Predicting DNA-binding proteins: approached from Chou's pseudo amino acid composition and other specific sequence features

Benchmarking recent computational tools for DNA-binding protein identification

Identification of DNA-binding proteins (DBPs) is a crucial task in genome annotation, as it aids in understanding gene regulation, DNA replication, transcriptional control, and various cellular processes. In this paper, we conduct an unbiased benchmarking of 11 state-of-the-art computational tools as well as traditional tools such as ScanProsite, BLAST, and HMMER for identifying DBPs. We highlight the data leakage issue in conventional datasets leading to inflated performance. We introduce new evaluation datasets to support further development. Through a comprehensive evaluation pipeline, we identify potential limitations in models, feature extraction techniques, and training methods, and recommend solutions regarding these issues. We show that combining the predictions of the two best computational tools with BLAST-based prediction significantly enhances DBP identification capability. We provide this consensus method as user-friendly software. The datasets and software are available at https://github.com/Rafeed-bot/DNA_BP_Benchmarking.

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.

DBPboost:A method of classification of DNA-binding proteins based on improved differential evolution algorithm and feature extraction

DNA-binding proteins are a class of proteins that can interact with DNA molecules through physical and chemical interactions. Their main functions include regulating gene expression, maintaining chromosome structure and stability, and more. DNA-binding proteins play a crucial role in cellular and molecular biology, as they are essential for maintaining normal cellular physiological functions and adapting to environmental changes. The prediction of DNA-binding proteins has been a hot topic in the field of bioinformatics. The key to accurately classifying DNA-binding proteins is to find suitable feature sources and explore the information they contain. Although there are already many models for predicting DNA-binding proteins, there is still room for improvement in mining feature source information and calculation methods. In this study, we created a model called DBPboost to better identify DNA-binding proteins. The innovation of this study lies in the use of eight feature extraction methods, the improvement of the feature selection step, which involves selecting some features first and then performing feature selection again after feature fusion, and the optimization of the differential evolution algorithm in feature fusion, which improves the performance of feature fusion. The experimental results show that the prediction accuracy of the model on the UniSwiss dataset is 89.32%, and the sensitivity is 89.01%, which is better than most existing models.

Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features

DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer drugs. Predicting them poses the most challenging task in proteomics research. Conventional experimental methods for DBP identification are costly and sometimes biased toward prediction. Therefore, developing powerful computational methods that can accurately and rapidly identify DBPs from sequence information is an urgent need. In this study, we propose a novel deep learning-based method called Deep-WET to accurately identify DBPs from primary sequence information. In Deep-WET, we employed three powerful feature encoding schemes containing Global Vectors, Word2Vec, and fastText to encode the protein sequence. Subsequently, these three features were sequentially combined and weighted using the weights obtained from the elements learned through the differential evolution (DE) algorithm. To enhance the predictive performance of Deep-WET, we applied the SHapley Additive exPlanations approach to remove irrelevant features. Finally, the optimal feature subset was input into convolutional neural networks to construct the Deep-WET predictor. Both cross-validation and independent tests indicated that Deep-WET achieved superior predictive performance compared to conventional machine learning classifiers. In addition, in extensive independent test, Deep-WET was effective and outperformed than several state-of-the-art methods for DBP prediction, with accuracy of 78.08%, MCC of 0.559, and AUC of 0.805. This superior performance shows that Deep-WET has a tremendous predictive capacity to predict DBPs. The web server of Deep-WET and curated datasets in this study are available at https://deepwet-dna.monarcatechnical.com/ . The proposed Deep-WET is anticipated to serve the community-wide effort for large-scale identification of potential DBPs.

Identification of DNA-binding protein based multiple kernel model

DNA-binding proteins (DBPs) play a critical role in the development of drugs for treating genetic diseases and in DNA biology research. It is essential for predicting DNA-binding proteins more accurately and efficiently. In this paper, a Laplacian Local Kernel Alignment-based Restricted Kernel Machine (LapLKA-RKM) is proposed to predict DBPs. In detail, we first extract features from the protein sequence using six methods. Second, the Radial Basis Function (RBF) kernel function is utilized to construct pre-defined kernel metrics. Then, these metrics are combined linearly by weights calculated by LapLKA. Finally, the fused kernel is input to RKM for training and prediction. Independent tests and leave-one-out cross-validation were used to validate the performance of our method on a small dataset and two large datasets. Importantly, we built an online platform to represent our model, which is now freely accessible via http://8.130.69.121:8082/.

