CB513 Dataset Research Articles

Protein Secondary Structure Prediction Using Convolutional Bidirectional GRU

In this paper, a protein secondary structure prediction method based on convolutional bidirectional GRU Model (CBi-GRU model) is adopted, which combines the advantages of sliding window in extracting local features of data. The use of CNN and Bi-GRU in the construction of the model improves the feature expression and data utilization, and improves the performance of the model. Protein data from FoxChase Institute were used, and high quality, complete and representative CullPDB dataset, CB513, CASP10 and CASP11 datasets were selected to train, test and validate the model. The results show that the proposed method achieves good prediction performance on CASP10 and CASP11 datasets, and the prediction accuracy of Q8 is 76.2% and 76.4%, respectively. Compared with RaptorX-SS, DeepCNF, CGAN-PSSP and other methods, the Q8 evaluation indicators are improved. Compared with the latest research data, our Q8 prediction accuracy is improved by 2% and 5.1%, which shows the effectiveness and superiority of the proposed model.

Prediction of protein secondary structure by the improved TCN-BiLSTM-MHA model with knowledge distillation

Secondary structure prediction is a key step in understanding protein function and biological properties and is highly important in the fields of new drug development, disease treatment, bioengineering, etc. Accurately predicting the secondary structure of proteins helps to reveal how proteins are folded and how they function in cells. The application of deep learning models in protein structure prediction is particularly important because of their ability to process complex sequence information and extract meaningful patterns and features, thus significantly improving the accuracy and efficiency of prediction. In this study, a combined model integrating an improved temporal convolutional network (TCN), bidirectional long short-term memory (BiLSTM), and a multi-head attention (MHA) mechanism is proposed to enhance the accuracy of protein prediction in both eight-state and three-state structures. One-hot encoding features and word vector representations of physicochemical properties are incorporated. A significant emphasis is placed on knowledge distillation techniques utilizing the ProtT5 pretrained model, leading to performance improvements. The improved TCN, achieved through multiscale fusion and bidirectional operations, allows for better extraction of amino acid sequence features than traditional TCN models. The model demonstrated excellent prediction performance on multiple datasets. For the TS115, CB513 and PDB (2018–2020) datasets, the prediction accuracy of the eight-state structure of the six datasets in this paper reached 88.2%, 84.9%, and 95.3%, respectively, and the prediction accuracy of the three-state structure reached 91.3%, 90.3%, and 96.8%, respectively. This study not only improves the accuracy of protein secondary structure prediction but also provides an important tool for understanding protein structure and function, which is particularly applicable to resource-constrained contexts and provides a valuable tool for understanding protein structure and function.

A novel approach for protein secondary structure prediction using encoder-decoder with attention mechanism model.

Computational biology faces many challenges like protein secondary structure prediction (PSS), prediction of solvent accessibility, etc. In this work, we addressed PSS prediction. PSS is based on sequence-structure mapping and interaction among amino acid residues. We proposed an encoder-decoder with an attention mechanism model, which considers the mapping of sequence structure and interaction among residues. The attention mechanism is used to select prominent features from amino acid residues. The proposed model is trained on CB513 and CullPDB open datasets using the Nvidia DGX system. We have tested our proposed method for Q 3 and Q 8 accuracy, segment of overlap, and Mathew correlation coefficient. We achieved 70.63 and 78.93% Q 3 and Q 8 accuracy, respectively, on the CullPDB dataset whereas 79.8 and 77.13% Q 3 and Q 8 accuracy on the CB513 dataset. We observed improvement in SOV up to 80.29 and 91.3% on CullPDB and CB513 datasets. We achieved the results using our proposed model in very few epochs, which is better than the state-of-the-art methods.

