Low-similarity Sequences Research Articles

Molecular characterization of divergent isolates of Citrus bent leaf viroid (CBLVd) from citrus cultivars of Punjab, Pakistan.

Citrus viroid infection is emerging as a serious threat because of its efficient systemic movement within the host plant and its quick spread due to contaminated pruning tools. A survey was conducted to investigate the primary distribution and molecular characterization of Citrus bent leaf viroid (CBLVd) and its variants in different citrus cultivars. A total of 154 symptomatic citrus samples were collected and detected by RT‒PCR with newly designed specific primers with the incidence of 36.33%. During biological indexing study on Etrog citron, expressions of reduced leaf size, yellowing with a light green pattern, and bending were observed. Amplified products were sequenced and analyzed using a nucleotide BLAST search, which showed 98% homology with other CBLVd isolates. The results of the phylogenetic tree analysis showed the presence of two main groups (A and B), with the predominant variants of CBLVd, i.e., CVd-I-LSS (Citrus viroid Low Sequence Similarity) sequences, clustering in subgroup A1 along with newly detected CVd-I-LSS from Palestinian sweet lime (Citrus limettioides), which has been identified as a new host of CVd-I-LSS in Pakistan. Further analysis of the sequences in subgroup A1 showed that the variant of CVd-I-LSS infecting citrus cultivars had a close relationship with isolates reported from China, Japan, and Iran, which may have resulted from the exchange of planting material. This study also unveiled the variability in nucleotide sequences of CBLVd, which made it unable to be detected by old primers. The results of this study indicate that the widespread presence of divergent variants of CBLVd is a major concern for the citrus industry in Pakistan and other countries where virulent isolates of CBLVd are prevalent. These findings suggest the need for future research on effective management and quarantine measures to stop the spread of CBLVd.

A hybrid deep learning and handcrafted feature approach for the prediction of protein structural class

The knowledge of the protein structural class is one of the most important sources of information related to protein structure or that about function analysis and drug design. But researchers still face difficulties to predict the protein structural class when it is a question about low-similarity sequences. In this paper, we propose to make the prediction using a hybrid deep learning method and handcrafted features instead of shallow classifier. We input only nine features, mostly from predicted secondary structure information, to a feed-forward deep neural network. The latter will automatically explore and extend those features through many layers and discover the representations needed for classification. The obtained results, when applying the jackknife test on two low-similarity benchmark datasets (25PDB and FC699), proved to be very significant. After comparing our method to others, it has turned out that using deep learning methods affords satisfactory performance in the field of protein structural class prediction.

Using Recursive Feature Selection with Random Forest to Improve Protein Structural Class Prediction for Low-Similarity Sequences.

Many combinations of protein features are used to improve protein structural class prediction, but the information redundancy is often ignored. In order to select the important features with strong classification ability, we proposed a recursive feature selection with random forest to improve protein structural class prediction. We evaluated the proposed method with four experiments and compared it with the available competing prediction methods. The results indicate that the proposed feature selection method effectively improves the efficiency of protein structural class prediction. Only less than 5% features are used, but the prediction accuracy is improved by 4.6-13.3%. We further compared different protein features and found that the predicted secondary structural features achieve the best performance. This understanding can be used to design more powerful prediction methods for the protein structural class.

Combination between DE and SVM to enhance Protein Structure Prediction based on Secondary Structural information

The effective selection of protein features and the accurate method for predicting protein structural class (PSP) is an important aspect in protein folding, especially for low-similarity sequences. Many promising approaches are proposed to solve this problem, mostly via computational intelligence methods. One of the main aspect of the prediction is the extraction of an excellent representation of a protein sequence. An integrated vector of dimensions 71 was extracted using secondary and hydropathy information in this study Using newly developed strategies for categorizing proteins into their respective main structures classes, which are all-α, all-β, α/β, and α+β. Support Vector Machine (SVM) and Differential Evolution (DE) were combined using the wrapper method to select the top N features based on the level of their respective importance. The classification can be made more accurate by tuning the kernel parameters for the SVM in the training phase. In this study, the mean of the classification rate from using the SVM classifier was used to evaluate the selected subset of features. This study was tested using two low - similarity data sets (D640 and ASTRAL). A comparison between the proposed (SVM + DE) based on DE feature selection approach and (SVM+DE) based on grid search (a traditional method to search for parameters) forms the core of this work. The proposed SVM+DE model is competitive and highly reliable in terms of time and performance accuracy compared with other reported methods in literature.

