Protein Structural Class Prediction Research Articles

Information entropy-based differential evolution with extremely randomized trees and LightGBM for protein structural class prediction

The discovery of protein tertiary structure is the basis of current genetic engineering, medicinal design, and other biological applications. Protein structural class plays a significant role in the tertiary structure folding and function analysis of protein. However, the growth rate of new amino acid sequence far exceeds the tertiary structure. Existing research methods of confirming protein folding cannot satisfy massive sequences and protein engineering. A high-accuracy prediction result of low-similarity protein dataset is particularly critical to generate the corresponding tertiary structure from the primary structure. In this paper, we construct a novel super-large-scale feature of the primary structure based on secondary structure, evolutionary information, chemical properties, and global descriptors. The diversified and massive features are utilized to predict the protein class based on a novel feature selection algorithm and a gradient boosting decision tree model. To testify the effectiveness and robustness of our proposed method, namely IDEGBM, we choose the 10-fold cross-validation for evaluating four benchmark datasets 25PDB, FC699, D1189 and D640. Experimental results exhibit that our method improves the accuracy in comparison with other state-of-the-art prediction models in terms of both accuracy and efficiency. Furthermore, a representative protein is used to validate that our proposed IDEGBM can be applied to improve the conformation prediction of protein tertiary structure.

An Hybrid of Ensemble Model and Feature Extraction Algorithm for Classification of Protein Structural Class Prediction

An Hybrid of Ensemble Model and Feature Extraction Algorithm for Classification of Protein Structural Class Prediction

Fuzzy spherical truncation-based multi-linear protein descriptors: From their definition to application in structural-related predictions.

This study introduces a set of fuzzy spherically truncated three-dimensional (3D) multi-linear descriptors for proteins. These indices codify geometric structural information from kth spherically truncated spatial-(dis)similarity two-tuple and three-tuple tensors. The coefficients of these truncated tensors are calculated by applying a smoothing value to the 3D structural encoding based on the relationships between two and three amino acids of a protein embedded into a sphere. At considering, the geometrical center of the protein matches with center of the sphere, the distance between each amino acid involved in any specific interaction and the geometrical center of the protein can be computed. Then, the fuzzy membership degree of each amino acid from an spherical region of interest is computed by fuzzy membership functions (FMFs). The truncation value is finally a combination of the membership degrees from interacting amino acids, by applying the arithmetic mean as fusion rule. Several fuzzy membership functions with diverse biases on the calculation of amino acids memberships (e.g., Z-shaped (close to the center), PI-shaped (middle region), and A-Gaussian (far from the center)) were considered as well as traditional truncation functions (e.g., Switching). Such truncation functions were comparatively evaluated by exploring: 1) the frequency of membership degrees, 2) the variability and orthogonality analyses among them based on the Shannon Entropy’s and Principal Component’s methods, respectively, and 3) the prediction performance of alignment-free prediction of protein folding rates and structural classes. These analyses unraveled the singularity of the proposed fuzzy spherically truncated MDs with respect to the classical (non-truncated) ones and respect to the MDs truncated with traditional functions. They also showed an improved prediction power by attaining an external correlation coefficient of 95.82% in the folding rate modelling and an accuracy of 100% in distinguishing structural protein classes. These outcomes are better than the ones attained by existing approaches, justifying the theoretical contribution of this report. Thus, the fuzzy spherically truncated-based protein descriptors from MuLiMs-MCoMPAs (http://tomocomd.com/mulims-mcompas) are promising alignment-free predictors for modeling protein functions and properties.

Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction.

In living organisms, proteins are considered as the executants of biological functions. Owing to its pivotal role played in protein folding patterns, comprehension of protein structure is a challenging issue. Moreover, owing to numerous protein sequence exploration in protein data banks and complication of protein structures, experimental methods are found to be inadequate for protein structural class prediction. Hence, it is very much advantageous to design a reliable computational method to predict protein structural classes from protein sequences. In the recent few years there has been an elevated interest in using deep learning to assist protein structure prediction as protein structure prediction models can be utilized to screen a large number of novel sequences. In this regard, we propose a model employing Energy Profile for atom pairs in conjunction with the Legion-Class Bayes function called Energy Profile Legion-Class Bayes Protein Structure Identification model. Followed by this, we use a Thompson Optimized convolutional neural network to extract features between amino acids and then the Thompson Optimized SoftMax function is employed to extract associations between protein sequences for predicting secondary protein structure. The proposed Energy Profile Bayes and Thompson Optimized Convolutional Neural Network (EPB-OCNN) method tested distinct unique protein data and was compared to the state-of-the-art methods, the Template-Based Modeling, Protein Design using Deep Graph Neural Networks, a deep learning-based S-glutathionylation sites prediction tool called a Computational Framework, the Deep Learning and a distance-based protein structure prediction using deep learning. The results obtained when applied with the Biopython tool with respect to protein structure prediction time, protein structure prediction accuracy, specificity, recall, F-measure, and precision, respectively, are measured. The proposed EPB-OCNN method outperformed the state-of-the-art methods, thereby corroborating the objective.

