A Naïve Bayes classifier was trained to predict whether or not a target residue in a protein sequence is an interface residue based on local protein sequence information. Several input encodings based on local sequence information were tried, with input consisting of: (a) the identities of 9 amino acid residues, corresponding to a window containing the target residue and 4 neighboring residues on each side of the target residue; and (b) the identities of 9 amino acid residues and the sequence entropy of the target residue (the entropy of the corresponding column in multiple alignment obtained by aligning the target sequence with its sequence homologs). In each case, Naïve Bayes classifiers were trained and evaluated using leave-one-out cross-validation on a set of 171 DNA-binding proteins

Table 1 shows that the classifier using amino acid identities as input achieved an overall accuracy of 71% with a correlation coefficient of 0.24, 35% of the residues predicted to be interface residues are actually interface residues, and 53% of interface residues are correctly identified. Adding the sequence entropy of the target residue (the entropy of the corresponding column in multiple alignment obtained by aligning the target sequence with its sequence homologs) to the input improved the performance of the classifier (Table 1). The resulting classifier achieved an overall accuracy of 78% with a correlation coefficient of 0.28, 44% specificity, and 41% sensitivity. In 33% (56 of 171) of the proteins, the classifier recognizes the interaction site by correctly identifying at least half of the interface residues, and in 87% (149 of 171) of the proteins, by correctly identifying at least 20% of the interface residues.

Inclusion of other features of the target residue, including relative solvent accessibility, secondary structure, electrostatic potential, and hydrophobicity as additional inputs to the classifier did not yield performance improvements (data not shown) relative to the classifier trained using only the amino acid identities of the target residue and its sequence neighbors. Classifiers trained using features other than the amino acid identities of target residue and its neighbors as input achieved performance that was lower than that of the classifier using amino acid identities of the corresponding residues as input (data not shown).

We examined in the context of the 3-dimensional structures of the protein-DNA complexes, the DNA-binding residue predictions generated by a Naïve Bayes classifier trained to identify such residues based on the amino acid identities of the target residue and its sequence neighbors. Two representative examples are shown in figure 1. Figure 1A shows the predictions on the transcription factor C/Ebpβ from PDB complex 1gu4. The predictions of the classifier rank the 3^rd best in terms of correlation efficient among the 171 proteins. We note that the classifier is able to recognize the DNA-binding site on the protein on the basis of sequence information alone. Figure 1B shows the predictions on the intron-associated endonuclease I-TevI from PDB complex 1i3j. The predictions of the classifier in this case rank the114^th best among the 171 proteins in terms of correlation efficient. I-TevI wraps around the DNA and has an unusually extended binding site. We note that the predicted DNA-binding residues cover the long segment of the protein that binds to the DNA.

In some situations (e.g., identification of critical interface residues for site-specific mutagenesis), it is desirable to predict interface residues with high precision at the cost of reduced coverage. In other situations, discovering more potential interface residues might be more useful. These different requirements can be met by modifying the threshold θ used by the Naïve Bayes classifier in this study. The Naïve Bayes classifier predicts a residue to be an interface residue if P(c=1|X=x1x2...xn)P(c=0|X=x1x2...xn)>θ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaWcaaqaaiabdcfaqjabcIcaOiabdogaJjabg2da9iabigdaXiabcYha8jabdIfayjabg2da9iabdIha4naaBaaaleaacqaIXaqmaeqaaOGaemiEaG3aaSbaaSqaaiabikdaYaqabaGccqGGUaGlcqGGUaGlcqGGUaGlcqWG4baEdaWgaaWcbaGaemOBa4gabeaakiabcMcaPaqaaiabdcfaqjabcIcaOiabdogaJjabg2da9iabicdaWiabcYha8jabdIfayjabg2da9iabdIha4naaBaaaleaacqaIXaqmaeqaaOGaemiEaG3aaSbaaSqaaiabikdaYaqabaGccqGGUaGlcqGGUaGlcqGGUaGlcqWG4baEdaWgaaWcbaGaemOBa4gabeaakiabcMcaPaaacqGH+aGpcqaH4oqCaaa@5936@. Figure 2 shows the Receiver Operating Characteristic curve (ROC curve) of the DNA-binding site predictor.

