DNA-binding Residues In Proteins Research Articles

A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen–Shannon Divergence

The knowledge of protein-DNA interactions is essential to fully understand the molecular activities of life. Many research groups have developed various tools which are either structure- or sequence-based approaches to predict the DNA-binding residues in proteins. The structure-based methods usually achieve good results, but require the knowledge of the 3D structure of protein; while sequence-based methods can be applied to high-throughput of proteins, but require good features. In this study, we present a new information theoretic feature derived from Jensen–Shannon Divergence (JSD) between amino acid distribution of a site and the background distribution of non-binding sites. Our new feature indicates the difference of a certain site from a non-binding site, thus it is informative for detecting binding sites in proteins. We conduct the study with a five-fold cross validation of 263 proteins utilizing the Random Forest classifier. We evaluate the functionality of our new features by combining them with other popular existing features such as position-specific scoring matrix (PSSM), orthogonal binary vector (OBV), and secondary structure (SS). We notice that by adding our features, we can significantly boost the performance of Random Forest classifier, with a clear increment of sensitivity and Matthews correlation coefficient (MCC).

RBscore&NBench: a high-level web server for nucleic acid binding residues prediction with a large-scale benchmarking database.

RBscore&NBench combines a web server, RBscore and a database, NBench. RBscore predicts RNA-/DNA-binding residues in proteins and visualizes the prediction scores and features on protein structures. The scoring scheme of RBscore directly links feature values to nucleic acid binding probabilities and illustrates the nucleic acid binding energy funnel on the protein surface. To avoid dataset, binding site definition and assessment metric biases, we compared RBscore with 18 web servers and 3 stand-alone programs on 41 datasets, which demonstrated the high and stable accuracy of RBscore. A comprehensive comparison led us to develop a benchmark database named NBench. The web server is available on: http://ahsoka.u-strasbg.fr/rbscorenbench/.

Predicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome.

As more and more protein sequences are uncovered from increasingly inexpensive sequencing techniques, an urgent task is to find their functions. This work presents a highly reliable computational technique for predicting DNA-binding function at the level of protein-DNA complex structures, rather than low-resolution two-state prediction of DNA-binding as most existing techniques do. The method first predicts protein-DNA complex structure by utilizing the template-based structure prediction technique HHblits, followed by binding affinity prediction based on a knowledge-based energy function (Distance-scaled finite ideal-gas reference state for protein-DNA interactions). A leave-one-out cross validation of the method based on 179 DNA-binding and 3797 non-binding protein domains achieves a Matthews correlation coefficient (MCC) of 0.77 with high precision (94%) and high sensitivity (65%). We further found 51% sensitivity for 82 newly determined structures of DNA-binding proteins and 56% sensitivity for the human proteome. In addition, the method provides a reasonably accurate prediction of DNA-binding residues in proteins based on predicted DNA-binding complex structures. Its application to human proteome leads to more than 300 novel DNA-binding proteins; some of these predicted structures were validated by known structures of homologous proteins in APO forms. The method [SPOT-Seq (DNA)] is available as an on-line server at http://sparks-lab.org.

Novel approach for selecting the best predictor for identifying the binding sites in DNA binding proteins

Protein–DNA complexes play vital roles in many cellular processes by the interactions of amino acids with DNA. Several computational methods have been developed for predicting the interacting residues in DNA-binding proteins using sequence and/or structural information. These methods showed different levels of accuracies, which may depend on the choice of data sets used in training, the feature sets selected for developing a predictive model, the ability of the models to capture information useful for prediction or a combination of these factors. In many cases, different methods are likely to produce similar results, whereas in others, the predictors may return contradictory predictions. In this situation, a priori estimates of prediction performance applicable to the system being investigated would be helpful for biologists to choose the best method for designing their experiments. In this work, we have constructed unbiased, stringent and diverse data sets for DNA-binding proteins based on various biologically relevant considerations: (i) seven structural classes, (ii) 86 folds, (iii) 106 superfamilies, (iv) 194 families, (v) 15 binding motifs, (vi) single/double-stranded DNA, (vii) DNA conformation (A, B, Z, etc.), (viii) three functions and (ix) disordered regions. These data sets were culled as non-redundant with sequence identities of 25 and 40% and used to evaluate the performance of 11 different methods in which online services or standalone programs are available. We observed that the best performing methods for each of the data sets showed significant biases toward the data sets selected for their benchmark. Our analysis revealed important data set features, which could be used to estimate these context-specific biases and hence suggest the best method to be used for a given problem. We have developed a web server, which considers these features on demand and displays the best method that the investigator should use. The web server is freely available at http://www.biotech.iitm.ac.in/DNA-protein/. Further, we have grouped the methods based on their complexity and analyzed the performance. The information gained in this work could be effectively used to select the best method for designing experiments.