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.

Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms

In the postgenomic age, rapid growth in the number of sequence-known proteins has been accompanied by much slower growth in the number of structure-known proteins (as a result of experimental limitations), and a widening gap between the two is evident. Because protein function is linked to protein structure, successful prediction of protein structure is of significant importance in protein function identification. Foreknowledge of protein structural class can help improve protein structure prediction with significant medical and pharmaceutical implications. Thus, a fast, suitable, reliable, and reasonable computational method for protein structural class prediction has become pivotal in bioinformatics. Here, we review recent efforts in protein structural class prediction from protein sequence, with particular attention paid to new feature descriptors, which extract information from protein sequence, and the use of machine learning algorithms in both feature selection and the construction of new classification models. These new feature descriptors include amino acid composition, sequence order, physicochemical properties, multiprofile Bayes, and secondary structure-based features. Machine learning methods, such as artificial neural networks (ANNs), support vector machine (SVM), K-nearest neighbor (KNN), random forest, deep learning, and examples of their application are discussed in detail. We also present our view on possible future directions, challenges, and opportunities for the applications of machine learning algorithms for prediction of protein structural classes.

Enhanced Protein Structural Class Prediction Using Effective Feature Modeling and Ensemble of Classifiers.

Protein Secondary Structural Class (PSSC) information is important in investigating further challenges of protein sequences like protein fold recognition, protein tertiary structure prediction, and analysis of protein functions for drug discovery. Identification of PSSC using biological methods is time-consuming and cost-intensive. Several computational models have been developed to predict the structural class; however, they lack in generalization of the model. Hence, predicting PSSC based on protein sequences is still proving to be an uphill task. In this article, we proposed an effective, novel and generalized prediction model consisting of a feature modeling and an ensemble of classifiers. The proposed feature modeling extracts discriminating information (features) by leveraging three techniques: (i) Embedding - features are extracted on the basis of spatial residue arrangements of the sequences using word embedding approaches; (ii) SkipXGram Bi-gram - various sets of skipped bi-gram features are extracted from the sequences; and (iii) General Statistical (GS) based features are extracted which covers the global information of structural sequences. The combined effective sets of features are trained and classified using an ensemble of three classifiers: Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM). The proposed model when assessed on five benchmark datasets (high and low sequence similarity), viz. z277, z498, 25PDB, 1189, and FC699, reported an overall accuracy of 93.55, 97.58, 81.82, 81.11, and 93.93 percent respectively. The proposed model is further validated on a large-scale updated low similarity ( ≤ 25%) dataset, where it achieved an overall accuracy of 81.11 percent. The proposed generalized model is robust and consistently outperformed several state-of-the-art models on all the five benchmark datasets.

Image-based effective feature generation for protein structural class and ligand binding prediction.

Proteins are the building blocks of all cells in both human and all living creatures of the world. Most of the work in the living organism is performed by proteins. Proteins are polymers of amino acid monomers which are biomolecules or macromolecules. The tertiary structure of protein represents the three-dimensional shape of a protein. The functions, classification and binding sites are governed by the protein’s tertiary structure. If two protein structures are alike, then the two proteins can be of the same kind implying similar structural class and ligand binding properties. In this paper, we have used the protein tertiary structure to generate effective features for applications in structural similarity to detect structural class and ligand binding. Firstly, we have analyzed the effectiveness of a group of image-based features to predict the structural class of a protein. These features are derived from the image generated by the distance matrix of the tertiary structure of a given protein. They include local binary pattern (LBP) histogram, Gabor filtered LBP histogram, separate row multiplication matrix with uniform LBP histogram, neighbor block subtraction matrix with uniform LBP histogram and atom bond. Separate row multiplication matrix and neighbor block subtraction matrix filters, as well as atom bond, are our novels. The experiments were done on a standard benchmark dataset. We have demonstrated the effectiveness of these features over a large variety of supervised machine learning algorithms. Experiments suggest support vector machines is the best performing classifier on the selected dataset using the set of features. We believe the excellent performance of Hybrid LBP in terms of accuracy would motivate the researchers and practitioners to use it to identify protein structural class. To facilitate that, a classification model using Hybrid LBP is readily available for use at http://brl.uiu.ac.bd/PL/. Protein-ligand binding is accountable for managing the tasks of biological receptors that help to cure diseases and many more. Therefore, binding prediction between protein and ligand is important for understanding a protein’s activity or to accelerate docking computations in virtual screening-based drug design. Protein-ligand binding prediction requires three-dimensional tertiary structure of the target protein to be searched for ligand binding. In this paper, we have proposed a supervised learning algorithm for predicting protein-ligand binding, which is a similarity-based clustering approach using the same set of features. Our algorithm works better than the most popular and widely used machine learning algorithms.

