BACKGROUND
in the post-genomic era, functional identification of proteins and their interactions became an essential step for the understanding of the molecular machinery of the cell.
advances in structural genomics and proteomics  <cit>  are producing a huge amount of information.
structural information is useful for inferring the protein biochemical function because often this function is strongly correlated with the protein 3d structure  <cit> .
moreover, structural properties can be useful for detecting a set of special amino acids that is strongly related to function.
these amino acids are the protein functional residues, e.g, active site and binding site residues.
functional residues are the key players in protein-protein interactions and in protein-ligand interactions.
some recent studies have sought to find the common maximal set of functional residues for related proteins  <cit> .
those studies aimed at finding ligand functional sites may have a major impact on drug discovery development  <cit> .
protein-ligand interactions can be solved by assessing the co-crystallized ligand structure.
nowadays, there are several applications for this purpose.
one such application is ligbase  <cit> , a 3d ligand-binding database derived from the protein data bank   <cit> .
in ligbase, a ligand-binding site is an amino acid with at least one atom within  <dig> Å from a ligand atom.
there are many other applications based on the same principle, including pdbsum  <cit> , bindingdb  <cit> , and pld  <cit> .
on the other hand, protein-protein interactions are hard to solve because large protein complexes are often more difficult to crystallize than protein-ligand complexes  <cit> .
however, when functional residues of a protein family are known, they can be used to predict the function of related proteins.
identifying these residues from amino acid sequences alone would be useful for the understanding of the interactions of proteins with no 3d structure defined.
in order to predict functional sites in proteins with unknown function, the prosite database  <cit>  uses a method based on regular expressions  <cit>   to detect functional site patterns and profiles within proteins with unknown function.
in addition, henschel and colleagues  <cit>  presented a method based on hidden markov models   <cit>  to predict functional sites in both protein-ligand and protein-protein interactions.
their research compiled sequential segments that constituted structural features of an interaction site into a hidden markov model, namely the hmm descriptor.
in their study, each multiple sequence alignment  that represents a protein family generates fragments according to the position of functional residues, and for each fragment an hmm is built.
segmented hmms are merged into one final hmm by their linking with insert states.
this final hmm is called the hmm descriptor.
collection of hmm descriptors can be used to screen sequence databases by predicting functional site positions.
henschel and colleagues  <cit>  focused on the protein-protein functional site predictions, but they also evaluated ligand-binding descriptors for the atp-binding.
herein, we present a method based on hidden markov models  that uses functional sites of known ligand-protein interactions to predict functional sites in query sequences.
our method was based on the study done by henschel et al  <cit> , with some differences.
first, we built the hmm from full msas rather than from functional sites of msa segments.
second, hmms for the prediction of functional residues were trained with both multiple sequence alignments based on structural alignments and with column alignments which corresponded to functional residue positions.
third, our approach focused on predictions of protein-ligand functional sites rather than predictions of protein-protein functional sites.
the ligand-binding residues are likely to be important in many applications such as in designing small molecular inhibitors for drug discovery.
finally, we developed a web-accessible database, called structural descriptor database  that uses our prediction method and allow the detection of functional sites, biological activity, and of ligand interactions in query sequences.
the sddb was built with information from several databases, namely the pdb  <cit> , scop  <cit> , pdb-sum  <cit> , and csa  <cit> .
each family in the scop database was associated with a library of hmm descriptors.
we selected  <dig>  scop families and  <dig>  pdb entries.
functional sites were obtained from pdbsum  and from csa .
structural alignments were derived from the 3dcoffee aligner  <cit> .
we used the hmmer package  <cit>  for building hmm descriptors.
we also built a user friendly website to turn both the database and the functional site prediction method available to anyone interested .
in this website, query sequences can be scored by using a library of hmm descriptors.
the scop classification, functional residue positions, and ligand binding interactions are considered in order to indicate the sequence most likely function.
also, sddb contains links to all related databases, which allows the user to have immediate access to additional information on the protein of interest.
