BACKGROUND
protein secondary structure and solvent accessibility predictions are an important stage towards the prediction of protein structure and function.
accurate secondary structure and solvent accessibility information is not only at the core of most ab initio methods for the prediction of protein structure  but is also effective in improving the sensitivity of fold recognition methods , and is routinely used in protein analysis and annotation  <cit> .
virtually all modern methods for the prediction of protein one-dimensional structural features  are based on machine learning techniques  <cit> , and exploit evolutionary information in the form of amino acid frequency profiles extracted from alignments of multiple sequences, generally of unknown structure.
the progress of these methods over the last  <dig> years has been slow, but steady, and is due to numerous factors: the ever-increasing size of training sets; more sensitive methods for the detection of homologues, such as psi-blast  <cit> ; the use of ensembles of multiple predictors trained independently, sometimes tens of them  <cit> ; more sophisticated machine learning techniques ; a combination of a number of the above  <cit> .
predictors of secondary structure and solvent accessibility are virtually always ab initio , meaning that they do not rely directly on similarity to proteins of known structure.
in fact, often, much care is taken to try to exclude any detectable similarity between training and test set instances when gauging predictive performances of structural feature predictors.
the main reason for this seems to be a short-circuit, which happened early on in the field and was never disputed, between the idea of hypothesis validation by strict training and test set separation , and the concept of ab initio prediction.
for training and test sets to be strictly distinct, they are required to not only contain different examples , but to contain examples that do not show significant sequence identity to one another, as detected by a standard blast  <cit>  search.
a hint of the historical, more than scientific, nature of this issue is the fact that when subtler algorithms for sequence similarity detection became available , the criteria for training vs. test set separation did not always change.
currently over half of all known protein sequences show some detectable degree of similarity to one or more sequences of known structure.
nearly 3/ <dig> of newly deposited structures in the pdb  <cit>  show significant similarity to previously deposited structures  <cit> .
over 60% of the queries received by the server porter  <cit>  in the first six months of year  <dig> have potential homologues in the pdb at the moment of submission , and another 25% have marginal similarity to some sequence in the pdb .
for the case of clear homology, direct structural information from the homologous proteins can be exploited for the prediction of structural features.
for instance, secondary structure extracted from full three-dimensional comparative models is known to be significantly more reliable than secondary structure obtained from ab initio predictors  <cit> .
moreover, even where alignments to pdb structures are of dubious reliability, or too short to reliably imply homology, these may carry information.
one of the main sources of improvement for fold recognition and ab initio structure prediction methods over the last few casp competitions  <cit>  has been the reliance on sets of possible conformations for short fragments of chain  <cit> , extracted from the pdb.
there is a number of reasons why direct, machine learning-based predictions of secondary structure or other structural features incorporating homology information are useful: nearly all the most reliable public predictors  <cit>   do not take structural information directly into account, which implies that over half of the responses provided to users could be improved, often dramatically; machine learning methods are robust with respect to noise – selecting a template from a set of candidate structures from the pdb may be less of a problem than in traditional comparative modelling, since a set or a profile of templates  may be provided to the method, rather than a single template which might be erroneous; machine learning methods are significantly faster than full comparative modelling methods – large-scale predictions may be generated with relatively modest computational resources, and feed into structure-based functional similarity algorithms, comparative modelling validation and template selection, protein analysis and proteome annotation efforts; low-similarity, short-alignment based predictions may improve on traditional ab initio ones in fold recognition or even novel fold cases.
here we develop high-throughput systems for the prediction of protein secondary structure and solvent accessibility, which exploit similarity to proteins of known structure, where available, in the form of simple structural frequency profiles from sets of pdb templates.
the systems have two stages: one in which a set of templates for a query sequence is generated based on a similarity search of the pdb; one in which this template information, plus the primary sequence, and evolutionary information in the form of multiple alignments is used as input to an ensemble of recursive neural networks to determine a query's secondary structure and solvent accessibility.
although here we use a simple psi-blast-based protocol to find suitable templates , our systems are fully modular and may easily accommodate more sophisticated stages with better sensitivity to remote homology .
it is important to stress that, when homology information is available, the systems we design here do not simply take it as the final answer, but rather use it as a further input.