Improving DNA-Binding Protein Prediction Using Three-Part Sequence-Order Feature Extraction and a Deep Neural Network Algorithm

Identification of the DNA-binding protein (DBP) helps dig out information embedded in the DNA-protein interaction, which is significant to understanding the mechanisms of DNA replication, transcription, and repair. Although existing computational methods for predicting the DBPs based on protein sequences have obtained great success, there is still room for improvement since the sequence-order information is not fully mined in these methods. In this study, a new three-part sequence-order feature extraction (called TPSO) strategy is developed to extract more discriminative information from protein sequences for predicting the DBPs. For each query protein, TPSO first divides its primary sequence features into N- and C-terminal fragments and then extracts the numerical pseudo features of three parts including the full sequence and these two fragments, respectively. Based on TPSO, a novel deep learning-based method, called TPSO-DBP, is proposed, which employs the sequence-based single-view features, the bidirectional long short-term memory (BiLSTM) and fully connected (FC) neural networks to learn the DBP prediction model. Empirical outcomes reveal that TPSO-DBP can achieve an accuracy of 87.01%, covering 85.30% of all DBPs, while achieving a Matthew's correlation coefficient value (0.741) that is significantly higher than most existing state-of-the-art DBP prediction methods. Detailed data analyses have indicated that the advantages of TPSO-DBP lie in the utilization of TPSO, which helps extract more concealed prominent patterns, and the deep neural network framework composed of BiLSTM and FC that learns the nonlinear relationships between input features and DBPs. The standalone package and web server of TPSO-DBP are freely available at https://jun-csbio.github.io/TPSO-DBP/.

Robust ensemble of handcrafted and learned approaches for DNA-binding proteins

Purpose

Automatic DNA-binding protein (DNA-BP) classification is now an essential proteomic technology. Unfortunately, many systems reported in the literature are tested on only one or two datasets/tasks. The purpose of this study is to create the most optimal and universal system for DNA-BP classification, one that performs competitively across several DNA-BP classification tasks.

Design/methodology/approach

Efficient DNA-BP classifier systems require the discovery of powerful protein representations and feature extraction methods. Experiments were performed that combined and compared descriptors extracted from state-of-the-art matrix/image protein representations. These descriptors were trained on separate support vector machines (SVMs) and evaluated. Convolutional neural networks with different parameter settings were fine-tuned on two matrix representations of proteins. Decisions were fused with the SVMs using the weighted sum rule and evaluated to experimentally derive the most powerful general-purpose DNA-BP classifier system.

Findings

The best ensemble proposed here produced comparable, if not superior, classification results on a broad and fair comparison with the literature across four different datasets representing a variety of DNA-BP classification tasks, thereby demonstrating both the power and generalizability of the proposed system.

Originality/value

Most DNA-BP methods proposed in the literature are only validated on one (rarely two) datasets/tasks. In this work, the authors report the performance of our general-purpose DNA-BP system on four datasets representing different DNA-BP classification tasks. The excellent results of the proposed best classifier system demonstrate the power of the proposed approach. These results can now be used for baseline comparisons by other researchers in the field.

Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning

DNA-binding proteins (DBPs) play pivotal roles in many biological functions such as alternative splicing, RNA editing, and methylation. Many traditional machine learning (ML) methods and deep learning (DL) methods have been proposed to predict DBPs. However, these methods either rely on manual feature extraction or fail to capture long-term dependencies in the DNA sequence. In this paper, we propose a method, called PDBP-Fusion, to identify DBPs based on the fusion of local features and long-term dependencies only from primary sequences. We utilize convolutional neural network (CNN) to learn local features and use bi-directional long-short term memory network (Bi-LSTM) to capture critical long-term dependencies in context. Besides, we perform feature extraction, model training, and model prediction simultaneously. The PDBP-Fusion approach can predict DBPs with 86.45% sensitivity, 79.13% specificity, 82.81% accuracy, and 0.661 MCC on the PDB14189 benchmark dataset. The MCC of our proposed methods has been increased by at least 9.1% compared to other advanced prediction models. Moreover, the PDBP-Fusion also gets superior performance and model robustness on the PDB2272 independent dataset. It demonstrates that the PDBP-Fusion can be used to predict DBPs from sequences accurately and effectively; the online server is at http://119.45.144.26:8080/PDBP-Fusion/.