Sequence-Based Prediction of DNA-Binding Residues in Proteins with Conservation and Correlation Information

The recognition of DNA-binding residues in proteins is critical to our understanding of the mechanisms of DNA-protein interactions, gene expression, and for guiding drug design. Therefore, a prediction method DNABR (DNA Binding Residues) is proposed for predicting DNA-binding residues in protein sequences using the random forest (RF) classifier with sequence-based features. Two types of novel sequence features are proposed in this study, which reflect the information about the conservation of physicochemical properties of the amino acids, and the correlation of amino acids between different sequence positions in terms of physicochemical properties. The first type of feature uses the evolutionary information combined with the conservation of physicochemical properties of the amino acids while the second reflects the dependency effect of amino acids with regards to polarity charge and hydrophobic properties in the protein sequences. Those two features and an orthogonal binary vector which reflect the characteristics of 20 types of amino acids are used to build the DNABR, a model to predict DNA-binding residues in proteins. The DNABR model achieves a value of 0.6586 for Matthew’s correlation coefficient (MCC) and 93.04 percent overall accuracy (ACC) with a68.47 percent sensitivity (SE) and 98.16 percent specificity (SP), respectively. The comparisons with each feature demonstrate that these two novel features contribute most to the improvement in predictive ability. Furthermore, performance comparisons with other approaches clearly show that DNABR has an excellent prediction performance for detecting binding residues in putative DNA-binding protein. The DNABR web-server system is freely available at http://www.cbi.seu.edu.cn/DNABR/.

Prediction of DNA-binding residues from protein sequence information using random forests

BackgroundProtein-DNA interactions are involved in many biological processes essential for cellular function. To understand the molecular mechanism of protein-DNA recognition, it is necessary to identify the DNA-binding residues in DNA-binding proteins. However, structural data are available for only a few hundreds of protein-DNA complexes. With the rapid accumulation of sequence data, it becomes an important but challenging task to accurately predict DNA-binding residues directly from amino acid sequence data.ResultsA new machine learning approach has been developed in this study for predicting DNA-binding residues from amino acid sequence data. The approach used both the labelled data instances collected from the available structures of protein-DNA complexes and the abundant unlabeled data found in protein sequence databases. The evolutionary information contained in the unlabeled sequence data was represented as position-specific scoring matrices (PSSMs) and several new descriptors. The sequence-derived features were then used to train random forests (RFs), which could handle a large number of input variables and avoid model overfitting. The use of evolutionary information was found to significantly improve classifier performance. The RF classifier was further evaluated using a separate test dataset, and the predicted DNA-binding residues were examined in the context of three-dimensional structures.ConclusionThe results suggest that the RF-based approach gives rise to more accurate prediction of DNA-binding residues than previous studies. A new web server called BindN-RF has thus been developed to make the RF classifier accessible to the biological research community.

Prediction of DNA-binding residues in proteins from amino acid sequences using a random forest model with a hybrid feature

Motivation: In this work, we aim to develop a computational approach for predicting DNA-binding sites in proteins from amino acid sequences. To avoid overfitting with this method, all available DNA-binding proteins from the Protein Data Bank (PDB) are used to construct the models. The random forest (RF) algorithm is used because it is fast and has robust performance for different parameter values. A novel hybrid feature is presented which incorporates evolutionary information of the amino acid sequence, secondary structure (SS) information and orthogonal binary vector (OBV) information which reflects the characteristics of 20 kinds of amino acids for two physical–chemical properties (dipoles and volumes of the side chains). The numbers of binding and non-binding residues in proteins are highly unbalanced, so a novel scheme is proposed to deal with the problem of imbalanced datasets by downsizing the majority class.Results: The results show that the RF model achieves 91.41% overall accuracy with Matthew's correlation coefficient of 0.70 and an area under the receiver operating characteristic curve (AUC) of 0.913. To our knowledge, the RF method using the hybrid feature is currently the computationally optimal approach for predicting DNA-binding sites in proteins from amino acid sequences without using three-dimensional (3D) structural information. We have demonstrated that the prediction results are useful for understanding protein–DNA interactions.Availability: DBindR web server implementation is freely available at http://www.cbi.seu.edu.cn/DBindR/DBindR.htm.Contact: xsun@seu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.

Protein–DNA interactions: structural, thermodynamic and clustering patterns of conserved residues in DNA-binding proteins

Amino acid residues, which play important roles in protein function, are often conserved. Here, we analyze thermodynamic and structural data of protein–DNA interactions to explore a relationship between free energy, sequence conservation and structural cooperativity. We observe that the most stabilizing residues or putative hotspots are those which occur as clusters of conserved residues. The higher packing density of the clusters and available experimental thermodynamic data of mutations suggest cooperativity between conserved residues in the clusters. Conserved singlets contribute to the stability of protein–DNA complexes to a lesser extent. We also analyze structural features of conserved residues and their clusters and examine their role in identifying DNA-binding sites. We show that about half of the observed conserved residue clusters are in the interface with the DNA, which could be identified from their amino acid composition; whereas the remaining clusters are at the protein–protein or protein–ligand interface, or embedded in the structural scaffolds. In protein–protein interfaces, conserved residues are highly correlated with experimental residue hotspots, contributing dominantly and often cooperatively to the stability of protein–protein complexes. Overall, the conservation patterns of the stabilizing residues in DNA-binding proteins also highlight the significance of clustering as compared to single residue conservation.