this, on average, leads to significant improvements over extracting secondary structure and solvent accessibility directly from the best single pdb template, and weighed and unweighed averages of the top  <dig> templates, or all templates identified, suggesting that the combination of sequence and template information carries more information than templates alone.
not surprisingly, when only very high quality templates are available , which are almost guaranteed to be close homologues, the improvements become marginal.
we also compare the predictive systems to to their state-of-the-art ab initio counterparts.
we show that similarity information, when available, greatly improves prediction quality.
for sequence similarity exceeding 30%, prediction quality is nearly at its theoretical maximum.
gains are significant for low sequence similarity when we design specialised systems for this case, and for alignments shorter than  <dig> residues, outside traditional comparative modelling territory, supporting the claim that these improved predictions may prove beneficial for fold recognition algorithms.
the predictive systems described in this paper are publicly available at the address  <cit> , as part of a suite of predictors of protein structural features.
when the user requests secondary structure  or solvent accessibility  predictions, homology-based results are automatically selected when suitable templates are available.
up to  <dig>  queries a day may be served by the  <dig> cpu cluster hosting the predictors.
RESULTS
the four systems we describe here are: an ab initio secondary structure predictor  in three classes; the same, but homology-based ; an ab initio predictor of relative solvent accessibility in  <dig> and  <dig> classes ; the same, but homology-based .
all systems are trained and tested in rigorous 5-fold cross-validation on the december  <dig> 25% pdb_select list  <cit>  .
we use this set to make direct comparisons with porter's published results, and performances as recorded by eva  <cit> .
the public versions of the servers will undergo regular trainings to keep them up-to-date with the expansion of the pdb.
porter classifies correctly  <dig> % of all residues.
porter has currently the highest performance of all predictors tested by independent assessor eva  <cit> , and is ranked first of all methods by eva based on the combination of pairwise comparisons of servers on identical sets.
overall, porter_h classifies correctly  <dig> % residues, or nearly 7% above porter.
if we consider only those residues for which pdb template information is available , porter_h's performance rises to  <dig> %, roughly 9% above porter.
if we further restrict our observation to those residues for which template information is available with a blast e-value of  <dig>  or smaller , porter_h's performance rises to  <dig> % .
it is worth reminding that the theoretical maximum for secondary structure prediction performance is well below 100% and is bounded by the intrinsic ambiguity of mapping three-dimensional atom coordinates into secondary structure classes.
any two automated programmes for secondary structure assignment  differ on at least 5% residues, with up to 20% residues assigned to different states in some cases  <cit> .
nonetheless, these larger margins are likely mainly due to different definitions of secondary structures by different automated assignment programs, and only by a smaller amount to actual uncertainties as to what the structure might be.
once a semantics is chosen  it is possible to classify secondary structure with an accuracy of more than 90%  <cit> .
hence the theoretical maximum for classification accuracy  is likely somewhere in the 90–95% region.
as shown in figure  <dig>  about 90% residues are correctly classified by porter_h in the 50%-100% similarity  region.
nearly 87% are correctly classified between  <dig> and 50% similarity.
even in the 20–30% similarity region, porter_h significantly outperforms porter .
for similarity below 20% porter_h performs slightly worse than porter.
the two main reasons for this are probably: the better specialisation of the latter system, which is trained on  <dig> template-less examples, while the former is trained only on about  <dig> examples with no or very low quality templates; the fact that psi-blast is increasingly inaccurate as sequence similarity becomes lower and noise in the templates eventually dominates over signal.
low-similarity templates
to investigate whether a specialised system can yield improvements when only low-similarity templates are available, we re-trained porter_h twice, with the further constraint that only templates with at most 30% and 20% sequence similarity are adopted, i.e.
all pdb templates showing more than 30%  sequence similarity to the query are eliminated.
we refer to the system with maximum 30% similarity templates as porter_h <dig> and with maximum 20% similarity templates as porter_h <dig>  the constraint imposed on template quality implies that many more examples are provided for which no template or only marginal templates are available.
porter_h30's performances in the 0–30% similarity range increase and improve over porter for all template lengths .
porter_h <dig> improves consistently over porter for sequence similarity greater than 17%.