Prediction of protein structural classes by different feature expressions based on 2-D wavelet denoising and fusion

BackgroundProtein structural class predicting is a heavily researched subject in bioinformatics that plays a vital role in protein functional analysis, protein folding recognition, rational drug design and other related fields. However, when traditional feature expression methods are adopted, the features usually contain considerable redundant information, which leads to a very low recognition rate of protein structural classes.ResultsWe constructed a prediction model based on wavelet denoising using different feature expression methods. A new fusion idea, first fuse and then denoise, is proposed in this article. Two types of pseudo amino acid compositions are utilized to distill feature vectors. Then, a two-dimensional (2-D) wavelet denoising algorithm is used to remove the redundant information from two extracted feature vectors. The two feature vectors based on parallel 2-D wavelet denoising are fused, which is known as PWD-FU-PseAAC. The related source codes are available at https://github.com/Xiaoheng-Wang12/Wang-xiaoheng/tree/master.ConclusionsExperimental verification of three low-similarity datasets suggests that the proposed model achieves notably good results as regarding the prediction of protein structural classes.

Impacts of Pseudo Amino Acid Components and 5-steps Rule to Proteomics and Proteome Analysis.

Stimulated by the 5-steps rule during the last decade or so, computational proteomics has achieved remarkable progresses in the following three areas: (1) protein structural class prediction; (2) protein subcellular location prediction; (3) post-translational modification (PTM) site prediction. The results obtained by these predictions are very useful not only for an in-depth study of the functions of proteins and their biological processes in a cell, but also for developing novel drugs against major diseases such as cancers, Alzheimer's, and Parkinson's. Moreover, since the targets to be predicted may have the multi-label feature, two sets of metrics are introduced: one is for inspecting the global prediction quality, while the other for the local prediction quality. All the predictors covered in this review have a userfriendly web-server, through which the majority of experimental scientists can easily obtain their desired data without the need to go through the complicated mathematics.

Predicting structural class for protein sequences of 40% identity based on features of primary and secondary structure using Random Forest algorithm

At present, tertiary structure discovery growth rate is lagging far behind discovery of primary structure. The prediction of protein structural class using Machine Learning techniques can help reduce this gap. The Structural Classification of Protein – Extended (SCOPe 2.07) is latest and largest dataset available at present. The protein sequences with less than 40% identity to each other are used for predicting α, β, α/β and α + β SCOPe classes. The sensitive features are extracted from primary and secondary structure representations of Proteins. Features are extracted experimentally from secondary structure with respect to its frequency, pitch and spatial arrangements. Primary structure based features contain species information for a protein sequence. The species parameters are further validated with uniref100 dataset using TaxId. As it is known, protein tertiary structure is manifestation of function. Functional differences are observed in species. Hence, the species are expected to have strong correlations with structural class, which is discovered in current work. It enhances prediction accuracy by 7%–10%. The subset of SCOPe 2.07 is trained using 65 dimensional feature vector using Random Forest classifier. The test result for the rest of the set gives consistent accuracy of better than 95%. The accuracy achieved on benchmark datasets ASTRAL 1.73, 25PDB and FC699 is better than 86%, 91% and 97% respectively, which is best reported to our knowledge.

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.   

Protein Structural Class Prediction Based on Distance-related Statistical Features from Graphical Representation of Predicted Secondary Structure

Protein structural class prediction is beneficial to protein structure and function analysis. Exploring good feature representation is a key step for this prediction task. Prior works have demonstrated the effectiveness of the secondary structure based feature extraction methods especially for lowsimilarity protein sequences. However, the prediction accuracies still remain limited. To explore the potential of secondary structure information, a novel feature extraction method based on a generalized chaos game representation of predicted secondary structure is proposed. Each protein sequence is converted into a 20-dimensional distance-related statistical feature vector to characterize the distribution of secondary structure elements and segments. The feature vectors are then fed into a support vector machine classifier to predict the protein structural class. Our experiments on three widely used lowsimilarity benchmark datasets (25PDB, 1189 and 640) show that the proposed method achieves superior performance to the state-of-the-art methods. It is anticipated that our method could be extended to other graphical representations of protein sequence and be helpful in future protein research.

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.

