BACKGROUND
protein three-dimensional structure prediction directly from amino acid sequence is an important issue in bioinformatics.
an intermediate approach to this problem is to predict the so-called one-dimensional structural properties of proteins.
the solvent accessibility or accessible surface area  of an amino acid residue in a protein structure is one such property and the knowledge of this property can significantly enhance the overall structure and function prediction of proteins  <cit> .
given an amino acid sequence, the goal of such prediction is to estimate the asa of each residue making use of previously observed asa values taken from known protein structures.
the knowledge from previously observed structures is modeled using machine learning and other methods  <cit> .
various methods of predicting asa from sequence or sequence-derived evolutionary information have been developed such as neural networks  <cit> , bayesian analysis  <cit> , information theory  <cit> , multiple linear regressions  <cit> , and support vector machine  <cit> .
among these, machine-learning methods such as neural networks  <cit>  and support vector machines  <cit>  have been shown to be the most effective for asa prediction.
although methods to predict solvent accessibility of each atom have also been developed, more actively pursued area is to estimate residue-wise asa  <cit> .
in almost all asa prediction methods, solvent accessibility is first normalized to its relative value.
on the one hand, it is required for training some computational models with bound-value outputs and on the other, it gives a better idea of fractional exposure of a residue normalized by a hypothetical maximally exposed residue.
mere restricting the model outputs to finite values could have been achieved by simply rescaling all residue asas ignoring their identity .
however, the values obtained by such transformation would have little physical meaning.
moreover, the trained parameters required to model such transformed values may make the relationship between residue environments and target asa values even more difficult to model.
thus, each residue asa is typically normalized by its corresponding extended state asa  values, which uses the reference state of ala-x-ala for normalizing asa of residue x  <cit> .
thus  <dig> esa values are used to normalize all residue asas.
we argue that this type of normalization- although better than a single value for all amino-acids, still suffers from two shortcomings.
first of all, currently employed extended state of a tripeptide has no practical meaning for the residues in folded proteins and hence reference states should come from folded proteins rather than extended states.
secondly, the structural constraints imposed by actual sequence neighbors of residues are different from the case when the residue is surrounded by ala residues on its c- and n- terminals.
first of these two questions  could be answered by using the highest values of observed asa as reference rather than the extended state.
the second question of sequence context may be answered by using  <dig> ×  <dig> ×  <dig> possible reference states instead of  <dig>  there will still be a limitation that the highest observed asa may not represent the highest possible asa value due to the insufficient number of solved structures, and one has to be content with the approximations introduced by this.
our primary benchmark in asa normalization here is to estimate the effect of improved scaling criterion on the improvement in its prediction from sequence.
an unbiased role of normalization in prediction can be assessed by using high quality data sets and developing prediction models for the two systems of normalization under similar conditions.
most non-redundant data sets of protein structures, including those used for asa prediction are based on similarity and resolution conditions and largely ignore the incidence of missing atoms in structures .
this may be especially important for accurate calculation of solvent accessibility for each residue.
to unambiguously determine the role of normalization in prediction, we develop new data sets of protein structures by systematic quality check on structures and removing samples with missing atoms.
neural networks are then trained by using two different sets of values as target vectors.
finally, the asa values from both cases are transformed to absolute values in area units and performance comparison is made in terms of the mean absolute error and coefficient of correlation between predicted and experimental values of asa in area units.
results indicate that hoa based normalization can improve the performance of neural network based prediction.
the improvement depends on the type of residue and the score used to measure.
improvement in the correlation coefficient between predicted and experimental values reaches to as high as 10% using hoa instead of esa for normalization.
we also demonstrate that this type of normalization may be effective in estimating interface residues simply from their over-exposed status.
