Volume 10 Supplement 3
DNA-binding residues and binding mode prediction with binding-mechanism concerned models
© Huang et al; licensee BioMed Central Ltd. 2009
Published: 3 December 2009
Protein-DNA interactions are essential for fundamental biological activities including DNA transcription, replication, packaging, repair and rearrangement. Proteins interacting with DNA can be classified into two categories of binding mechanisms - sequence-specific and non-specific binding. Protein-DNA specific binding provides a mechanism to recognize correct nucleotide base pairs for sequence-specific identification. Protein-DNA non-specific binding shows sequence independent interaction for accelerated targeting by interacting with DNA backbone. Both sequence-specific and non-specific binding residues contribute to their roles for interaction.
The proposed framework has two stage predictors: DNA-binding residues prediction and binding mode prediction. In the first stage - DNA-binding residues prediction, the predictor for DNA specific binding residues achieves 96.45% accuracy with 50.14% sensitivity, 99.31% specificity, 81.70% precision, and 62.15% F-measure. The predictor for DNA non-specific binding residues achieves 89.14% accuracy with 53.06% sensitivity, 95.25% specificity, 65.47% precision, and 58.62% F-measure. While combining prediction results of sequence-specific and non-specific binding residues with OR operation, the predictor achieves 89.26% accuracy with 56.86% sensitivity, 95.63% specificity, 71.92% precision, and 63.51% F-measure. In the second stage, protein-DNA binding mode prediction achieves 75.83% accuracy while using support vector machine with multi-class prediction.
This article presents the design of a sequence based predictor aiming to identify sequence-specific and non-specific binding residues in a transcription factor with DNA binding-mechanism concerned. The protein-DNA binding mode prediction was introduced to help improve DNA-binding residues prediction. In addition, the results of this study will help with the design of binding-mechanism concerned predictors for other families of proteins interacting with DNA.
Protein-DNA interactions play important roles for the regulation of key biological functions like DNA transcription, replication, packaging and recombination. With the increasing number of high quality structure of complexes in Protein Data Bank (PDB)  and Nucleic Acid Database (NDB) , the collection of atomic interaction information for their structural and functional integrity is sufficiently complete for analysis and prediction of protein-nucleic acid interactions. Atomic level analyses have been investigated to understand how amino acids interact with nucleotide bases or sugar-phosphate backbones through hydrogen bonds, van der Waals contacts, or water-mediated hydrogen bonds , depending on the amino acid propensities [4, 5]. In recent years, the prediction of residues in a protein chain that interact with DNA has been a research topic that attracts a high level of interest. Some of the studies were purely based on analysis of the protein polypeptide sequence [6–11], while the others took the structural information into account [12–17]. Particularly, the issue for sequence-specific binding residue prediction has been also mentioned recently . Transcription factors (TFs) are proteins that regulate gene expression, which serve as integration centers of the different signal-transduction pathways affecting a given gene . TFs regulate cell development, differentiation, and cell growth by binding to a specific DNA site and regulating gene expression [20–22]. As it has been reported in a recent article that the tertiary structures of a large number of TFs are mostly disordered , sequence based analysis aimed at identifying the residues in a highly-disordered TF that play key roles in interaction with the DNA is essential for obtaining a comprehensive picture of how TFs function.
As studied in previous research, proteins that interact with DNA will change their conformations from their free states, changing non-specific complexes to specific complexes . During the course of DNA-recognition, residues play different roles to either recognize nucleotide bases or stabilize the protein-DNA conformation. In this work, we try to identify whether the residue performs sequence-specific or non-specific binding. There are two types of binding mechanisms involved in amino acid - nucleotide interactions, namely sequence-specific and non-specific site binding [25–29]. Sequence-specific binding occurs between protein side-chains and nucleotide bases, while non-specific binding occurs between protein side-chains and the DNA sugar/phosphate backbone . In general, sequence-specific binding is also named as specific binding. Specific binding corresponds to sequence-specific recognition of a gene and therefore is essential for the correct regulation of genes. Non-specific binding shows relatively little base-sequence preference and binds preferentially to either single or double-stranded DNA. The role for non-specific binding residues is to stabilize the interactions between protein and nucleotide backbone to help specific binding residues in recognizing base pairs correctly. As reported in the review article by Luscombe et al. , protein-DNA interactions can be grouped into eight different structural/functional groups based on the structures of the DNA-binding region in the proteins, which is also referred to as the binding mode of the protein [30–32]. There are eight such binding modes including (I) Helix-Turn-Helix, HTH (including "winged" HTH), (II) zinc-coordinating, (III) zipper-type, (IV) other α- helix, (V) β- sheet, (VI) β-hairpin/ribbon, (VII) other, (VIII) enzymes. Related research has investigated the classification of protein-DNA complexes and structural domains [33–35]. Proteins in the same class have similar binding site conformations despite having different DNA targets. The importance of introducing the DNA-binding mode information is to find the binding pattern that a protein uses to interact with the target DNA [36, 37], which could help to identify the location of sequence-specific and non-specific binding residues.