The results summarized above show that a Naïve Bayes classifier trained on a set of DNA-binding proteins can successfully identify protein-DNA interface residues from amino acid sequence. This raises the question as to how the sequence features that are identified as predictive of DNA-binding residues by Naïve Bayes classifier relate to known DNA-binding motifs. To explore this question, we used the ps_scan program to search for PROSITE motifs in our data set of 171 DNA-binding proteins. PROSITE motifs were found in 53 of the 171 proteins (a total of 73 hits). Of these 73 hits, 61 overlap with actual protein-DNA binding sites. The DNA-binding site predictions produced by the Naïve Bayes classifier (in the leave-one-out cross-validation setting) using the identities of a window of 9 residues and the sequence entropy of the target residue as input, substantially overlap with 56 of the 61 PROSITE DNA-binding motifs (Figure 3). It is worth noting that 118 of the 171 DNA-binding proteins in our data set contain no PROSITE motif whose annotation suggests a role in protein-DNA interactions. PROSITE motifs cover more than 50% of interface residues in only 11% (18 out of 171) of the proteins and cover at least 20% of interface residues in only 20% (34 out of 171) of the proteins. In contrast, the Naïve Bayes classifier identifies at least 50% of the interface residues in 33% (56 out of 171) of the proteins and at least 20% of the interface residues in 87% (149 out of 171) of the DNA-binding proteins used in this study. These results suggest the possibility of using a Naïve Bayes classifier trained to predict DNA-binding residues to identify putative DNA-binding motifs.

Ahmad and Sarai have developed a Position Specific Scoring Matrix (PSSM) based neural network classifier for predicting DNA-binding sites 15. To the best of our knowledge, this is the only previously published study which reports the performance of a DNA-binding site prediction using only sequence information on a "per residue" basis. Ahmad and Sarai have made available an online server that predicts DNA-binding residues using a PSSM-based neural network classifier 16. The server makes predictions for protein sequences that are 40 to 200 amino acid residues in length. In our data set of 171 DNA-binding proteins, 86 have length in this range. The predictions of the PSSM-based classifier on these 86 proteins were obtained by submitting the sequences to the online server. The server returns, for each residue in the submitted sequence, the estimated probability that the residue is a DNA-binding residue. These probabilities can be compared with a threshold to obtain a prediction as to whether a residue is a DNA-binding residue. Different choices of threshold yield different predictions. We varied the threshold from 0.01 to 0.99 in increments of 0.02 to generate an ROC curve for the PSSM-based neural network classifier. For comparison, we trained and evaluated using leave-one-out cross-validation, a Naïve Bayes classifier using as input the identities of 9 amino acid residues on the subset of 86 proteins (ranging from 40 to 200 amino acids in length). Figure 4 shows the comparison of the ROC curves of the PSSM-based neural network classifier with that of the Naïve Bayes classifier on the data set of 86 proteins. The results show that the Naïve Bayes classifier achieves higher hit rate, for any given choice of the false alarm rate, than the current implementation of the PSSM-based neural network classifier in the online server.

Restriction-modification (R-M) systems play important role in the recognition and elimination of foreign DNA. In type I R-M systems, S subunit determines the specificity of DNA recognition. The interaction mode between S subunit and DNA is still unknown. Recently, Kim et al. 17 solved the crystal structure of the S subunit from M. jannaschii, the only crystal structure ever reported for the S subunit of type I (R-M) systems. To further evaluate the Naïve Bayes classifier, we used the classifier trained on our data set of 171 DNA-binding proteins (using identities of the target residue, and 4 sequence neighbors on either side along with the sequence entropy of the target residue as input) to identify DNA-binding residues on the S subunit of the type I R-M system from M. jannaschii. Figure 5 shows the predicted DNA-binding residues in red and spacefill. Note that Kim et al. 17 reported, based on the solved crystal structure of the S subunit of M. jannaschii, that the structures of the two target recognition domains (TRD1, residue 1–168 and TRD2, residue 209–378) of the S subunit are similar to the DNA binding domain of TaqI-MTase. By aligning the structures of TRD1 and TRD2 with the structure of TaqI-MTase/DNA complex, Kim et al. 17 proposed a model for the interaction between the S subunit and DNA. In figure 5, the DNA molecules in Kim's model are shown in blue. Comparison of Kim's model with the DNA-binding site predictions produced by our Naïve Bayes classifier shows that the Naive Bayes classifier agrees with the locations of the two potential DNA-binding sites on the S subunit in Kim's interaction model.

