Background Detection of DNA-binding sites in proteins is of enormous interest for technologies targeting gene regulation and manipulation. We have previously shown that a residue and its sequence neighbor information can be used to predict DNA-binding candidates in a protein sequence. This sequence-based prediction method is applicable even if no sequence homology with a previously known DNA-binding protein is observed. Here we implement a neural network based algorithm to utilize evolutionary information of amino acid sequences in terms of their position specific scoring matrices (PSSMs) for a better prediction of DNA-binding sites.
Background
Detection of DNA-binding sites in proteins is of enormous interest for technologies targeting gene regulation and manipulation. We have previously shown that a residue and its sequence neighbor information can be used to predict DNA-binding candidates in a protein sequence. This sequence-based prediction method is applicable even if no sequence homology with a previously known DNA-binding protein is observed. Here we implement a neural network based algorithm to utilize evolutionary information of amino acid sequences in terms of their position specific scoring matrices (PSSMs) for a better prediction of DNA-binding sites.Results An average of sensitivity and specificity using PSSMs is up to 8.7% better than the prediction with sequence information only. Much smaller data sets could be used to generate PSSM with minimal loss of prediction accuracy.
Results
An average of sensitivity and specificity using PSSMs is up to 8.7% better than the prediction with sequence information only. Much smaller data sets could be used to generate PSSM with minimal loss of prediction accuracy.Conclusion One problem in using PSSM-derived prediction is obtaining lengthy and time-consuming alignments against large sequence databases. In order to speed up the process of generating PSSMs, we tried to use different reference data sets (sequence space) against which a target protein is scanned for PSI-BLAST iterations. We find that a very small set of proteins can actually be used as such a reference data without losing much of the prediction value. This makes the process of generating PSSMs very rapid and even amenable to be used at a genome level. A web server has been developed to provide these predictions of DNA-binding sites for any new protein from its amino acid sequence.
Conclusion
One problem in using PSSM-derived prediction is obtaining lengthy and time-consuming alignments against large sequence databases. In order to speed up the process of generating PSSMs, we tried to use different reference data sets (sequence space) against which a target protein is scanned for PSI-BLAST iterations. We find that a very small set of proteins can actually be used as such a reference data without losing much of the prediction value. This makes the process of generating PSSMs very rapid and even amenable to be used at a genome level. A web server has been developed to provide these predictions of DNA-binding sites for any new protein from its amino acid sequence.Availability Online predictions based on this method are available at
Availability
Online predictions based on this method are available at

Background There has been a growing interest in the prediction of DNA-binding sites in proteins which play crucial roles in gene regulation [ 1 - 4 ]. We have previously developed a method of predicting DNA-binding sites of proteins from the sequence information [ 5 ]. We reported development of a neural network and corresponding web server to predict amino acid residues which are likely to bind DNA. The only input to the neural network in this algorithm was the identity of the amino acid residue and its two sequence neighbors on C- and N- terminals. We also developed a method to identify DNA-binding proteins using electrical moments from structural information of proteins [ 6 ]. On the other hand, several investigators have reported that the use of evolutionary information in sequence-based predictions of secondary structure and solvent accessibility can improve the prediction capacity of a neural network [ 7 - 10 ]. Here we report the use of such evolutionary information in improving the prediction of DNA-binding sites of proteins. We note that one of the major problems in applying evolutionary information by way of position specific scoring matrices (PSSMs) for sequence based prediction is that such matrices are generated over large data sets and take a long time to complete. Thus large scale predictions remain inaccessible to moderately capable computers. This is a serious limitation in the portability of neural network based predictions using PSSMs [ 8 ]. In this work, we report that evolutionary profiles or PSSMs against much smaller representative reference data sets may be utilized to achieve almost the same levels of prediction as would be obtained from alignments with large sequence data sets representing entire available sequence space. We have used four different reference data sets of PSSMs for 62 representative protein sequences. These are (1) PDNA-RDN: a data set of protein sequences from all Protein-DNA complexes from the PDB, (2) PDNA-NR90: a non-redundant data set compiled from PDNA-RDN, (3) PDB-ALL: a data set of all amino acid sequences from PDB and (4) NCBI-NR: a non-redundant data set of all protein sequences taken from sequence and structure databases and compiled by NCBI (see Methods). We find that the net prediction (an average of sensitivity and specificity) of the best of these systems (using PIR sequence data as reference) improves to 67.1% from the value of 58.4% reported earlier for a sequence-only prediction. We also report that a small reference data set of 375 sequences (PDNA-NR90) can give a 64.6% net prediction – just 2.5% poorer than the best- while reducing the PSSM calculation time from more than two hours (against NCBI-NR) to just about one minute. A better compromise could be the use of PDNA-RDN data for which 65.2% net prediction #150; 1.9% less than the best- was obtained, while about 2 and a half minutes are taken to generate their PSSMs. It is also reported that the presence of redundancy is helpful in improving the prediction whereas presence of data not relevant for DNA-binding may in some cases reduce predictive performance.