Detection of Human Protein Structures by Select Deep Learning Models and Dynamic Systems

Peptide bonds bind amino acids together to form proteins. Because of the way amino acids fold, proteins have a three-dimensional structure. Increased data, or samples, improves machine learning models that learn from examples. ANNs, a single-layer learning system, require sophisticated systems and time to learn massive volumes of data over time. Today, the multi-layer deep learning technique is preferred. Deep learning uses artificial neural networks. This part of the work provides technical details about the deep learning methods used. This research's deep learning methods include CNN, Hidden Markov Model (HMM), Recurrent Weighted Average (RWA), and Conditional Random Fields (CRF). The training and testing results of these models are presented in this work. For this work, the CNN, HMM, RWA, and CRF algorithms were all compared using the CB513 dataset. In addition, each method was analyzed and contrasted with other research conducted previously. In this particular research endeavour, the respective success rates of the four models that were built for predicting the protein secondary structure were as follows: 82.54%, 81.06%, 81.10%, and 81.48%. The models and working environment produced in this study can be used to predict protein secondary structure quickly. In deep learning experiments, data amount affects learning. Increasing data will test the study's models

Empirical Study of Protein Feature Representation on Deep Belief Networks Trained With Small Data for Secondary Structure Prediction.

Protein secondary structure (SS) prediction is a classic problem of computational biology and is widely used in structural characterization and to infer homology. While most SS predictors have been trained on thousands of sequences, a previous approach had developed a compact model of training proteins that used a C-Alpha, C-Beta Side Chain (CABS)-algorithm derived energy based feature representation. Here, the previous approach is extended to Deep Belief Networks (DBN). Deep learning methods are notorious for requiring large datasets and there is a wide consensus that training deep models from scratch on small datasets, works poorly. By contrast, we demonstrate a simple DBN architecture containing a single hidden layer, trained only on the CB513 dataset. Testing on an independent set of G Switch proteins improved the Q 3 score of the previous compact model by almost 3%. The findings are further confirmed by comparison to several deep learning models which are trained on thousands of proteins. Finally, the DBN performance is also compared with Position Specific Scoring Matrix (PSSM)-profile based feature representation. The importance of (i) structural information in protein feature representation and (ii) complementary small dataset learning approaches for detection of structural fold switching are demonstrated.

RiRPSSP: A unified deep learning method for prediction of regular and irregular protein secondary structures

Protein secondary structure prediction (PSSP) is an important and challenging task in protein bioinformatics. Protein secondary structures (SSs) are categorized in regular and irregular structure classes. Regular SSs, representing nearly 50% of amino acids consist of helices and sheets, whereas the remaining amino acids represent irregular SSs. [Formula: see text]-turns and [Formula: see text]-turns are the most abundant irregular SSs present in proteins. Existing methods are well developed for separate prediction of regular and irregular SSs. However, for more comprehensive PSSP, it is essential to develop a uniform model to predict all types of SSs simultaneously. In this work, using a novel dataset comprising dictionary of secondary structure of protein (DSSP)-based SSs and PROMOTIF-based [Formula: see text]-turns and [Formula: see text]-turns, we propose a unified deep learning model consisting of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) for simultaneous prediction of regular and irregular SSs. To the best of our knowledge, this is the first study in PSSP covering both regular and irregular structures. The protein sequences in our constructed datasets, RiR6069 and RiR513, have been borrowed from benchmark CB6133 and CB513 datasets, respectively. The results are indicative of increased PSSP accuracy.

Protein encoder: An autoencoder-based ensemble feature selection scheme to predict protein secondary structure

