Identification Of DNA-binding Proteins Research Articles

LGC-DBP: the method of DNA-binding protein identification based on PSSM and deep learning.

The recognition of DNA Binding Proteins (DBPs) plays a crucial role in understanding biological functions such as replication, transcription, and repair. Although current sequence-based methods have shown some effectiveness, they often fail to fully utilize the potential of deep learning in capturing complex patterns. This study introduces a novel model, LGC-DBP, which integrates Long Short-Term Memory (LSTM), Gated Inception Convolution, and Improved Channel Attention mechanisms to enhance the prediction of DBPs. Initially, the model transforms protein sequences into Position Specific Scoring Matrices (PSSM), then processed through our deep learning framework. Within this framework, Gated Inception Convolution merges the concepts of gating units with the advantages of Graph Convolutional Network (GCN) and Dilated Convolution, significantly surpassing traditional convolution methods. The Improved Channel Attention mechanism substantially enhances the model's responsiveness and accuracy by shifting from a single input to three inputs and integrating three sigmoid functions along with an additional layer output. These innovative combinations have significantly improved model performance, enabling LGC-DBP to recognize and interpret the complex relationships within DBP features more accurately. The evaluation results show that LGC-DBP achieves an accuracy of 88.26% and a Matthews correlation coefficient of 0.701, both surpassing existing methods. These achievements demonstrate the model's strong capability in integrating and analyzing multi-dimensional data and mark a significant advancement over traditional methods by capturing deeper, nonlinear interactions within the data.

ProkDBP: Toward more precise identification of prokaryotic DNA binding proteins.

Prokaryotic DNA binding proteins (DBPs) play pivotal roles in governing gene regulation, DNA replication, and various cellular functions. Accurate computational models for predicting prokaryotic DBPs hold immense promise in accelerating the discovery of novel proteins, fostering a deeper understanding of prokaryotic biology, and facilitating the development of therapeutics targeting for potential disease interventions. However, existing generic prediction models often exhibit lower accuracy in predicting prokaryotic DBPs. To address this gap, we introduce ProkDBP, a novel machine learning-driven computational model for prediction of prokaryotic DBPs. For prediction, a total of nine shallow learning algorithms and five deep learning models were utilized, with the shallow learning models demonstrating higher performance metrics compared to their deep learning counterparts. The light gradient boosting machine (LGBM), coupled with evolutionarily significant features selected via random forest variable importance measure (RF-VIM) yielded the highest five-fold cross-validation accuracy. The model achieved the highest auROC (0.9534) and auPRC (0.9575) among the 14 machine learning models evaluated. Additionally, ProkDBP demonstrated substantial performance with an independent dataset, exhibiting higher values of auROC (0.9332) and auPRC (0.9371). Notably, when benchmarked against several cutting-edge existing models, ProkDBP showcased superior predictive accuracy. Furthermore, to promote accessibility and usability, ProkDBP (https://iasri-sg.icar.gov.in/prokdbp/) is available as an online prediction tool, enabling free access to interested users. This tool stands as a significant contribution, enhancing the repertoire of resources for accurate and efficient prediction of prokaryotic DBPs.

Predictive Recognition of DNA-binding Proteins Based on Pre-trained Language Model BERT.

Identifying proteins is crucial for disease diagnosis and treatment. With the increase of known proteins, large-scale batch predictions are essential. However, traditional biological experiments being time-consuming and expensive are difficult to accomplish this task efficiently. Nevertheless, deep learning algorithms based on big data analysis have manifested potential in this aspect. In recent years, language representation models, especially BERT, have made significant advancements in natural language processing. In this paper, using three protein segmentation methods and three encoder numbers, nine BERT models with different sizes are constructed to predict whether known proteins are DNA-binding proteins or not. Furthermore, based on the concept of protein motifs, multi-scale convolutional networks are fused into the models to extract the local features of DNA-binding proteins. Finally, we find that the larger the number of encoders, the better the model predictions under the condition of considering each amino acid in the protein as a word. Our proposed algorithm achieves 81.88% sensitivity and 0.39 MCC value on the test set. Furthermore, it achieves 62.41% accuracy on the independent test set PDB2272. It is evident that our proposed method can be a tool to assist in the identification of DNA-binding proteins.