RESULTS
distribution of context-dependent asas
many tripeptides have higher asa in folded state than the ala-x-ala extended state
extended state asa has been used for normalization under the assumption that it represents unfolded state of the protein and would probably be more than any observed asa in real proteins.
however, as seen from figure  <dig>  the extended state asa  are not on the extreme right in any of the plots.
thus, for each residue there exist a number of tripeptide environments in which their asa can exceed ala-x-ala esa values.
figure  <dig> shows the fractional number of residues whose asa is more than the extended state asa as described above.
detailed numbers are provided in table  <dig>  clearly there are a significant number of such over-exposed residues, especially with polar or charged side chains.
this result is potentially of interest for computing free energy of denaturation, but we limit ourselves to the effect on sequence-based predictions.
typically, polar and charged residues have a significant number of such cases.
in some tripeptides esa values are never observed
hoa histograms of some residue types have sharp peaks
some residues such as his, lys and ser show a sharp peak in their histograms and most hoa values are close to esa values.
this suggests that the highest exposure these residues can have, depends only weakly on their neighbors and hoa-normalized values will at best rescale the esa-normalized values in these cases.
on the other hand residues such as cys and trp have less sharp peaks in their histogram, showing that the highest asa of these residues strongly depends on their sequence neighbors.
however, flatness of histograms is also caused by a low frequency of these residues in protein structures.
it remains to be explored if these residues will continue to have leptokurtosis in their histograms when more data on their tripeptides becomes available.
observation of asa values higher than esa has led in the past to relative asa being more than 100%.
using hoa-normalization, relative values will never cross 100% and may be more suitable for using a machine learning method for prediction.
however, the tripeptide data may be modified when more data becomes available and the results presented here may also need minor revisions with more solved structures.
correlations between hoa- and esa- normalized values are strong with subtle differences
correlation coefficient  between relative asa obtained after normalizing by the ala-x-ala extended state  and tripeptide highest observed asa  is also provided in the last column.
cc values are based on all residues with different sequence neighbor.
residues are arranged in the order of cc values.
abbreviations: res: residue id, freq: number of residues in ala-x-ala conformation, esa: extended state asa of ala-x-ala conformation, hoa: highest observed asa in y-x-z conformation , cc: correlation coefficient between esa-normalized and hoa-normalized values in the benchmark data set.
further statistics and implications of even these small differences in hoa and esa-normalized values are examined in the following sections.
overall asa distribution of a tripeptide
although, we are looking for the highest observed asa in tripeptides, we also analyzed the distribution of asa within all tripeptides of a given type.
a typical distribution is shown in figure  <dig> and it is observed that the tripeptides have asa diversity similar to the overall asa distribution of all residues.
hoa residues primarily come from coil, turn and bend conformations
secondary
for each tripeptide environment,  residue with highest asa is selected and distribution of such residues in various secondary structures is calculated.
why do hoa values differ from esa values?
hoa values obtained in the current work differ from the traditional esa values in the following ways.
actual protein environment instead of a purely computational extended state is used in our proposed method.
traditionally ala-x-ala or gly-x-gly environments are generated in simulation software.
these software programs produce a hypothetical state of a tripeptide, which may never actually be observed in a protein or even a fully capped tripeptide.
in our approach, we scan the entire set of observed tripeptides in actual crystal structures and the effect of solvent, charge screening and the effect of subsequent peptide bonds on neighbors are implicitly taken care of.
thus the new approach of finding a normalization value is more realistic than currently used method.
instead of assuming ala residues on both sides, a detailed residue context is used that allow for taking care of additional constraints as well as potential role in over-exposing some of the residues in a given context.
hoa from native structures versus molecular calculations
usual practice to estimate the highest possible asa of a residue is to carry out molecular calculations assuming simple aliphatic side chain neighbors on its n- and c-terminal positions.
as stated above, we depart from the conventional methods by  choosing native protein structures instead of simulated tripeptides to address the role of global protein environment and  considering n- and c- terminal context by obtaining  <dig> reference states rather than currently available  <dig>  in making these two departures, following questions may rightly be raised.
are there sufficient examples in pdb sto obtain highest observed asa  close enough to highest possible asa  for each tripeptide environment?
is there any advantage of using native protein structures over conformations derived from molecular simulations of tripeptides?
we discuss these issues in the following.
is there sufficient data for obtaining hoa values?
as stated above, highest observed asa , analyzed here may be different in some cases than the highest possible asa , because the ensemble size formed from the data set may not have sufficient number of representatives in the protein structures solved so far.
this insufficiency has partly been the reason that extended state has been generally used as a reference state.
to address this concern, we first note that the overall number of residues from which hoa values have been extracted here is ~ <dig>  million .