Background
There has been a growing interest in the prediction of DNA-binding sites in proteins which play crucial roles in gene regulation [ 1 - 4 ]. We have previously developed a method of predicting DNA-binding sites of proteins from the sequence information [ 5 ]. We reported development of a neural network and corresponding web server to predict amino acid residues which are likely to bind DNA. The only input to the neural network in this algorithm was the identity of the amino acid residue and its two sequence neighbors on C- and N- terminals. We also developed a method to identify DNA-binding proteins using electrical moments from structural information of proteins [ 6 ]. On the other hand, several investigators have reported that the use of evolutionary information in sequence-based predictions of secondary structure and solvent accessibility can improve the prediction capacity of a neural network [ 7 - 10 ].Here we report the use of such evolutionary information in improving the prediction of DNA-binding sites of proteins. We note that one of the major problems in applying evolutionary information by way of position specific scoring matrices (PSSMs) for sequence based prediction is that such matrices are generated over large data sets and take a long time to complete. Thus large scale predictions remain inaccessible to moderately capable computers. This is a serious limitation in the portability of neural network based predictions using PSSMs [ 8 ]. In this work, we report that evolutionary profiles or PSSMs against much smaller representative reference data sets may be utilized to achieve almost the same levels of prediction as would be obtained from alignments with large sequence data sets representing entire available sequence space. We have used four different reference data sets of PSSMs for 62 representative protein sequences. These are (1) PDNA-RDN: a data set of protein sequences from all Protein-DNA complexes from the PDB, (2) PDNA-NR90: a non-redundant data set compiled from PDNA-RDN, (3) PDB-ALL: a data set of all amino acid sequences from PDB and (4) NCBI-NR: a non-redundant data set of all protein sequences taken from sequence and structure databases and compiled by NCBI (see Methods). We find that the net prediction (an average of sensitivity and specificity) of the best of these systems (using PIR sequence data as reference) improves to 67.1% from the value of 58.4% reported earlier for a sequence-only prediction. We also report that a small reference data set of 375 sequences (PDNA-NR90) can give a 64.6% net prediction – just 2.5% poorer than the best- while reducing the PSSM calculation time from more than two hours (against NCBI-NR) to just about one minute. A better compromise could be the use of PDNA-RDN data for which 65.2% net prediction #150; 1.9% less than the best- was obtained, while about 2 and a half minutes are taken to generate their PSSMs. It is also reported that the presence of redundancy is helpful in improving the prediction whereas presence of data not relevant for DNA-binding may in some cases reduce predictive performance.Results and discussion Position Specific Iterative BLAST (PSI BLAST) is a strong measure of residue conservation in a given location. In the absence of any alignments, PSI BLAST simply returns a 20-dimensional vector representing probabilities of conservation against mutations to 20 different amino acids including itself. A matrix consisting of such vector representations for all residues in a given sequence is called Position Specific Scoring Matrix or PSSM. When a residue is conserved through cycles of PSI BLAST, it is likely to be due to a purpose i.e. biological function. It has been established by several authors cited in the introduction that the prediction of structural properties is significantly enhanced by the use of PSSMs compared to predictions based on unique representations of amino acid sequence and its environment. Protein structure universe is vast and a prediction of structural properties should span the entire range of this diversity. However, the question of predicting DNA-binding sites is much narrower and hence the significance of conservation of residues at specific locations may be limited to a subset of this protein space. Such reduction in the protein search space or the reference data sets against which PSSM-based predictions should be attempted is desired for a rapid prediction of binding sites as well as portability of prediction methods. Compact reference data size can not only answer these questions of speed and portability but also try to minimize noise in information contents and improve prediction quality. Table  1  shows the results of DNA-binding site prediction using different sets of PSSMs as the neural network inputs. The best net prediction results were 67.1% which is 8.7% better than the predictions with sequence information only. These results were obtained for PSSMs against PIR sequence data. An even larger NCBI-NR data set showed a slightly smaller (66.7%) net prediction. The fact that NCBI-NR reference data sets produce somewhat worse results than PIR sequence suggests that the redundancy present in the PIR sequence data could be the factor responsible for giving better PSSMs than those of a non-redundant NCBI sequence data. Thus an overall redundancy in the data turns out to be helpful in improving the prediction of binding sites. The question is how rapidly the prediction ability will fall if we reduce the redundancy even further, replacing the larger data sets with smaller ones until a small representative data set is left. This question is partly answered by first using a sequence database of the entire protein data bank (PDB-ALL), which gives an accuracy of 64.7% (about 2.4% poorer than the best). Further reducing the data set to protein sequences from only the Protein-DNA complexes surprisingly increases the net prediction to 65.2%. We suggest that the increase in net prediction on the PDNA-RDN over the entire PDB is caused by the fact that PDNA-RDN contains all the data from PDB which is relevant for the DNA-binding. However, an additional data in the PDB-ALL represents conservation scores in regions not involved in DNA-binding and hence lead to a somewhat lower net prediction. Going further down from a redundant (PDNA-RDN) to a non-redundant (PDNA-NR90) sequence data of Protein-DNA complexes, we observe a 0.6% fall in net prediction- just about the same we observed from PIR to NCBI-NR. We attribute this fall in net prediction to the reduction in the redundancy in the sequence data sets, which is concluded to be useful in better prediction of DNA-binding sites. In terms of CPU time, it may be noted that the time taken by 62 protein sequences used here is about one hour for the best (PIR) data sets. These times are prohibitively large for making predictions at a genomic scale or for providing rapid web services. A compromise could be obtained by using PDNA-RDN instead, which reduces the CPU time by a factor more than 8. The loss of net prediction for this compromise is about 1.9%, which is still 6.8% better than the predictions obtained from sequence information only. PSSMs against this data set for a typical protein of 500 residues can be generated in about 1 s, making it possible to run large scale predictions. A smaller size of reference data and high speed of PSSMs also make this method portable and light weight with a strong predictive ability. Binary decision function of the neural network (see Methods) assigns a value of zero (not binding) or 1 (binding) based on a threshold on the real value output received at the output node. Most of the accuracy scores presented here have been obtained by using 0.5 as the cutoff (mid point of the transfer function range). By changing this threshold from 0.5 to higher and lower values, the balance between sensitivity and specificity can be adjusted. In our online prediction we also present the scores obtained for a ROC analysis of such adjustments (Figure  2 ). ROC for only one reference data set has been shown here as most other graphs show a similar behavior.