porter_h <dig> improves consistently over porter for sequence similarity greater than 13%.
overall, porter_h <dig> is not statistically distinguishable from porter in the 10–20% similarity range, while porter_h <dig> actually outperforms porter in this range.
these results suggest that, although noisy, psi-blast-based templates in the 10–20% similarity region still retain information that can be sifted out by a machine learning system, provided that enough examples are available.
this also suggests that more subtle similarity tools  are likely to yield better results in marginal/fold recognition regions if coupled with our predictor.
we are in the process of investigating this further point.
an example of prediction by porter_h is reported in figure  <dig>
porter_h vs. templates
templates up to 95%, 30% and 20% maximum similarity allowed  for baseline and porter_h.
performances measured only on residues for which a template has been identified.
results in 5-fold cross-validation on the s <dig> set .
we also tested different baselines in which, instead of just the top template, respectively, the top  <dig> templates  and all the templates are used to predict the secondary structure of a protein.
in both cases the prediction is obtained as a majority vote among the templates covering each residue.
we tested both an unweighed vote  and a vote in which each template is weighed by its sequence similarity to the query, cubed.
the latter weighing scheme is identical to the one used to present the templates to the network , and we refer to it as baseline_input.
in all cases the predictions are worse than those obtained by only considering the top template , hence at least 4% worse than porter_h.
when we consider templates with sequence similarity of up to 95% and exclude marginal hits , porter_h still outperforms the baseline by a significant margin, although reduced .
this continues to be true when the threshold for excluding hits is lowered, down to 10- <dig>  beyond which the differences between the best baseline and the predictors become negligible.
table  <dig> reports the level of disagreement between porter_h and baseline_input, which is the baseline it agrees most with.
disagreement is measured simply as the percentage of residues on which porter_h's and baseline_input's predictions are different.
the overall disagreement is  <dig> %, which decreases to  <dig> % on residues for which templates exist with e <  <dig> , and grows to over 30% when templates with e <  <dig>  are excluded.
results on all residues for which a template exists , residues for which a template exists with blast e-value smaller than  <dig>   and all residues for which a template exists after having removed templates with blast e-value smaller than  <dig>  .
results in 5-fold cross-validation on the s <dig> set .
these results suggest that combining sequence and structure information is a better choice than only relying on templates, i.e.
the sequence contains enough information to resolve at least some of the ambiguity contained in sets of templates retrieved by sequence similarity.
the distribution of prediction performances as a function of the quality of the best hit  is shown in figure  <dig>  homology-based predictions are better for all intervals of quality, but not surprisingly the gains decrease with the decrease in quality of the templates.
somewhat surprisingly in the case of nmr templates the gains  are comparable to those obtained with high quality templates from x-ray crystallography.
we also checked whether the presence of membrane proteins in the sets we use for training and testing has any influence on the results.
in total there are  <dig> membrane proteins out of  <dig> in the set, covering roughly 5% of all amino acids.
on these proteins porter_h outperforms porter by  <dig> % , less than on the whole set.
removing membrane proteins from the set changes the performances of both porter and porter_h by less than  <dig> %, and keeps the difference between the methods statistically unchanged.
lastly, we tested porter_h on the eva common <dig> set as available in november  <dig>  containing  <dig> proteins.
on this set, a version of porter retrained from scratch, after having excluded from its training set all sequences with more than 25% similarity to any sequence in the set, achieves  <dig> % correct prediction, better by at least  <dig> % than all the other servers evaluated.
on the same set, a similarly retrained porter_h achieves  <dig> % correct prediction when templates with more than 95% sequence similarity to the query are ignored.
in this set for  <dig> out of  <dig> sequences the best template is below 30% sequence similarity and for  <dig> below 20%.
solvent accessibility prediction
similar results are obtained for solvent accessibility prediction, as shown in figures  <dig> and  <dig>  and table  <dig>  the figures and table refer to 4-class solvent accessibility prediction.
the template-less predictor  achieves  <dig> % correct 4-class prediction.
if the  <dig> classes are reassigned into  <dig> with a 25% accessibility threshold  the performance rises to  <dig> %.
although comparisons on different sets are always not entirely fair, this is at least as good as the most recent 2-class predictors adopting the same class threshold, e.g.