this is a sufficiently large data size and if some of the  <dig> tripeptide patterns have not shown up sufficiently in hoa ensemble of millions of residues, they must be indeed rare and that is unlikely to affect the results of current analysis.
we only use hoa of a residue for normalization if the hoa query for that tripeptide was based on a minimum number of observations in the universal ensemble of tripeptides .
to estimate the effect of insufficiency of hoa search space, we present some additional statistics as follows.
of all the  <dig> tripeptide patterns, more than 97% occur at least  <dig> times in the overall search space .
a frequency of  <dig> in oss corresponds to only a few occurrences  in the normalization benchmark dataset  whose size  is about 4% of oss.
it may be noted that occurrence of 4% of  <dig> patterns does not mean that there are anywhere close to 4% residues which were normalized by infrequent pattern hoa data because 4% refers to  <dig> possible tripeptide patterns and rarer of them are even rarer in the nbd dataset as 96% of  <dig> residue patterns are far more degenerate than are these 4% cases.
thus, although hoa values for very few tripeptides are based on a small number of observations, it is unlikely to affect the results of the current study.
the statistics will also be refined from time to time to take into account if any newly observed asa of a tripeptide suprpasses its currenly listed hoa value.
is there any advantage in using native conformations over simulated structures?
there are several advantages of using native-structure reference states rather than simulated structures.
first of all molecular simulation of a tripeptide requires definition of a conformation.
extended tripeptide state has been assumed to be the most exposed conformation presumably because it is expected that any folding should reduce the asa of a residue.
however, we observe that number of residues may be significantly more exposed in native proteins than their extended state.
for example figure  <dig> shows a ser residue to be more exposed in gln-ser-gly conformation  compared with its extended state  in ala-ser-ala derived from molecular simulations.
such observations of higher asa than esa are frequent as discussed throughout this work.
however, to establish that higher asa is not due to different c- and n-terminal residues only, we compared  <dig> esa values of ala-x-ala tripeptides with their corresponding  <dig> hoa values .
it is observed that in  <dig> of the  <dig> cases we did observe an asa of a residue even in identical ala-x-ala environment, which was higher than its corresponding extended state asa.
this shows that the native structure not only constraints a residue's asa but also makes it more exposed in certain conformations.
out of  <dig> remaining residue types pro and val, show a difference of less than  <dig> Å <dig> but in six cases  hoa's are always significantly lower than esa values despite a sufficiently large number of their occurrence  respectively and it remains doubtful  that they will ever be observed close to their esa conformation in real proteins.
thus native state hoa based reference state carry information which would not be available from structures generated by molecular simulations.
implications to asa prediction
abbreviations: res: residue id, mae: mean absolute error in prediction measured in percentage points, cc: correlation coefficient, rel: relative asa values, abs: using absolute area units, t-test: student's t-test statistics, u-test: mann-whitney u-test
we also compared the mean absolute error in absolute asa prediction for data corresponding to each of the  <dig> tripeptide and found that in  <dig>  cases mean absolute error of hoa-normalized tripeptides was lower compared to  <dig>  cases in which hoa-normalized tripeptide prediction error was higher .
this shows that there are many more tripeptides contexts in which prediction performance is improved by using hoa-normalization, than those whose performance fell .
test of significance on individual tripeptides is not possible because the prediction is performed on a  non-redundant data set of proteins.
there are about  <dig> residue-wise predictions for  <dig> patterns implying ~ <dig> instances per tripeptide type on the average.
given that prediction itself is not 100% accurate and prediction errors have large standard deviations, it is not possible to detect statistical significance in the differences between prediction performances for each of these tripeptide patterns.
application to interface prediction
the aim of the present work is to analyze the statistics of highest observed asa of residues in proteins and its implications to prediction of asa itself.
however, it is useful to see if the newly defined relative asa values carry any more physical meaning than what is already available in relative asa obtained by esa-based normalization.
the most important meaning of relative solvent accessibility , in contrast to absolute accessible surface area  is to characterize them between exposed and buried residues at given cutoffs.
one of the most important applications of this characterization is to determine a relationship between ''unusual exposure'' and ''interface propensity''  <cit> .
based on this characterization, it is expected that the exposed residues contain a greater fractional number of binding sites than the buried ones.