Results and discussion
Position Specific Iterative BLAST (PSI BLAST) is a strong measure of residue conservation in a given location. In the absence of any alignments, PSI BLAST simply returns a 20-dimensional vector representing probabilities of conservation against mutations to 20 different amino acids including itself. A matrix consisting of such vector representations for all residues in a given sequence is called Position Specific Scoring Matrix or PSSM. When a residue is conserved through cycles of PSI BLAST, it is likely to be due to a purpose i.e. biological function. It has been established by several authors cited in the introduction that the prediction of structural properties is significantly enhanced by the use of PSSMs compared to predictions based on unique representations of amino acid sequence and its environment. Protein structure universe is vast and a prediction of structural properties should span the entire range of this diversity. However, the question of predicting DNA-binding sites is much narrower and hence the significance of conservation of residues at specific locations may be limited to a subset of this protein space. Such reduction in the protein search space or the reference data sets against which PSSM-based predictions should be attempted is desired for a rapid prediction of binding sites as well as portability of prediction methods. Compact reference data size can not only answer these questions of speed and portability but also try to minimize noise in information contents and improve prediction quality.Table  1  shows the results of DNA-binding site prediction using different sets of PSSMs as the neural network inputs. The best net prediction results were 67.1% which is 8.7% better than the predictions with sequence information only. These results were obtained for PSSMs against PIR sequence data. An even larger NCBI-NR data set showed a slightly smaller (66.7%) net prediction. The fact that NCBI-NR reference data sets produce somewhat worse results than PIR sequence suggests that the redundancy present in the PIR sequence data could be the factor responsible for giving better PSSMs than those of a non-redundant NCBI sequence data. Thus an overall redundancy in the data turns out to be helpful in improving the prediction of binding sites. The question is how rapidly the prediction ability will fall if we reduce the redundancy even further, replacing the larger data sets with smaller ones until a small representative data set is left. This question is partly answered by first using a sequence database of the entire protein data bank (PDB-ALL), which gives an accuracy of 64.7% (about 2.4% poorer than the best). Further reducing the data set to protein sequences from only the Protein-DNA complexes surprisingly increases the net prediction to 65.2%. We suggest that the increase in net prediction on the PDNA-RDN over the entire PDB is caused by the fact that PDNA-RDN contains all the data from PDB which is relevant for the DNA-binding. However, an additional data in the PDB-ALL represents conservation scores in regions not involved in DNA-binding and hence lead to a somewhat lower net prediction. Going further down from a redundant (PDNA-RDN) to a non-redundant (PDNA-NR90) sequence data of Protein-DNA complexes, we observe a 0.6% fall in net prediction- just about the same we observed from PIR to NCBI-NR. We attribute this fall in net prediction to the reduction in the redundancy in the sequence data sets, which is concluded to be useful in better prediction of DNA-binding sites.In terms of CPU time, it may be noted that the time taken by 62 protein sequences used here is about one hour for the best (PIR) data sets. These times are prohibitively large for making predictions at a genomic scale or for providing rapid web services. A compromise could be obtained by using PDNA-RDN instead, which reduces the CPU time by a factor more than 8. The loss of net prediction for this compromise is about 1.9%, which is still 6.8% better than the predictions obtained from sequence information only. PSSMs against this data set for a typical protein of 500 residues can be generated in about 1 s, making it possible to run large scale predictions. A smaller size of reference data and high speed of PSSMs also make this method portable and light weight with a strong predictive ability.Binary decision function of the neural network (see Methods) assigns a value of zero (not binding) or 1 (binding) based on a threshold on the real value output received at the output node. Most of the accuracy scores presented here have been obtained by using 0.5 as the cutoff (mid point of the transfer function range). By changing this threshold from 0.5 to higher and lower values, the balance between sensitivity and specificity can be adjusted. In our online prediction we also present the scores obtained for a ROC analysis of such adjustments (Figure  2 ). ROC for only one reference data set has been shown here as most other graphs show a similar behavior.Online predictions We have provided online predictions based on the above method at our web site [ 16 ]. The raw probability scores, their annotations at different sensitivity thresholds, and a reference scale for expected sensitivity and specificity have been provided. In addition, results of sequence alignments obtained after PSI BLAST iterations against a reference data (PDNA-RDN) are also provided. This allows us to have a complete picture of similarity of a given sequence with known DNA-binding proteins and predictions based on neural network using alignment profiles in the form of PSSM. The only input to this neural network is the amino acid sequence of the protein. The web server will automatically generate PSSMs of the given sequence against a reference data and use them as the input to a neural network, trained for predictions of 62 DNA-binding proteins.
Online predictions
We have provided online predictions based on the above method at our web site [ 16 ]. The raw probability scores, their annotations at different sensitivity thresholds, and a reference scale for expected sensitivity and specificity have been provided. In addition, results of sequence alignments obtained after PSI BLAST iterations against a reference data (PDNA-RDN) are also provided. This allows us to have a complete picture of similarity of a given sequence with known DNA-binding proteins and predictions based on neural network using alignment profiles in the form of PSSM. The only input to this neural network is the amino acid sequence of the protein. The web server will automatically generate PSSMs of the given sequence against a reference data and use them as the input to a neural network, trained for predictions of 62 DNA-binding proteins.Conclusion A PSSM-based neural network method for predicting DNA-binding sites in proteins has been developed. PSSMs were developed against different data sets and it was observed that significant computer time can be saved by replacing the reference data sets with much smaller reference data sets without loss of much prediction ability. Redundant reference data sets show a better prediction than the non-redundant data sets. A web server was developed to provide prediction of DNA-binding sites based on this method. In addition, the web server provides BLAST alignments against a reference data set of known DNA-binding proteins.