Proteins play a vital role in the human body as they perform important metabolic tasks. Experimental identification of protein structure is expensive and time consuming. The prediction of protein secondary structure is significant to identify the protein tertiary structure and its folds. The feature subset selection from high dimensional protein primary sequence is a key to improve the accuracy of Protein Secondary Structure Prediction (PSSP). Therefore, it is essential to select the relevant features from high dimensional data to predict the protein secondary structure. This work presents a novel method for the PSSP problem based on a two-phase feature selection technique. The first stage utilizes an unsupervised autoencoder for feature extractions. Whereas, the second stage is an ensemble of three feature selection methods, namely, generic univariate select, recursive feature elimination, and Pearson's correlation. This phase combines multiple feature subsets using mutual information to select the optimum feature subset. For classification, different resultant subset features are used. These include random forest, decision tree, and multilayer perceptron. Two sets of experiments are performed on five datasets for the assessment of proposed work. The proposed solution is compared with three state-of-the-art methods based on Q3 accuracy, Q8 accuracy, and segment overlap score. Obtained results show that the proposed framework performs better in the majority of the cases than the past contributions. The proposed framework achieves Q8 accuracies of 82%, 80%, 79%, 73%, and 74% and Q3 accuracies of 90%, 90%, 92%, 79%, and 74% on CB6133, CB6133-filtered, CB513, CASP10, and CASP11 datasets, respectively.

Deep Ensemble Learning with Atrous Spatial Pyramid Networks for Protein Secondary Structure Prediction.

The secondary structure of proteins is significant for studying the three-dimensional structure and functions of proteins. Several models from image understanding and natural language modeling have been successfully adapted in the protein sequence study area, such as Long Short-term Memory (LSTM) network and Convolutional Neural Network (CNN). Recently, Gated Convolutional Neural Network (GCNN) has been proposed for natural language processing. It has achieved high levels of sentence scoring, as well as reduced the latency. Conditionally Parameterized Convolution (CondConv) is another novel study which has gained great success in the image processing area. Compared with vanilla CNN, CondConv uses extra sample-dependant modules to conditionally adjust the convolutional network. In this paper, we propose a novel Conditionally Parameterized Convolutional network (CondGCNN) which utilizes the power of both CondConv and GCNN. CondGCNN leverages an ensemble encoder to combine the capabilities of both LSTM and CondGCNN to encode protein sequences by better capturing protein sequential features. In addition, we explore the similarity between the secondary structure prediction problem and the image segmentation problem, and propose an ASP network (Atrous Spatial Pyramid Pooling (ASPP) based network) to capture fine boundary details in secondary structure. Extensive experiments show that the proposed method can achieve higher performance on protein secondary structure prediction task than existing methods on CB513, Casp11, CASP12, CASP13, and CASP14 datasets. We also conducted ablation studies over each component to verify the effectiveness. Our method is expected to be useful for any protein related prediction tasks, which is not limited to protein secondary structure prediction.

Multistage Combination Classifier Augmented Model for Protein Secondary Structure Prediction.

In the field of bioinformatics, understanding protein secondary structure is very important for exploring diseases and finding new treatments. Considering that the physical experiment-based protein secondary structure prediction methods are time-consuming and expensive, some pattern recognition and machine learning methods are proposed. However, most of the methods achieve quite similar performance, which seems to reach a model capacity bottleneck. As both model design and learning process can affect the model learning capacity, we pay attention to the latter part. To this end, a framework called Multistage Combination Classifier Augmented Model (MCCM) is proposed to solve the protein secondary structure prediction task. Specifically, first, a feature extraction module is introduced to extract features with different levels of learning difficulties. Second, multistage combination classifiers are proposed to learn decision boundaries for easy and hard samples, respectively, with the latter penalizing the loss value of the hard samples and finally improving the prediction performance of hard samples. Third, based on the Dirichlet distribution and information entropy measurement, a sample difficulty discrimination module is designed to assign samples with different learning difficulty levels to the aforementioned classifiers. The experimental results on the publicly available benchmark CB513 dataset show that our method outperforms most state-of-the-art models.