Figure 5 also shows that two additional DNA-binding sites predicted by the Naïve Bayes classifier overlap with the potential interaction sites between the S subunit and R subunits of the protein (shown as circles in figure 5) as proposed in Kim's model. This observation raises the intriguing possibility that protein-DNA interfaces and protein-protein interfaces might have some common features.

Given that the Naïve Bayes classifier was trained to identify DNA-binding residues in proteins that are known to bind to DNA, it is interesting to examine their predictions on a set of proteins for which at present, there is no evidence suggesting a DNA-binding role. We assembled a non-redundant data set of 2,323 proteins which, based on our analysis of Gene Ontology annotations, appear to have no evidence suggesting a DNA-binding role. A Naïve Bayes classifier trained on our data set of 171 DNA-binding proteins to identify the DNA-binding residues (using amino acid identities of the target residue and its sequence neighbors together with the sequence entropy of the target residue as input) was applied to the 2,323 proteins with no known DNA-binding role. The Naïve Bayes classifier predicted 11% of the 613,754 residues from these 2,323 proteins as potentially DNA-binding residues. It would be inappropriate to conclude that 11% is a per residue basis false positive rate of our classifier because absence of DNA-binding evidence in GO annotation does not necessarily imply that the protein in question does not have a DNA-binding role. It is quite possible that at least some of these 2,323 proteins indeed bind to DNA. It should be emphasized that our classifier was not trained to distinguish the class of DNA-binding proteins from those that are not DNA-binding (Training such a classifier would involve using representatives of both DNA-binding and non DNA-binding proteins in the training set). It is interesting to note that in 156 of the 2,323 proteins, no residues were predicted to be DNA-binding by our classifier; 264 had fewer than 5 predicted DNA-binding residues; 502 had fewer than 10 predicted DNA-binding residues, and 999 with fewer than 20 DNA-binding residues. Exploring the implications of these observations would require experimentally testing some of the proteins on which our Naïve Bayes classifier predicts putative DNA-binding sites for DNA-binding activity. Another potentially interesting direction would be to train classifiers to distinguish proteins that are DNA-binding (without necessarily identifying the DNA-binding residues) from those that are not.

In this paper, we have described a computationally efficient approach to identifying putative DNA-binding residues of DNA-binding proteins using Naïve Bayes classifiers trained to predict DNA-binding residues using amino acid identities of the target residue and its sequence neighbors. The resulting classifier achieves 71% overall accuracy with a correlation coefficient of 0.24, 35% specificity and 53% sensitivity in identifying interface residues as evaluated by leave-one-out cross-validation. Our results indicate the feasibility of identifying interface residues based on local sequence information alone.

We found that the performance of the classifier is improved by using sequence entropy of the target residue (the entropy of the corresponding column in multiple alignment obtained by aligning the target sequence with its sequence homologs) as additional input. This observation is consistent with the suggestion that DNA-binding residues are likely to be conserved (because of their function). The resulting classifier achieves 78% overall accuracy with a correlation coefficient of 0.28, 44% specificity and 41% sensitivity in identifying interface residues.

Incorporating additional structure-derived information such as solvent accessibility, electrostatic potential, hydrophobicity or secondary structure of the target residue as additional input, however, did not improve the performance in this study. This should not be taken to mean that these features are not useful predictors of a residue's functionality. In particular, electrostatic potential has been shown to be useful in identification of protein-DNA interface residues 1011. The fact that this information does not improve performance of our Naïve Bayes classifiers might have to do with the properties of input encoding or the classification method. Specifically, the additional features were simply added as additional input. The underlying assumption of the Naïve Bayes classifier that the inputs are independent given the class almost certainly does not hold in the case of protein sequences. Hence, more systematic analysis is needed to identify features that are useful for identification of interface residues and develop methods of representing them in input to a broad range of classifiers. Jones and Thornton 18 analyzed six features of surface patches in protein-protein interaction sites and developed an approach to identify protein-protein interfaces based on the scores combining the six features. Sen et al. 19 developed an ensemble method to identify protease-inhibitor binding sites based on sequence, structure and evolution information. It would be interesting to explore such methods for computational prediction of protein-DNA interfaces.

Ahmad and Sarai 15 used a PSSM-based neural network classifier to identify interface residues in protein-DNA interactions. Our comparison of the PSSM-based classifier with the Naïve Bayes classifier shows that the Naïve Bayes classifier achieves higher hit rate than the PSSM-based classifier for any given choice of the false alarm rate.