we examined if such a distinction could be improved by the proposed method of normalization.
figure  <dig> shows a graph of the difference between fractional number of binding and non-binding residues in various ranges of rsa based on esa and hoa definitions of normalization.
for comparison a third plot based on absolute asa, scaled to  <dig> has also been shown.
binding sites data is as reported by us in our previous works  <cit> .
from this graph a clear improvement is seen in the enriched number of binding residues in the exposed regions defined by the hoa normalization.
this demonstrates another useful application of fine-tuning the rsa calculations as described in this work.
availability
a perl program to normalize asa values by the proposed method has been provided for download on the web at .
this program converts absolute asa values to hoa- or esa-normalized values and vice-versa.
users can also provide their own hoa data, which enables a quick update or return to esa values for some of the tripeptides.
hoa data will be regularly updated if higher asa values are observed for a new tripeptide.
CONCLUSIONS
in this study, we developed the statistics of highest observed asa in various tripeptide environments of residues.
using asa data normalized by these asa values, we could predict asa with ~15% mae and  <dig>  correlations from evolutionary information.
individual residues show varied degrees of improvement in their prediction when trained with data normalized by new method.
we also show that the exposed regions defined by newly developed method of normalization are better enriched in binding sites for the dna-binding proteins.
it remains to be seen, if the proposed method of normalization has other universal applications, although the present observations suggest that trend.

 protein three-dimensional structure prediction directly from amino acid sequence is an important issue in bioinformatics. an intermediate approach to this problem is to predict the so-called one-dimensional structural properties of proteins. the solvent accessibility or accessible surface area of an amino acid residue in a protein structure is one such property and the knowledge of this property can significantly enhance the overall structure and function prediction of proteins. given an amino acid sequence, the goal of such prediction is to estimate the asa of each residue making use of previously observed asa values taken from known protein structures. the knowledge from previously observed structures is modeled using machine learning and other methods. various methods of predicting asa from sequence or sequence-derived evolutionary information have been developed such as neural networks, bayesian analysis, information theory, multiple linear regressions, and support vector machine. among these, machine-learning methods such as neural networks and support vector machines have been shown to be the most effective for asa prediction. although methods to predict solvent accessibility of each atom have also been developed, more actively pursued area is to estimate residue-wise asa. in almost all asa prediction methods, solvent accessibility is first normalized to its relative value. on the one hand, it is required for training some computational models with bound-value outputs and on the other, it gives a better idea of fractional exposure of a residue normalized by a hypothetical maximally exposed residue. mere restricting the model outputs to finite values could have been achieved by simply rescaling all residue asas ignoring their identity. however, the values obtained by such transformation would have little physical meaning. moreover, the trained parameters required to model such transformed values may make the relationship between residue environments and target asa values even more difficult to model. thus, each residue asa is typically normalized by its corresponding extended state asa values, which uses the reference state of ala-x-ala for normalizing asa of residue x. thus esa values are used to normalize all residue asas. we argue that this type of normalization- although better than a single value for all amino-acids, still suffers from two shortcomings. first of all, currently employed extended state of a tripeptide has no practical meaning for the residues in folded proteins and hence reference states should come from folded proteins rather than extended states. secondly, the structural constraints imposed by actual sequence neighbors of residues are different from the case when the residue is surrounded by ala residues on its c- and n- terminals. first of these two questions could be answered by using the highest values of observed asa as reference rather than the extended state. the second question of sequence context may be answered by using × × possible reference states instead of there will still be a limitation that the highest observed asa may not represent the highest possible asa value due to the insufficient number of solved structures, and one has to be content with the approximations introduced by this. our primary benchmark in asa normalization here is to estimate the effect of improved scaling criterion on the improvement in its prediction from sequence. an unbiased role of normalization in prediction can be assessed by using high quality data sets and developing prediction models for the two systems of normalization under similar conditions. most non-redundant data sets of protein structures, including those used for asa prediction are based on similarity and resolution conditions and largely ignore the incidence of missing atoms in structures. this may be especially important for accurate calculation of solvent accessibility for each residue. to unambiguously determine the role of normalization in prediction, we develop new data sets of protein structures by systematic quality