Prediction of Protein Secondary Structure Based on WS-BiLSTM Model

Protein secondary structure prediction is an important topic in bioinformatics. This paper proposed a novel model named WS-BiLSTM, which combined the wavelet scattering convolutional network and the long-short-term memory network for the first time to predict protein secondary structure. This model captures nonlocal interactions between amino acid sequences and remembers long-range interactions between amino acids. In our WS-BiLSTM model, the wavelet scattering convolutional network is used to extract protein features from the PSSM sliding window; the extracted features are combined with the original PSSM data as the input features of the long-short-term memory network to predict protein secondary structure. It is worth noting that the wavelet scattering convolutional network is asymmetric as a member of the continuous wavelet family. The Q3 accuracy on the test set CASP9, CASP10, CASP11, CASP12, CB513, and PDB25 reached 85.26%, 85.84%, 84.91%, 85.13%, 86.10%, and 85.52%, which were higher 2.15%, 2.16%, 3.5%, 3.19%, 4.22%, and 2.75%, respectively, than using the long-short-term memory network alone. Comparing our results with the state-of-art methods shows that our proposed model achieved better results on the CB513 and CASP12 data sets. The experimental results show that the features extracted from the wavelet scattering convolutional network can effectively improve the accuracy of protein secondary structure prediction.

Protein Secondary Structure Prediction based on CNN and Machine Learning Algorithms

One of the most important topics in computational biology is protein secondary structure prediction. Primary, secondary, tertiary, and quaternary structure are the four levels of complexity that can be used to characterize the entire structure of a protein that are totally ordered by the amino acid sequences. The polypeptide backbone of a protein's local configuration is referred to as a secondary structure. In this paper, three prediction algorithms have been proposed which will predict the protein secondary structure based on machine learning. These prediction methods have been improved by the model structure of convolutional neural networks (CNN). The Rectified Linear Units (ReLU) has been used as the activation function. The 2D CNN has been trained with machine learning algorithms, including Support Vector Machine, Naive Bays and Random Forest. The SVM is used to correctly classify the unseen data. Naïve Bays (NB) and Random Forest (RF) are also applied to solve the prediction problems for not only classification problems but also regression problems. The 2D CNN, hybrid of 2D CNN -SVM, CNN-RF and CNN-NB have been proposed in this experiment. These different methods are implemented with the RS126, 25PDB and CB513 dataset. Further, all prediction Q3 accuracy is compared and improved with their datasets.

Cascading 1D-Convnet Bidirectional Long Short Term Memory Network with Modified COCOB Optimizer: A Novel Approach for Protein Secondary Structure Prediction

The functional properties of proteins play a key job in numerous organic procedures. These functional properties rely on the structure of the specific protein. Prediction of protein structure can be performed by experimental setup as well as by computational methods. In addition to statistics and probabilistic approaches, the computational methods also include artificial intelligence-based techniques. We designed a model which consists of 1D-Convnet and modified recurrent neural network with modified continuous coin betting optimizer for the prediction of protein structure. In the modified continuous coin betting (COCOB) algorithm the probability of getting a head or a tail is based on tossing the coin twice for getting the outcome of a coin flip. It shows significant improvement in gradient calculation. As per our knowledge this is the first approach to predict protein secondary structure using modified COCOB optimization in deep learning domain. We have performed this experiment on Nvidia DGX station having four GPU core. We have used bidirectional architecture of long short term memory for this research work. We designed a 1D-Convnet model and assessed it on CB513 and CullPDB dataset. Further we combined 1D-Convnet model with bidirectional long short term memory to assess CB513 and CullPDB dataset. Both the models are evaluated by Q8 and Q3 accuracy. We compared COCOB and modified COCOB on our model and found that modified COCOB performs better than COCOB. However, our 1D-Convnet-BLSTM model achieves 76.55% (Q3) and 74.45% (Q8) accuracy on CB513 dataset and 68.91% (Q3) and 75.04% (Q8) accuracy on CullPDB dataset.

Protein Secondary Structure Prediction With a Reductive Deep Learning Method.

Protein secondary structures have been identified as the links in the physical processes of primary sequences, typically random coils, folding into functional tertiary structures that enable proteins to involve a variety of biological events in life science. Therefore, an efficient protein secondary structure predictor is of importance especially when the structure of an amino acid sequence fragment is not solved by high-resolution experiments, such as X-ray crystallography, cryo-electron microscopy, and nuclear magnetic resonance spectroscopy, which are usually time consuming and expensive. In this paper, a reductive deep learning model MLPRNN has been proposed to predict either 3-state or 8-state protein secondary structures. The prediction accuracy by the MLPRNN on the publicly available benchmark CB513 data set is comparable with those by other state-of-the-art models. More importantly, taking into account the reductive architecture, MLPRNN could be a baseline for future developments.