DBPMod: a supervised learning model for computational recognition of DNA-binding proteins in model organisms.

DNA-binding proteins (DBPs) play critical roles in many biological processes, including gene expression, DNA replication, recombination and repair. Understanding the molecular mechanisms underlying these processes depends on the precise identification of DBPs. In recent times, several computational methods have been developed to identify DBPs. However, because of the generic nature of the models, these models are unable to identify species-specific DBPs with higher accuracy. Therefore, a species-specific computational model is needed to predict species-specific DBPs. In this paper, we introduce the computational DBPMod method, which makes use of a machine learning approach to identify species-specific DBPs. For prediction, both shallow learning algorithms and deep learning models were used, with shallow learning models achieving higher accuracy. Additionally, the evolutionary features outperformed sequence-derived features in terms of accuracy. Five model organisms, including Caenorhabditis elegans, Drosophila melanogaster, Escherichia coli, Homo sapiens and Mus musculus, were used to assess the performance of DBPMod. Five-fold cross-validation and independent test set analyses were used to evaluate the prediction accuracy in terms of area under receiver operating characteristic curve (auROC) and area under precision-recall curve (auPRC), which was found to be ~89-92% and ~89-95%, respectively. The comparative results demonstrate that the DBPMod outperforms 12 current state-of-the-art computational approaches in identifying the DBPs for all five model organisms. We further developed the web server of DBPMod to make it easier for researchers to detect DBPs and is publicly available at https://iasri-sg.icar.gov.in/dbpmod/. DBPMod is expected to be an invaluable tool for discovering DBPs, supplementing the current experimental and computational methods.

Identification of DNA-binding proteins by Kernel Sparse Representation via [formula omitted]-matrix norm

An understanding of DNA-binding proteins is helpful in exploring the role that proteins play in cell biology. Furthermore, the prediction of DNA-binding proteins is essential for the chemical modification and structural composition of DNA, and is of great importance in protein functional analysis and drug design. In recent years, DNA-binding protein prediction has typically used machine learning-based methods. The prediction accuracy of various classifiers has improved considerably, but researchers continue to spend time and effort on improving prediction performance. In this paper, we combine protein sequence evolutionary information with a classification method based on kernel sparse representation for the prediction of DNA-binding proteins, and based on the field of machine learning, a model for the identification of DNA-binding proteins by sequence information was finally proposed. Based on the confirmation of the final experimental results, we achieved good prediction accuracy on both the PDB1075 and PDB186 datasets. Our training result for cross-validation on PDB1075 was 81.37%, and our independent test result on PDB186 was 83.9%, both of which outperformed the other methods to some extent. Therefore, the proposed method in this paper is proven to be effective and feasible for predicting DNA-binding proteins.

MV-H-RKM: A Multiple View-Based Hypergraph Regularized Restricted Kernel Machine for Predicting DNA-Binding Proteins.

DNA-binding proteins (DBPs) have a significant impact on many life activities, so identification of DBPs is a crucial issue. And it is greatly helpful to understand the mechanism of protein-DNA interactions. In traditional experimental methods, it is significant time-consuming and labor-consuming to identify DBPs. In recent years, many researchers have proposed lots of different DBP identification methods based on machine learning algorithm to overcome shortcomings mentioned above. However, most existing methods cannot get satisfactory results. In this paper, we focus on developing a new predictor of DBPs, called Multi-View Hypergraph Restricted Kernel Machines (MV-H-RKM). In this method, we extract five features from the three views of the proteins. To fuse these features, we couple them by means of the shared hidden vector. Besides, we employ the hypergraph regularization to enforce the structure consistency between original features and the hidden vector. Experimental results show that the accuracy of MV-H-RKM is 84.09% and 85.48% on PDB1075 and PDB186 data set respectively, and demonstrate that our proposed method performs better than other state-of-the-art approaches. The code is publicly available at https://github.com/ShixuanGG/MV-H-RKM.