check on structures and removing samples with missing atoms. neural networks are then trained by using two different sets of values as target vectors. finally, the asa values from both cases are transformed to absolute values in area units and performance comparison is made in terms of the mean absolute error and coefficient of correlation between predicted and experimental values of asa in area units. results indicate that hoa based normalization can improve the performance of neural network based prediction. the improvement depends on the type of residue and the score used to measure. improvement in the correlation coefficient between predicted and experimental values reaches to as high as 10% using hoa instead of esa for normalization. we also demonstrate that this type of normalization may be effective in estimating interface residues simply from their over-exposed status.  distribution of context-dependent asas many tripeptides have higher asa in folded state than the ala-x-ala extended state extended state asa has been used for normalization under the assumption that it represents unfolded state of the protein and would probably be more than any observed asa in real proteins. however, as seen from figure the extended state asa are not on the extreme right in any of the plots. thus, for each residue there exist a number of tripeptide environments in which their asa can exceed ala-x-ala esa values. figure shows the fractional number of residues whose asa is more than the extended state asa as described above. detailed numbers are provided in table clearly there are a significant number of such over-exposed residues, especially with polar or charged side chains. this result is potentially of interest for computing free energy of denaturation, but we limit ourselves to the effect on sequence-based predictions. typically, polar and charged residues have a significant number of such cases. in some tripeptides esa values are never observed hoa histograms of some residue types have sharp peaks some residues such as his, lys and ser show a sharp peak in their histograms and most hoa values are close to esa values. this suggests that the highest exposure these residues can have, depends only weakly on their neighbors and hoa-normalized values will at best rescale the esa-normalized values in these cases. on the other hand residues such as cys and trp have less sharp peaks in their histogram, showing that the highest asa of these residues strongly depends on their sequence neighbors. however, flatness of histograms is also caused by a low frequency of these residues in protein structures. it remains to be explored if these residues will continue to have leptokurtosis in their histograms when more data on their tripeptides becomes available. observation of asa values higher than esa has led in the past to relative asa being more than 100%. using hoa-normalization, relative values will never cross 100% and may be more suitable for using a machine learning method for prediction. however, the tripeptide data may be modified when more data becomes available and the results presented here may also need minor revisions with more solved structures. correlations between hoa- and esa- normalized values are strong with subtle differences correlation coefficient between relative asa obtained after normalizing by the ala-x-ala extended state and tripeptide highest observed asa is also provided in the last column. cc values are based on all residues with different sequence neighbor. residues are arranged in the order of cc values. abbreviations: res: residue id, freq: number of residues in ala-x-ala conformation, esa: extended state asa of ala-x-ala conformation, hoa: highest observed asa in y-x-z conformation, cc: correlation coefficient between esa-normalized and hoa-normalized values in the benchmark data set. further statistics and implications of even these small differences in hoa and esa-normalized values are examined in the following sections. overall asa distribution of a tripeptide although, we are looking for the highest observed asa in tripeptides, we also analyzed the distribution of asa within all tripeptides of a given type. a typical distribution is shown in figure and it is observed that the tripeptides have asa diversity similar to the overall asa distribution of all residues. hoa residues primarily come from coil, turn and bend conformations secondary for each tripeptide environment, residue with highest asa is selected and distribution of such residues in various secondary structures is calculated. why do hoa values differ from esa values? hoa values obtained in the current work differ from the traditional esa values in the following ways. actual protein environment instead of a purely computational extended state is used in our proposed method. traditionally ala-x-ala or gly-x-gly environments are generated in simulation software. these software programs produce a hypothetical state of a tripeptide, which may never actually be observed in a protein or even a fully capped tripeptide. in our approach, we scan the entire set of observed tripeptides in actual crystal structures and the effect of solvent, charge screening and the effect of subsequent peptide bonds on neighbors are implicitly taken care of. thus the new approach of finding a normalization value is more realistic than currently used method. instead of assuming ala residues on both sides, a detailed residue context is used that allow for taking care of additional constraints as well as potential role in over-exposing some of the residues in a given context. hoa from native structures versus molecular calculations usual practice to estimate the highest possible asa of a residue is to carry out molecular calculations assuming simple aliphatic side chain neighbors on its n- and c-terminal positions. as stated above, we