Results and discussion
Overall performance of proposed approach
# of residues
Performance of the binding site prediction in terms of secondary structure elements
Secondary structure elements
# of residues
Specific + Non-specific
Overall performance of protein-DNA binding mode prediction
Protein-DNA binding mode
# of protein chains
Performance delivered by alternative predictors of DNA-binding residues, where the F-measure is the harmonic mean of precision and sensitivity
Ahmad and Sarai
Yan et al.
BindN (Wang and Brown)
DP-Bind (Hwang et al.)
In addition, we have . Therefore, for each related study, we can derive the actual value of the fourth performance metric based on the values of the other three performance metrics that were provided. The only exception is precision for the predictor proposed by Hwang et al. . By definition, the accuracy cannot be higher than the sensitivity and the specificity simultaneously, which is the case with the numbers reported by Hwang et al. Therefore, there is no way to derive the exact value of precision for their predictor.
According to the observation of the predicted results, the predictor of non-specific binding residues tries to locate positive charged patches. However, not all positive charged patches in a protein will come into contact with single- or double-strand DNA. It might be the reason of the performance gap between sequence-specific and non-specific binding residue prediction. While combining prediction results of sequence-specific and non-specific binding residues, sensitivity is higher than other predictors. The reason is that non-specific binding residues help a protein to slide along the target DNA, and specific binding residues will recognize base pairs while sliding along the target DNA. The role the non-specific binding residues play is to help specific binding residues recognize base pairs precisely. Therefore, the prediction of non-specific binding residues can increase the predictor's capability for predicting DNA-binding residues.
This article presents the design of a sequence based predictor that aims to identify the sequence-specific and non-specific DNA-binding residues in a TF. As a recent study has revealed that the tertiary structures of a large number of transcription factors are mostly disordered, a sequence based predictor is essential for analyzing how a TF interacts with DNA. Furthermore, it is highly desirable to have a predictor capable of identifying the residues involved in sequence-specific binding with DNA, since sequence-specific binding corresponds to sequence-specific recognition of a gene and is therefore essential for correct gene regulation. However, non-specific binding residues can help specific binding residues to increase binding specificity as well.
In the experiments reported in this article, our proposed approach has been able to deliver precision 81.70% and 65.47% in sequence-specific and non-specific binding residue prediction respectively. Precision of 81.70% implies that about 4 out of 5 predicted binding residues are really involved in sequence-specific binding with the DNA. Precision of 65.47% implies that about 7 out of 10 predicted binding residues are really involved in non-specific binding with the DNA. While combining prediction results, the performance for DNA-binding residue prediction can deliver sensitivity 56.85%. Sensitivity of 56.85% implies that our proposed approach can catch about 6 out of 10 residues involved in DNA binding with the DNA. In the DNA-binding segment of the protein, regions where non-specific binding residues are located will cover the regions where specific binding residues are located. Therefore, improvement can be achieved for DNA-binding residues prediction while combining prediction results of specific and non-specific binding residues. The protein-DNA binding mode prediction is also proposed in this framework, and we select 1LMB:4 as an example to reveal how can be helpful for improving DNA-binding residue prediction.
It is anticipated that the prediction accuracy delivered by our proposed approach will continue to improve as the number of TF-DNA complexes deposited in the PDB continues to grow which will increase the number of training samples for use in our learning algorithm. Nevertheless, the primary interest of computational biologists is to develop more advanced prediction mechanisms. In this respect, we believe that as the number of TF-DNA complexes deposited in the PDB increases, we can obtain more insights about the key physiochemical properties that play essential roles in TF-DNA interactions to be used to develop more advanced prediction mechanisms. In addition, we will exploit the experiences learned in this study in order to design binding-mechanism concerned predictors for other families of proteins interacting with DNA. We believe that different families of proteins may have very different characteristics. Therefore, a specifically-designed predictor should be created for each specific type of protein to be able to deliver superior performance in comparison with a general-purpose predictor.