Predicting protein structural classes for low-similarity sequences by evaluating different features

Protein structural class could provide important clues for understanding protein fold, evolution and function. However, it is still a challenging problem to accurately predict protein structural classes for low-similarity sequences. This paper was devoted to develop a powerful method to predict protein structural classes for low-similarity sequences. On the basis of a very objective and strict benchmark dataset, we firstly extracted optimal tripeptide compositions (OTC) which was picked out by using feature selection technique to formulate protein samples. And an overall accuracy of 91.1% was achieved in jackknife cross-validation. Subsequently, we investigated the accuracies of three popular features: position-specific scoring matrix (PSSM), predicted secondary structure information (PSSI) and the average chemical shift (ACS) for comparison. Finally, to further improve the prediction performance, we examined all combinations of the four kinds of features and achieved the maximum accuracy of 96.7% in jackknife cross-validation by combining OTC with ACS, demonstrating that the model is efficient and powerful. Our study will provide an important guide to extract valuable information from protein sequences.

An improved method to enhance protein structural class prediction using their secondary structure sequences and genetic algorithm

Many approaches have been proposed to enhance the accuracy of protein structural class. However, such approaches did not cover the low-similarity sequences which are proved to be quite challenging. In this study, a 71-dimensional integrated feature vector is extracted from the predicted secondary structure and hydropathy sequence using newly devised strategies for the purpose of categorising proteins into their major structural classes: all-α, all-β, α/β and α+β. A new combined method containing two machine learning algorithms has been proposed for feature selections in this study. Support vector machine (SVM) and genetic algorithm (GA) are combined using the wrapper method for the purpose of selecting top N features based on the level of their importance. The proposed method is evaluated using the jackknife upon two low-similarity sequences datasets, i.e. ASTRAL and D640. The overall accuracies of 83.93 and 92.2% are reported for the predictions pertaining to ASTRALtesting and D640 benchmarks, exceeding most of the current approaches.

Predicting Protein Structural Class for Low-Similarity Sequences via Novel Evolutionary Modes of PseAAC and Recursive Feature Elimination

Predicting Protein Structural Class for Low-Similarity Sequences via Novel Evolutionary Modes of PseAAC and Recursive Feature Elimination

Prediction of protein structural class for low-similarity sequences using Chou’s pseudo amino acid composition and wavelet denoising

Prediction of protein structural class plays an important role in protein structure and function analysis, drug design and many other biological applications. Prediction of protein structural class for low-similarity sequences is still a challenging task. Based on the theory of wavelet denoising, this paper presents a novel method of prediction of protein structural class for the first time. Firstly, the features of the protein sequence are extracted by using Chou’s pseudo amino acid composition (PseAAC). Then the extracted feature information is denoised by two-dimensional (2D) wavelet. Finally, the optimal feature vectors are input to support vector machine (SVM) classifier to predict protein structural classes. We obtained significant predictive results using jackknife test on three low-similarity protein structural class datasets 25PDB, 1189 and 640, and compared our method with previous methods The results indicate that the method proposed in this paper can effectively improve the prediction accuracy of protein structural class, which will be a reliable tool for prediction of protein structural class, especially for low-similarity sequences.

Improving the Prediction of Protein Structural Class for Low-Similarity Sequences by Incorporating Evolutionaryand Structural Information

Protein structural class prediction is beneficial to study protein function, regulation and interactions. However, protein structural class prediction for low-similarity sequences (i.e., below 40% in pairwise sequence similarity) remains a challenging problem at present. In this study, a novel computational method is proposed to accurately predict protein structural class for low-similarity sequences. This method is based on support vector machine in conjunction with integrated features from evolutionary information generated with position specific iterative basic local alignment search tool (PSI-BLAST) and predicted secondary structure. Various prediction accuracies evaluated by the jackknife tests are reported on two widely-used low-similarity benchmark datasets (25PDB and 1189), reaching overall accuracies 89.3% and 87.9%, which are significantly higher than those achieved by state-of-the-art in protein structural class prediction. The experimental results suggest that our method could serve as an effective alternative to existing methods in protein structural classification, especially for low-similarity sequences.