We note that the PSSM-based classifier's ROC originally reported by Ahmad and Sarai 15 is better than the PSSM-based classifier's ROC achieved by their online server 16 on the data set used in our comparison. A few factors may have contributed to this difference: (1) the data set used by Ahmad and Sarai in their original study is different from the data set of 86 proteins used here. It is possible that the current implementation of the PSSM-based method is well optimized for their original data set, but not for the 86 proteins used here; (2) the ROC reported by Ahmad and Sarai includes predictions on proteins of all lengths, whereas the online server only makes predictions for proteins with a length in the range of 40–200. We chose to compare the Naïve Bayes classifier with the online server because the server is publicly available and it provides the raw probabilities of the predictions making it possible to compare the ROC curves of the two classifiers on the same data set. However, it should be noted that in the case of Naïve Bayes classifier, our use of leave-one-out cross-validation ensures that the training and test data do not overlap. We have no control over the training data used by the PSSM-based classifier. Nevertheless, a comparison of the two ROC curves suggests that the Naïve Bayes classifier achieves higher hit rate than the current implementation of the PSSM-based neural network classifier for any given choice of the false alarm rate.

A thorough assessment of the performance of the Naïve Bayes classifier relative to the PSSM-based classifier requires systematic comparisons using leave-one-out cross-validation on identical data sets – which is at present, not feasible without access to an implementation of the algorithm and the precise parameter settings used to train the PSSM-based classifier. Plans are underway to perform such a comparison using identical data sets and evaluation procedures, in collaboration with Ahmad and Sarai.

It should be noted that the Naïve Bayes classifier described in this paper offers several advantages over the PSSM-based neural network classifier: (a) The Naïve Bayes classifier can be trained in a single pass through the training data whereas training a neural network classifier requires many, often hundreds of passes through the training data. (b) Training the Naïve Bayes classifier, unlike the neural network classifier, requires no time-consuming and computationally expensive exploration of many possible choices of network architecture (e.g., number of hidden neurons) and parameter settings (e.g., learning rate). (c) The Naïve Bayes classifier, as well as predictions generated by it is amenable to a straightforward probabilistic interpretation whereas the neural network classifier is more of a "black box".

These advantages, together with the superior performance of the Naïve Bayes classifier relative to the current implementation of the PSSM-based neural network classifier, make it an attractive alternative to the latter in identifying DNA-binding residues from a protein sequence. However, the neural network classifier is not limited by the strong independence assumption of the Naïve Bayes classifier. Hence, it would be interesting to explore whether a neural network classifier or a variant of it could be optimized to yield results that are better than that of the simple Naïve Bayes classifier.

Protein sequence motifs (defined here as sequence segments associated with specific protein functions or structural families) are often used to identify putative DNA-binding domains. Discovery of such motifs requires alignment of protein sequences that are known to have the same or similar functions. Generating multiple sequence alignments that reveal useful sequence motifs requires significant human expertise to identify a suitable set of sequences to be aligned and to manually refine, through an iterative process of trial and error, the multiple sequence alignment. Against this background, it is interesting to note that in 118 out of 171 DNA-binding proteins used in this study, we found no PROSITE motifs whose annotations suggest a possible DNA-binding role. In the remaining proteins, 61 PROSITE motifs were found to overlap with protein-DNA binding sites. The DNA-binding sites predicted by the Naïve Bayes classifier significantly overlapped with 56 of the 61 PROSITE motifs that overlapped with DNA-binding sites. PROSITE motifs cover at least 20% of the DNA-binding residues in only 20% (34 out of 171) of the proteins. In contrast, the Naïve Bayes classifier identifies at least 20% of the interface residues in 87% (149 out of 171) of the DNA-binding proteins used in this study. This raises the possibility of identifying novel sequence motifs that correspond to protein-DNA interfaces by using a Naïve Bayes classifier trained to identify protein-DNA binding sites. More systematic comparison of this approach with alternative approaches to identification of putative DNA-binding motifs using other motif libraries and different motif finding methods is needed to evaluate its efficacy relative to other approaches.

DNA-binding proteins: A data set of DNA-binding proteins was extracted from structures of known protein-DNA complexes in the Protein Data Bank 23. The dataset was culled using PISCES 24. The resulting dataset consists of 171 proteins with mutual sequence identity <= 30% and each protein has at least 40 amino acid residues. All the structures have resolution better than 3.0 Å and R factor less than 0.3.