RESULTS
in order to validate our prediction method, we performed experiments using three different data sets.
we started by assessing one specific family, namely the trypsion-like serine proteases.
next, we assessed the remaining scop families.
finally, we compared our results with those on the atp-binding study done by henschel and colleagues  <cit> .
in the first experiment, the functional residue positions were defined from protease study  <cit> .
in the second and third experiments, the functional residue positions were extracted from the pdbsum and the csa databases.
in the first and second experiments we wanted to assess our prediction method when both data was available, curated data supplied by expert knowledge and automated data calculated from computational methods, as done previously by others  <cit> .
trypsion-like serine proteases data set
in our first experiment, we only considered scop sequences from the trypsion-like serine protease family.
we selected a total of  <dig> proteins.
sequences were separated into  <dig> groups, according to ligand specificity.
these groups are chymotrypsin, elastase, granzyme, thrombin, urokinase, trypsin, plasminogen activator, and a hybrid group formed by procarboxypeptidase a-s <dig> subunit iii, granzyme k, and neuropsin.
table  <dig> shows for each group the pdb identify, chain, start residue and end residue that refer to the pdb coordinate file for each protein selected.
also, table  <dig> shows alignment identity for each group.
group name, pdb coordinates details, such as pdb-id, chain, start residue, and end residue of each protein used in first experiment.
in addition, the later column shows the identity of alignment of each protein group.
we performed cross validation by applying the leaving one out method  <cit> , in each group.
first, training set sequences were aligned using the 3dcoffee tool  <cit> .
next, for each alignment, positions of binding site alignment and of active site alignment were defined, according to  <cit> .
hmm descriptors were built using both alignments and alignment-columns, which corresponded to the residues of the functional sites.
these hmm descriptors were used for screening and predicting functional residue positions in the test-sequence set.
precison and recall for each group in trypsion-like serine protease data set
scop data set
the second experiment was performed considering the scop families selected for this study , with the exception of the trypsion-like serine protease family.
a total of  <dig>  families were selected.
similarly as in the first experiment, we performed cross validation by applying the leave one out test  <cit> .
sequences in each family were separated into groups according to their ligand interactions.
training sequences were aligned using 3dcoffee  <cit> .
binding and active site alignment positions were obtained by running an algorithm that recognizes functional ligand residues in alignments, as explained in methods.
next, hmm descriptors were built using both alignment and functional ligand positions.
finally, each hmm descriptor was used for screening, predicting, and classifying a test-sequence.
the scop families experiment achieved precision of 84% and recall of  <dig> %, in predicting the active site.
for binding site prediction, precision and recall were 78% and 57%, respectively.
as observed in the first experiment, our methodology obtained better results in predicting active sites rather than in predicting binding sites.
indeed, active site residues are more conserved than binding residues.
therefore, we applied different levels of tolerance when defining an alignment column into a binding or active site.
for instance, we set as binding site columns those that presented more than 50% of their binding residues aligned, whereas for active sites columns we required 80% of active residues aligned.
these parameter settings narrowed the active site predictions because they decreased the number of false positives that could be incorporated in the training data, which in turn increases precision.
the recall also increases because fewer cases of false negatives are incorporated in the active site column.
we assessed the classification with respect to the scop database, and found that all proteins were correctly classified into the scop class, fold, super family, and family.
around 96% of the proteins were classified into their correct domain.
atp-binding data set
we compared our results with those obtained by henschel and colleagues  <cit> .
for that, we assessed hmm descriptors performance for the particular study of the atp-binding site.
we considered  <dig> scop families in which a atp-ligand or adp-ligand was present, totalizing  <dig>  sequences.
notice that their tests were performed on swiss-prot  <cit>  database, and they used  <dig>  sequences, while we used the scop database, a dataset smaller than swiss-prot.
unfortunately, direct comparisons are not possible because their source code is not available.
for this experiment we assessed precision-recall for e-values <  <dig>  table  <dig> shows our results and those of henschel and colleagues'.
their original results can be found in table  <dig> <cit> .
they achieved a precision of  <dig> %, whereas we achieved  <dig> %.
however, our results had a recall rate of  <dig> %, whereas theirs was  <dig> %.
this indicates that our prediction method has produced fewer false negatives.
while our method detected more false positives, it also detected more true positives.
precison and recall for both henschel and colleagues prediction method, and our approach .
assessing hmm descriptors performance
to evaluate the performance of hmm descriptors, we built precision-recall curves for our first and second experiments.
figure 1-a shows precision-recall curves for active site predictions, whereas figure 1-b reports precision-recall curves for binding site predictions.
table  <dig> shows protein function classification for both experiments.
we noted that the first experiment performed better than the second one in all cases.
we compared the experiment performance for active site prediction only, and we observed that the area under the graphic is greater for the trypsion-like serine protease experiment than for the scop family experiment .
also, figures 1-b shows greater areas for the trypsion-like serine protease experiment.
results in the first experiment were expected because the training data set is a curated data, as they were collected from the literature.
we believe, they are more reliable than computationally-calculated functional residues.
this significant difference in the performance of these experiments may be due to separation into groups according to ligand specificity.
proteins from the same family can present different functional sites when interacting with the same ligand, i.e., the type of amino acids involved in interaction might not be the same ones.
thus, these proteins present different ligand specificity.
naturally, the alignment of proteins with similar ligand specificity keeps the functional sites aligned.
as a result, hmm descriptors derived from these alignments perform better than those built by using alignments that contain all the proteins that interact with the same ligand, which explains the better results obtained in the first experiment.
in any way, hmm descriptor performed better achieved precision above 70% and recall above 50%, in both cases.
scop classification for trypsion-like serine protease and scop data sets.