Conclusion
A PSSM-based neural network method for predicting DNA-binding sites in proteins has been developed. PSSMs were developed against different data sets and it was observed that significant computer time can be saved by replacing the reference data sets with much smaller reference data sets without loss of much prediction ability. Redundant reference data sets show a better prediction than the non-redundant data sets. A web server was developed to provide prediction of DNA-binding sites based on this method. In addition, the web server provides BLAST alignments against a reference data set of known DNA-binding proteins.Methods Data sets PDNA-62 This is the (non redundant) target data set of 62 DNA-binding proteins from Protein Data Bank (PDB) [ 11 ]. The same data set has been used in our related studies [ 5 , 12 ]. PDNA-RDN This is a new data set, developed for this work. We have selected all Protein-DNA complexes from PDB and separated their chains. 1386 protein chains were obtained in this way. FASTA formatted sequences were subsequently formatted using  formatdb  program of the BLAST package [ 13 ]. PDNA-NR90 The data set ( PDNA-RDN ), obtained from the procedure mentioned above was filtered to remove redundancy at 90% sequence identity level by using sequence clustering program BLASTCLUST [ 13 ]. Resulting data set now contains 375 sequences which are formatted for use as a reference data set using  formatdb . This data set is called  PDNA-NR90 . Other data sets PDB-ALL  (47,189 sequences) is a data set of all protein sequences obtained from NCBI.  PIR  is the sequence data set (283,177 sequences) of Protein Information Resource at Georgetown University [ 14 ]. NCBI-NR is a non-redundant data set of all protein sequences compiled from GeneBank, PIR, SwissProt, PDB and other resources by NCBI [ 17 ]. Generation of PSSMs Target sequences are scanned against the reference data sets to compile a set of alignment profiles or position specific scoring matrices (PSSMs) using Position Specific Iterative BLAST (PSI BLAST) program [ 15 ]. Three cycles of PSI-BLAST were run for each protein and the scores were saved as profile matrices (PSSMs). Neural network Neural network inputs Conservation scores in 20 amino acid positions for every residues form 20 columns (column 3 onwards) of corresponding row in a PSI-BLAST PSSM. For every residue, we make a binary or real-value (interpreted as probability) prediction of that residue being a binding site or not. Input for every prediction is the PSSM score on the row corresponding to this target residue and two more rows on either side, totaling 20 × 5 = 100 inputs (Figure  1 ). Network architecture and transfer function We use a neural network with one hidden layer (two nodes) in addition to the input layer described above and a single node output layer. Large number of units in the hidden layer and additional layers were not tried because the data size does not justify an unreasonably large neural network. Network signal is transferred to subsequent layers by an algebraic summation of inputs from the previous layer. Total signal in the last unit is transformed to a real output by a binary decision function much in the same way as in our previous work except that the input to the network is now replaced by PSSM scores rather than 20 bit binary coding [ 5 ]. Training and validation A six-fold cross-validation has been used in this work. Out of 62 proteins, 10 were removed at one time and the remaining 52 were trained until the accuracy on the left-out 10 also improved. Six random sets are created in this way and the figures in Table  1  report the averages on all six runs of each set of 10 proteins. Training error function and measure of prediction quality Data imbalance in the two binary categories for this neural network makes the choice of error function particularly important. We have used an accuracy score called Net Prediction, which is the average of sensitivity and specificity values defined below. Neural network learns to maximize this accuracy score rather than minimizing an error function. Sensitivity is defined as the number of correct prediction in the binding category relative to total number of such items in the original data and specificity is the number of correctly rejected residues in this category relative to the total number of non-binding residues in the original data. Sensitivity (S1) = 100 * TP/(TP+FN)     (1) Specificity (S2) = 100 * TN/(TN+FP)     (2) where TP: True Positive; TN: True Negative; FP: False Positive; FN: False Negative Relative number of true positive (TP) values in the prediction was termed as accuracy in our previous work. We have avoided using that term here, as we prefer a more operational definition of accuracy measures here. The imbalance of sensitivity (S1) and specificity (S2) is taken care of by comparing the Net Prediction of the models which gives a better comparison when S1 and S2 vary from one sample to the other. Thus, Net Prediction (NP) = (S1+S2)/2     (3)
Methods
Data sets PDNA-62 This is the (non redundant) target data set of 62 DNA-binding proteins from Protein Data Bank (PDB) [ 11 ]. The same data set has been used in our related studies [ 5 , 12 ]. PDNA-RDN This is a new data set, developed for this work. We have selected all Protein-DNA complexes from PDB and separated their chains. 1386 protein chains were obtained in this way. FASTA formatted sequences were subsequently formatted using  formatdb  program of the BLAST package [ 13 ]. PDNA-NR90 The data set ( PDNA-RDN ), obtained from the procedure mentioned above was filtered to remove redundancy at 90% sequence identity level by using sequence clustering program BLASTCLUST [ 13 ]. Resulting data set now contains 375 sequences which are formatted for use as a reference data set using  formatdb . This data set is called  PDNA-NR90 . Other data sets PDB-ALL  (47,189 sequences) is a data set of all protein sequences obtained from NCBI.  PIR  is the sequence data set (283,177 sequences) of Protein Information Resource at Georgetown University [ 14 ]. NCBI-NR is a non-redundant data set of all protein sequences compiled from GeneBank, PIR, SwissProt, PDB and other resources by NCBI [ 17 ].
Data sets
PDNA-62 This is the (non redundant) target data set of 62 DNA-binding proteins from Protein Data Bank (PDB) [ 11 ]. The same data set has been used in our related studies [ 5 , 12 ].
PDNA-62
This is the (non redundant) target data set of 62 DNA-binding proteins from Protein Data Bank (PDB) [ 11 ]. The same data set has been used in our related studies [ 5 , 12 ].PDNA-RDN This is a new data set, developed for this work. We have selected all Protein-DNA complexes from PDB and separated their chains. 1386 protein chains were obtained in this way. FASTA formatted sequences were subsequently formatted using  formatdb  program of the BLAST package [ 13 ].
PDNA-RDN
This is a new data set, developed for this work. We have selected all Protein-DNA complexes from PDB and separated their chains. 1386 protein chains were obtained in this way. FASTA formatted sequences were subsequently formatted using  formatdb  program of the BLAST package [ 13 ].formatdbPDNA-NR90 The data set ( PDNA-RDN ), obtained from the procedure mentioned above was filtered to remove redundancy at 90% sequence identity level by using sequence clustering program BLASTCLUST [ 13 ]. Resulting data set now contains 375 sequences which are formatted for use as a reference data set using  formatdb . This data set is called  PDNA-NR90 .
PDNA-NR90
The data set ( PDNA-RDN ), obtained from the procedure mentioned above was filtered to remove redundancy at 90% sequence identity level by using sequence clustering program BLASTCLUST [ 13 ]. Resulting data set now contains 375 sequences which are formatted for use as a reference data set using  formatdb . This data set is called  PDNA-NR90 .PDNA-RDNformatdbPDNA-NR90Other data sets PDB-ALL  (47,189 sequences) is a data set of all protein sequences obtained from NCBI.  PIR  is the sequence data set (283,177 sequences) of Protein Information Resource at Georgetown University [ 14 ]. NCBI-NR is a non-redundant data set of all protein sequences compiled from GeneBank, PIR, SwissProt, PDB and other resources by NCBI [ 17 ].