Predicting Secondary Structure of Protein Using Hybrid of Convolutional Neural Network and Support Vector Machine

Protein secondary structure prediction is one of the problems in the Bioinformatics field, which conducted to find the function of proteins. Protein secondary structure prediction is done by classifying each sequence of protein primary structure into the sequence of protein secondary structure, which fall in sequence labelling problems and can be solved with the machine learning. Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are 2 methods that often used to solve classification problems. In this research, we proposed a hybrid of 1-Dimensional CNN and SVM to predict the secondary structure of the protein. In this research, we used a novel hybrid 1-Dimensional CNN and SVM for sequence labelling, specifically to predict the secondary structure of the protein. Our hybrid model managed to outperform previous studies in term of Q3 and Q8 accuracy on CB513 dataset.

Comprehensive Study on Enhancing Low-Quality Position-Specific Scoring Matrix with Deep Learning for Accurate Protein Structure Property Prediction: Using Bagging Multiple Sequence Alignment Learning.

Accurate predictions of protein structure properties, for example, secondary structure and solvent accessibility, are essential in analyzing the structure and function of a protein. Position-specific scoring matrix (PSSM) features are widely used in the structure property prediction. However, some proteins may have low-quality PSSM features due to insufficient homologous sequences, leading to limited prediction accuracy. To address this limitation, we propose an enhancing scheme for PSSM features. We introduce the "Bagging MSA" (multiple sequence alignment) method to calculate PSSM features used to train our model, adopt a convolutional network to capture local context features and bidirectional long short-term memory for long-term dependencies, and integrate them under an unsupervised framework. Structure property prediction models are then built upon such enhanced PSSM features for more accurate predictions. Moreover, we develop two frameworks to evaluate the effectiveness of the enhanced PSSM features, which also bring proposed method into real-world scenarios. Empirical evaluation of CB513, CASP11, and CASP12 data sets indicates that our unsupervised enhancing scheme indeed generates more informative PSSM features for structure property prediction.

OCLSTM: Optimized convolutional and long short-term memory neural network model for protein secondary structure prediction.

Protein secondary structure prediction is extremely important for determining the spatial structure and function of proteins. In this paper, we apply an optimized convolutional neural network and long short-term memory neural network models to protein secondary structure prediction, which is called OCLSTM. We use an optimized convolutional neural network to extract local features between amino acid residues. Then use the bidirectional long short-term memory neural network to extract the remote interactions between the internal residues of the protein sequence to predict the protein structure. Experiments are performed on CASP10, CASP11, CASP12, CB513, and 25PDB datasets, and the good performance of 84.68%, 82.36%, 82.91%, 84.21% and 85.08% is achieved respectively. Experimental results show that the model can achieve better results.

An enhanced protein secondary structure prediction using deep learning framework on hybrid profile based features

Accurate protein secondary structure prediction (PSSP) is essential to identify structural classes, protein folds, and its tertiary structure. To identify the secondary structure, experimental methods exhibit higher precision with the trade-off of high cost and time. In this study, we propose an effective prediction model which consists of hybrid features of 42-dimensions with the combination of convolutional neural network (CNN) and bidirectional recurrent neural network (BRNN). The proposed model is accessed on four benchmark datasets such as CB6133, CB513, CASP10, and CAP11 using Q3, Q8, and segment overlap (Sov) metrics. The proposed model reported Q3 accuracy of 85.4%, 85.4%, 83.7%, 81.5%, and Q8 accuracy 75.8%, 73.5%, 72.2%, and 70% on CB6133, CB513, CASP10, and CAP11 datasets respectively. The results of the proposed model are improved by a minimum factor of 2.5% and 2.1% in Q3 and Q8 accuracy respectively, as compared to the popular existing models on CB513 dataset. Further, the quality of the Q3 results is validated by structural class prediction and compared with PSI-PRED. The experiment showed that the quality of the Q3 results of the proposed model is higher than that of PSI-PRED.

PiPred \u2013 a deep-learning method for prediction of \u03c0-helices in protein sequences

Canonical π-helices are short, relatively unstable secondary structure elements found in proteins. They comprise seven or more residues and are present in 15% of all known protein structures, often in functionally important regions such as ligand- and ion-binding sites. Given their similarity to α-helices, the prediction of π-helices is a challenging task and none of the currently available secondary structure prediction methods tackle it. Here, we present PiPred, a neural network-based tool for predicting π-helices in protein sequences. By performing a rigorous benchmark we show that PiPred can detect π-helices with a per-residue precision of 48% and sensitivity of 46%. Interestingly, some of the α-helices mispredicted by PiPred as π-helices exhibit a geometry characteristic of π-helices. Also, despite being trained only with canonical π-helices, PiPred can identify 6-residue-long α/π-bulges. These observations suggest an even higher effective precision of the method and demonstrate that π-helices, α/π-bulges, and other helical deformations may impose similar constraints on sequences. PiPred is freely accessible at: https://toolkit.tuebingen.mpg.de/#/tools/quick2d. A standalone version is available for download at: https://github.com/labstructbioinf/PiPred, where we also provide the CB6133, CB513, CASP10, and CASP11 datasets, commonly used for training and validation of secondary structure prediction methods, with correctly annotated π-helices.

Prediction of 8-state protein secondary structures by a novel deep learning architecture

BackgroundProtein secondary structure can be regarded as an information bridge that links the primary sequence and tertiary structure. Accurate 8-state secondary structure prediction can significantly give more precise and high resolution on structure-based properties analysis.ResultsWe present a novel deep learning architecture which exploits an integrative synergy of prediction by a convolutional neural network, residual network, and bidirectional recurrent neural network to improve the performance of protein secondary structure prediction. A local block comprised of convolutional filters and original input is designed for capturing local sequence features. The subsequent bidirectional recurrent neural network consisting of gated recurrent units can capture global context features. Furthermore, the residual network can improve the information flow between the hidden layers and the cascaded recurrent neural network. Our proposed deep network achieved 71.4% accuracy on the benchmark CB513 dataset for the 8-state prediction; and the ensemble learning by our model achieved 74% accuracy. Our model generalization capability is also evaluated on other three independent datasets CASP10, CASP11 and CASP12 for both 8- and 3-state prediction. These prediction performances are superior to the state-of-the-art methods.ConclusionOur experiment demonstrates that it is a valuable method for predicting protein secondary structure, and capturing local and global features concurrently is very useful in deep learning.

Protein Secondary Structure Prediction Based on Data Partition and Semi-Random Subspace Method

Protein secondary structure prediction is one of the most important and challenging problems in bioinformatics. Machine learning techniques have been applied to solve the problem and have gained substantial success in this research area. However there is still room for improvement toward the theoretical limit. In this paper, we present a novel method for protein secondary structure prediction based on a data partition and semi-random subspace method (PSRSM). Data partitioning is an important strategy for our method. First, the protein training dataset was partitioned into several subsets based on the length of the protein sequence. Then we trained base classifiers on the subspace data generated by the semi-random subspace method, and combined base classifiers by majority vote rule into ensemble classifiers on each subset. Multiple classifiers were trained on different subsets. These different classifiers were used to predict the secondary structures of different proteins according to the protein sequence length. Experiments are performed on 25PDB, CB513, CASP10, CASP11, CASP12, and T100 datasets, and the good performance of 86.38%, 84.53%, 85.51%, 85.89%, 85.55%, and 85.09% is achieved respectively. Experimental results showed that our method outperforms other state-of-the-art methods.