CONCLUSIONS
with this study we aimed to develop a web-based tool that allowed the use of a computational method for the prediction of functional sites and protein function.
for that, we created a prediction method based on the study of henschel and colleagues  <cit>  in which they developed a method for predicting functional sites in proteins with unknown function.
our web-based tool does two main tasks: it screens the scop database for the prediction of the protein function, and it pint-points the positions of its functional residues.
likewise the study of henschel and colleagues, our prediction method is based on the hidden markov model descriptors and structural properties.
however, our hmms were built using information from structural alignments, ligand binding sites, and active sites.
our web-accessible database is a user friendly interface that allows users to analyze their own data.
after setting the parameters, users can submit their protein sequences.
each protein is then analyzed and results are sent by email.
each sequence submitted is scanned against a library of hmm descriptors and results are displayed in two levels.
in the first level of results, it shows all predicted functional residues and protein interactions with its ligands.
in the second, it shows the predicted functional residues for each interacting ligand, individually.
hmms and alignments are available for download in the result page.
to validate our prediction method, we performed experiments that achieved significant precision-recall ranges.
our approach is accurate in determining protein-ligand interactions and interaction-residues positions.
therefore, sddb is useful for functional annotation and for predicting functional residues.
knowing the positions of functional residues may provide insights into the biological activity of the protein and reveal new targets for drug development.

 in the post-genomic era, functional identification of proteins and their interactions became an essential step for the understanding of the molecular machinery of the cell. advances in structural genomics and proteomics are producing a huge amount of information. structural information is useful for inferring the protein biochemical function because often this function is strongly correlated with the protein 3d structure. moreover, structural properties can be useful for detecting a set of special amino acids that is strongly related to function. these amino acids are the protein functional residues, e.g, active site and binding site residues. functional residues are the key players in protein-protein interactions and in protein-ligand interactions. some recent studies have sought to find the common maximal set of functional residues for related proteins. those studies aimed at finding ligand functional sites may have a major impact on drug discovery development. protein-ligand interactions can be solved by assessing the co-crystallized ligand structure. nowadays, there are several applications for this purpose. one such application is ligbase, a 3d ligand-binding database derived from the protein data bank. in ligbase, a ligand-binding site is an amino acid with at least one atom within Å from a ligand atom. there are many other applications based on the same principle, including pdbsum, bindingdb, and pld. on the other hand, protein-protein interactions are hard to solve because large protein complexes are often more difficult to crystallize than protein-ligand complexes. however, when functional residues of a protein family are known, they can be used to predict the function of related proteins. identifying these residues from amino acid sequences alone would be useful for the understanding of the interactions of proteins with no 3d structure defined. in order to predict functional sites in proteins with unknown function, the prosite database uses a method based on regular expressions to detect functional site patterns and profiles within proteins with unknown function. in addition, henschel and colleagues presented a method based on hidden markov models to predict functional sites in both protein-ligand and protein-protein interactions. their research compiled sequential segments that constituted structural features of an interaction site into a hidden markov model, namely the hmm descriptor. in their study, each multiple sequence alignment that represents a protein family generates fragments according to the position of functional residues, and for each fragment an hmm is built. segmented hmms are merged into one final hmm by their linking with insert states. this final hmm is called the hmm descriptor. collection of hmm descriptors can be used to screen sequence databases by predicting functional site positions. henschel and colleagues focused on the protein-protein functional site predictions, but they also evaluated ligand-binding descriptors for the atp-binding. herein, we present a method based on hidden markov models that uses functional sites of known ligand-protein interactions to predict functional sites in query sequences. our method was based on the study done by henschel et al, with some differences. first, we built the hmm from full msas rather than from functional sites of msa segments. second, hmms for the prediction of functional residues were trained with both multiple sequence alignments based on structural alignments and with column alignments which corresponded to functional residue positions. third, our approach focused on predictions of protein-ligand functional sites rather than predictions of protein-protein functional sites. the ligand-binding residues are likely to be important in many applications such as in designing small molecular inhibitors for drug discovery. finally, we developed a web-accessible database, called structural descriptor database that uses our prediction method and allow the detection of functional sites, biological activity, and of ligand interactions in query sequences. the sddb was built with information from several databases, namely the pdb, scop, pdb-sum, and csa. each family in the scop database was associated with a library of hmm descriptors. we selected scop families and pdb entries. functional sites were obtained from pdbsum and from csa. structural alignments were derived from the 3dcoffee aligner. we used the hmmer package for building hmm descriptors. we also built a user friendly website to turn both the database and the functional site prediction method available to anyone interested. in this website, query sequences can be scored by using a library of hmm descriptors. the scop classification, functional residue positions, and ligand binding interactions are considered in order to indicate the sequence most likely function. also, sddb contains links to all related databases, which allows the user to have immediate access to additional information on the protein of interest.  