<cit>  ,  <cit>  ,  <cit>  ,  <cit>  .
templates up to 95%, 30% and 20% maximum similarity allowed  for baseline and paleale_h.
performances measured only on residues for which a template has been identified.
results in 5-fold cross-validation on the s <dig> set .
performances of the various methods from  <cit> .
the class threshold is 25% for all methods.
templates up to 95% adopted by paleale_h.
CONCLUSIONS
we have developed high-throughput systems for the prediction of protein secondary structure and solvent accessibility, exploiting similarity to proteins of known structure.
these systems, based on machine learning techniques, greatly outperform their ab initio counterparts when pdb templates are available, are capable of combining sequence information and structural information from multiple templates, and outperform simpler strategies such as the extraction of the structural properties in question from the best available template in the pdb, or from weighed and unweighed profiles of templates.
moreover, they are entirely automated, and can be run on multi-genomic or bioengineering scales.
on a small cluster of machines, hundreds of thousands of protein structural features may be predicted in days.
what is especially encouraging is that performance gains are significant even for marginal sequence similarity when we design specialised systems for this case.
this suggests that our strategy may feed into fold recognition systems, which currently rely on ab initio secondary structure predictors.
a closed-loop strategy in which the results of fold recognition searches are fed back into the predictors is also possible, and is the object of our current investigation.
all predictive systems are available at the address  <cit> .
template-based predictions are automatically returned by the secondary structure prediction server  and the solvent accessibility server  when templates showing more than 20% sequence similarity to the query are detected.
given the current distribution of queries, this will yield greatly improved predictions for well over half of all requests.

 protein secondary structure and solvent accessibility predictions are an important stage towards the prediction of protein structure and function. accurate secondary structure and solvent accessibility information is not only at the core of most ab initio methods for the prediction of protein structure but is also effective in improving the sensitivity of fold recognition methods, and is routinely used in protein analysis and annotation. virtually all modern methods for the prediction of protein one-dimensional structural features are based on machine learning techniques, and exploit evolutionary information in the form of amino acid frequency profiles extracted from alignments of multiple sequences, generally of unknown structure. the progress of these methods over the last years has been slow, but steady, and is due to numerous factors: the ever-increasing size of training sets; more sensitive methods for the detection of homologues, such as psi-blast; the use of ensembles of multiple predictors trained independently, sometimes tens of them; more sophisticated machine learning techniques; a combination of a number of the above. predictors of secondary structure and solvent accessibility are virtually always ab initio, meaning that they do not rely directly on similarity to proteins of known structure. in fact, often, much care is taken to try to exclude any detectable similarity between training and test set instances when gauging predictive performances of structural feature predictors. the main reason for this seems to be a short-circuit, which happened early on in the field and was never disputed, between the idea of hypothesis validation by strict training and test set separation, and the concept of ab initio prediction. for training and test sets to be strictly distinct, they are required to not only contain different examples, but to contain examples that do not show significant sequence identity to one another, as detected by a standard blast search. a hint of the historical, more than scientific, nature of this issue is the fact that when subtler algorithms for sequence similarity detection became available, the criteria for training vs. test set separation did not always change. currently over half of all known protein sequences show some detectable degree of similarity to one or more sequences of known structure. nearly 3/ of newly deposited structures in the pdb show significant similarity to previously deposited structures. over 60% of the queries received by the server porter in the first six months of year have potential homologues in the pdb at the moment of submission, and another 25% have marginal similarity to some sequence in the pdb. for the case of clear homology, direct structural information from the homologous proteins can be exploited for the prediction of structural features. for instance, secondary structure extracted from full three-dimensional comparative models is known to be significantly more reliable than secondary structure obtained from ab initio predictors. moreover, even where alignments to pdb structures are of dubious reliability, or too short to reliably imply homology, these may carry information. one of the main sources of improvement for fold recognition and ab initio structure prediction methods over the last few casp competitions has been the reliance on sets of possible conformations for short fragments of chain, extracted from the pdb. there is a number of reasons why direct, machine learning-based predictions of secondary structure or other structural features incorporating homology information are useful: nearly all the most reliable public predictors do not take structural information directly into account, which implies that over half of the responses provided