved the best performance. Its overall accuracies for datasets 25 PDB, D640, FC699 and 1189 are 88.8%, 90.9%, 96.4% and 87.4%, which are 4.5%, 7.6%, 2% and 3.9% higher than the existing best-performing method. This understanding can be used to guide development of more powerful methods for protein structural class prediction. The software and supplement material are freely available at http://bioinfo.zstu.edu.cn/PseAA-SSP.

Prediction of protein structure classes with flexible neural tree.

Prediction of protein structural classes is of great significance to better understand protein folding patterns. An array of methods has been proposed to predict these structures based on sequences. However, the accuracy is strongly affected by the homology of sequences. In the present study, the features based on correlation coefficient of sequence and amino acid composition are extracted. Flexible neutral tree is employed as the classification model. To examine the performance of this method, four benchmark datasets are selected. Altogether, the results show that a higher prediction accuracy of alpha/beta can be achieved by the method compared to others.

Proposing a highly accurate protein structural class predictor using segmentation-based features

BackgroundPrediction of the structural classes of proteins can provide important information about their functionalities as well as their major tertiary structures. It is also considered as an important step towards protein structure prediction problem. Despite all the efforts have been made so far, finding a fast and accurate computational approach to solve protein structural class prediction problem still remains a challenging problem in bioinformatics and computational biology.ResultsIn this study we propose segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins. By applying SVM to our extracted features, for the first time we enhance the protein structural class prediction accuracy to over 90% and 85% for two popular low-homology benchmarks that have been widely used in the literature. We report 92.2% and 86.3% prediction accuracies for 25PDB and 1189 benchmarks which are respectively up to 7.9% and 2.8% better than previously reported results for these two benchmarks.ConclusionBy proposing segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins, we are able to enhance the protein structural class prediction performance significantly.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-S1-S2) contains supplementary material, which is available to authorized users.

Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces

Proteins are the executants of biological functions in living organisms. Comprehension of protein structure is a challenging problem in the era of proteomics, computational biology, and bioinformatics because of its pivotal role in protein folding patterns. Owing to the large exploration of protein sequences in protein databanks and intricacy of protein structures, experimental and theoretical methods are insufficient for prediction of protein structure classes. Therefore, it is highly desirable to develop an accurate, reliable, and high throughput computational model to predict protein structure classes correctly from polygenetic sequences. In this regard, we propose a promising model employing hybrid descriptor space in conjunction with optimized evidence-theoretic K-nearest neighbor algorithm. Hybrid space is the composition of two descriptor spaces including Multi-profile Bayes and bi-gram probability. In order to enhance the generalization power of the classifier, we have selected high discriminative descriptors from the hybrid space using particle swarm optimization, a well-known evolutionary feature selection technique. Performance evaluation of the proposed model is performed using the jackknife test on three low similarity benchmark datasets including 25PDB, 1189, and 640. The success rates of the proposed model are 87.0%, 86.6%, and 88.4%, respectively on the three benchmark datasets. The comparative analysis exhibits that our proposed model has yielded promising results compared to the existing methods in the literature. In addition, our proposed prediction system might be helpful in future research particularly in cases where the major focus of research is on low similarity datasets.

Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou's pseudo amino acid composition

Extracting good representation from protein sequence is fundamental for protein structural classes prediction tasks. In this paper, we propose a novel and powerful method to predict protein structural classes based on the predicted secondary structure information. At the feature extraction stage, a 13-dimensional feature vector is extracted to characterize general contents and spatial arrangements of the secondary structural elements of a given protein sequence. Specially, four segment-level features are designed to elevate discriminative ability for proteins from the α/β and α+β classes. After the features are extracted, a multi-class non-linear support vector machine classifier is used to implement protein structural classes prediction. We report extensive experiments comparing the proposed method to the state-of-the-art in protein structural classes prediction on three widely used low-similarity benchmark datasets: FC699, 1189 and 640. Our method achieves competitive performance on prediction accuracies, especially for the overall prediction accuracies which have exceeded the best reported results on all of the three datasets.

Protein structural class prediction based on multi-feature and Ma-Ada multi-classifier fusion

Protein sequence feature and machine learning algorithm are two important aspects to determine the results of protein structural class prediction. In this study,we established 17-D and 57-D feature information sets through fusing the sequence information,physical and chemical information with the secondary structure information based on the k-word statistical frequency and the k-fragment distribution feature extraction method.By introducing Multi-Agent 's idea into Adaboost. M1 algorithm,a novel method for protein structural class prediction,called Ma-Ada multi-classifier fusion algorithm,was proposed,which fully utilized the information of the single classifier metric layer and the fusion of information among individual classifiers. Four protein datasets including Z277,Z498,1189,D640 were used to validate the performance of the Ma-Ada algorithm.Classification accuracies are 91. 3 %,96. 8 %,85. 3% and 87. 2 % with 57- D features,and 90. 6 %,95.8 %,84. 8 % and 88. 3 % with 17 D features on datasets Z277,Z498,1189 and D640,respectively. The experimental results show better.