in order to validate our prediction method, we performed experiments using three different data sets. we started by assessing one specific family, namely the trypsion-like serine proteases. next, we assessed the remaining scop families. finally, we compared our results with those on the atp-binding study done by henschel and colleagues. in the first experiment, the functional residue positions were defined from protease study. in the second and third experiments, the functional residue positions were extracted from the pdbsum and the csa databases. in the first and second experiments we wanted to assess our prediction method when both data was available, curated data supplied by expert knowledge and automated data calculated from computational methods, as done previously by others. trypsion-like serine proteases data set in our first experiment, we only considered scop sequences from the trypsion-like serine protease family. we selected a total of proteins. sequences were separated into groups, according to ligand specificity. these groups are chymotrypsin, elastase, granzyme, thrombin, urokinase, trypsin, plasminogen activator, and a hybrid group formed by procarboxypeptidase a-s subunit iii, granzyme k, and neuropsin. table shows for each group the pdb identify, chain, start residue and end residue that refer to the pdb coordinate file for each protein selected. also, table shows alignment identity for each group. group name, pdb coordinates details, such as pdb-id, chain, start residue, and end residue of each protein used in first experiment. in addition, the later column shows the identity of alignment of each protein group. we performed cross validation by applying the leaving one out method, in each group. first, training set sequences were aligned using the 3dcoffee tool. next, for each alignment, positions of binding site alignment and of active site alignment were defined, according to. hmm descriptors were built using both alignments and alignment-columns, which corresponded to the residues of the functional sites. these hmm descriptors were used for screening and predicting functional residue positions in the test-sequence set. precison and recall for each group in trypsion-like serine protease data set scop data set the second experiment was performed considering the scop families selected for this study, with the exception of the trypsion-like serine protease family. a total of families were selected. similarly as in the first experiment, we performed cross validation by applying the leave one out test. sequences in each family were separated into groups according to their ligand interactions. training sequences were aligned using 3dcoffee. binding and active site alignment positions were obtained by running an algorithm that recognizes functional ligand residues in alignments, as explained in methods. next, hmm descriptors were built using both alignment and functional ligand positions. finally, each hmm descriptor was used for screening, predicting, and classifying a test-sequence. the scop families experiment achieved precision of 84% and recall of %, in predicting the active site. for binding site prediction, precision and recall were 78% and 57%, respectively. as observed in the first experiment, our methodology obtained better results in predicting active sites rather than in predicting binding sites. indeed, active site residues are more conserved than binding residues. therefore, we applied different levels of tolerance when defining an alignment column into a binding or active site. for instance, we set as binding site columns those that presented more than 50% of their binding residues aligned, whereas for active sites columns we required 80% of active residues aligned. these parameter settings narrowed the active site predictions because they decreased the number of false positives that could be incorporated in the training data, which in turn increases precision. the recall also increases because fewer cases of false negatives are incorporated in the active site column. we assessed the classification with respect to the scop database, and found that all proteins were correctly classified into the scop class, fold, super family, and family. around 96% of the proteins were classified into their correct domain. atp-binding data set we compared our results with those obtained by henschel and colleagues. for that, we assessed hmm descriptors performance for the particular study of the atp-binding site. we considered scop families in which a atp-ligand or adp-ligand was present, totalizing sequences. notice that their tests were performed on swiss-prot database, and they used sequences, while we used the scop database, a dataset smaller than swiss-prot. unfortunately, direct comparisons are not possible because their source code is not available. for this experiment we assessed precision-recall for e-values < table shows our results and those of henschel and colleagues'. their original results can be found in table. they achieved a precision of %, whereas we achieved %. however, our results had a recall rate of %, whereas theirs was %. this indicates that our prediction method has produced fewer false negatives. while our method detected more false positives, it also detected more true positives. precison and recall for both henschel and colleagues prediction method, and our approach. assessing hmm descriptors performance to evaluate the performance of hmm