depart from the conventional methods by choosing native protein structures instead of simulated tripeptides to address the role of global protein environment and considering n- and c- terminal context by obtaining reference states rather than currently available in making these two departures, following questions may rightly be raised. are there sufficient examples in pdb sto obtain highest observed asa close enough to highest possible asa for each tripeptide environment? is there any advantage of using native protein structures over conformations derived from molecular simulations of tripeptides? we discuss these issues in the following. is there sufficient data for obtaining hoa values? as stated above, highest observed asa, analyzed here may be different in some cases than the highest possible asa, because the ensemble size formed from the data set may not have sufficient number of representatives in the protein structures solved so far. this insufficiency has partly been the reason that extended state has been generally used as a reference state. to address this concern, we first note that the overall number of residues from which hoa values have been extracted here is ~ million. this is a sufficiently large data size and if some of the tripeptide patterns have not shown up sufficiently in hoa ensemble of millions of residues, they must be indeed rare and that is unlikely to affect the results of current analysis. we only use hoa of a residue for normalization if the hoa query for that tripeptide was based on a minimum number of observations in the universal ensemble of tripeptides. to estimate the effect of insufficiency of hoa search space, we present some additional statistics as follows. of all the tripeptide patterns, more than 97% occur at least times in the overall search space. a frequency of in oss corresponds to only a few occurrences in the normalization benchmark dataset whose size is about 4% of oss. it may be noted that occurrence of 4% of patterns does not mean that there are anywhere close to 4% residues which were normalized by infrequent pattern hoa data because 4% refers to possible tripeptide patterns and rarer of them are even rarer in the nbd dataset as 96% of residue patterns are far more degenerate than are these 4% cases. thus, although hoa values for very few tripeptides are based on a small number of observations, it is unlikely to affect the results of the current study. the statistics will also be refined from time to time to take into account if any newly observed asa of a tripeptide suprpasses its currenly listed hoa value. is there any advantage in using native conformations over simulated structures? there are several advantages of using native-structure reference states rather than simulated structures. first of all molecular simulation of a tripeptide requires definition of a conformation. extended tripeptide state has been assumed to be the most exposed conformation presumably because it is expected that any folding should reduce the asa of a residue. however, we observe that number of residues may be significantly more exposed in native proteins than their extended state. for example figure shows a ser residue to be more exposed in gln-ser-gly conformation compared with its extended state in ala-ser-ala derived from molecular simulations. such observations of higher asa than esa are frequent as discussed throughout this work. however, to establish that higher asa is not due to different c- and n-terminal residues only, we compared esa values of ala-x-ala tripeptides with their corresponding hoa values. it is observed that in of the cases we did observe an asa of a residue even in identical ala-x-ala environment, which was higher than its corresponding extended state asa. this shows that the native structure not only constraints a residue's asa but also makes it more exposed in certain conformations. out of remaining residue types pro and val, show a difference of less than Å but in six cases hoa's are always significantly lower than esa values despite a sufficiently large number of their occurrence respectively and it remains doubtful that they will ever be observed close to their esa conformation in real proteins. thus native state hoa based reference state carry information which would not be available from structures generated by molecular simulations. implications to asa prediction abbreviations: res: residue id, mae: mean absolute error in prediction measured in percentage points, cc: correlation coefficient, rel: relative asa values, abs: using absolute area units, t-test: student's t-test statistics, u-test: mann-whitney u-test we also compared the mean absolute error in absolute asa prediction for data corresponding to each of the tripeptide and found that in cases mean absolute error of hoa-normalized tripeptides was lower compared to cases in which hoa-normalized tripeptide prediction error was higher. this shows that there are many more tripeptides contexts in which prediction performance is improved by using hoa-normalization, than those whose performance fell. test of significance on individual tripeptides is not possible because the prediction is performed on a non-redundant data set of proteins. there are about residue-wise predictions for patterns implying ~ instances per tripeptide type on the average. given that prediction itself is not 100% accurate and prediction errors have large standard deviations, it is not possible to detect statistical significance in the differences between prediction performances for each of these tripeptide patterns. application to interface prediction the aim of the present work is to analyze the statistics of highest observed asa of residues in proteins and its implications to prediction of asa itself. however, it is useful to see if the newly defined relative asa values carry any more physical