PseKNC and Adaboost-Based Method for DNA-Binding Proteins Recognition

DNA-binding proteins are an essential part of the DNA. It also an integral component during life processes of various organisms, for instance, DNA recombination, replication, and so on. Recognition of such proteins helps medical researchers pinpoint the cause of disease. Traditional techniques of identifying DNA-binding proteins are expensive and time-consuming. Machine learning methods can identify these proteins quickly and efficiently. However, the accuracies of the existing related methods were not high enough. In this paper, we propose a framework to identify DNA-binding proteins. The proposed framework first uses PseKNC (ps), MomoKGap (mo), and MomoDiKGap (md) methods to combine three algorithms to extract features. Further, we apply Adaboost weight ranking to select optimal feature subsets from the above three types of features. Based on the selected features, three algorithms (k-nearest neighbor (knn), Support Vector Machine (SVM), and Random Forest (RF)) are applied to classify it. Finally, three predictors for identifying DNA-binding proteins are established, including [Formula: see text], [Formula: see text], [Formula: see text]. We utilize benchmark and independent datasets to train and evaluate the proposed framework. Three tests are performed, including Jackknife test, 10-fold cross-validation and independent test. Among them, the accuracy of ps+md is the highest. We named the model with the best result as psmdDBPs and applied it to identify DNA-binding proteins.

Amino Acid Composition in Various Types of Nucleic Acid-Binding Proteins

Nucleic acid-binding proteins are traditionally divided into two categories: With the ability to bind DNA or RNA. In the light of new knowledge, such categorizing should be overcome because a large proportion of proteins can bind both DNA and RNA. Another even more important features of nucleic acid-binding proteins are so-called sequence or structure specificities. Proteins able to bind nucleic acids in a sequence-specific manner usually contain one or more of the well-defined structural motifs (zinc-fingers, leucine zipper, helix-turn-helix, or helix-loop-helix). In contrast, many proteins do not recognize nucleic acid sequence but rather local DNA or RNA structures (G-quadruplexes, i-motifs, triplexes, cruciforms, left-handed DNA/RNA form, and others). Finally, there are also proteins recognizing both sequence and local structural properties of nucleic acids (e.g., famous tumor suppressor p53). In this mini-review, we aim to summarize current knowledge about the amino acid composition of various types of nucleic acid-binding proteins with a special focus on significant enrichment and/or depletion in each category.

TargetDBP+: Enhancing the Performance of Identifying DNA-Binding Proteins via Weighted Convolutional Features

Protein-DNA interactions exist ubiquitously and play important roles in the life cycles of living cells. The accurate identification of DNA-binding proteins (DBPs) is one of the key steps to understand the mechanisms of protein-DNA interactions. Although many DBP identification methods have been proposed, the current performance is still unsatisfactory. In this study, a new method, called TargetDBP+, is developed to further enhance the performance of identifying DBPs. In TargetDBP+, five convolutional features are first extracted from five feature sources, i.e., amino acid one-hot matrix (AAOHM), position-specific scoring matrix (PSSM), predicted secondary structure probability matrix (PSSPM), predicted solvent accessibility probability matrix (PSAPM), and predicted probabilities of DNA-binding sites (PPDBSs); second, the five features are weightedly and serially combined using the weights of all of the elements learned by the differential evolution algorithm; and finally, the DBP identification model of TargetDBP+ is trained using the support vector machine (SVM) algorithm. To evaluate the developed TargetDBP+ and compare it with other existing methods, a new gold-standard benchmark data set, called UniSwiss, is constructed, which consists of 4881 DBPs and 4881 non-DBPs extracted from the UniprotKB/Swiss-Prot database. Experimental results demonstrate that TargetDBP+ can obtain an accuracy of 85.83% and precision of 88.45% covering 82.41% of all DBP data on the independent validation subset of UniSwiss, with the MCC value (0.718) being significantly higher than those of other state-of-the-art control methods. The web server of TargetDBP+ is accessible at http://csbio.njust.edu.cn/bioinf/targetdbpplus/; the UniSwiss data set and stand-alone program of TargetDBP+ are accessible at https://github.com/jun-csbio/TargetDBPplus.

DBP-PSSM: Combination of Evolutionary Profiles with the XGBoost Algorithm to Improve the Identification of DNA-binding Proteins

BACKGROUND AND OBJECTIVE

DNA-binding proteins play important roles in a variety of biological processes, such as gene transcription and regulation, DNA replication and repair, DNA recombination and packaging, and the formation of chromatin and ribosomes. Therefore, it is urgent to develop a computational method to improve the recognition efficiency of DNA-binding proteins.