descriptors, we built precision-recall curves for our first and second experiments. figure 1-a shows precision-recall curves for active site predictions, whereas figure 1-b reports precision-recall curves for binding site predictions. table shows protein function classification for both experiments. we noted that the first experiment performed better than the second one in all cases. we compared the experiment performance for active site prediction only, and we observed that the area under the graphic is greater for the trypsion-like serine protease experiment than for the scop family experiment. also, figures 1-b shows greater areas for the trypsion-like serine protease experiment. results in the first experiment were expected because the training data set is a curated data, as they were collected from the literature. we believe, they are more reliable than computationally-calculated functional residues. this significant difference in the performance of these experiments may be due to separation into groups according to ligand specificity. proteins from the same family can present different functional sites when interacting with the same ligand, i.e., the type of amino acids involved in interaction might not be the same ones. thus, these proteins present different ligand specificity. naturally, the alignment of proteins with similar ligand specificity keeps the functional sites aligned. as a result, hmm descriptors derived from these alignments perform better than those built by using alignments that contain all the proteins that interact with the same ligand, which explains the better results obtained in the first experiment. in any way, hmm descriptor performed better achieved precision above 70% and recall above 50%, in both cases. scop classification for trypsion-like serine protease and scop data sets.  with this study we aimed to develop a web-based tool that allowed the use of a computational method for the prediction of functional sites and protein function. for that, we created a prediction method based on the study of henschel and colleagues in which they developed a method for predicting functional sites in proteins with unknown function. our web-based tool does two main tasks: it screens the scop database for the prediction of the protein function, and it pint-points the positions of its functional residues. likewise the study of henschel and colleagues, our prediction method is based on the hidden markov model descriptors and structural properties. however, our hmms were built using information from structural alignments, ligand binding sites, and active sites. our web-accessible database is a user friendly interface that allows users to analyze their own data. after setting the parameters, users can submit their protein sequences. each protein is then analyzed and results are sent by email. each sequence submitted is scanned against a library of hmm descriptors and results are displayed in two levels. in the first level of results, it shows all predicted functional residues and protein interactions with its ligands. in the second, it shows the predicted functional residues for each interacting ligand, individually. hmms and alignments are available for download in the result page. to validate our prediction method, we performed experiments that achieved significant precision-recall ranges. our approach is accurate in determining protein-ligand interactions and interaction-residues positions. therefore, sddb is useful for functional annotation and for predicting functional residues. knowing the positions of functional residues may provide insights into the biological activity of the protein and reveal new targets for drug development. 
structural alignments and functional residues of a known protein set are used to build special hidden markov models called hmm descriptors. sddb uses previously calculated and stored hmm descriptors for predicting active sites, binding residues, and protein function. it accepts queries in fasta format and predicts functional residue positions, protein-ligand interactions, and protein function, based on the scop database. the structural descriptor database is a web-based tool that predicts the function of proteins and functional site positions based on the structural properties of related protein families. to assess the sddb performance, we used different data sets. the trypsion-like serine protease data set assessed how well sddb predicts functional sites when curated data is available. nevertheless, by using our prediction method we obtained results with precision above 70%. sddb worked better in predicting active sites rather than binding sites because the former are more conserved than the latter.
 the structural descriptor database is a web-based tool that predicts the function of proteins and functional site positions based on the structural properties of related protein families. structural alignments and functional residues of a known protein set are used to build special hidden markov models called hmm descriptors. sddb uses previously calculated and stored hmm descriptors for predicting active sites, binding residues, and protein function. the database integrates biologically relevant data filtered from several databases such as pdb, pdbsum, csa and scop. it accepts queries in fasta format and predicts functional residue positions, protein-ligand interactions, and protein function, based on the scop database.  to assess the sddb performance, we used different data sets. the trypsion-like serine protease data set assessed how well sddb predicts functional sites when curated data is available. the scop family data set was used to analyze sddb performance by using training data extracted from pdbsum and from csa. the atp-binding experiment was used to compare our approach with the most current method. for all evaluations, significant improvements were obtained with sddb.  sddb performed better when trusty training data was available. sddb worked better in predicting active sites rather than binding sites because the former are more conserved than the latter. nevertheless, by using our prediction method we obtained results with precision above 70%. 