IDRBP-EL: Identifying DNA- and RNA- Binding Proteins Based on Hierarchical Ensemble Learning.

Identification of DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) from the primary sequences is essential for further exploring protein-nucleic acid interactions. Previous studies have shown that machine-learning-based methods can efficiently identify DBPs or RBPs. However, the information used in these methods is slightly unitary, and most of them only can predict DBPs or RBPs. In this study, we proposed a computational predictor iDRBP-EL to identify DNA- and RNA- binding proteins, and introduced hierarchical ensemble learning to integrate three level information. The method can integrate the information of different features, machine learning algorithms and data into one multi-label model. The ablation experiment showed that the fusion of different information can improve the prediction performance and overcome the cross-prediction problem. Experimental results on the independent datasets showed that iDRBP-EL outperformed all the other competing methods. Moreover, we established a user-friendly webserver iDRBP-EL (http://bliulab.net/iDRBP-EL), which can predict both DBPs and RBPs only based on protein sequences.

Identification of DNA-binding proteins via Multi-view LSSVM with independence criterion.

DNA-binding proteins actively participate in life activities such as DNA replication, recombination, gene expression and regulation and play a prominent role in these processes. As DNA-binding proteins continue to be discovered and increase, it is imperative to design an efficient and accurate identification tool. Considering the time-consuming and expensive traditional experimental technology and the insufficient number of samples in the biological computing method based on structural information, we proposed a machine learning algorithm based on sequence information to identify DNA binding proteins, named multi-view Least Squares Support Vector Machine via Hilbert-Schmidt Independence Criterion (multi-view LSSVM via HSIC). This method took 6 feature sets as multi-view input and trains a single view through the LSSVM algorithm. Then, we integrated HSIC into LSSVM as a regular term to reduce the dependence between views and explored the complementary information of multiple views. Subsequently, we trained and coordinated the submodels and finally combined the submodels in the form of weights to obtain the final prediction model. On training set PDB1075, the prediction results of our model were better than those of most existing methods. Independent tests are conducted on the datasets PDB186 and PDB2272. The accuracy of the prediction results was 85.5% and 79.36%, respectively. This result exceeded the current state-of-the-art methods, which showed that the multi-view LSSVM via HSIC can be used as an efficient predictor.

DBP-iDWT: Improving DNA-Binding Proteins Prediction Using Multi-Perspective Evolutionary Profile and Discrete Wavelet Transform.

DNA-binding proteins (DBPs) have crucial biotic activities including DNA replication, recombination, and transcription. DBPs are highly concerned with chronic diseases and are used in the manufacturing of antibiotics and steroids. A series of predictors were established to identify DBPs. However, researchers are still working to further enhance the identification of DBPs. This research designed a novel predictor to identify DBPs more accurately. The features from the sequences are transformed by F-PSSM (Filtered position-specific scoring matrix), PSSM-DPC (Position specific scoring matrix-dipeptide composition), and R-PSSM (Reduced position-specific scoring matrix). To eliminate the noisy attributes, we extended DWT (discrete wavelet transform) to F-PSSM, PSSM-DPC, and R-PSSM and introduced three novel descriptors, namely, F-PSSM-DWT, PSSM-DPC-DWT, and R-PSSM-DWT. Onward, the training of the four models were performed using LiXGB (Light eXtreme gradient boosting), XGB (eXtreme gradient boosting, ERT (extremely randomized trees), and Adaboost. LiXGB with R-PSSM-DWT has attained 6.55% higher accuracy on training and 5.93% on testing dataset than the best existing predictors. The results reveal the excellent performance of our novel predictor over the past studies. DBP-iDWT would be fruitful for establishing more operative therapeutic strategies for fatal disease treatment.

DBP-DeepCNN: Prediction of DNA-binding proteins using wavelet-based denoising and deep learning

DNA-binding proteins (DBPs) are highly concerned with several types of cancers (lung, breast, and liver), other fatal diseases (AIDS/HIV, asthma), and are used in the designing of drug. A series of predictors were constructed for identification of DBPs. However, a more accurate computational predictor is still essential for further performance improvement. In this work, a deep learning-based predictor (DBP-DeepCNN) is proposed for improving DBPs prediction. The salient features are derived by a novel method, namely, R-PSSM-DWT (Reduced position-specific scoring matrix-discrete wavelet transform) as well as Lead-BiPSSM (Lead-bigram-position specific scoring matrix), PSSM-DPC (Position specific scoring matrix-dipeptide composition), ED-PSSM (Evolutionary difference position specific scoring matrix), and F-PSSM (Filtered position specific scoring matrix). Further, the models are trained with 2D CNN (two-dimensional convolutional neural network), XGB (eXtreme gradient boosting), Adaboost, and ERT (extremely randomized trees). 2D CNN-based model with R-PSSM-DWT produced 6.92% and 1.32% higher accuracies than existing approach on training and independent datasets, respectively. These outcomes verified the superlative success rate of our novel predictor over the existing studies. In addition to being a promising method for large scale prediction of DBPs. DBP-DeepCNN would be fruitful for establishing more promising therapeutic strategies for chronic disease treatment.

Comparative Analysis on Alignment-Based and Pretrained Feature Representations for the Identification of DNA-Binding Proteins

The interaction between DNA and protein is vital for the development of a living body. Previous numerous studies on in silico identification of DNA-binding proteins (DBPs) usually include features extracted from the alignment-based (pseudo) position-specific scoring matrix (PSSM), leading to limited application due to its time-consuming generation. Few researchers have paid attention to the application of pretrained language models at the scale of evolution to the identification of DBPs. To this end, we present comprehensive insights into a comparison study on alignment-based PSSM and pretrained evolutionary scale modeling (ESM) representations in the field of DBP classification. The comparison is conducted by extracting information from PSSM and ESM representations using four unified averaging operations and by performing various feature selection (FS) methods. Experimental results demonstrate that the pretrained ESM representation outperforms the PSSM-derived features in a fair comparison perspective. The pretrained feature presentation deserves wide application to the area of in silico DBP identification as well as other function annotation issues. Finally, it is also confirmed that an ensemble scheme by aggregating various trained FS models can significantly improve the classification performance of DBPs.

Research on DNA-Binding Protein Identification Method Based on LSTM-CNN Feature Fusion.

Protein is closely related to life activities. As a kind of protein, DNA-binding protein plays an irreplaceable role in life activities. Therefore, it is very important to study DNA-binding protein, which is a subject worthy of study. Although traditional biotechnology has high precision, its cost and efficiency are increasingly unable to meet the needs of modern society. Machine learning methods can make up for the deficiencies of biological experimental techniques to a certain extent, but they are not as simple and fast as deep learning for data processing. In this paper, a deep learning framework based on parallel long and short-term memory(LSTM) and convolutional neural networks(CNN) was proposed to identify DNA-binding protein. This model can not only further extract the information and features of protein sequences, but also the features of evolutionary information. Finally, the two features are combined for training and testing. On the PDB2272 dataset, compared with PDBP_Fusion model, Accuracy(ACC) and Matthew's Correlation Coefficient (MCC) increased by 3.82% and 7.98% respectively. The experimental results of this model have certain advantages.

DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features.

In the domain of genome annotation, the identification of DNA-binding protein is one of the crucial challenges. DNA is considered a blueprint for the cell. It contained all necessary information for building and maintaining the trait of an organism. It is DNA, which makes a living thing, a living thing. Protein interaction with DNA performs an essential role in regulating DNA functions such as DNA repair, transcription, and regulation. Identification of these proteins is a crucial task for understanding the regulation of genes. Several methods have been developed to identify the binding sites of DNA and protein depending upon the structures and sequences, but they were costly and time-consuming. Therefore, we propose a methodology named “DNAPred_Prot”, which uses various position and frequency-dependent features from protein sequences for efficient and effective prediction of DNA-binding proteins. Using testing techniques like 10-fold cross-validation and jackknife testing an accuracy of 94.95% and 95.11% was yielded, respectively. The results of SVM and ANN were also compared with those of a random forest classifier. The robustness of the proposed model was evaluated by using the independent dataset PDB186, and an accuracy of 91.47% was achieved by it. From these results, it can be predicted that the suggested methodology performs better than other extant methods for the identification of DNA-binding proteins.

DeepDNAbP: A deep learning-based hybrid approach to improve the identification of deoxyribonucleic acid-binding proteins

Accurate identification of DNA-binding proteins (DBPs) is critical for both understanding protein function and drug design. DBPs also play essential roles in different kinds of biological activities such as DNA replication, repair, transcription, and splicing. As experimental identification of DBPs is time-consuming and sometimes biased toward prediction, constructing an effective DBP model represents an urgent need, and computational methods that can accurately predict potential DBPs based on sequence information are highly desirable. In this paper, a novel predictor called DeepDNAbP has been developed to accurately predict DBPs from sequences using a convolutional neural network (CNN) model. First, we perform three feature extraction methods, namely position-specific scoring matrix (PSSM), pseudo-amino acid composition (PseAAC) and tripeptide composition (TPC), to represent protein sequence patterns. Secondly, SHapley Additive exPlanations (SHAP) are employed to remove the redundant and irrelevant features for predicting DBPs. Finally, the best features are provided to the CNN classifier to construct the DeepDNAbP model for identifying DBPs. The final DeepDNAbP predictor achieves superior prediction performance in K-fold cross-validation tests and outperforms other existing predictors of DNA–protein binding methods. DeepDNAbP is poised to be a powerful computational resource for the prediction of DBPs. The web application and curated datasets in this study are freely available at: http://deepdbp.sblog360.blog/.

HKAM-MKM: A hybrid kernel alignment maximization-based multiple kernel model for identifying DNA-binding proteins

The identification of DNA-binding proteins (DBPs) has always been a hot issue in the field of sequence classification. However, considering that the experimental identification method is very resource-intensive, the construction of a computational prediction model is worthwhile. This study developed and evaluated a hybrid kernel alignment maximization-based multiple kernel model (HKAM-MKM) for predicting DBPs. First, we collected two datasets and performed feature extraction on the sequences to obtain six feature groups, and then constructed the corresponding kernels. To ensure the effective utilisation of the base kernel and avoid ignoring the difference between the sample and its neighbours, we proposed local kernel alignment to calculate the kernel between the sample and its neighbours, with each sample as the centre. We combined the global and local kernel alignments to develop a hybrid kernel alignment model, and balance the relationship between the two through parameters. By maximising the hybrid kernel alignment value, we obtained the weight of each kernel and then linearly combined the kernels in the form of weights. Finally, the fused kernel was input into a support vector machine for training and prediction.Finally, in the independent test sets PDB186 and PDB2272, we obtained the highest Matthew's correlation coefficient (MCC) (0.768 and 0.5962, respectively) and the highest accuracy (87.1% and 78.43%, respectively), which were superior to the other predictors. Therefore, HKAM-MKM is an efficient prediction tool for DBPs.

Identification of DNA-Binding Proteins via Hypergraph Based Laplacian Support Vector Machine

Background: The identification of DNA binding proteins (DBP) is an important research field. Experiment-based methods are time-consuming and labor-intensive for detecting DBP. Objective: To solve the problem of large-scale DBP identification, some machine learning methods are proposed. However, these methods have insufficient predictive accuracy. Our aim is to develop a sequence- based machine learning model to predict DBP. Methods: In our study, we extracted six types of features (including NMBAC, GE, MCD, PSSM-AB, PSSM-DWT, and PsePSSM) from protein sequences. We used Multiple Kernel Learning based on Hilbert- Schmidt Independence Criterion (MKL-HSIC) to estimate the optimal kernel. Then, we constructed a hypergraph model to describe the relationship between labeled and unlabeled samples. Finally, Laplacian Support Vector Machines (LapSVM) is employed to train the predictive model. Our method is tested on PDB186, PDB1075, PDB2272 and PDB14189 data sets. Result: Compared with other methods, our model achieved best results on benchmark data sets. Conclusion: The accuracy of 87.1% and 74.2% are achieved on PDB186 (Independent test of PDB1075) and PDB2272 (Independent test of PDB14189), respectively.

Identification of DNA-Binding Proteins via Hypergraph Based Laplacian Support Vector Machine

Identification of DNA-Binding Proteins via Hypergraph Based Laplacian Support Vector Machine

Reannotation of Mycoplasma hyopneumoniae hypothetical proteins revealed novel potential virulence factors

Mycoplasma hyopneumoniae is a bacterium that inhabits the swine respiratory tract, causing porcine enzootic pneumonia, which generates significant economic losses to the swine industry worldwide. The knowledge on M. hyopneumoniae biology and virulence have been significantly increased by genomics studies. However, around 30% of the predicted proteins remained of unknown function so far. According to the original annotation, the genome of M. hyopneumoniae 7448, a Brazilian pathogenic strain, had 693 coding DNA sequences, 244 of which were annotated as coding for hypothetical or uncharacterized proteins. Among them, there may be still several genes coding for unknown virulence factors. Therefore, this study aimed to functionally reannotate the whole set of 244 M. hyopneumoniae 7448 proteins of unknown function based on currently available database and bioinformatic tools, in order to predict novel potential virulence factors. Predictions of physicochemical properties, subcellular localization, function, overall association to virulence and antigenicity are provided. With that, 159 out of the set of 244 proteins of unknown function had a putative function associated to them, allowing identification of novel enzymes, membrane transporters, lipoproteins, DNA-binding proteins and adhesins. Furthermore, 139 proteins were generally associated to virulence, 14 of which had a function assigned and were differentially expressed between pathogenic and non-pathogenic strains of M. hyopneumoniae. Moreover, all extracellular or cytoplasmic membrane predicted proteins had putative epitopes identified. Overall, these analyses improved the functional annotation of M. hyopneumoniae 7448 genome from 65% to 87% and allowed the identification of new potential virulence factors.

KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest.

DNA-binding protein (DBP) is a protein with a special DNA binding domain that is associated with many important molecular biological mechanisms. Rapid development of computational methods has made it possible to predict DBP on a large scale; however, existing methods do not fully integrate DBP-related features, resulting in rough prediction results. In this article, we develop a DNA-binding protein identification method called KK-DBP. To improve prediction accuracy, we propose a feature extraction method that fuses multiple PSSM features. The experimental results show a prediction accuracy on the independent test dataset PDB186 of 81.22%, which is the highest of all existing methods.

A Useful Tool for the Identification of DNA-Binding Proteins Using Graph Convolutional Network

Background: DNA and protein are important components of living organisms. DNA binding protein is a helicase, which is a protein specifically responsible for binding to DNA single- stranded regions. It is a necessary component for DNA replication, recombination and repair, and plays a key role in the function of various biomolecules. Although there are already some classification prediction methods for this protein, the use of graph neural networks for this work is still limited. Objective: The classification of unknown protein sequences into the correct categories, subcategories and families is important for biological sciences. In this article, using graph neural networks, we developed a novel predictor GCN-DBP for protein classification prediction. Methods: Each protein sequence is treated as a document in this study, and then segment the words according to the concept of k-mer, thereby, finally achieving the purpose of segmenting the document. This research aims to use document word relationships and word co-occurrence as a corpus to construct a text graph, and then learn protein sequence information by two-layer graph convolutional networks. Results: Finally, we tested GCN-DBP on the independent data set PDB2272, and its accuracy reached 64.17% and MCC was 28.32%. Moreover, in order to compare the proposed method with other existing methods, we have conducted more experiments. Conclusion: The results show that the proposed method is superior to the other four methods and will be a useful tool.