A novel improved prediction of protein structural class using deep recurrent neural network

For last few decades, sequence arrangement of amino acids have been utilized for the prediction of protein secondary structure. Recent methods have applied high dimensional natural language based features in machine learning models. Performance measures of machine learning based models are significantly affected by data size and data dimensionality. It is a huge challenge to develop a generic model which can be trained to perform both for small and large sized datasets in a low dimensional framework. In the present research, we suggest a low dimensional representation for both small and large sized datasets. A hybrid space of Atchley’s factors II, IV, V, electron ion interaction potential and SkipGram based word2vec have been employed for amino acid sequence representation. Subsequently Stockwell transformation is applied to the representation to preserve features both in time and frequency domains. Finally, deep gated recurrent network with dropout, categorical-cross entropy error estimation and Adam optimization is used for classification purpose. The introduced method results in better prediction accuracies for both small (204,277, and 498) and large sized (PDB25, Protein 640 and FC699) bench mark data sets of low sequence similarity (25–40%). The obtained classification accuracies for PDB25, 640, FC699, 498, 277, 204 datasets are 84.2%, 94.31%, 93.1%, 95.9%, 94.5% and 85.36% respectively. The major contributions in this research is that, for the first time, we verify the protein secondary structural class prediction in a very low dimensional (18-D) feature space with a novel feature representation method. Secondly, we also verify for the first time, the behaviour of deep networks for low dimensional small sized data sets.

Enhanced Artificial Neural Network for Protein Fold Recognition and Structural Class Prediction

In Bioinformatics Protein Fold Recognition (PFR) and Structural Class Prediction (SCP) is a significant problem in predicting protein with a three dimensional structure. Extraction of valuable features of protein that consists of 20 amino acids to acquire more desirable classifiers is fundamental to this PFR and SCP. Feature extraction technique predominantly exploits Forward Consecutive Search Scheme (FCS) that supplements syntactical-based, evolutionary-based and physicochemical-based information. In this research work, a classifier known as Enhanced Artificial Neural Network (ANN) is employed as it is more efficient than Forward Consecutive Search scheme in order to improve the performance of PFR and SCP. The Enhanced ANN algorithm is an improved version of Artificial Neural Network when compared with various existing algorithms such as Support Vector Machine (SVM), ANN, K-Nearest Neighbor (KNN) and the Bayesian. The experiments are conducted on four datasets namely DD, EDD, TG and RDD. Ultimately, the statistical imputation of Enhanced ANN algorithm hypothesizes gives better results than other algorithms to improve the performance of PFR and SCP.

A novel feature selection method to predict protein structural class

Integrating various features from different protein properties helps to improve the prediction accuracy of protein structural class but need to deal with the corresponding integrated high-dimensional data. Thus, the feature selection process used to select the informative features from the integrated features also becomes an indispensable key step. This paper proposes a novel feature selection method, Partial-Maximum-Correlation-Information based Recursive Feature Elimination (PMCI-RFE), to quickly select the best feature subset from the integrated high-dimensional protein features set to improve the prediction performance of protein structural class. PMCI-RFE can also be used to find different types of informative features to further analyze some biological relationships. The proposed PMCI-RFE method uses the correlation information between the feature space and class encoding space to select informative features based on the idea of orthogonal component projection in the feature space. The experimental results on six widely used benchmark datasets show that PMCI-RFE is a fast and effective method compare to other four state-of-the-art feature selection methods, which indeed can make full use of different protein property information and improve the predictability of protein structural class.

Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC

In this study, I introduce novel global and local 0D-protein descriptors based on a statistical quantity named Total Sum of Squares (TSS). This quantity represents the sum of the squares differences of amino acid properties from the arithmetic mean property. As an extension, the amino acid-types and amino acid-groups formalisms are used for describing zones of interest in proteins. To assess the effectiveness of the proposed descriptors, a Nearest Neighbor model for predicting the major four protein structural classes was built. This model has a success rate of 98.53% on the jackknife cross-validation test; this performance being superior to other reported methods despite the simplicity of the predictor. Additionally, this predictor has an average success rate of 98.35% in different cross-validation tests performed. A value of 0.98 for the Kappa statistic clearly discriminates this model from a random predictor. The results obtained by the Nearest Neighbor model demonstrated the ability of the proposed descriptors not only to reflect relevant biochemical information related to the structural classes of proteins but also to allow appropriate interpretability. It can thus be expected that the current method may play a supplementary role to other existing approaches for protein structural class prediction and other protein attributes.

Low-Homology Protein Structural Class Prediction from Secondary Structure Based on Visibility and Horizontal Visibility Network

In this study, based on the predicted secondary structures of proteins, we propose a new approach to predict protein structural classes (α,β,α/β,α+β) for three widely used low-homology data sets. Fist, we obtain two time siries from the chaos game representation of each predicted secondary structure; second, based on two time series, we construct visibility and horizontal visibility network, respectively and generate a set of features using 17 network features; finaly, we predict each protein structure class using support vector machine and Fisher’s linear discriminant algorithm, respectively. In order to evaluate our method, the leave one out cross-validating test is employed on three data sets. Results show that our approach has been provided as a effective tool for the prediction of low-homology protein structural classes.