On the Accuracy of Sequence-Based Computational Inference of Protein Residues Involved in Interactions with DNA

Methods for computational inference of DNA-binding residues in DNA-binding proteins are usually developed using classification techniques trained to distinguish between binding and non-binding residues on the basis of known examples observed in experimentally determined high-resolution structures of protein-DNA complexes. What degree of accuracy can be expected when a computational methods is applied to a particular novel protein remains largely unknown. We test the utility of classification methods on the example of Kernel Logistic Regression (KLR) predictors of DNA-binding residues. We show that predictors that utilize sequence properties of proteins can successfully predict DNA-binding residues in proteins from a novel structural class. We use Multiple Linear Regression (MLR) to establish a quantitative relationship between protein properties and the expected accuracy of KLR predictors. Present results indicate that in the case of novel proteins the expected accuracy provided by an MLR model is close to the actual accuracy and can be used to assess the overall confidence of the prediction.

DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins

This article describes DP-Bind, a web server for predicting DNA-binding sites in a DNA-binding protein from its amino acid sequence. The web server implements three machine learning methods: support vector machine, kernel logistic regression and penalized logistic regression. Prediction can be performed using either the input sequence alone or an automatically generated profile of evolutionary conservation of the input sequence in the form of PSI-BLAST position-specific scoring matrix (PSSM). PSSM-based kernel logistic regression achieves the accuracy of 77.2%, sensitivity of 76.4% and specificity of 76.6%. The outputs of all three individual methods are combined into a consensus prediction to help identify positions predicted with high level of confidence. Freely available at http://lcg.rit.albany.edu/dp-bind. http://lcg.rit.albany.edu/dp-bind/dpbind_supplement.html.

PREDICTION OF DNA-BINDING RESIDUES FROM SEQUENCE FEATURES

Protein-DNA interaction plays a pivotal role in transcriptional regulation, DNA metabolism and chromatin formation. Although structural data are available for a few hundreds of protein-DNA complexes, the molecular recognition pattern is still poorly understood. With the rapid accumulation of sequence data from many genomes, it is important to develop predictive methods for identification of potential DNA-binding residues in proteins. In this study, neural networks have been trained using five sequence-derived features for prediction of DNA-binding residues. These features include the molecular mass, hydrophobicity index, side chain pKa value, solvent accessible surface area and conservation score of an amino acid. Interestingly, the side chain pKa value appears to be the best feature for prediction, suggesting that the ionization state of amino acid side chains is important for DNA-binding. The predictive performance is enhanced by using multiple features for classifier construction. The classifier that has been constructed using all the five features predicts at 72.71% sensitivity and 67.73% specificity. This is by far the most accurate classifier reported for prediction of DNA-binding residues from sequence data. The classifier has also been evaluated by using the Receiver Operating Characteristic curve and by examining the predictions made for different classes of DNA-binding proteins. Supplementary materials including the datasets are available at http://bioinformatics.ksu.edu/pdi/feature.html.

Cationic peptides containing tyrosine protect against radiation-induced oxidative DNA damage

Purpose: To examine the effect of the amino acid tyrosine on oxidatively or direct-type damaged DNA damage when it is present in a DNA binding ligand.Materials and methods: We made use of tetralysine ligands to ensure binding to DNA and to condense the DNA, and simulated direct-type damage by using gamma irradiation in the presence of thiocyanate ions. These ligands contained an additional C terminal amino acid. Phenylalanine was used as a control for tyrosine. These ligands were used in conjuction with a plasmid substrate to quantify strand break yields. Base damage yields were estimated by measuring the strand break yield after incubation of the plasmid with the bacterial base excision repair enzyme formamidopyrimidine-DNA N-glycosylase (FPG).Results: When the condensing ligand contains an additional tyrosine or tryptophan residue, the plasmid is protected against the effects of a single electron oxidation, as assayed by sensitivity to a base excision repair enzyme. This protection is significantly greater in condensed plasmid where the amino acid residues are in close proximity to the DNA, and can be observed even when only a small fraction of the ligand contains tyrosine.Conclusions: Bound tyrosine residues located in close proximity to DNA are capable of reversing oxidative DNA damage far more efficiently than when present unbound in the bulk solution. This suggests that tyrosine residues in DNA binding proteins may participate in the repair of DNA that has been oxidatively damaged by ionizing radiation.