Other data sets
PDB-ALL  (47,189 sequences) is a data set of all protein sequences obtained from NCBI.  PIR  is the sequence data set (283,177 sequences) of Protein Information Resource at Georgetown University [ 14 ]. NCBI-NR is a non-redundant data set of all protein sequences compiled from GeneBank, PIR, SwissProt, PDB and other resources by NCBI [ 17 ].PDB-ALLPIRGeneration of PSSMs Target sequences are scanned against the reference data sets to compile a set of alignment profiles or position specific scoring matrices (PSSMs) using Position Specific Iterative BLAST (PSI BLAST) program [ 15 ]. Three cycles of PSI-BLAST were run for each protein and the scores were saved as profile matrices (PSSMs).
Generation of PSSMs
Target sequences are scanned against the reference data sets to compile a set of alignment profiles or position specific scoring matrices (PSSMs) using Position Specific Iterative BLAST (PSI BLAST) program [ 15 ]. Three cycles of PSI-BLAST were run for each protein and the scores were saved as profile matrices (PSSMs).Neural network Neural network inputs Conservation scores in 20 amino acid positions for every residues form 20 columns (column 3 onwards) of corresponding row in a PSI-BLAST PSSM. For every residue, we make a binary or real-value (interpreted as probability) prediction of that residue being a binding site or not. Input for every prediction is the PSSM score on the row corresponding to this target residue and two more rows on either side, totaling 20 × 5 = 100 inputs (Figure  1 ). Network architecture and transfer function We use a neural network with one hidden layer (two nodes) in addition to the input layer described above and a single node output layer. Large number of units in the hidden layer and additional layers were not tried because the data size does not justify an unreasonably large neural network. Network signal is transferred to subsequent layers by an algebraic summation of inputs from the previous layer. Total signal in the last unit is transformed to a real output by a binary decision function much in the same way as in our previous work except that the input to the network is now replaced by PSSM scores rather than 20 bit binary coding [ 5 ]. Training and validation A six-fold cross-validation has been used in this work. Out of 62 proteins, 10 were removed at one time and the remaining 52 were trained until the accuracy on the left-out 10 also improved. Six random sets are created in this way and the figures in Table  1  report the averages on all six runs of each set of 10 proteins. Training error function and measure of prediction quality Data imbalance in the two binary categories for this neural network makes the choice of error function particularly important. We have used an accuracy score called Net Prediction, which is the average of sensitivity and specificity values defined below. Neural network learns to maximize this accuracy score rather than minimizing an error function. Sensitivity is defined as the number of correct prediction in the binding category relative to total number of such items in the original data and specificity is the number of correctly rejected residues in this category relative to the total number of non-binding residues in the original data. Sensitivity (S1) = 100 * TP/(TP+FN)     (1) Specificity (S2) = 100 * TN/(TN+FP)     (2) where TP: True Positive; TN: True Negative; FP: False Positive; FN: False Negative Relative number of true positive (TP) values in the prediction was termed as accuracy in our previous work. We have avoided using that term here, as we prefer a more operational definition of accuracy measures here. The imbalance of sensitivity (S1) and specificity (S2) is taken care of by comparing the Net Prediction of the models which gives a better comparison when S1 and S2 vary from one sample to the other. Thus, Net Prediction (NP) = (S1+S2)/2     (3)
Neural network
Neural network inputs Conservation scores in 20 amino acid positions for every residues form 20 columns (column 3 onwards) of corresponding row in a PSI-BLAST PSSM. For every residue, we make a binary or real-value (interpreted as probability) prediction of that residue being a binding site or not. Input for every prediction is the PSSM score on the row corresponding to this target residue and two more rows on either side, totaling 20 × 5 = 100 inputs (Figure  1 ).
Neural network inputs
Conservation scores in 20 amino acid positions for every residues form 20 columns (column 3 onwards) of corresponding row in a PSI-BLAST PSSM. For every residue, we make a binary or real-value (interpreted as probability) prediction of that residue being a binding site or not. Input for every prediction is the PSSM score on the row corresponding to this target residue and two more rows on either side, totaling 20 × 5 = 100 inputs (Figure  1 ).Network architecture and transfer function We use a neural network with one hidden layer (two nodes) in addition to the input layer described above and a single node output layer. Large number of units in the hidden layer and additional layers were not tried because the data size does not justify an unreasonably large neural network. Network signal is transferred to subsequent layers by an algebraic summation of inputs from the previous layer. Total signal in the last unit is transformed to a real output by a binary decision function much in the same way as in our previous work except that the input to the network is now replaced by PSSM scores rather than 20 bit binary coding [ 5 ].
Network architecture and transfer function
We use a neural network with one hidden layer (two nodes) in addition to the input layer described above and a single node output layer. Large number of units in the hidden layer and additional layers were not tried because the data size does not justify an unreasonably large neural network. Network signal is transferred to subsequent layers by an algebraic summation of inputs from the previous layer. Total signal in the last unit is transformed to a real output by a binary decision function much in the same way as in our previous work except that the input to the network is now replaced by PSSM scores rather than 20 bit binary coding [ 5 ].Training and validation A six-fold cross-validation has been used in this work. Out of 62 proteins, 10 were removed at one time and the remaining 52 were trained until the accuracy on the left-out 10 also improved. Six random sets are created in this way and the figures in Table  1  report the averages on all six runs of each set of 10 proteins.