Materials and methods
Dataset of 253 TF-DNA complexes for DNA-binding residues prediction
253 TF-DNA Complexes
Defining the DNA-binding residue
Previous research used various distance cut-offs from 3.5 Å to 6 Å to define DNA-binding residues between proteins and DNA [6–10, 14, 40, 42]. Most, if not all, of the cut-off distance is measured between the atoms of amino acid and the atoms of nucleotide bases or sugar-phosphate backbones. Most DNA-binding residue prediction tools used 3.5 Å or 4.5 Å as the distance cut-off in general. Considering electrostatic interaction, hydrogen bonding, water-mediated hydrogen bonding, and van der Waals contacts, we use 4.5 Å distance cut-off to label DNA-binding residues. A residue is regarded as involved in sequence-specific binding with DNA if one or more heavy atoms on its side-chain are within 4.5 Å from the nucleic bases of the DNA. A residue is regarded as involved in non-specific binding with the DNA, if one or more heavy atoms on its side-chain are within 4.5 Å from the sugar/phosphate backbone of the DNA. In all 253 TF-DNA complexes, there are 1526 binding residues and 23371 non-binding resides for sequence-specific binding residue prediction. The ratio of positive to negative samples is 1:15 in sequence-specific binding. For non-specific binding residue prediction, there are 3831 binding residues and 21066 non-binding residues. The ratio of positive to negative samples is 1:5 in non-specific binding. The number of non-specific binding residues is twice as many as the number of sequence-specific binding residues. Without distinguishing between sequence-specific and non-specific binding residues, there are 4360 binding residues and 20537 non-binding residues. All missing residues which do not have coordinate information in the PDB data file, will be excluded from the training and testing datasets.
Framework of DNA-binding residues and binding mode prediction using support vector machine
We proposed the two stage framework to predict the DNA-binding residues in a protein and the corresponding binding mode for a query protein respectively. Figure 3 shows the overall framework for binding residue prediction and a binding mode prediction. The first stage predicts the DNA binding residues and the second stage predicts the protein-DNA binding mode. In the first stage, a well-known machine leaning approach has been used for prediction from amino acid sequences which uses support vector machine with features created by the evolutionary profile of the proteins [43, 44]. The evolutionary profile of position-specific scoring matrices (PSSM) is computed by PSI-BLAST  against the NR database for a protein sequence. In addition, in order to keep evolutionary information of neighborhood residues information, we use the principle of sliding window to calculate the backward (or/and forward) metrics over a limited region of the received sequence. For each residue in a protein sequence, we use a sliding window of size 11 to describe neighborhood information; therefore, we have a 11 * 21 = 231 dimension feature factor in addition to the 20 amino acids and a boundary flag. In the end, we used LIBSVM  as predictor to predict DNA-binding residues. The best parameters selected for DNA-binding residues prediction is decided by leave-one-out cross validation (LOOCV).
Protein-DNA binding modes and their corresponding Pfam domains
Protein-DNA Binding mode
Illustration of feature set for protein-DNA binding mode prediction
5 protein-DNA binding modes
2. helix-turn-helix (HTH)
20 dimensions of amino acid
3 dimensions of secondary structure elements
# of binding residues
Protein chain information
3 dimensions of secondary structure elements
# of total residues in a protein chain
Predictor performance measures
Other papers from the meeting have been published as part of BMC Bioinformatics Volume 10 Supplement 15, 2009: Eighth International Conference on Bioinformatics (InCoB2009): Bioinformatics, available online at http://www.biomedcentral.com/1471-2105/10?issue=S15.
List of abbreviations used
Nucleic Acid Database
Protein Data Bank
Support vector machine
This research has been supported by the National Science Council and National Taiwan University. Funding for open access charge: National Science Council, NSC 97-2627-P-001-002.
This article has been published as part of BMC Genomics Volume 10 Supplement 3, 2009: Eighth International Conference on Bioinformatics (InCoB2009): Computational Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2164/10?issue=S3.
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