METHODS

We proposed a novel method, DBP-PSSM, which constructed the features from amino acid composition and evolutionary information of protein sequences. The maximum relevance, minimum redundancy (mRMR) was employed to select the optimal features for establishing the XGBoost classifier, therefore, the novel model of prediction DNA-binding proteins, DBP-PSSM, was established with 5-fold cross-validation on the training dataset.

RESULTS

DBP-PSSM achieved an accuracy of 81.18% and MCC of 0.657 in a test dataset, which outperformed the many existing methods. These results demonstrated that our method can effectively predict DNA-binding proteins.

CONCLUSION

The data and source code are provided at https://github.com/784221489/DNA-binding.

TargetDBP: Accurate DNA-Binding Protein Prediction Via Sequence-Based Multi-View Feature Learning

Accurately identifying DNA-binding proteins (DBPs) from protein sequence information is an important but challenging task for protein function annotations. In this paper, we establish a novel computational method, named TargetDBP, for accurately targeting DBPs from primary sequences. In TargetDBP, four single-view features, i.e., AAC (Amino Acid Composition), PsePSSM (Pseudo Position-Specific Scoring Matrix), PsePRSA (Pseudo Predicted Relative Solvent Accessibility), and PsePPDBS (Pseudo Predicted Probabilities of DNA-Binding Sites), are first extracted to represent different base features, respectively. Second, differential evolution algorithm is employed to learn the weights of four base features. Using the learned weights, we weightedly combine these base features to form the original super feature. An excellent subset of the super feature is then selected by using a suitable feature selection algorithm SVM-REF+CBR (Support Vector Machine Recursive Feature Elimination with Correlation Bias Reduction). Finally, the prediction model is learned via using support vector machine on the selected feature subset. We also construct a new gold-standard and non-redundant benchmark dataset from PDB database to evaluate and compare the proposed TargetDBP with other existing predictors. On this new dataset, TargetDBP can achieve higher performance than other state-of-the-art predictors. The TargetDBP web server and datasets are freely available at http://csbio.njust.edu.cn/bioinf/targetdbp/ for academic use.

An Insightful 10-year Recollection Since the Emergence of the 5-steps Rule

OBJECTIVE

One of the most challenging and also the most difficult problems is how to formulate a biological sequence with a vector but considerably keep its sequence order information.

METHODS

To address such a problem, the approach of Pseudo Amino Acid Components or PseAAC has been developed.

RESULTS AND CONCLUSION

It has become increasingly clear via the 10-year recollection that the aforementioned proposal has been indeed very powerful.

Sequence-based Detection of DNA-binding Proteins using Multiple-view Features Allied with Feature Selection

DNA-binding proteins play essential roles in many molecular functions and gene regulation. Therefore, it becomes highly desirable to develop effective computational techniques for detecting DNA-binding proteins. In this paper, we proposed a new method, iDBP-DEP, which performs DNA-binding prediction by using the discriminative feature derived from multi-view feature sources including evolutionary profile, dipeptide composition, and physicochemical properties with feature selection. We evaluated iDBP-DEP on two benchmark datasets, i. e., PDB1075 and PDB594 by rigorous Jackknife test. Compared with the state-of-the-art sequence-based DNA-binding predictors, the proposed iDBP-DEP achieved 1.8 % and 3.0 % improvements of accuracy (Acc) and Mathew's Correlation Coefficient (MCC), respectively, on PDB1075 dataset; 7.4 % and 14.8 % improvements of Acc and MCC, respectively, on PDB594. The independent validation test with PDB186 show that the proposed method achieved the best performances on Acc (80.1 %) and MCC (0.684), which further demonstrated the robustness of iDBP-DEP for the detection of DNA-binding proteins. Datasets and codes used in this study are freely available at https://githup.com/Zll-codeside/iDBP-DEP.

Some illuminating remarks on molecular genetics and genomics as well as drug development

Facing the explosive growth of biological sequences unearthed in the post-genomic age, one of the most important but also most difficult problems in computational biology is how to express a biological sequence with a discrete model or a vector, but still keep it with considerable sequence-order information or its special pattern. To deal with such a challenging problem, the ideas of "pseudo amino acid components" and "pseudo K-tuple nucleotide composition" have been proposed. The ideas and their approaches have further stimulated the birth for "distorted key theory", "wenxing diagram", and substantially strengthening the power in treating the multi-label systems, as well as the establishment of the famous "5-steps rule". All these logic developments are quite natural that are very useful not only for theoretical scientists but also for experimental scientists in conducting genetics/genomics analysis and drug development. Presented in this review paper are also their future perspectives; i.e., their impacts will become even more significant and propounding.