meaning than what is already available in relative asa obtained by esa-based normalization. the most important meaning of relative solvent accessibility, in contrast to absolute accessible surface area is to characterize them between exposed and buried residues at given cutoffs. one of the most important applications of this characterization is to determine a relationship between''unusual exposure'' and''interface propensity''. based on this characterization, it is expected that the exposed residues contain a greater fractional number of binding sites than the buried ones. we examined if such a distinction could be improved by the proposed method of normalization. figure shows a graph of the difference between fractional number of binding and non-binding residues in various ranges of rsa based on esa and hoa definitions of normalization. for comparison a third plot based on absolute asa, scaled to has also been shown. binding sites data is as reported by us in our previous works. from this graph a clear improvement is seen in the enriched number of binding residues in the exposed regions defined by the hoa normalization. this demonstrates another useful application of fine-tuning the rsa calculations as described in this work. availability a perl program to normalize asa values by the proposed method has been provided for download on the web at. this program converts absolute asa values to hoa- or esa-normalized values and vice-versa. users can also provide their own hoa data, which enables a quick update or return to esa values for some of the tripeptides. hoa data will be regularly updated if higher asa values are observed for a new tripeptide.  in this study, we developed the statistics of highest observed asa in various tripeptide environments of residues. using asa data normalized by these asa values, we could predict asa with ~15% mae and correlations from evolutionary information. individual residues show varied degrees of improvement in their prediction when trained with data normalized by new method. we also show that the exposed regions defined by newly developed method of normalization are better enriched in binding sites for the dna-binding proteins. it remains to be seen, if the proposed method of normalization has other universal applications, although the present observations suggest that trend. 
here, we analyze the statistics of these constraints and examine how the normalization of absolute asa values using context-dependent highest observed asa instead of context-free extended state asa of residues can influence the performance of sequence-based prediction of solvent accessibility. a neural networks trained with hoa-normalized data outperforms the one trained with esa-normalized values. we observe that many trippetides are more exposed than esa and that hoa residues are often found in turn, coil and bend conformations. there may still be some difference between the highest possible asa and highest observed asa due to an insufficiently covered space of asa distribution in the pdb, which limit the overall improvement in prediction to a relatively modest degree. exact sequence context, the folding state of the residues, and the actual environment of a folded protein, which do impose additional constraints on the highest possible values of asa, are currently ignored. hoa based normalization of solvent accessibility from native structures is proposed and it shows improvement in sequence-based predictability, as well as enrichment in interface residues on surface. solvent accessibility of amino acid residues is often transformed from absolute values of exposed surface area to their normalized relative values. on the other hand several residues are never observed in an exposure state close to esa values.
 solvent accessibility of amino acid residues is often transformed from absolute values of exposed surface area to their normalized relative values. this normalization is typically attained by assuming a highest exposure conformation based on extended state of that residue when it is surrounded by ala or gly on both sides i.e. ala-x-ala or gly-x-gly solvent exposed area. exact sequence context, the folding state of the residues, and the actual environment of a folded protein, which do impose additional constraints on the highest possible values of asa, are currently ignored. here, we analyze the statistics of these constraints and examine how the normalization of absolute asa values using context-dependent highest observed asa instead of context-free extended state asa of residues can influence the performance of sequence-based prediction of solvent accessibility. characterization of burial and exposed states of residues based on this normalization has also been shown to provide better enrichment of dna-binding sites in exposed residues.  we compiled the statistics of highest observed asa of residues in their different contexts and analyzed their distribution in all possible combinations for each residue type. we observe that many trippetides are more exposed than esa and that hoa residues are often found in turn, coil and bend conformations. on the other hand several residues are never observed in an exposure state close to esa values. a neural networks trained with hoa-normalized data outperforms the one trained with esa-normalized values. however, the improvements are subtle in some residues, while they are more significant in others.  hoa based normalization of solvent accessibility from native structures is proposed and it shows improvement in sequence-based predictability, as well as enrichment in interface residues on surface. there may still be some difference between the highest possible asa and highest observed asa due to an insufficiently covered space of asa distribution in the pdb, which limit the overall improvement in prediction to a relatively modest degree. 