to users could be improved, often dramatically; machine learning methods are robust with respect to noise – selecting a template from a set of candidate structures from the pdb may be less of a problem than in traditional comparative modelling, since a set or a profile of templates may be provided to the method, rather than a single template which might be erroneous; machine learning methods are significantly faster than full comparative modelling methods – large-scale predictions may be generated with relatively modest computational resources, and feed into structure-based functional similarity algorithms, comparative modelling validation and template selection, protein analysis and proteome annotation efforts; low-similarity, short-alignment based predictions may improve on traditional ab initio ones in fold recognition or even novel fold cases. here we develop high-throughput systems for the prediction of protein secondary structure and solvent accessibility, which exploit similarity to proteins of known structure, where available, in the form of simple structural frequency profiles from sets of pdb templates. the systems have two stages: one in which a set of templates for a query sequence is generated based on a similarity search of the pdb; one in which this template information, plus the primary sequence, and evolutionary information in the form of multiple alignments is used as input to an ensemble of recursive neural networks to determine a query's secondary structure and solvent accessibility. although here we use a simple psi-blast-based protocol to find suitable templates, our systems are fully modular and may easily accommodate more sophisticated stages with better sensitivity to remote homology. it is important to stress that, when homology information is available, the systems we design here do not simply take it as the final answer, but rather use it as a further input. this, on average, leads to significant improvements over extracting secondary structure and solvent accessibility directly from the best single pdb template, and weighed and unweighed averages of the top templates, or all templates identified, suggesting that the combination of sequence and template information carries more information than templates alone. not surprisingly, when only very high quality templates are available, which are almost guaranteed to be close homologues, the improvements become marginal. we also compare the predictive systems to to their state-of-the-art ab initio counterparts. we show that similarity information, when available, greatly improves prediction quality. for sequence similarity exceeding 30%, prediction quality is nearly at its theoretical maximum. gains are significant for low sequence similarity when we design specialised systems for this case, and for alignments shorter than residues, outside traditional comparative modelling territory, supporting the claim that these improved predictions may prove beneficial for fold recognition algorithms. the predictive systems described in this paper are publicly available at the address, as part of a suite of predictors of protein structural features. when the user requests secondary structure or solvent accessibility predictions, homology-based results are automatically selected when suitable templates are available. up to queries a day may be served by the cpu cluster hosting the predictors.  the four systems we describe here are: an ab initio secondary structure predictor in three classes; the same, but homology-based; an ab initio predictor of relative solvent accessibility in and classes; the same, but homology-based. all systems are trained and tested in rigorous 5-fold cross-validation on the december 25% pdb_select list. we use this set to make direct comparisons with porter's published results, and performances as recorded by eva. the public versions of the servers will undergo regular trainings to keep them up-to-date with the expansion of the pdb. porter classifies correctly % of all residues. porter has currently the highest performance of all predictors tested by independent assessor eva, and is ranked first of all methods by eva based on the combination of pairwise comparisons of servers on identical sets. overall, porter_h classifies correctly % residues, or nearly 7% above porter. if we consider only those residues for which pdb template information is available, porter_h's performance rises to %, roughly 9% above porter. if we further restrict our observation to those residues for which template information is available with a blast e-value of or smaller, porter_h's performance rises to %. it is worth reminding that the theoretical maximum for secondary structure prediction performance is well below 100% and is bounded by the intrinsic ambiguity of mapping three-dimensional atom coordinates into secondary structure classes. any two automated programmes for secondary structure assignment differ on at least 5% residues, with up to 20% residues assigned to different states in some cases. nonetheless, these larger margins are likely mainly due to different definitions of secondary structures by different automated assignment programs, and only by a smaller amount to actual uncertainties as to what the structure might be. once a semantics is chosen it is possible to classify secondary structure with an accuracy of more than 90%. hence the theoretical maximum for classification accuracy is likely somewhere in the 90–95% region. as shown in figure about 90% residues are correctly classified by porter_h in the 50%-100% similarity region. nearly 87% are correctly classified between and 50% similarity. even in the 20–30% similarity region, porter_h significantly outperforms porter. for similarity below 20% porter_h performs slightly worse than porter. the two main reasons for this are probably: the better