A Review of DNA-binding Proteins Prediction Methods

Background:

DNA-binding proteins, binding to DNA, widely exist in living cells, participating in many cell activities. They can participate some DNA-related cell activities, for instance DNA replication, transcription, recombination, and DNA repair.

Objective:

Given the importance of DNA-binding proteins, studies for predicting the DNA-binding proteins have been a popular issue over the past decades. In this article, we review current machine-learning methods which research on the prediction of DNA-binding proteins through feature representation methods, classifiers, measurements, dataset and existing web server.

Method:

The prediction methods of DNA-binding protein can be divided into two types, based on amino acid composition and based on protein structure. In this article, we accord to the two types methods to introduce the application of machine learning in DNA-binding proteins prediction.

Results:

Machine learning plays an important role in the classification of DNA-binding proteins, and the result is better. The best ACC is above 80%.

Conclusion:

Machine learning can be widely used in many aspects of biological information, especially in protein classification. Some issues should be considered in future work. First, the relationship between the number of features and performance must be explored. Second, many features are used to predict DNA-binding proteins and propose solutions for high-dimensional spaces.

Effective DNA binding protein prediction by using key features via Chou's general PseAAC

DNA-binding proteins (DBPs) are responsible for several cellular functions, starting from our immunity system to the transport of oxygen. In the recent studies, scientists have used supervised machine learning based methods that use information from the protein sequence only to classify the DBPs. Most of the methods work effectively on the train sets but performance of most of them degrades in the independent test set. It shows a room for improving the prediction method by reducing over-fitting. In this paper, we have extracted several features solely using the protein sequence and carried out two different types of feature selection on them. Our results have proven comparable on training set and significantly improved on the independent test set. On the independent test set our accuracy was 82.26% which is 1.62% improved compared to the previous best state-of-the-art methods. Performance in terms of sensitivity and area under receiver operating characteristic curve for the independent test set was also higher and they were 0.95 and 0.823 respectively.

DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC

A DNA-binding protein (DNA-BP) is a protein that can bind and interact with a DNA. Identification of DNA-BPs using experimental methods is expensive as well as time consuming. As such, fast and accurate computational methods are sought for predicting whether a protein can bind with a DNA or not. In this paper, we focus on building a new computational model to identify DNA-BPs in an efficient and accurate way. Our model extracts meaningful information directly from the protein sequences, without any dependence on functional domain or structural information. After feature extraction, we have employed Random Forest (RF) model to rank the features. Afterwards, we have used Recursive Feature Elimination (RFE) method to extract an optimal set of features and trained a prediction model using Support Vector Machine (SVM) with linear kernel. Our proposed method, named as DNA-binding Protein Prediction model using Chou's general PseAAC (DPP-PseAAC), demonstrates superior performance compared to the state-of-the-art predictors on standard benchmark dataset. DPP-PseAAC achieves accuracy values of 93.21%, 95.91% and 77.42% for 10-fold cross-validation test, jackknife test and independent test respectively. The source code of DPP-PseAAC, along with relevant dataset and detailed experimental results, can be found at https://github.com/srautonu/DNABinding. A publicly accessible web interface has also been established at: http://77.68.43.135:8080/DPP-PseAAC/.

HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features

DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

iDNAProt-ES: Identification of DNA-binding Proteins Using Evolutionary and Structural Features

DNA-binding proteins play a very important role in the structural composition of the DNA. In addition, they regulate and effect various cellular processes like transcription, DNA replication, DNA recombination, repair and modification. The experimental methods used to identify DNA-binding proteins are expensive and time consuming and thus attracted researchers from computational field to address the problem. In this paper, we present iDNAProt-ES, a DNA-binding protein prediction method that utilizes both sequence based evolutionary and structure based features of proteins to identify their DNA-binding functionality. We used recursive feature elimination to extract an optimal set of features and train them using Support Vector Machine (SVM) with linear kernel to select the final model. Our proposed method significantly outperforms the existing state-of-the-art predictors on standard benchmark dataset. The accuracy of the predictor is 90.18% using jack knife test and 88.87% using 10-fold cross validation on the benchmark dataset. The accuracy of the predictor on the independent dataset is 80.64% which is also significantly better than the state-of-the-art methods. iDNAProt-ES is a novel prediction method that uses evolutionary and structural based features. We believe the superior performance of iDNAProt-ES will motivate the researchers to use this method to identify DNA-binding proteins. iDNAProt-ES is publicly available as a web server at: http://brl.uiu.ac.bd/iDNAProt-ES/.