Prediction of Protein Structural Classes for Low-Similarity Sequences Based On Predicted Secondary Structure

Prediction of Protein Structural Classes for Low-Similarity Sequences Based On Predicted Secondary Structure

A highly accurate protein structural class prediction approach using auto cross covariance transformation and recursive feature elimination

Structural class characterizes the overall folding type of a protein or its domain. Many methods have been proposed to improve the prediction accuracy of protein structural class in recent years, but it is still a challenge for the low-similarity sequences. In this study, we introduce a feature extraction technique based on auto cross covariance (ACC) transformation of position-specific score matrix (PSSM) to represent a protein sequence. Then support vector machine-recursive feature elimination (SVM-RFE) is adopted to select top K features according to their importance and these features are input to a support vector machine (SVM) to conduct the prediction. Performance evaluation of the proposed method is performed using the jackknife test on three low-similarity datasets, i.e., D640, 1189 and 25PDB. By means of this method, the overall accuracies of 97.2%, 96.2%, and 93.3% are achieved on these three datasets, which are higher than those of most existing methods. This suggests that the proposed method could serve as a very cost-effective tool for predicting protein structural class especially for low-similarity datasets.

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 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.

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.

Improving the prediction accuracy of protein structural class: Approached with alternating word frequency and normalized Lempel–Ziv complexity

Prediction of protein structural class for low-similarity sequences remains a challenging problem. In this study, the new computational method has been developed to predict protein structural class by incorporating alternating word frequency and normalized Lempel–Ziv complexity. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on three widely used benchmark datasets, 25PDB, 1189 and 640, respectively. We report 83.6%, 81.8% and 83.6% prediction accuracies for 25PDB, 1189 and 640 benchmarks, respectively. Comparison of our results with other methods shows that the proposed method is very promising and may provide a cost-effective alternative to predict protein structural class in particular for low-similarity datasets and may at least play an important complementary role to existing methods.

A protein structural classes prediction method based on predicted secondary structure and PSI-BLAST profile

Knowledge of protein secondary structural classes plays an important role in understanding protein folding patterns. In this paper, 25 features based on position-specific scoring matrices are selected to reflect evolutionary information. In combination with other 11 rational features based on predicted protein secondary structure sequences proposed by the previous researchers, a 36-dimensional representation feature vector is presented to predict protein secondary structural classes for low-similarity sequences. ASTRALtraining dataset is used to train and design our method, other three low-similarity datasets ASTRALtest, 25PDB and 1189 are used to test the proposed method. Comparisons with other methods show that our method is effective to predict protein secondary structural classes. Stand alone version of the proposed method (PSSS-PSSM) is written in MATLAB language and it can be downloaded from http://letsgob.com/bioinfo_PSSS_PSSM/.

Predicting the accuracy of multiple sequence alignment algorithms by using computational intelligent techniques

Multiple sequence alignments (MSAs) have become one of the most studied approaches in bioinformatics to perform other outstanding tasks such as structure prediction, biological function analysis or next-generation sequencing. However, current MSA algorithms do not always provide consistent solutions, since alignments become increasingly difficult when dealing with low similarity sequences. As widely known, these algorithms directly depend on specific features of the sequences, causing relevant influence on the alignment accuracy. Many MSA tools have been recently designed but it is not possible to know in advance which one is the most suitable for a particular set of sequences. In this work, we analyze some of the most used algorithms presented in the bibliography and their dependences on several features. A novel intelligent algorithm based on least square support vector machine is then developed to predict how accurate each alignment could be, depending on its analyzed features. This algorithm is performed with a dataset of 2180 MSAs. The proposed system first estimates the accuracy of possible alignments. The most promising methodologies are then selected in order to align each set of sequences. Since only one selected algorithm is run, the computational time is not excessively increased.

Using principal component analysis and support vector machine to predict protein structural class for low-similarity sequences via PSSM

The accurate identification of protein structure class solely using extracted information from protein sequence is a complicated task in the current computational biology. Prediction of protein structural class for low-similarity sequences remains a challenging problem. In this study, the new computational method has been developed to predict protein structural class by fusing the sequence information and evolution information to represent a protein sample. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on two widely used benchmark data-sets, 1189 and 25PDB with sequence similarity lower than 40 and 25%, respectively. Comparison of our results with other methods shows that the proposed method by us is very promising and may provide a cost-effective alternative to predict protein structural class in particular for low-similarity data-sets.