specialisation of the latter system, which is trained on template-less examples, while the former is trained only on about examples with no or very low quality templates; the fact that psi-blast is increasingly inaccurate as sequence similarity becomes lower and noise in the templates eventually dominates over signal. low-similarity templates to investigate whether a specialised system can yield improvements when only low-similarity templates are available, we re-trained porter_h twice, with the further constraint that only templates with at most 30% and 20% sequence similarity are adopted, i.e. all pdb templates showing more than 30% sequence similarity to the query are eliminated. we refer to the system with maximum 30% similarity templates as porter_h and with maximum 20% similarity templates as porter_h the constraint imposed on template quality implies that many more examples are provided for which no template or only marginal templates are available. porter_h30's performances in the 0–30% similarity range increase and improve over porter for all template lengths. porter_h improves consistently over porter for sequence similarity greater than 17%. porter_h improves consistently over porter for sequence similarity greater than 13%. overall, porter_h is not statistically distinguishable from porter in the 10–20% similarity range, while porter_h actually outperforms porter in this range. these results suggest that, although noisy, psi-blast-based templates in the 10–20% similarity region still retain information that can be sifted out by a machine learning system, provided that enough examples are available. this also suggests that more subtle similarity tools are likely to yield better results in marginal/fold recognition regions if coupled with our predictor. we are in the process of investigating this further point. an example of prediction by porter_h is reported in figure porter_h vs. templates templates up to 95%, 30% and 20% maximum similarity allowed for baseline and porter_h. performances measured only on residues for which a template has been identified. results in 5-fold cross-validation on the s set. we also tested different baselines in which, instead of just the top template, respectively, the top templates and all the templates are used to predict the secondary structure of a protein. in both cases the prediction is obtained as a majority vote among the templates covering each residue. we tested both an unweighed vote and a vote in which each template is weighed by its sequence similarity to the query, cubed. the latter weighing scheme is identical to the one used to present the templates to the network, and we refer to it as baseline_input. in all cases the predictions are worse than those obtained by only considering the top template, hence at least 4% worse than porter_h. when we consider templates with sequence similarity of up to 95% and exclude marginal hits, porter_h still outperforms the baseline by a significant margin, although reduced. this continues to be true when the threshold for excluding hits is lowered, down to 10- beyond which the differences between the best baseline and the predictors become negligible. table reports the level of disagreement between porter_h and baseline_input, which is the baseline it agrees most with. disagreement is measured simply as the percentage of residues on which porter_h's and baseline_input's predictions are different. the overall disagreement is %, which decreases to % on residues for which templates exist with e <, and grows to over 30% when templates with e < are excluded. results on all residues for which a template exists, residues for which a template exists with blast e-value smaller than and all residues for which a template exists after having removed templates with blast e-value smaller than. results in 5-fold cross-validation on the s set. these results suggest that combining sequence and structure information is a better choice than only relying on templates, i.e. the sequence contains enough information to resolve at least some of the ambiguity contained in sets of templates retrieved by sequence similarity. the distribution of prediction performances as a function of the quality of the best hit is shown in figure homology-based predictions are better for all intervals of quality, but not surprisingly the gains decrease with the decrease in quality of the templates. somewhat surprisingly in the case of nmr templates the gains are comparable to those obtained with high quality templates from x-ray crystallography. we also checked whether the presence of membrane proteins in the sets we use for training and testing has any influence on the results. in total there are membrane proteins out of in the set, covering roughly 5% of all amino acids. on these proteins porter_h outperforms porter by %, less than on the whole set. removing membrane proteins from the set changes the performances of both porter and porter_h by less than %, and keeps the difference between the methods statistically unchanged. lastly, we tested porter_h on the eva common set as available in november containing proteins. on this set, a version of porter retrained from scratch, after having excluded from its training set all sequences with more than 25% similarity to any sequence in the set, achieves % correct prediction, better by at least % than all the other servers evaluated. on the same set, a similarly retrained porter_h achieves % correct prediction when templates with more than 95% sequence similarity to the query are ignored. in this set for out of sequences the best template is below 30% sequence similarity and for below 20%. solvent accessibility prediction similar results are obtained for solvent accessibility prediction, as shown in figures and and table the figures and table refer to 4-class solvent accessibility prediction. the template-less predictor achieves % correct 4-class prediction. if the classes are reassigned into with a 25% accessibility threshold the performance rises to %. although comparisons on different sets are always not entirely fair, this is at least as good as the most recent 2-class predictors adopting the same class threshold, e.g.,,,. templates up to 95%, 30% and 20% maximum similarity allowed for baseline and paleale_h. performances measured only on residues for which a template has been identified. results in 5-fold cross-validation on the s set. performances of the various methods from. the class threshold is 25% for all methods. templates up to 95% adopted by paleale_h.  we have developed high-throughput systems for the prediction of protein secondary structure and solvent accessibility, exploiting similarity to proteins of known structure. these systems, based on machine learning techniques, greatly outperform their ab initio counterparts when pdb templates are available, are capable of combining sequence information and structural information from multiple templates, and outperform simpler strategies such as the extraction of the structural properties in question from the best available template in the pdb, or from weighed and unweighed profiles of templates. moreover, they are entirely automated, and can be run on multi-genomic or bioengineering scales. on a small cluster of machines, hundreds of thousands of protein structural features may be predicted in days. what is especially encouraging is that performance gains are significant even for marginal sequence similarity when we design specialised systems for this case. this suggests that our strategy may feed into fold recognition systems, which currently rely on ab initio secondary structure predictors. a closed-loop strategy in which the results of fold recognition searches are fed back into the predictors is also possible, and is the object of our current investigation. all predictive systems are available at the address. template-based predictions are automatically returned by the secondary structure prediction server and the solvent accessibility server when templates showing more than 20% sequence similarity to the query are detected. given the current distribution of queries, this will yield greatly improved predictions for well over half of all requests. 
structural properties of proteins such as secondary structure and solvent accessibility contribute to three-dimensional structure prediction, not only in the ab initio case but also when homology information to known structures is available. structural properties are also routinely used in protein analysis even when homology is available, largely because homology modelling is lower throughput than, say, secondary structure prediction. nonetheless, predictors of secondary structure and solvent accessibility are virtually always ab initio. here we develop high-throughput machine learning systems for the prediction of protein secondary structure and solvent accessibility that exploit homology to proteins of known structure, where available, in the form of simple structural frequency profiles extracted from sets of pdb templates. we compare these systems to their state-of-the-art ab initio counterparts, and with a number of baselines in which secondary structures and solvent accessibilities are extracted directly from the templates. we show that structural information from templates greatly improves secondary structure and solvent accessibility prediction quality, and that, on average, the systems significantly enrich the information contained in the templates. for sequence similarity exceeding 30%, secondary structure prediction quality is approximately 90%, close to its theoretical maximum, and 2-class solvent accessibility roughly 85%. gains are robust with respect to template selection noise, and significant for marginal sequence similarity and for short alignments, supporting the claim that these improved predictions may prove beneficial beyond the case in which clear homology is available.
 structural properties of proteins such as secondary structure and solvent accessibility contribute to three-dimensional structure prediction, not only in the ab initio case but also when homology information to known structures is available. structural properties are also routinely used in protein analysis even when homology is available, largely because homology modelling is lower throughput than, say, secondary structure prediction. nonetheless, predictors of secondary structure and solvent accessibility are virtually always ab initio.  here we develop high-throughput machine learning systems for the prediction of protein secondary structure and solvent accessibility that exploit homology to proteins of known structure, where available, in the form of simple structural frequency profiles extracted from sets of pdb templates. we compare these systems to their state-of-the-art ab initio counterparts, and with a number of baselines in which secondary structures and solvent accessibilities are extracted directly from the templates. we show that structural information from templates greatly improves secondary structure and solvent accessibility prediction quality, and that, on average, the systems significantly enrich the information contained in the templates. for sequence similarity exceeding 30%, secondary structure prediction quality is approximately 90%, close to its theoretical maximum, and 2-class solvent accessibility roughly 85%. gains are robust with respect to template selection noise, and significant for marginal sequence similarity and for short alignments, supporting the claim that these improved predictions may prove beneficial beyond the case in which clear homology is available.  the predictive system are publicly available at the address. 