Improved detection of DNA-binding proteins via compression technology on PSSM information

Since the importance of DNA-binding proteins in multiple biomolecular functions has been recognized, an increasing number of researchers are attempting to identify DNA-binding proteins. In recent years, the machine learning methods have become more and more compelling in the case of protein sequence data soaring, because of their favorable speed and accuracy. In this paper, we extract three features from the protein sequence, namely NMBAC (Normalized Moreau-Broto Autocorrelation), PSSM-DWT (Position-specific scoring matrix—Discrete Wavelet Transform), and PSSM-DCT (Position-specific scoring matrix—Discrete Cosine Transform). We also employ feature selection algorithm on these feature vectors. Then, these features are fed into the training SVM (support vector machine) model as classifier to predict DNA-binding proteins. Our method applys three datasets, namely PDB1075, PDB594 and PDB186, to evaluate the performance of our approach. The PDB1075 and PDB594 datasets are employed for Jackknife test and the PDB186 dataset is used for the independent test. Our method achieves the best accuracy in the Jacknife test, from 79.20% to 86.23% and 80.5% to 86.20% on PDB1075 and PDB594 datasets, respectively. In the independent test, the accuracy of our method comes to 76.3%. The performance of independent test also shows that our method has a certain ability to be effectively used for DNA-binding protein prediction. The data and source code are at https://doi.org/10.6084/m9.figshare.5104084.

PSFM-DBT: Identifying DNA-Binding Proteins by Combing Position Specific Frequency Matrix and Distance-Bigram Transformation

DNA-binding proteins play crucial roles in various biological processes, such as DNA replication and repair, transcriptional regulation and many other biological activities associated with DNA. Experimental recognition techniques for DNA-binding proteins identification are both time consuming and expensive. Effective methods for identifying these proteins only based on protein sequences are highly required. The key for sequence-based methods is to effectively represent protein sequences. It has been reported by various previous studies that evolutionary information is crucial for DNA-binding protein identification. In this study, we employed four methods to extract the evolutionary information from Position Specific Frequency Matrix (PSFM), including Residue Probing Transformation (RPT), Evolutionary Difference Transformation (EDT), Distance-Bigram Transformation (DBT), and Trigram Transformation (TT). The PSFMs were converted into fixed length feature vectors by these four methods, and then respectively combined with Support Vector Machines (SVMs); four predictors for identifying these proteins were constructed, including PSFM-RPT, PSFM-EDT, PSFM-DBT, and PSFM-TT. Experimental results on a widely used benchmark dataset PDB1075 and an independent dataset PDB186 showed that these four methods achieved state-of-the-art-performance, and PSFM-DBT outperformed other existing methods in this field. For practical applications, a user-friendly webserver of PSFM-DBT was established, which is available at http://bioinformatics.hitsz.edu.cn/PSFM-DBT/.

DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues

DNA-binding proteins are fundamentally important in cellular processes. Several computational-based methods have been developed to improve the prediction of DNA-binding proteins in previous years. However, insufficient work has been done on the prediction of DNA-binding proteins from protein sequence information. In this paper, a novel predictor, DNABP (DNA-binding proteins), was designed to predict DNA-binding proteins using the random forest (RF) classifier with a hybrid feature. The hybrid feature contains two types of novel sequence features, which reflect information about the conservation of physicochemical properties of the amino acids, and the binding propensity of DNA-binding residues and non-binding propensities of non-binding residues. The comparisons with each feature demonstrated that these two novel features contributed most to the improvement in predictive ability. Furthermore, to improve the prediction performance of the DNABP model, feature selection using the minimum redundancy maximum relevance (mRMR) method combined with incremental feature selection (IFS) was carried out during the model construction. The results showed that the DNABP model could achieve 86.90% accuracy, 83.76% sensitivity, 90.03% specificity and a Matthews correlation coefficient of 0.727. High prediction accuracy and performance comparisons with previous research suggested that DNABP could be a useful approach to identify DNA-binding proteins from sequence information. The DNABP web server system is freely available at http://www.cbi.seu.edu.cn/DNABP/.

Prediction of G-protein coupled receptors and their subfamilies by incorporating various sequence features into Chou's general PseAAC

BACKGROUND AND OBJECTIVE

The G-protein coupled receptors are the largest superfamilies of membrane proteins and important targets for the drug design. G-protein coupled receptors are responsible for many physiochemical processes such as smell, taste, vision, neurotransmission, metabolism, cellular growth and immune response. So it is necessary to design a robust and efficient approach for the prediction of G-protein coupled receptors and their subfamilies.