Proteins that do not have evidence of a DNA-binding role: A non-redundant set of proteins with mutual identity less than 30% was extracted from the PDB using the cluster file from the Protein Data Bank 25. Structures with resolution worse than 2.5 Å were removed. The annotations for each protein were retrieved from the Gene Ontology Annotation (GOA) 26. Proteins with annotations indicative of a DNA-binding role were eliminated, leaving a data set of 2,313 proteins with no evidence of a DNA-binding role.

Interface residues are defined as described in Jones et al. 10. Accessible surface area (ASA) was computed for each residue in the unbound protein (in absence of DNA) and in the protein-DNA complex using NACCESS 27. A residue is defined to be an interface residue if its ASA in the protein-DNA complex is less than its ASA in the unbound protein by at least 1Ų. The 171 proteins have 38,649 residues in total and 5,050 of them are interface residues.

We used the Naïve Bayes implementation in the Weka package from the University of Waikato, New Zealand 2829. For each input target residue, the classifier produces a Boolean output (with 1 denoting an interface residue and 0 denoting a non-interface residue). The Naïve Bayes classifier assumes independence of the attributes given the class. The Naïve Bayes classifier performs as well as more sophisticated methods on many classification tasks 30. For an input X = x₁ x₂ ,...,x_n , a Naïve Bayes classifier assigns it a class label c by optimizing the posterior: c=arg⁡max⁡cP(c|X=x1x2...xn)=arg⁡max⁡cP(c)∏i=1nP(xi|c) MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaacqWGJbWycqGH9aqpcyGGHbqycqGGYbGCcqGGNbWzdaWfqaqaaiGbc2gaTjabcggaHjabcIha4bWcbaGaem4yamgabeaakiabdcfaqjabcIcaOiabdogaJjabcYha8jabdIfayjabg2da9iabdIha4naaBaaaleaacqaIXaqmaeqaaOGaemiEaG3aaSbaaSqaaiabikdaYaqabaGccqGGUaGlcqGGUaGlcqGGUaGlcqWG4baEdaWgaaWcbaGaemOBa4gabeaakiabcMcaPiabg2da9iGbcggaHjabckhaYjabcEgaNnaaxababaGagiyBa0MaeiyyaeMaeiiEaGhaleaacqWGJbWyaeqaamXvP5wqSXMqHnxAJn0BKvguHDwzZbqegyvzYrwyUfgaiqGakiaa=bfacqqGOaakcaWFJbGaeeykaKYaaebCaeaacaWFqbGaeiikaGIaemiEaG3aaSbaaSqaaiabdMgaPbqabaGccqGG8baFcqWGJbWycqGGPaqkaSqaaiaa=LgacqGH9aqpiqaacaGFXaaabaGaa8NBaaqdcqGHpis1aaaa@7297@. In the case of two class classification (c ∈ {0, 1}), this is equivalent to determining c by comparing the ratio likelihood with a parameter θ as in equation (1).

P ( c = 1 | X = x 1 x 2 ... x n ) P ( c = 0 | X = x 1 x 2 ... x n ) = P ( c = 1 ) ∏ i = 1 n P ( x i | c = 1 ) P ( c = 0 ) ∏ i = 1 n P ( x i | c = 0 ) > θ       ( 1 ) MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=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@8D7B@

c is predicted to be 1 if the ratio likelihood is greater than θ, and 0 otherwise. When a local sequence around the target residue was encoded using numeric features such as hydrophobicity, the numerical values were discretized using the discretization filter of Weka.

In a standard Naïve Bayes classifier, θ takes the value of 1. The predictions of Naïve Bayes classifier are biased in favor of the majority class when the dataset consists of unequal numbers of examples for the two classes. Hence, we trained θ to optimize classification performance on training data. We used leave-one-out cross-validation to train and test the classifier. In each round of experiment, all proteins except one were used as the training set and the remaining protein was used to test the classifier. In the training stage, the conditional probability table P(x_i | c) and prior probability p (c) were estimated using the training set. To determine θ, the classifier was applied to the training set and different values of θ ranging from 0.01 to 1 were tested, in increments of 0.01. The value of θ for which the classifier yields the highest correlation coefficient was used to make predictions on the test set.

The input to the Naïve Bayes classifier contains the identities of 2n+1 residues in the form of X = (x_t-n , x_t-n+1 ,...,x_t-1 ,x_t ,x_t-1 ,...,x_t+n-1 , x_t+n ), where x_t is the identity of target residue, x_t-n , x_t-n+1 ,...,x_t-1 and x_t+1 , x_t+n-1 , x_t+n are the identities of n residues on each side of the target residue. Different values of n from 1 to 10 were tried and the best performance was obtained when n = 4 (corresponding to a window size of 9). A training example is an ordered pair (X, c), where c ∈ {0, 1}. 1 indicates that the target residue (the residue in the center of the input window) is an interface residue and 0 indicates that target residue is not an interface residue. For a test example X, the classifier outputs 1 (i.e., X is predicted to be an interface residue) or 0 (i.e., X is predicted to be a non-interface residue) as the class label of X.

Relative solvent accessibility (rASA), sequence entropy, secondary structure, electrostatic potential and hydrophobicity were considered. When a feature of the target residue is added into the input of amino acid identities of residues in a 9-residue window, the input to the classifier is encoded as X = (x_t-n , x_t-n+1 ,...,x_t-1 ,x_t ,x_t+1 ,...,x_t+n-1 , x_t+n , f_t ), with f_t standing for the corresponding feature of the target residue (e.g., sequence entropy, hydrophobicity, etc.), and x_i denotes the amino acid identity of the corresponding position within the sequence window. When a feature other than residue identity of the input window (i.e., the target residue and its sequence neighbors) is used to encode the local sequence around the target residue, the input to the classifier has the form of X = (f_t-n , f_t-n+1 ,...,f_t-1 , f_t , f_t+1 ,...,f_t+n-1 , f_t+n ), where f_i is the corresponding feature (e.g., hydrophobicity) of the residue i.

The relative solvent accessible surface area (rASA) of each residue (in the absence of DNA) was computed using NACCESS 27. Entropy of each sequence position (the sequence entropy for the corresponding column in multiple of the multiple sequence alignment) was extracted from the HSSP database 31. The sequence entropy is normalized to the range of 0–100, with lower entropy values corresponding to more conserved sequence positions. Secondary structure for each residue was extracted from the PDB database 25. Electrostatic potential for each atom was calculated using Delphi 3233, using parameters based on the study of Jones et al. 10. The electrostatic potential for each residue was calculated in a similar way as the study of Jones et al. 10: the electrostatic potential of an atom is set to 0 if its solvent accessibility is less than 1Ų and the electrostatic potential of a residue is the average over all its atoms. Hydrophobicity of each residue is obtained from the consensus normalized hydrophobicity scale derived by Eisenberg et al. 34.

Because no single performance measure provides a complete picture of performance of the classifier 35, we used a combination of accuracy, correlation coefficient (CC), specificity and sensitivity. These measures are defined as described in Baldi et al. 35. Accuracy=TP+TNN;  CC=TP×TN−FP×FN(TP+FN)(TP+FP)(TN+FP)(TN+FN);Sensitivity=TPTP+FN;Specificity=TPTP+FP MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=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@A1BF@, where TP= the number of true positives (residues predicted to be DNA-binding residues that are in fact interface residues); FP = the number of false positives (residues predicted to be DNA-binding residues that are in fact not interface residues); TN = the number of true negatives (residues predicted to be non DNA-binding residues that are in fact not DNA-binding residues); FN = the number of false negatives (residues predicted to be non DNA-binding residues that are in fact DNA-binding residues); N = TP+TN+FP+FN (the total number of examples).

Sensitivity is the fraction of positive examples (DNA-binding residues) that are predicted as such by the classifier. Specificity is the fraction of positive predictions (residues predicted to be DNA-binding residues) that are actually interface residues. Accuracy is the fraction of overall predictions that are correct. Correlation coefficient measures the correlation between predictions and actual class labels.

The Receiver Operating Characteristic curve (ROC curve) is a plot of the "hit rate" (TP/(TP+FN)) versus the "false alarm rate" (FP/(TN+FP)) 35. It shows the tradeoff between hit rate and false alarm rate when different threshold values are used for the classifier.

The PROSITE motif database was downloaded from the PROSITE 36. Protein sequences were scanned using the ps-scan program 37 to identify motifs. Frequently matching (unspecific) patterns and profiles were omitted by setting the "-s" and "-r" options of ps-scan.