Training and validation
A six-fold cross-validation has been used in this work. Out of 62 proteins, 10 were removed at one time and the remaining 52 were trained until the accuracy on the left-out 10 also improved. Six random sets are created in this way and the figures in Table  1  report the averages on all six runs of each set of 10 proteins.Training error function and measure of prediction quality Data imbalance in the two binary categories for this neural network makes the choice of error function particularly important. We have used an accuracy score called Net Prediction, which is the average of sensitivity and specificity values defined below. Neural network learns to maximize this accuracy score rather than minimizing an error function. Sensitivity is defined as the number of correct prediction in the binding category relative to total number of such items in the original data and specificity is the number of correctly rejected residues in this category relative to the total number of non-binding residues in the original data. Sensitivity (S1) = 100 * TP/(TP+FN)     (1) Specificity (S2) = 100 * TN/(TN+FP)     (2) where TP: True Positive; TN: True Negative; FP: False Positive; FN: False Negative Relative number of true positive (TP) values in the prediction was termed as accuracy in our previous work. We have avoided using that term here, as we prefer a more operational definition of accuracy measures here. The imbalance of sensitivity (S1) and specificity (S2) is taken care of by comparing the Net Prediction of the models which gives a better comparison when S1 and S2 vary from one sample to the other. Thus, Net Prediction (NP) = (S1+S2)/2     (3)
Training error function and measure of prediction quality
Data imbalance in the two binary categories for this neural network makes the choice of error function particularly important. We have used an accuracy score called Net Prediction, which is the average of sensitivity and specificity values defined below. Neural network learns to maximize this accuracy score rather than minimizing an error function.Sensitivity is defined as the number of correct prediction in the binding category relative to total number of such items in the original data and specificity is the number of correctly rejected residues in this category relative to the total number of non-binding residues in the original data.Sensitivity (S1) = 100 * TP/(TP+FN)     (1)Specificity (S2) = 100 * TN/(TN+FP)     (2)where TP: True Positive; TN: True Negative; FP: False Positive; FN: False NegativeRelative number of true positive (TP) values in the prediction was termed as accuracy in our previous work. We have avoided using that term here, as we prefer a more operational definition of accuracy measures here. The imbalance of sensitivity (S1) and specificity (S2) is taken care of by comparing the Net Prediction of the models which gives a better comparison when S1 and S2 vary from one sample to the other. Thus,Net Prediction (NP) = (S1+S2)/2     (3)List of abbreviations PSSM: Position Specific Substitution Matrix PSI BLAST: Position Specific Iterative Basic Local Alignment Search Tool
List of abbreviations
PSSM: Position Specific Substitution MatrixPSI BLAST: Position Specific Iterative Basic Local Alignment Search ToolAuthors' contributions SA conceived and implemented this project. AS contributed in manuscript preparation, results analysis and discussions.
Authors' contributions
SA conceived and implemented this project. AS contributed in manuscript preparation, results analysis and discussions.


Background Clitoromegaly is a frequent congenital malformation, but acquired clitoral enlargement is relatively rare.
Background
Clitoromegaly is a frequent congenital malformation, but acquired clitoral enlargement is relatively rare.Methods Two acquired clitoromegaly cases treated in Atatürk Training Hospital, Izmir, Turkey are presented.
Methods
Two acquired clitoromegaly cases treated in Atatürk Training Hospital, Izmir, Turkey are presented.Results History from both patients revealed clitoromegaly over the last three years. Neither gynecological nor systemic abnormalities were detected in either patient. Karyotype analyses and hormonal tests were normal. Abdominal and gynaecological ultrasound did not show any cystic lesion or other abnormal finding. Computerized tomography scan of the adrenal glands was normal. Clitoroplasty with preservation of neurovascular pedicles was performed for the treatment of clitoromegaly.
Results
History from both patients revealed clitoromegaly over the last three years. Neither gynecological nor systemic abnormalities were detected in either patient. Karyotype analyses and hormonal tests were normal. Abdominal and gynaecological ultrasound did not show any cystic lesion or other abnormal finding. Computerized tomography scan of the adrenal glands was normal. Clitoroplasty with preservation of neurovascular pedicles was performed for the treatment of clitoromegaly.Conclusion The patients were diagnosed as "idiopathic isolated" clitoromegaly. To the best of our knowledge, there has been no detailed report about idiopathic clitoromegaly in the literature.
Conclusion
The patients were diagnosed as "idiopathic isolated" clitoromegaly. To the best of our knowledge, there has been no detailed report about idiopathic clitoromegaly in the literature.

Case reports Two cases with clitoromegaly were treated in Atatürk Training Hospital, Izmir, Turkey. A 22-year-old gravida 0 (case 1) and 19-year-old gravida 0 (case 2) presented with acquired clitoromegaly, leading to psychological distress. Histories taken from both patients revealed a gradually growing clitoris in the last three years, no history of drug abuse or family history of clitoromegaly and no clitoral irritation secondary to masturbation or other sexual functions. Case one had a phallus 20 mm in length that increased to 30 mm on arousal (Figure  1 ) and case two had a phallus 30 mm in length, which increased to 40 mm with arousal (Figure  2 ). Secondary sexual features were normal in both cases. Sexual hair was normal and there were no signs of hirsutism. The patients were not obese, weighing 65 and 68 kg, respectively. Neither patient had any sign of polycystic ovaries. No gynaecological or systemic abnormalities were detected in either patient. The only clinical finding was 'isolated clitoromegaly' on physical examination. Figure 1 View of the Case 1. Figure 2 View of the Case 2. Karyotype analysis was done in both cases and reported as 46, XX. Results of routine laboratory tests were normal. In addition, serum electrolytes, oestradiol, sex hormone binding globulin (SHBG), testosterone, androstenedione, dehydroepiandrosterone sulphate (DHEA-S), follicule stimulating hormone (FSH), luteinizing hormone (LH), 17 hydroxy progesterone (17-OH-P), prolactin, adrenocorticotropic hormone (ACTH), cortisol, placental lactogen (PL), deoxycorticosterone, deoxycortisol 11, triiodothyronine (T3), thyroxine (T4), thyroid stimulating hormone (TSH), beta-human chorionic gonadotrophin (β-hCG), carcinoembryonic antigen (CEA) were measured before the operations and the results were normal. 17-ketosteroid output in 24-hour-urine specimen was normal in both patients. Abdominal and gynecological ultrasound did not show any cystic lesion or abnormal finding. Computed tomography scan of the adrenal glands was normal. No abnormality suggestive of a possible relation to clitoromegaly was found in all laboratory and radiological tests. Both patients underwent clitoroplasty with preservation of the neurovascular pedicles under general anesthesia. A traction suture of 3/0 nylon was placed in the glans of clitoris (Figure  3 ). An incision was made on the lateral phallus perpendicular to the axis of the clitoral shaft, and carried through a 270 degree semicircular arc to the base of the glans as described by Papageorgoiou et al [ 1 ]. Two longitudinal incisions were made lateral to the dorsal neurovascular bundle. Two crura were identified, clamped and the mid-body of the clitoris was resected. The base of the glans was sutured to the divided corpora with 4/0 vicryl, and proximal and distal ends of the corpora were closed with 4/0 vicryl. The skin was closed with 4/0 vicryl sutures as well. Histopathological examinations of the resected specimens showed "normal corporal tissue". There was no abnormal finding on microscopic examination of the specimen obtained from clitoral and submucosal tissue. Figure 3 Traction of the clitoris per-operatively. Patients were followed up for one year after the operation. There was no early or late post-operative complication. Sensation was normal and patients were satisfied with the aesthetical and functional results.
Case reports
Two cases with clitoromegaly were treated in Atatürk Training Hospital, Izmir, Turkey.A 22-year-old gravida 0 (case 1) and 19-year-old gravida 0 (case 2) presented with acquired clitoromegaly, leading to psychological distress. Histories taken from both patients revealed a gradually growing clitoris in the last three years, no history of drug abuse or family history of clitoromegaly and no clitoral irritation secondary to masturbation or other sexual functions. Case one had a phallus 20 mm in length that increased to 30 mm on arousal (Figure  1 ) and case two had a phallus 30 mm in length, which increased to 40 mm with arousal (Figure  2 ). Secondary sexual features were normal in both cases. Sexual hair was normal and there were no signs of hirsutism. The patients were not obese, weighing 65 and 68 kg, respectively. Neither patient had any sign of polycystic ovaries. No gynaecological or systemic abnormalities were detected in either patient. The only clinical finding was 'isolated clitoromegaly' on physical examination.Karyotype analysis was done in both cases and reported as 46, XX. Results of routine laboratory tests were normal. In addition, serum electrolytes, oestradiol, sex hormone binding globulin (SHBG), testosterone, androstenedione, dehydroepiandrosterone sulphate (DHEA-S), follicule stimulating hormone (FSH), luteinizing hormone (LH), 17 hydroxy progesterone (17-OH-P), prolactin, adrenocorticotropic hormone (ACTH), cortisol, placental lactogen (PL), deoxycorticosterone, deoxycortisol 11, triiodothyronine (T3), thyroxine (T4), thyroid stimulating hormone (TSH), beta-human chorionic gonadotrophin (β-hCG), carcinoembryonic antigen (CEA) were measured before the operations and the results were normal. 17-ketosteroid output in 24-hour-urine specimen was normal in both patients. Abdominal and gynecological ultrasound did not show any cystic lesion or abnormal finding. Computed tomography scan of the adrenal glands was normal.No abnormality suggestive of a possible relation to clitoromegaly was found in all laboratory and radiological tests.Both patients underwent clitoroplasty with preservation of the neurovascular pedicles under general anesthesia. A traction suture of 3/0 nylon was placed in the glans of clitoris (Figure  3 ). An incision was made on the lateral phallus perpendicular to the axis of the clitoral shaft, and carried through a 270 degree semicircular arc to the base of the glans as described by Papageorgoiou et al [ 1 ]. Two longitudinal incisions were made lateral to the dorsal neurovascular bundle. Two crura were identified, clamped and the mid-body of the clitoris was resected. The base of the glans was sutured to the divided corpora with 4/0 vicryl, and proximal and distal ends of the corpora were closed with 4/0 vicryl. The skin was closed with 4/0 vicryl sutures as well. Histopathological examinations of the resected specimens showed "normal corporal tissue". There was no abnormal finding on microscopic examination of the specimen obtained from clitoral and submucosal tissue.Patients were followed up for one year after the operation. There was no early or late post-operative complication. Sensation was normal and patients were satisfied with the aesthetical and functional results.Discussion Clitoromegaly is a frequently seen congenital malformation, but acquired clitoral enlargement is rarely detected [ 2 ]. A detailed history and physical examination are required for the evaluation of clitoral enlargement because clitoromegaly may result from a variety of conditions [ 3 ]. The causes of clitoromegaly can be classified into four groups; hormonal conditions, non-hormonal conditions, pseudoclitoromegaly and idiopathic clitoromegaly (Table  1 ). Table 1 Classification of the clitoromegaly based on causative factors Causative factors of clitoromegaly A.  Hormonal conditions 1. Endocrinopathies 2. Masculinizing tumors 3. Exposure to the androgens 4. Syndromes B.  Non-Hormonal conditions 1. Neurofibromatosis 2. Epidermoid cysts 3. Syndromes 4. Nevus C.  Pseudoclitoromegaly D.  Idiopathic Endocrinopathies, masculinizing tumors, exposure to the androgens and various syndromes are the main hormonal causes of clitoromegaly. The most common cause is female pseudohermaphroditism secondary to congenital adrenal hyperplasia (CAH) or adrenogenital syndrome, caused by an enzyme defect in the normal pathway of steroid biosynthesis [ 4 ]. Virilization of the external genitalia may cause profound clitoromegaly but rarely causes formation of a true penile urethra. However, clitoromegaly may be accompanied by fusion of the labioscrotal folds and perineoscrotal hypospadias, and a persistence of the urogenital sinus closing the external opening of the vagina [ 5 ]. Bilateral hilus cell tumors of the ovary, steroid producing gonadal tumors, adrenal androgen-secreting carcinoma, Leydig cell tumor of the ovaries and metastatic carcinosarcoma of the urinary bladder have been reported to cause clitoromegaly [ 6 - 9 ]. Fetal exposure to danazol has been described as cause for clitoromegaly [ 10 ]. An interesting case was reported by Akcam and Topaloglu of clitoromegaly possibly following blood transfusion from an adult in a premature infant [ 11 ]. Among the non-hormonal conditions are neurofibromatosis (NF)[ 12 ], epidermoid cysts[ 3 ], various syndromes and nevus lipomatous cutaneous superficialis. The majority of clitoromegaly cases related to NF are congenital. Clitoral cysts arise from epidermis displaced into the dermis or the subcutaneous tissue either during the prenatal period or after a trauma. Various syndromes resulting from non-hormonal conditions may cause clitoromegaly. Kazlauskaite et al reported a case diagnosed as congenital generalized lipodystrophy (CGL) presenting with generalized body-fat loss, prominent musculature, hepatomegaly, clitoromegaly and mild hirsutism [ 13 ]. CGL is an autosomal recessive disorder characterized by severe metabolic derangement associated with the absence of subcutaneous adipose tissue and clitoromegaly. Turner syndrome (TS) is a chromosomal disorder in females and results from a partial or complete loss of an X chromosome. Abnormalities include short stature and gonadal dysgenesis. Haddad et al presented a case of clitoromegaly and TS [ 15 ]. Fraser syndrome is another rare cause of clitoromegaly [ 14 ]. Androgen insensitivity syndrome is a heterogeneous disorder with a wide spectrum of phenotypic abnormalities, ranging from a complete female phenotype to ambiguous forms that more closely resemble males. The primary abnormality is a defective androgen receptor protein due to mutation of the androgen receptor gene. Nevus lipomatous cutaneous superficialis (NLCS) is a relatively rare condition characterized by groups of ectopic fat cells dispersed in various parts of the body [ 16 ] that may cause clitoromegaly when located on the clitoris. Pseudohypertrophy of the clitoris has been reported in small girls due to masturbation: manipulations of the skin of prepuce leads to repeated mechanical trauma, which expands the prepuce and labia minora, thus imitating true clitoral enlargement [ 2 ]. The objectives of clitoroplasty are preservation of sexual arousal function and sensation, and cosmetic. Historically, until 1960s, clitoral hypertrophy was treated surgically by amputation (clitoridectomy)[ 4 ]. Surgical methods for correction of clitoral hypertrophy were first described in 1934 by Young, who performed an operation for clitoral reduction in a child with CAH [ 17 ]. Several clitoroplasty methods have been reported, but few describe preservation of dorsal and ventral neurovascular bundles in sexually mature women. Clitoroplasty with preservation of the neurovascular pedicle may be the optimal operative technique for the treatment of clitoromegaly.
Discussion
Clitoromegaly is a frequently seen congenital malformation, but acquired clitoral enlargement is rarely detected [ 2 ]. A detailed history and physical examination are required for the evaluation of clitoral enlargement because clitoromegaly may result from a variety of conditions [ 3 ]. The causes of clitoromegaly can be classified into four groups; hormonal conditions, non-hormonal conditions, pseudoclitoromegaly and idiopathic clitoromegaly (Table  1 ).Classification of the clitoromegaly based on causative factorsHormonal conditionsNon-Hormonal conditionsPseudoclitoromegalyIdiopathicEndocrinopathies, masculinizing tumors, exposure to the androgens and various syndromes are the main hormonal causes of clitoromegaly. The most common cause is female pseudohermaphroditism secondary to congenital adrenal hyperplasia (CAH) or adrenogenital syndrome, caused by an enzyme defect in the normal pathway of steroid biosynthesis [ 4 ]. Virilization of the external genitalia may cause profound clitoromegaly but rarely causes formation of a true penile urethra. However, clitoromegaly may be accompanied by fusion of the labioscrotal folds and perineoscrotal hypospadias, and a persistence of the urogenital sinus closing the external opening of the vagina [ 5 ].Bilateral hilus cell tumors of the ovary, steroid producing gonadal tumors, adrenal androgen-secreting carcinoma, Leydig cell tumor of the ovaries and metastatic carcinosarcoma of the urinary bladder have been reported to cause clitoromegaly [ 6 - 9 ].Fetal exposure to danazol has been described as cause for clitoromegaly [ 10 ]. An interesting case was reported by Akcam and Topaloglu of clitoromegaly possibly following blood transfusion from an adult in a premature infant [ 11 ].Among the non-hormonal conditions are neurofibromatosis (NF)[ 12 ], epidermoid cysts[ 3 ], various syndromes and nevus lipomatous cutaneous superficialis. The majority of clitoromegaly cases related to NF are congenital.Clitoral cysts arise from epidermis displaced into the dermis or the subcutaneous tissue either during the prenatal period or after a trauma.Various syndromes resulting from non-hormonal conditions may cause clitoromegaly. Kazlauskaite et al reported a case diagnosed as congenital generalized lipodystrophy (CGL) presenting with generalized body-fat loss, prominent musculature, hepatomegaly, clitoromegaly and mild hirsutism [ 13 ]. CGL is an autosomal recessive disorder characterized by severe metabolic derangement associated with the absence of subcutaneous adipose tissue and clitoromegaly. Turner syndrome (TS) is a chromosomal disorder in females and results from a partial or complete loss of an X chromosome. Abnormalities include short stature and gonadal dysgenesis. Haddad et al presented a case of clitoromegaly and TS [ 15 ]. Fraser syndrome is another rare cause of clitoromegaly [ 14 ].Androgen insensitivity syndrome is a heterogeneous disorder with a wide spectrum of phenotypic abnormalities, ranging from a complete female phenotype to ambiguous forms that more closely resemble males. The primary abnormality is a defective androgen receptor protein due to mutation of the androgen receptor gene.Nevus lipomatous cutaneous superficialis (NLCS) is a relatively rare condition characterized by groups of ectopic fat cells dispersed in various parts of the body [ 16 ] that may cause clitoromegaly when located on the clitoris.Pseudohypertrophy of the clitoris has been reported in small girls due to masturbation: manipulations of the skin of prepuce leads to repeated mechanical trauma, which expands the prepuce and labia minora, thus imitating true clitoral enlargement [ 2 ].The objectives of clitoroplasty are preservation of sexual arousal function and sensation, and cosmetic. Historically, until 1960s, clitoral hypertrophy was treated surgically by amputation (clitoridectomy)[ 4 ]. Surgical methods for correction of clitoral hypertrophy were first described in 1934 by Young, who performed an operation for clitoral reduction in a child with CAH [ 17 ]. Several clitoroplasty methods have been reported, but few describe preservation of dorsal and ventral neurovascular bundles in sexually mature women. Clitoroplasty with preservation of the neurovascular pedicle may be the optimal operative technique for the treatment of clitoromegaly.Competing interests The authors declare that they have no competing interests.
Competing interests
The authors declare that they have no competing interests.Authors' contributions EC  conceived the study and prepared the manuscript draft for submission.  AA, NS, OC  and  YO  did the literature search and participated in the preparation of the manuscript. All authors read and approved the final manuscript.
Authors' contributions
EC  conceived the study and prepared the manuscript draft for submission.  AA, NS, OC  and  YO  did the literature search and participated in the preparation of the manuscript.ECAA, NS, OCYOAll authors read and approved the final manuscript.