METHODS

In this paper, the protein samples are represented by amino acid composition, dipeptide composition, correlation features, composition, transition, distribution, sequence order descriptors and pseudo amino acid composition with total 1497 number of sequence derived features. To address the issue of efficient classification of G-protein coupled receptors and their subfamilies, we propose to use a weighted k-nearest neighbor classifier with UNION of best 50 features, selected by Fisher score based feature selection, ReliefF, fast correlation based filter, minimum redundancy maximum relevancy, and support vector machine based recursive elimination feature selection methods to exploit the advantages of these feature selection methods.

RESULTS

The proposed method achieved an overall accuracy of 99.9%, 98.3%, 95.4%, MCC values of 1.00, 0.98, 0.95, ROC area values of 1.00, 0.998, 0.996 and precision of 99.9%, 98.3% and 95.5% using 10-fold cross-validation to predict the G-protein coupled receptors and non-G-protein coupled receptors, subfamilies of G-protein coupled receptors, and subfamilies of class A G-protein coupled receptors, respectively.

CONCLUSIONS

The high accuracies, MCC, ROC area values, and precision values indicate that the proposed method is better for the prediction of G-protein coupled receptors families and their subfamilies.

gDNA-Prot: Predict DNA-binding proteins by employing support vector machine and a novel numerical characterization of protein sequence

DNA-binding proteins are the functional proteins in cells, which play an important role in various essential biological activities. An effective and fast computational method gDNA-Prot is proposed to predict DNA-binding proteins in this paper, which is a DNA-binding predictor that combines the support vector machine classifier and a novel kind of feature called graphical representation. The DNA-binding protein sequence information was described with the 20 probabilities of amino acids and the 23 new numerical graphical representation features of a protein sequence, based on 23 physicochemical properties of 20 amino acids. The Principal Components Analysis (PCA) was employed as feature selection method for removing the irrelevant features and reducing redundant features. The Sigmod function and Min-max normalization methods for PCA were applied to accelerate the training speed and obtain higher accuracy. Experiments demonstrated that the Principal Components Analysis with Sigmod function generated the best performance. The gDNA-Prot method was also compared with the DNAbinder, iDNA-Prot and DNA-Prot. The results suggested that gDNA-Prot outperformed the DNAbinder and iDNA-Prot. Although the DNA-Prot outperformed gDNA-Prot, gDNA-Prot was faster and convenient to predict the DNA-binding proteins. Additionally, the proposed gNDA-Prot method is available at http://sourceforge.net/projects/gdnaprot.

DNA-binding protein prediction using plant specific support vector machines: validation and application of a new genome annotation tool

There are currently 151 plants with draft genomes available but levels of functional annotation for putative protein products are low. Therefore, accurate computational predictions are essential to annotate genomes in the first instance, and to provide focus for the more costly and time consuming functional assays that follow. DNA-binding proteins are an important class of proteins that require annotation, but current computational methods are not applicable for genome wide predictions in plant species. Here, we explore the use of species and lineage specific models for the prediction of DNA-binding proteins in plants. We show that a species specific support vector machine model based on Arabidopsis sequence data is more accurate (accuracy 81%) than a generic model (74%), and based on this we develop a plant specific model for predicting DNA-binding proteins. We apply this model to the tomato proteome and demonstrate its ability to perform accurate high-throughput prediction of DNA-binding proteins. In doing so, we have annotated 36 currently uncharacterised proteins by assigning a putative DNA-binding function. Our model is publically available and we propose it be used in combination with existing tools to help increase annotation levels of DNA-binding proteins encoded in plant genomes.

Classification of membrane protein types using Voting Feature Interval in combination with Chou's Pseudo Amino Acid Composition

Membrane protein is a major constituent of cell, performing numerous crucial functions in the cell. These functions are mostly concerned with membrane protein's types. Initially, membrane proteins types are classified through traditional methods and reasonable results were obtained using these methods. However, due to large exploration of protein sequences in databases, it is very difficult or sometimes impossible to classify through conventional methods, because it is laborious and wasting of time. Therefore, a new powerful discriminating model is indispensable for classification of membrane protein's types with high precision. In this work, a quite promising classification model is developed having effective discriminating power of membrane protein's types. In our classification model, silent features of protein sequences are extracted via Pseudo Amino Acid Composition. Five classification algorithms were utilized. Among these classification algorithms Voting Feature Interval has obtained outstanding performance in all the three datasets. The accuracy of proposed model is 93.9% on dataset S1, 89.33% on S2 and 86.9% on dataset S3, respectively, applying 10-fold cross validation test. The success rates revealed that our proposed model has obtained the utmost outcomes than other existing models in literatures so far and will be played a substantial role in the fields of drug design and pharmaceutical industry.

An efficient approach for the prediction of ion channels and their subfamilies

Ion channels are integral membrane proteins that are responsible for controlling the flow of ions across the cell. There are various biological functions that are performed by different types of ion channels. Therefore for new drug discovery it is necessary to develop a novel computational intelligence techniques based approach for the reliable prediction of ion channels families and their subfamilies. In this paper random forest based approach is proposed to predict ion channels families and their subfamilies by using sequence derived features. Here, seven feature vectors are used to represent the protein sample, including amino acid composition, dipeptide composition, correlation features, composition, transition and distribution and pseudo amino acid composition. The minimum redundancy and maximum relevance feature selection is used to find the optimal number of features for improving the prediction performance. The proposed method achieved an overall accuracy of 100%, 98.01%, 91.5%, 93.0%, 92.2%, 78.6%, 95.5%, 84.9%, MCC values of 1.00, 0.92, 0.88, 0.88, 0.90, 0.79, 0.91, 0.81 and ROC area values of 1.00, 0.99, 0.99, 0.99, 0.99, 0.95, 0.99 and 0.96 using 10-fold cross validation to predict the ion channels and non-ion channels, voltage gated ion channels and ligand gated ion channels, four subfamilies (calcium, potassium, sodium and chloride) of voltage gated ion channels, and four subfamilies of ligand gated ion channels and predict subfamilies of voltage gated calcium, potassium, sodium and chloride ion channels respectively.

Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data

Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented.

MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multi-label classifier

MOTIVATION

Identifying protein subchloroplast localization in chloroplast organelle is very helpful for understanding the function of chloroplast proteins. There have existed a few computational prediction methods for protein subchloroplast localization. However, these existing works have ignored proteins with multiple subchloroplast locations when constructing prediction models, so that they can predict only one of all subchloroplast locations of this kind of multilabel proteins.

RESULTS

To address this problem, through utilizing label-specific features and label correlations simultaneously, a novel multilabel classifier was developed for predicting protein subchloroplast location(s) with both single and multiple location sites. As an initial study, the overall accuracy of our proposed algorithm reaches 55.52%, which is quite high to be able to become a promising tool for further studies.

AVAILABILITY AND IMPLEMENTATION

An online web server for our proposed algorithm named MultiP-SChlo was developed, which are freely accessible at http://biomed.zzuli.edu.cn/bioinfo/multip-schlo/.

CONTACT

pandaxiaoxi@gmail.com or gzli@tongji.edu.cn

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation

BACKGROUND

DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation. There have been several computational methods proposed in the literature to deal with the DNA-binding protein identification. However, most of them can't provide an invaluable knowledge base for our understanding of DNA-protein interactions.

RESULTS

We firstly presented a new protein sequence encoding method called PSSM Distance Transformation, and then constructed a DNA-binding protein identification method (SVM-PSSM-DT) by combining PSSM Distance Transformation with support vector machine (SVM). First, the PSSM profiles are generated by using the PSI-BLAST program to search the non-redundant (NR) database. Next, the PSSM profiles are transformed into uniform numeric representations appropriately by distance transformation scheme. Lastly, the resulting uniform numeric representations are inputted into a SVM classifier for prediction. Thus whether a sequence can bind to DNA or not can be determined. In benchmark test on 525 DNA-binding and 550 non DNA-binding proteins using jackknife validation, the present model achieved an ACC of 79.96%, MCC of 0.622 and AUC of 86.50%. This performance is considerably better than most of the existing state-of-the-art predictive methods. When tested on a recently constructed independent dataset PDB186, SVM-PSSM-DT also achieved the best performance with ACC of 80.00%, MCC of 0.647 and AUC of 87.40%, and outperformed some existing state-of-the-art methods.

CONCLUSIONS

The experiment results demonstrate that PSSM Distance Transformation is an available protein sequence encoding method and SVM-PSSM-DT is a useful tool for identifying the DNA-binding proteins. A user-friendly web-server of SVM-PSSM-DT was constructed, which is freely accessible to the public at the web-site on http://bioinformatics.hitsz.edu.cn/PSSM-DT/.

Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences

With the avalanche of DNA/RNA sequences generated in the post-genomic age, it is urgent to develop automated methods for analyzing the relationship between the sequences and their functions.

A survey of computational intelligence techniques in protein function prediction

During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction.