BACKGROUND
available three-dimensional structures of proteins of unknown biological role are rapidly increasing as a result of structural genomics initiatives  <cit> .
this prompted the development of methods for annotating protein structures at the residue level and inferring binding sites using information from related proteins  <cit> .
a common approach to detect functional sites in proteins has been to identify evolutionarily conserved residues that are spatially contiguous in the protein structure  <cit> .
this approach has lately been extended and integrated with detailed analyses of structural features, related protein structures and sequence information by several groups  <cit> .
but the lack of related proteins, a common occurrence with structural genomics targets, hinders the wide applicability of many of these methods  <cit> .
in addition, there are now many examples where above-average sequence variability rather than sequence conservation is associated with functional regions  <cit> .
for these reasons, methods capable of identifying functional residues in absence of information on conserved residues  <cit>  have attracted considerable attention.
available methods of this type are based on the detection of particular geometrical features in the protein structure, such as clefts  <cit> , proximity of residues to the protein center  <cit> , mutual spatial proximity of residues  <cit> , or spatial motifs such as the well-known catalytic triad in serine proteases  <cit> .
more recently, methods using a combination of evolutionary, geometrical and stability-related information to identify functional residues have also been proposed  <cit> .
there are good indications that evolution often optimizes functional properties at the expense of thermodynamic stability.
site-directed mutagenesis of the catalytic residues in t <dig> lysozyme yields inactive mutant proteins that are more stable than the wild-type  <cit> , indicating that the catalytic residues destabilize the wild-type enzyme.
similar conclusions were drawn from mutagenesis experiments on other proteins  <cit> .
in particular, the catalytic power of enzymes is believed to result from the presence of specific constellations of polar residues in the active site, which can introduce either electrostatic  <cit>  or steric  <cit>  strain into the folded protein conformation in absence of the bound ligand  <cit> .
related to this observation is the recent finding that residues in left handed helices, which occur rarely in proteins, are often important for function  <cit> .
further evidence that protein sequences may not be optimized for protein stability has been provided by studies using computational protein design procedures.
those procedures select sequences that optimize the stability of a given protein three dimensional structure.
they were recently shown to generate native-like sequences in the protein core but not on the surface, suggesting that surface residues may be selected primarily for functional reasons at the expense of stability  <cit> .
following this reasoning, several studies have shown that functional sites in protein structures can be detected by identifying residues positioned in unfavorable or unusual energetic environments.
this includes the analysis of ionisable groups with perturbed titration curves  <cit>  and the use of continuum electrostatics methods for the identification of polar residues engaged in unfavorable electrostatic interactions  <cit>  in enzyme active sites  <cit> .
binding sites in proteins were also shown to consist of neighbouring regions of low and high stability  <cit> .
a major challenge for functional site prediction methods in proteins is their validation against a benchmark set of known functional sites.
such benchmark must be large and diverse enough so as to cover many types of functional sites.
these sites should furthermore be described in a standard fashion and this description should be based on all available information .
unfortunately, such benchmarks are still unavailable, although efforts are currently in progress to address this issue.
resources like the catalytic site atlas  <cit>  are very helpful, but are limited to catalytic residues in enzymes.
they hence lack information on non-catalytic ligand-binding residues or other types of functionally important residues.
resources such as pdbsum  <cit> , pdb site records  <cit>  or swissprot  <cit>  also provide useful information, but only for a subset of the entries.
the bind database  <cit>  provides annotations for residues involved in ligand binding and different types of function, but makes no distinction between biologically relevant association modes and non-relevant ones.
other structure-based binding site databases suffer from that same limitation, and other issues that cannot be addressed without manual verification, such as inclusion of residues known to be important for function from non-structural evidence  <cit> .
providing a comprehensive and relevant functional site benchmark for proteins is not straightforward and reflects the difficulty to define what a functional site is.
where should one draw the limit?
should residues important for maintaining the native 3d structure or for enabling conformational changes, both of which may be required for function, also be considered as functional residues?
even when focusing on ligand binding alone, important choices need to be made in defining the ligand binding residues.
are those the residues that are directly involved in non-bonded interactions with the ligand in the holo-protein, or should one include other residues in the neighbourhood?
one may choose to define ligand binding residues as those contributing significantly to the protein-ligand binding free energy.
but this may likewise require the consideration of residues remote from the binding site, which might be involved in electrostatic steering effects  <cit> .
waiting for these issues to be addressed, functional site predictions are currently validated against information that is either approximate or incomplete.
some methods have been validated against sets of functional residues defined on the basis of thorough literature curation, but those are generally restricted to a very small numbers of proteins, and the definitions used are somewhat ad-hoc .
this paper presents a procedure for detecting destabilizing regions in protein three-dimensional structures solely on the basis of objective energetic criteria.
the correspondence between these regions and known functional sites is quantitatively evaluated in order to assess the effectiveness of energetic criteria alone in functional site prediction.
our analysis focuses entirely on ligand-binding sites.
to enable adequate validation we build a benchmark of  <dig> such binding sites from a non-redundant set  of  <dig> proteins having a high quality crystal apo-structure, and at least one characterised binding site.
these are defined here using a set of objective criteria and information extracted from the 3d structures and from a comprehensive analysis of the associated literature.
in our procedure the contribution of each residue to the protein folding free energy  is evaluated using an all-atom force-field developed previously for protein design applications  <cit> .
residues providing destabilizing free energy contributions are identified and grouped together to yield the destabilizing regions in a stepwise protocol, which takes into account their proximity in the 3d structure and the level of their destabilizing contribution.
this protocol is governed by  <dig> adjustable parameters, which have straightforward physical meanings.
these parameters are adjusted so as to optimize the overlap between the identified destabilizing regions and known binding sites in a set of  <dig> proteins , which are unrelated  to the set of  <dig> proteins used for the analysis .
a systematic comparison between the destabilizing regions identified by our procedure in the  <dig> apo-protein structures and the known ligand binding sites reveals that their overlap is on average limited, but significantly better than random.
a statistically significant overlap between the two types of regions  is obtained in 77% of the proteins in which destabilizing regions are detected.
most interestingly, our study shows that the extent of overlap largely depends on the type of ligand whose binding site is being considered.
largest overlaps are obtained for sites binding small ligands and polysaccharides, while very poor overlap is almost systematically obtained with nucleic acid-binding sites.
these differences are rationalized in terms of the geometric and energetic properties of the various binding sites, and the potential of using energetic criteria such as those proposed here for the prediction of functional sites in solved protein structures with no known relatives is discussed.
the software desite for identifying destabilizing regions in protein structure is available upon request.
RESULTS
functional sites in proteins
in order to evaluate the degree of overlap between the destabilizing regions identified with our procedure and the regions that actually mediate function in the proteins of interest, an objective and unified description of the latter regions, termed here functional sites is required.
to derive such description a detailed analysis that combined information from pdb entries and from biochemical and mutagenesis data extracted from the literature  was conducted on the  <dig> proteins of our test set .
a functional site was defined as a group of residues.
in the vast majority of the cases the defined groups represent residues involved in ligand binding, where the ligands encompass molecules of different types and sizes.
the defined sites are therefore strictly speaking ligand-binding sites.
hence residues not directly involved in ligand binding, but required for maintaining the stability of the native conformation or for enabling conformational changes required for function are not explicitly considered as being part of functional sites.
properties of known binding sites of the dataset proteins.
binding sites are classified according to their type of ligand.
the last  <dig> categories refer to binding sites where  <dig> types of ligand can bind.
a pdb identifier of structure used for energy calculations.
b pdb identifiers of the structures of the protein-ligand complex used to define the binding site.
c number of residues in binding site.
d fraction of protein residues in binding site .
e total asa of binding site residues.
f fraction of protein asa in binding site .
g true  if binding site sits in a cleft, false  otherwise.
h 1gus appears twice here because it has  <dig> distinct binding sites for small ligands.
the same observation applies to 1e6l and 1uns that have  <dig> distinct binding sites for different proteins.
the  <dig> proteins of our dataset were found to contain a total of  <dig> binding sites, with  <dig> proteins containing  <dig> binding sites, and one protein  with  <dig> binding sites.
the analyzed proteins contain  <dig> nucleic acid-binding sites,  <dig> polysaccharide binding sites,  <dig> interaction sites with other proteins,  <dig> peptide binding sites,  <dig> for lipids,  <dig> for metal ions, and  <dig> small ligand-binding sites.
seven sites were found to bind multiple ligands.
the size of the binding sites, expressed in terms of the number of residues per site copy, varies from  <dig> to  <dig> residues, with an average of  <dig> residues.
the fraction of the total number of residues contained in individual binding sites ranges between  <dig> % and  <dig> % .
but most binding sites  have a small relative size of less than 2% of the total number of residues in the protein.
the average amino acid composition found in binding sites is illustrated in fig.
<dig>  in comparison to the amino acid composition of the complete proteins of our set , binding sites are enriched in aromatic residues , h, r, and n, and to a lesser extent in m and d. on the other hand these sites are depleted in p, k, e and the aliphatic residues .
the lower occurrence of lysine residues in binding sites, as compared with other polar residues, thus follows the trend observed previously for protein-protein interfaces  <cit>  and catalytic sites  <cit> .
the enrichment in aromatic and polar residues such as h, t, r, and n most probably reflects the hydrogen bonding potential of the corresponding side chains, with the aromatics mostly occuring in sugar-binding sites.
the frequent occurrence of arg residues has previously been reported in interfaces of protein complexes  <cit> .
binding sites have been reported to often occur in large clefts at the protein surface  <cit> .
this is particularly frequent for sites that bind small ligands as it enables the protein to surround such ligands almost completely in order to minimize contact with the solvent  <cit> .
to investigate the extent to which this was also the case for the binding sites identified here, we checked whether the sites were located in one of the  <dig> largest clefts identified by surfnet  <cit> , following a set of recent rules used in conjunction with this program  <cit> .
we consider a binding site to be part of one of these clefts if at least 75% of its residues are cleft residues as well.
residues lining surfnet clefts are defined with the mask program  using default values  <cit> .
inspection of table  <dig> reveals that out of the  <dig> binding sites in our dataset,  <dig> are located in such clefts, including the majority of the sites that bind small ligands  and polysaccharides .
the frequency is lower for other ligand types, particularly for peptide and protein binding sites .
as a consequence of their preferred location in clefts, residues belonging to functional sites generally have lower solvent accessibilities  than non-functional surface residues , with about 25% of residues in these sites being completely buried.
it should also be noted that the largest cleft identified by surfnet is usually much larger than any of the functional sites defined here , with the largest clefts containing at least  <dig> residues and often as many as several hundreds .
the probability that the overlap of such large clefts with the much smaller functional sites might occur by chance can therefore be quite high .
destabilizing regions
salient features
destabilizing regions are defined as groups of spatially neighbouring residues whose contribution to the free energy of the native state is unfavorable .
such groups are identified by first computing the contributions of individual residues to the protein folding free energy, selecting the residues with the most unfavorable contributions and delimiting regions in the protein that contain a high density of such residues, as described in methods.
a pdb identifier of structure used for energy calculations.
b number of equivalent destabilizing regions identified in the structure .
c number of residues in destabilizing region.
d fraction of protein residues in destabilizing region .
e total asa of destabilizing region residues.
f fraction of protein asa in destabilizing region .
g true  if destabilizing region sits in a cleft, false  otherwise.
in homo-multimeric assemblies the described destabilizing regions represent the smallest of the equivalent destabilizing regions identified in different subunits, and can be considered as the common core of these regions.
a similar procedure was applied to define the common core of intersecting regions between binding sites and destabilizing regions, in multimeric proteins .
a total of  <dig> destabilizing regions are detected in the dataset, but none are found in  <dig> proteins .
the number of destabilizing regions per protein ranges between zero in these  <dig> cases and  <dig> in one protein.
their size varies from  <dig> to  <dig> residues, averaging around  <dig> residues, and most destabilizing regions  contain less than  <dig> residues.
residues in a single destabilizing region represent between <1% and ~15% of the total number of residues in the protein, with an average of ~4%.
less than half  of the destabilizing regions map into one of the  <dig> largest clefts in the protein.
the average amino acid composition of destabilizing regions is illustrated in fig.
<dig>  alongside of the composition in binding sites and in the full proteins of our dataset.
relative to the amino acid composition of the full protein, these regions are highly enriched in s and d, and to a lesser extent in e, r, t and h, and are largely depleted in the aliphatic residues , as well as in k, w, g, m and c. the amino acid composition of the destabilizing regions thus displays some similarities to the composition of the binding sites  but also differs from it, most notably by the lower content of aromatics, and much higher content of s, t, d and e. overall, polar and charged residues account for more than 75% of the residues in the destabilizing regions.
origins of the unfavorable energetic contributions
to gain insight into the origins of the unfavorable energy contributions of the so-called destabilizing residues, the contribution of individual residues to the folding free energy of the protein Δgf is decomposed into individual terms as follows:
Δgf = Δgvdw + Δgelec + Δgsolvation
where Δgvdw, Δgelec and Δgsolvation are respectively the differences in van der waals, electrostatic and solvation free energies between the folded state and the reference state for a given residue .
figure  <dig> shows the average values and standard deviations for the different terms in eq.
<dig> and the total free energy difference, computed for the  <dig> amino acid types, of all the residues of our dataset  and of the residues identified as destabilizing by our analysis  .
the polar residues in our dataset generally exhibit an unfavorable contribution to the solvation free energy difference  because their polar groups are often partially buried in the folded protein.
however, with those groups often engaged in hydrogen bonds with neighbouring residues in the protein, the unfavorable effect of desolvation tends to be at least partially compensated by more favorable electrostatic and van der waals interactions made in the folded protein than in water.
for k, n, q and y residues these compensatory effects roughly balance each other, yielding a net contribution to the folding free energy that is near zero .
in r, d, e, h, s and t the unfavorable contribution due to desolvation is less effectively compensated by other terms, leading to a net destabilizing contribution.
it is thus not surprising that these residues are significantly over-represented in destabilizing regions.
for the hydrophobic residues desolvation generally results in a near zero or favorable contribution, whereas the contribution from van der waals interactions tends to be stabilizing, especially for f, y and w, reflecting their tight packing in the folded state  <cit> .
the values calculated for the electrostatic contribution are close to zero for most residue types, except for arginines where they seem to be greatly stabilizing.
for destabilizing residues  the contribution from desolvation is in general more destabilizing for all residues types, and the favorable contributions from the van der waals and electrostatic terms is in general weaker, leading to a net destabilizing effect overall.
these trends are most salient for polar residues, which represent the major fraction of the destabilizing residues identified in our dataset.
when hydrophobic residues are identified as destabilizing this is often due to the solvation and van der waals terms being unfavorable.
in comparison to the hydrophobic residues in the full dataset whose contribution to the van der waals term is in general quite stabilizing, the absence of favorable van der waals contributions in destabilizing hydrophobic residues is particularly striking .
relation between destabilizing regions and known binding sites
in total, there are more destabilizing regions  than known binding sites , but destabilizing regions tend to be smaller, with ~ <dig> residues on average vs.  <dig> in functional sites.
this results in similar average numbers of residues in destabilizing regions and known binding sites, per protein .
the average fraction of protein residues located in binding sites  is however much larger than that located in destabilizing regions  because several analyzed proteins with known binding sites do not contain destabilizing regions.
we also see that known binding sites are more frequently located in large clefts than destabilizing regions .
intersection of functional sites and destabilizing regions
detected destabilizing regions and known binding sites show overlap in  <dig> out of the total of  <dig> proteins in our dataset.
residues shared by a known binding site and a destabilizing region constitute what we call here the intersection region .
a no destabilizing regions were detected in  <dig> entries of the dataset  and these entries are not listed in this table.
b pdb identifier of structure used for energy calculations.
c number of residues in binding site.
d number of residues in destabilising region.
e number of residues in intersection region.
f sensitivity .
g positive predictive value .
h expected number of residues in intersection region .
i 1gus appears twice here because it has  <dig> distinct binding sites for small ligands.
the same observation applies to 1e6l and 1uns that have  <dig> distinct binding sites for different proteins.
j average sensitivity  for the given ligand type.
k average ppv  for the given ligand type.
the majority of the ir's  are located in one of the four largest clefts of each protein.
the number of residues per ir varies from  <dig> to  <dig>  with an average of  <dig>  most contain less than  <dig> residues.
can destabilizing regions be used to predict functional sites?
using the results on the overlap between destabilizing regions and known functional sites listed in table  <dig>  we now evaluate the extent to which destabilizing regions, as identified here, can be used to predict functional sites in a protein structure in absence of prior knowledge.
to that end the sensitivity s and positive predictive value  of the destabilizing regions were computed.
for a given protein, sobs is the fraction of residues in known binding sites that are also part of destabilizing regions; and ppvobs is the fraction of residues in destabilizing regions that is intersecting with known binding sites .
since our procedure was not trained on our dataset but on a different group of  <dig> proteins, this dataset can be used to compute these quantities.
the average values of sobs and ppvobsin the dataset are respectively  <dig> % and  <dig> %.
this means that, on average, about one fourth of the residues in a given binding site are also part of destabilizing regions, and that a little more than one fourth of the residues in destabilizing regions are also part of binding sites.
the observed s and ppv values for each protein in which destabilizing regions were identified are listed in table  <dig>
although this overlap is modest and would not allow to identify functional sites in a quantitative fashion, it is statistically significant.
the number of overlapping residues expected by chance for each binding site-destabilizing region pair is computed using the hypergeometric distribution .
these expected numbers of intersecting residues were used to calculate the expected sensitivity  and ppv  values for each protein.
the average sexp and ppvexp are  <dig> % and  <dig> %, respectively.
a one-tailed wilcoxon signed-rank test  <cit>  was then applied to the full set of observed and expected sensitivity and ppv values, and indicates that sobs are significantly larger than sexp and that ppvobsare significantly larger than ppvexp.
taking a p-value threshold of  <dig>  in considering an overlap as statistically significant, yielded statistically significant overlaps in  <dig> of the  <dig> proteins in which at least one destabilizing region has been identified.
in other words, if destabilizing regions are identified in a protein, there is 77%  probability that at least one of these regions will display a significant overlap with at least one known binding site.
in the following we describe how these significant overlaps are distributed amongst the functional sites associated with different types of bound ligands.
overlap with destabilizing regions as a function of ligand types
the extent of overlap between destabilizing regions and functional sites varies significantly with the type of ligand binding to these sites .
this is not too surprising given that the shape and composition of the functional site is in principle optimized to fit the type of ligand that binds to it, and that occurrence of destabilizing regions depends on shape and amino acid composition.
the average sensitivity is highest  for sites that bind polysaccharides and lowest  for nucleic acid binding sites, whereas the average ppv ranges from  <dig> % for protein binding sites to 10% for nucleic-acid binding sites .
contingency table used to perform a fisher exact test of homogeneity, among different categories of binding sites , of the fraction of statistically significant overlaps  between destabilizing regions and known binding sites.
abbreviations used: nu, nucleic-acid; po, polysaccharide; pr, protein; pe, peptide; sm, small compound; me, metal ion; li, lipid; sig.
overlap, statistically significant overlap; no.
overlap, statistically non-significant or absent overlap.
calculated expected numbers of statistically significant overlaps are given between brackets, below the corresponding observed numbers.
destabilizing regions identified here are reasonable predictors of binding sites for polysaccharides and small ligands, but very poor predictors of sites involved in nucleic acid binding, with the prediction performance for other types of sites being of intermediate reliability .
the better overlap with sites that bind small ligands and polysaccharides can be explained by the fact that many small ligands and polysaccharides bind to clefts enriched in polar and/or charged residues  <cit> .
the polar residues in these sites therefore tend to be more buried than average, thereby providing a destabilizing contribution to the folding free energy in absence of the ligand  <cit> .
the same applies to metal-binding sites located in deep clefts, with some exceptions however, as in phospholipase c  where the zinc-binding site undergoes very large conformational change upon binding  <cit> .
in internalin a , chey  and endonuclease vii , the metal ion binds in shallow clefts or flat surfaces where the residues have freedom to move to adapt to the absence of the ion.
in contrast, nucleic acids generally bind to larger regions with flat or convex surface shape.
as a result, the binding site residues can be well solvated in absence of the bound ligand.
furthermore, nucleic acid binding regions usually include a sizable fraction of aliphatic and aromatic residues  <cit> , which are poorly represented in the destabilizing regions identified here.
the overlap with lipid-binding sites, which are often located in deep clefts rich in hydrophobic residues, is in general rather limited.
significant overlap is however observed with sites in phospholipase c  and crustacyanin c <dig> .
these proteins bind polar heads carrying phospholipids, and the corresponding sites bury these heads inside polar clefts on the protein surface.
it is these polar clefts that tend to overlap with the destabilizing regions identified in these proteins.
protein and peptide binding sites are very diverse, and so is their overlap with destabilizing regions.
some functional sites or part of such sites are located in disordered regions and cannot be identified by our method .
discussion and
CONCLUSIONS
the basic assumption in this work has been that functional sites in proteins are very likely to contain residues that contribute unfavorably to the stability of the native conformation, due to evolutionary selection pressure for optimizing functional efficiency or specificity.
this idea has been formulated previously by several authors  <cit> , and illustrated in several proteins systems  <cit> .
more recently, links have been established between functional sites and unfavorable solvation effects  <cit>  or electrostatic interactions  <cit> .
so far however, the relation between protein residues providing destabilizing contributions and functional sites has not been systematically investigated with rigorous statistical backing.
the present study attempted to fill this gap.
it described a procedure for identifying regions in protein structures, containing residues that contribute unfavorably to the thermodynamic stability of the folded state.
this stability was assessed from the experimentally determined atomic coordinates on the basis of a classical empirical energy function and standard parameters available in the charmm package, augmented with a surface area dependent solvation term.
contributions of individual residues were computed using a thermodynamic cycle that incorporates a simplified model for the unfolded state.
clusters of the most destabilizing residues were identified and extended to include their immediate spatial neighbours, yielding the so-called destabilizing regions defined in this study.
applying our procedure to a set of  <dig> high resolution protein crystal structures with well annotated ligand binding sites, but representing the apo-form of the protein, we were able to measure the overlap between these annotated sites and the identified destabilizing regions, assess its statistical significance and evaluate the effectiveness of using destabilizing regions for the prediction of ligand binding sites in proteins.
although our results show that only about 25% of the residues in destabilizing regions, as defined here, belong to ligand binding sites and vice versa, this overlap is well above what would be expected by chance .
we find furthermore, that when destabilizing regions are detected, they display statistically significant overlap with at least one known binding site in 77% of proteins examined here.
another important finding of our analysis is that the extent of overlap between destabilizing regions and binding sites is highly dependent on the type of ligand bound to these sites.
more extensive overlap is observed with binding sites for small ligands and polysaccharides whereas the overlap with nucleic acid binding sites is extremely poor.
these differences are rationalized by the observation that the binding sites for small ligands and polysaccharides occur mostly in clefts lined with polar residues.
those become partially desolvated, as a result, leading to unfavorable contributions.
in contrast, the nucleic acid-binding sites often consist of convex surfaces that are particularly rich in positively charged and polar residues.
the latter are hence optimally solvated in absence of the bound nucleotides and therefore provide a favorable energetic contribution.
but the relationship between destabilizing regions and functional sites cannot be reduced to geometric features or amino acid composition.
for instance, polysaccharide-binding sites are rich in residue types that are rare in destabilizing regions but overlap well with the latter.
likewise, some clefts are not detected as destabilizing  whereas flat and convex regions are occasionally detected as such.
there is little doubt that the occurrence of ligand binding sites in clefts often makes physical and chemical sense.
using the definition of clefts in a protein structure to predict functional sites is however far more challenging.
surfnet is a program that identifies clefts in protein structures.
it was claimed by the authors that the largest cleft identified by surfnet contains the protein binding site in a large majority of cases  <cit> .
applying surfnet to our  <dig> structures and checking the overlap of the largest cleft identified by this procedure with the functional sites defined in our dataset, shows that surfnet is 'better' at predicting functional site .
however surfnet clefts are usually much larger than the functional sites , and therefore tend to include these entirely in addition to including a large number of 'false positive' residues.
the ppv of the surfnet method is consequently much lower  compared to our method .
for the same reason, the overlap noted here with destabilizing regions may often not be statistically significant .
it was recently shown  <cit>  that the poor specificity of surfnet could be improved by using it in combination with the conservation-based method consurf  <cit> .
similarly, complementing surfnet with functional site prediction approaches not based on conservation, like the one presented here, may prove useful for cases where not enough homologues are available.
the energetic criteria used here to define destabilizing regions would also need improvements.
these criteria currently rely on standard force fields and approaches, that suffer from many well documented limitations.
the representation of electrostatic and solvation effects is far from optimal, although some of us have recently demonstrated that the addition of the simple surface area terms to the charmm potential, as done here, is superior to many of the more sophisticated continuum electrostatic models  <cit> .
more importantly, our analysis completely neglects polypeptide chain entropy contributions to the free energy in both the folded and unfolded states, and our model for the unfolded state is extremely crude.
due in part to these limitations we chose not to consider residues with very high temperature factors in our calculations, as the atomic coordinates of those residues are likely to be inaccurate.
however, it is well known that flexible regions often tend to be involved in recognition.
it is therefore not surprising that our choice not to consider residues with high temperature factors resulted in the elimination of several destabilizing regions that show significant overlap with functional sites .
a further factor that most certainly influenced the results of our analysis is the incomplete knowledge that we currently have of the functional sites of proteins on the one hand, and the lack of consistent annotations for the known sites on the other.
even one of the best characterized proteins, such as hen egg white lysozyme, features a myriad of binding and functional sites that have not been annotated and archived in databases.
the so-called 'moonlighting' proteins where new binding sites and activities are discovered long after their first function was characterised, illustrate well this point  <cit> .
to compensate at least in part for this shortcoming, the biologically relevant binding sites in the  <dig> proteins used in our analysis were manually annotated using all available literature evidence.
the annotated binding sites are freely available at  <cit> .
clearly though, much more work is needed in order to produce both more consistent definitions of functional sites and to proceed with their annotations.
despite the current limitations in using energetic criteria to identify functional sites in proteins, we believe that methods such as those presented here and future improved versions, will play an increasingly important role.
indeed the fact that they do not rely on information on related proteins, as most other methods presently do  <cit> , should make them particularly useful for assigning function to proteins with no known relatives, of which a growing number is being currently discovered .
since functional site prediction methods that use information on sequences and structures of related proteins may also suffer from limitations due to the lack of sequence data, an approach in which such methods are combined with energetic criteria should help improve performance, as previously suggested  <cit> .
of particular interest are methods that detect spatial clusters of conserved residues, which were shown to greatly improve the performance of functional site prediction  <cit> .
further improvements may be obtained if those methods are combined with the search of spatial clusters of destabilizing residues, as done in this study, instead of considering only individual destabilizing residues  <cit> .

 available three-dimensional structures of proteins of unknown biological role are rapidly increasing as a result of structural genomics initiatives. this prompted the development of methods for annotating protein structures at the residue level and inferring binding sites using information from related proteins. a common approach to detect functional sites in proteins has been to identify evolutionarily conserved residues that are spatially contiguous in the protein structure. this approach has lately been extended and integrated with detailed analyses of structural features, related protein structures and sequence information by several groups. but the lack of related proteins, a common occurrence with structural genomics targets, hinders the wide applicability of many of these methods. in addition, there are now many examples where above-average sequence variability rather than sequence conservation is associated with functional regions. for these reasons, methods capable of identifying functional residues in absence of information on conserved residues have attracted considerable attention. available methods of this type are based on the detection of particular geometrical features in the protein structure, such as clefts, proximity of residues to the protein center, mutual spatial proximity of residues, or spatial motifs such as the well-known catalytic triad in serine proteases. more recently, methods using a combination of evolutionary, geometrical and stability-related information to identify functional residues have also been proposed. there are good indications that evolution often optimizes functional properties at the expense of thermodynamic stability. site-directed mutagenesis of the catalytic residues in t lysozyme yields inactive mutant proteins that are more stable than the wild-type, indicating that the catalytic residues destabilize the wild-type enzyme. similar conclusions were drawn from mutagenesis experiments on other proteins. in particular, the catalytic power of enzymes is believed to result from the presence of specific constellations of polar residues in the active site, which can introduce either electrostatic or steric strain into the folded protein conformation in absence of the bound ligand. related to this observation is the recent finding that residues in left handed helices, which occur rarely in proteins, are often important for function. further evidence that protein sequences may not be optimized for protein stability has been provided by studies using computational protein design procedures. those procedures select sequences that optimize the stability of a given protein three dimensional structure. they were recently shown to generate native-like sequences in the protein core but not on the surface, suggesting that surface residues may be selected primarily for functional reasons at the expense of stability. following this reasoning, several studies have shown that functional sites in protein structures can be detected by identifying residues positioned in unfavorable or unusual energetic environments. this includes the analysis of ionisable groups with perturbed titration curves and the use of continuum electrostatics methods for the identification of polar residues engaged in unfavorable electrostatic interactions in enzyme active sites. binding sites in proteins were also shown to consist of neighbouring regions of low and high stability. a major challenge for functional site prediction methods in proteins is their validation against a benchmark set of known functional sites. such benchmark must be large and diverse enough so as to cover many types of functional sites. these sites should furthermore be described in a standard fashion and this description should be based on all available information. unfortunately, such benchmarks are still unavailable, although efforts are currently in progress to address this issue. resources like the catalytic site atlas are very helpful, but are limited to catalytic residues in enzymes. they hence lack information on non-catalytic ligand-binding residues or other types of functionally important residues. resources such as pdbsum, pdb site records or swissprot also provide useful information, but only for a subset of the entries. the bind database provides annotations for residues involved in ligand binding and different types of function, but makes no distinction between biologically relevant association modes and non-relevant ones. other structure-based binding site databases suffer from that same limitation, and other issues that cannot be addressed without manual verification, such as inclusion of residues known to be important for function from non-structural evidence. providing a comprehensive and relevant functional site benchmark for proteins is not straightforward and reflects the difficulty to define what a functional site is. where should one draw the limit? should residues important for maintaining the native 3d structure or for enabling conformational changes, both of which may be required for function, also be considered as functional residues? even when focusing on ligand binding alone, important choices need to be made in defining the ligand binding residues. are those the residues that are directly involved in non-bonded interactions with the ligand in the holo-protein, or should one include other residues in the neighbourhood? one may choose to define ligand binding residues as those contributing significantly to the protein-ligand binding free energy. but this may likewise require the consideration of residues remote from the binding site, which might be involved in electrostatic steering effects. waiting for these issues to be addressed, functional site predictions are currently validated against information that is either approximate or incomplete. some methods have been validated against sets of functional residues defined on the basis of thorough literature curation, but those are generally restricted to a very small numbers of proteins, and the definitions used are somewhat ad-hoc. this paper presents a procedure for detecting destabilizing regions in protein three-dimensional structures solely on the basis of objective energetic criteria. the correspondence between these regions and known functional sites is quantitatively evaluated in order to assess the effectiveness of energetic criteria alone in functional site prediction. our analysis focuses entirely on ligand-binding sites. to enable adequate validation we build a benchmark of such binding sites from a non-redundant set of proteins having a high quality crystal apo-structure, and at least one characterised binding site. these are defined here using a set of objective criteria and information extracted from the 3d structures and from a comprehensive analysis of the associated literature. in our procedure the contribution of each residue to the protein folding free energy is evaluated using an all-atom force-field developed previously for protein design applications. residues providing destabilizing free energy contributions are identified and grouped together to yield the destabilizing regions in a stepwise protocol, which takes into account their proximity in the 3d structure and the level of their destabilizing contribution. this protocol is governed by adjustable parameters, which have straightforward physical meanings. these parameters are adjusted so as to optimize the overlap between the identified destabilizing regions and known binding sites in a set of proteins, which are unrelated to the set of proteins used for the analysis. a systematic comparison between the destabilizing regions identified by our procedure in the apo-protein structures and the known ligand binding sites reveals that their overlap is on average limited, but significantly better than random. a statistically significant overlap between the two types of regions is obtained in 77% of the proteins in which destabilizing regions are detected. most interestingly, our study shows that the extent of overlap largely depends on the type of ligand whose binding site is being considered. largest overlaps are obtained for sites binding small ligands and polysaccharides, while very poor overlap is almost systematically obtained with nucleic acid-binding sites. these differences are rationalized in terms of the geometric and energetic properties of the various binding sites, and the potential of using energetic criteria such as those proposed here for the prediction of functional sites in solved protein structures with no known relatives is discussed. the software desite for identifying destabilizing regions in protein structure is available upon request.  functional sites in proteins in order to evaluate the degree of overlap between the destabilizing regions identified with our procedure and the regions that actually mediate function in the proteins of interest, an objective and unified description of the latter regions, termed here functional sites is required. to derive such description a detailed analysis that combined information from pdb entries and from biochemical and mutagenesis data extracted from the literature was conducted on the proteins of our test set. a functional site was defined as a group of residues. in the vast majority of the cases the defined groups represent residues involved in ligand binding, where the ligands encompass molecules of different types and sizes. the defined sites are therefore strictly speaking ligand-binding sites. hence residues not directly involved in ligand binding, but required for maintaining the stability of the native conformation or for enabling conformational changes required for function are not explicitly considered as being part of functional sites. properties of known binding sites of the dataset proteins. binding sites are classified according to their type of ligand. the last categories refer to binding sites where types of ligand can bind. a pdb identifier of structure used for energy calculations. b pdb identifiers of the structures of the protein-ligand complex used to define the binding site. c number of residues in binding site. d fraction of protein residues in binding site. e total asa of binding site residues. f fraction of protein asa in binding site. g true if binding site sits in a cleft, false otherwise. h 1gus appears twice here because it has distinct binding sites for small ligands. the same observation applies to 1e6l and 1uns that have distinct binding sites for different proteins. the proteins of our dataset were found to contain a total of binding sites, with proteins containing binding sites, and one protein with binding sites. the analyzed proteins contain nucleic acid-binding sites, polysaccharide binding sites, interaction sites with other proteins, peptide binding sites, for lipids, for metal ions, and small ligand-binding sites. seven sites were found to bind multiple ligands. the size of the binding sites, expressed in terms of the number of residues per site copy, varies from to residues, with an average of residues. the fraction of the total number of residues contained in individual binding sites ranges between % and %. but most binding sites have a small relative size of less than 2% of the total number of residues in the protein. the average amino acid composition found in binding sites is illustrated in fig. in comparison to the amino acid composition of the complete proteins of our set, binding sites are enriched in aromatic residues, h, r, and n, and to a lesser extent in m and d. on the other hand these sites are depleted in p, k, e and the aliphatic residues. the lower occurrence of lysine residues in binding sites, as compared with other polar residues, thus follows the trend observed previously for protein-protein interfaces and catalytic sites. the enrichment in aromatic and polar residues such as h, t, r, and n most probably reflects the hydrogen bonding potential of the corresponding side chains, with the aromatics mostly occuring in sugar-binding sites. the frequent occurrence of arg residues has previously been reported in interfaces of protein complexes. binding sites have been reported to often occur in large clefts at the protein surface. this is particularly frequent for sites that bind small ligands as it enables the protein to surround such ligands almost completely in order to minimize contact with the solvent. to investigate the extent to which this was also the case for the binding sites identified here, we checked whether the sites were located in one of the largest clefts identified by surfnet, following a set of recent rules used in conjunction with this program. we consider a binding site to be part of one of these clefts if at least 75% of its residues are cleft residues as well. residues lining surfnet clefts are defined with the mask program using default values. inspection of table reveals that out of the binding sites in our dataset, are located in such clefts, including the majority of the sites that bind small ligands and polysaccharides. the frequency is lower for other ligand types, particularly for peptide and protein binding sites. as a consequence of their preferred location in clefts, residues belonging to functional sites generally have lower solvent accessibilities than non-functional surface residues, with about 25% of residues in these sites being completely buried. it should also be noted that the largest cleft identified by surfnet is usually much larger than any of the functional sites defined here, with the largest clefts containing at least residues and often as many as several hundreds. the probability that the overlap of such large clefts with the much smaller functional sites might occur by chance can therefore be quite high. destabilizing regions salient features destabilizing regions are defined as groups of spatially neighbouring residues whose contribution to the free energy of the native state is unfavorable. such groups are identified by first computing the contributions of individual residues to the protein folding free energy, selecting the residues with the most unfavorable contributions and delimiting regions in the protein that contain a high density of such residues, as described in methods. a pdb identifier of structure used for energy calculations. b number of equivalent destabilizing regions identified in the structure. c number of residues in destabilizing region. d fraction of protein residues in destabilizing region. e total asa of destabilizing region residues. f fraction of protein asa in destabilizing region. g true if destabilizing region sits in a cleft, false otherwise. in homo-multimeric assemblies the described destabilizing regions represent the smallest of the equivalent destabilizing regions identified in different subunits, and can be considered as the common core of these regions. a similar procedure was applied to define the common core of intersecting regions between binding sites and destabilizing regions, in multimeric proteins. a total of destabilizing regions are detected in the dataset, but none are found in proteins. the number of destabilizing regions per protein ranges between zero in these cases and in one protein. their size varies from to residues, averaging around residues, and most destabilizing regions contain less than residues. residues in a single destabilizing region represent between <1% and ~15% of the total number of residues in the protein, with an average of ~4%. less than half of the destabilizing regions map into one of the largest clefts in the protein. the average amino acid composition of destabilizing regions is illustrated in fig. alongside of the composition in binding sites and in the full proteins of our dataset. relative to the amino acid composition of the full protein, these regions are highly enriched in s and d, and to a lesser extent in e, r, t and h, and are largely depleted in the aliphatic residues, as well as in k, w, g, m and c. the amino acid composition of the destabilizing regions thus displays some similarities to the composition of the binding sites but also differs from it, most notably by the lower content of aromatics, and much higher content of s, t, d and e. overall, polar and charged residues account for more than 75% of the residues in the destabilizing regions. origins of the unfavorable energetic contributions to gain insight into the origins of the unfavorable energy contributions of the so-called destabilizing residues, the contribution of individual residues to the folding free energy of the protein Δgf is decomposed into individual terms as follows: Δgf = Δgvdw + Δgelec + Δgsolvation where Δgvdw, Δgelec and Δgsolvation are respectively the differences in van der waals, electrostatic and solvation free energies between the folded state and the reference state for a given residue. figure shows the average values and standard deviations for the different terms in eq. and the total free energy difference, computed for the amino acid types, of all the residues of our dataset and of the residues identified as destabilizing by our analysis. the polar residues in our dataset generally exhibit an unfavorable contribution to the solvation free energy difference because their polar groups are often partially buried in the folded protein. however, with those groups often engaged in hydrogen bonds with neighbouring residues in the protein, the unfavorable effect of desolvation tends to be at least partially compensated by more favorable electrostatic and van der waals interactions made in the folded protein than in water. for k, n, q and y residues these compensatory effects roughly balance each other, yielding a net contribution to the folding free energy that is near zero. in r, d, e, h, s and t the unfavorable contribution due to desolvation is less effectively compensated by other terms, leading to a net destabilizing contribution. it is thus not surprising that these residues are significantly over-represented in destabilizing regions. for the hydrophobic residues desolvation generally results in a near zero or favorable contribution, whereas the contribution from van der waals interactions tends to be stabilizing, especially for f, y and w, reflecting their tight packing in the folded state. the values calculated for the electrostatic contribution are close to zero for most residue types, except for arginines where they seem to be greatly stabilizing. for destabilizing residues the contribution from desolvation is in general more destabilizing for all residues types, and the favorable contributions from the van der waals and electrostatic terms is in general weaker, leading to a net destabilizing effect overall. these trends are most salient for polar residues, which represent the major fraction of the destabilizing residues identified in our dataset. when hydrophobic residues are identified as destabilizing this is often due to the solvation and van der waals terms being unfavorable. in comparison to the hydrophobic residues in the full dataset whose contribution to the van der waals term is in general quite stabilizing, the absence of favorable van der waals contributions in destabilizing hydrophobic residues is particularly striking. relation between destabilizing regions and known binding sites in total, there are more destabilizing regions than known binding sites, but destabilizing regions tend to be smaller, with ~ residues on average vs. in functional sites. this results in similar average numbers of residues in destabilizing regions and known binding sites, per protein. the average fraction of protein residues located in binding sites is however much larger than that located in destabilizing regions because several analyzed proteins with known binding sites do not contain destabilizing regions. we also see that known binding sites are more frequently located in large clefts than destabilizing regions. intersection of functional sites and destabilizing regions detected destabilizing regions and known binding sites show overlap in out of the total of proteins in our dataset. residues shared by a known binding site and a destabilizing region constitute what we call here the intersection region. a no destabilizing regions were detected in entries of the dataset and these entries are not listed in this table. b pdb identifier of structure used for energy calculations. c number of residues in binding site. d number of residues in destabilising region. e number of residues in intersection region. f sensitivity. g positive predictive value. h expected number of residues in intersection region. i 1gus appears twice here because it has distinct binding sites for small ligands. the same observation applies to 1e6l and 1uns that have distinct binding sites for different proteins. j average sensitivity for the given ligand type. k average ppv for the given ligand type. the majority of the ir's are located in one of the four largest clefts of each protein. the number of residues per ir varies from to with an average of most contain less than residues. can destabilizing regions be used to predict functional sites? using the results on the overlap between destabilizing regions and known functional sites listed in table we now evaluate the extent to which destabilizing regions, as identified here, can be used to predict functional sites in a protein structure in absence of prior knowledge. to that end the sensitivity s and positive predictive value of the destabilizing regions were computed. for a given protein, sobs is the fraction of residues in known binding sites that are also part of destabilizing regions; and ppvobs is the fraction of residues in destabilizing regions that is intersecting with known binding sites. since our procedure was not trained on our dataset but on a different group of proteins, this dataset can be used to compute these quantities. the average values of sobs and ppvobsin the dataset are respectively % and %. this means that, on average, about one fourth of the residues in a given binding site are also part of destabilizing regions, and that a little more than one fourth of the residues in destabilizing regions are also part of binding sites. the observed s and ppv values for each protein in which destabilizing regions were identified are listed in table although this overlap is modest and would not allow to identify functional sites in a quantitative fashion, it is statistically significant. the number of overlapping residues expected by chance for each binding site-destabilizing region pair is computed using the hypergeometric distribution. these expected numbers of intersecting residues were used to calculate the expected sensitivity and ppv values for each protein. the average sexp and ppvexp are % and %, respectively. a one-tailed wilcoxon signed-rank test was then applied to the full set of observed and expected sensitivity and ppv values, and indicates that sobs are significantly larger than sexp and that ppvobsare significantly larger than ppvexp. taking a p-value threshold of in considering an overlap as statistically significant, yielded statistically significant overlaps in of the proteins in which at least one destabilizing region has been identified. in other words, if destabilizing regions are identified in a protein, there is 77% probability that at least one of these regions will display a significant overlap with at least one known binding site. in the following we describe how these significant overlaps are distributed amongst the functional sites associated with different types of bound ligands. overlap with destabilizing regions as a function of ligand types the extent of overlap between destabilizing regions and functional sites varies significantly with the type of ligand binding to these sites. this is not too surprising given that the shape and composition of the functional site is in principle optimized to fit the type of ligand that binds to it, and that occurrence of destabilizing regions depends on shape and amino acid composition. the average sensitivity is highest for sites that bind polysaccharides and lowest for nucleic acid binding sites, whereas the average ppv ranges from % for protein binding sites to 10% for nucleic-acid binding sites. contingency table used to perform a fisher exact test of homogeneity, among different categories of binding sites, of the fraction of statistically significant overlaps between destabilizing regions and known binding sites. abbreviations used: nu, nucleic-acid; po, polysaccharide; pr, protein; pe, peptide; sm, small compound; me, metal ion; li, lipid; sig. overlap, statistically significant overlap; no. overlap, statistically non-significant or absent overlap. calculated expected numbers of statistically significant overlaps are given between brackets, below the corresponding observed numbers. destabilizing regions identified here are reasonable predictors of binding sites for polysaccharides and small ligands, but very poor predictors of sites involved in nucleic acid binding, with the prediction performance for other types of sites being of intermediate reliability. the better overlap with sites that bind small ligands and polysaccharides can be explained by the fact that many small ligands and polysaccharides bind to clefts enriched in polar and/or charged residues. the polar residues in these sites therefore tend to be more buried than average, thereby providing a destabilizing contribution to the folding free energy in absence of the ligand. the same applies to metal-binding sites located in deep clefts, with some exceptions however, as in phospholipase c where the zinc-binding site undergoes very large conformational change upon binding. in internalin a, chey and endonuclease vii, the metal ion binds in shallow clefts or flat surfaces where the residues have freedom to move to adapt to the absence of the ion. in contrast, nucleic acids generally bind to larger regions with flat or convex surface shape. as a result, the binding site residues can be well solvated in absence of the bound ligand. furthermore, nucleic acid binding regions usually include a sizable fraction of aliphatic and aromatic residues, which are poorly represented in the destabilizing regions identified here. the overlap with lipid-binding sites, which are often located in deep clefts rich in hydrophobic residues, is in general rather limited. significant overlap is however observed with sites in phospholipase c and crustacyanin c. these proteins bind polar heads carrying phospholipids, and the corresponding sites bury these heads inside polar clefts on the protein surface. it is these polar clefts that tend to overlap with the destabilizing regions identified in these proteins. protein and peptide binding sites are very diverse, and so is their overlap with destabilizing regions. some functional sites or part of such sites are located in disordered regions and cannot be identified by our method. discussion and  the basic assumption in this work has been that functional sites in proteins are very likely to contain residues that contribute unfavorably to the stability of the native conformation, due to evolutionary selection pressure for optimizing functional efficiency or specificity. this idea has been formulated previously by several authors, and illustrated in several proteins systems. more recently, links have been established between functional sites and unfavorable solvation effects or electrostatic interactions. so far however, the relation between protein residues providing destabilizing contributions and functional sites has not been systematically investigated with rigorous statistical backing. the present study attempted to fill this gap. it described a procedure for identifying regions in protein structures, containing residues that contribute unfavorably to the thermodynamic stability of the folded state. this stability was assessed from the experimentally determined atomic coordinates on the basis of a classical empirical energy function and standard parameters available in the charmm package, augmented with a surface area dependent solvation term. contributions of individual residues were computed using a thermodynamic cycle that incorporates a simplified model for the unfolded state. clusters of the most destabilizing residues were identified and extended to include their immediate spatial neighbours, yielding the so-called destabilizing regions defined in this study. applying our procedure to a set of high resolution protein crystal structures with well annotated ligand binding sites, but representing the apo-form of the protein, we were able to measure the overlap between these annotated sites and the identified destabilizing regions, assess its statistical significance and evaluate the effectiveness of using destabilizing regions for the prediction of ligand binding sites in proteins. although our results show that only about 25% of the residues in destabilizing regions, as defined here, belong to ligand binding sites and vice versa, this overlap is well above what would be expected by chance. we find furthermore, that when destabilizing regions are detected, they display statistically significant overlap with at least one known binding site in 77% of proteins examined here. another important finding of our analysis is that the extent of overlap between destabilizing regions and binding sites is highly dependent on the type of ligand bound to these sites. more extensive overlap is observed with binding sites for small ligands and polysaccharides whereas the overlap with nucleic acid binding sites is extremely poor. these differences are rationalized by the observation that the binding sites for small ligands and polysaccharides occur mostly in clefts lined with polar residues. those become partially desolvated, as a result, leading to unfavorable contributions. in contrast, the nucleic acid-binding sites often consist of convex surfaces that are particularly rich in positively charged and polar residues. the latter are hence optimally solvated in absence of the bound nucleotides and therefore provide a favorable energetic contribution. but the relationship between destabilizing regions and functional sites cannot be reduced to geometric features or amino acid composition. for instance, polysaccharide-binding sites are rich in residue types that are rare in destabilizing regions but overlap well with the latter. likewise, some clefts are not detected as destabilizing whereas flat and convex regions are occasionally detected as such. there is little doubt that the occurrence of ligand binding sites in clefts often makes physical and chemical sense. using the definition of clefts in a protein structure to predict functional sites is however far more challenging. surfnet is a program that identifies clefts in protein structures. it was claimed by the authors that the largest cleft identified by surfnet contains the protein binding site in a large majority of cases. applying surfnet to our structures and checking the overlap of the largest cleft identified by this procedure with the functional sites defined in our dataset, shows that surfnet is'better' at predicting functional site. however surfnet clefts are usually much larger than the functional sites, and therefore tend to include these entirely in addition to including a large number of'false positive' residues. the ppv of the surfnet method is consequently much lower compared to our method. for the same reason, the overlap noted here with destabilizing regions may often not be statistically significant. it was recently shown that the poor specificity of surfnet could be improved by using it in combination with the conservation-based method consurf. similarly, complementing surfnet with functional site prediction approaches not based on conservation, like the one presented here, may prove useful for cases where not enough homologues are available. the energetic criteria used here to define destabilizing regions would also need improvements. these criteria currently rely on standard force fields and approaches, that suffer from many well documented limitations. the representation of electrostatic and solvation effects is far from optimal, although some of us have recently demonstrated that the addition of the simple surface area terms to the charmm potential, as done here, is superior to many of the more sophisticated continuum electrostatic models. more importantly, our analysis completely neglects polypeptide chain entropy contributions to the free energy in both the folded and unfolded states, and our model for the unfolded state is extremely crude. due in part to these limitations we chose not to consider residues with very high temperature factors in our calculations, as the atomic coordinates of those residues are likely to be inaccurate. however, it is well known that flexible regions often tend to be involved in recognition. it is therefore not surprising that our choice not to consider residues with high temperature factors resulted in the elimination of several destabilizing regions that show significant overlap with functional sites. a further factor that most certainly influenced the results of our analysis is the incomplete knowledge that we currently have of the functional sites of proteins on the one hand, and the lack of consistent annotations for the known sites on the other. even one of the best characterized proteins, such as hen egg white lysozyme, features a myriad of binding and functional sites that have not been annotated and archived in databases. the so-called'moonlighting' proteins where new binding sites and activities are discovered long after their first function was characterised, illustrate well this point. to compensate at least in part for this shortcoming, the biologically relevant binding sites in the proteins used in our analysis were manually annotated using all available literature evidence. the annotated binding sites are freely available at. clearly though, much more work is needed in order to produce both more consistent definitions of functional sites and to proceed with their annotations. despite the current limitations in using energetic criteria to identify functional sites in proteins, we believe that methods such as those presented here and future improved versions, will play an increasingly important role. indeed the fact that they do not rely on information on related proteins, as most other methods presently do, should make them particularly useful for assigning function to proteins with no known relatives, of which a growing number is being currently discovered. since functional site prediction methods that use information on sequences and structures of related proteins may also suffer from limitations due to the lack of sequence data, an approach in which such methods are combined with energetic criteria should help improve performance, as previously suggested. of particular interest are methods that detect spatial clusters of conserved residues, which were shown to greatly improve the performance of functional site prediction. further improvements may be obtained if those methods are combined with the search of spatial clusters of destabilizing residues, as done in this study, instead of considering only individual destabilizing residues. 
individual residue contributions to protein stability are evaluated using detailed atomic models and a force-field successfully applied in computational protein design. most methods for predicting functional sites in protein 3d structures, rely on information on related proteins and cannot be applied to proteins with no known relatives. another limitation of these methods is the lack of a well annotated set of functional sites to use as benchmark for validating their predictions. we examine the possibility of systematically exploiting this intrinsic property to identify functional sites using an original procedure that detects destabilizing regions in protein structures. in addition, to relate destabilizing regions to known functional sites, a novel benchmark consisting of a diverse set of hand-curated protein functional sites is derived. we find that although destabilizing regions as detected here can in general not be used to predict binding sites in protein structures, they can provide useful information, particularly on the location of functional sites that bind polysaccharides and small ligands. our publicly available benchmark of hand-curated functional sites in proteins should help other workers derive and validate new prediction methods. results depend on the type of bound ligand. a procedure for detecting clusters of destabilizing residues in protein structures is presented. this information can be exploited in methods for predicting function in protein structures with no known relatives. the biologically relevant binding sites of these proteins were annotated using all available information, including structural data and literature curation, resulting in the largest hand-curated dataset of binding sites in proteins available to date. significant overlap is obtained for most polysaccharide- and small ligand-binding sites, whereas no overlap is observed for most nucleic acid binding sites.
 most methods for predicting functional sites in protein 3d structures, rely on information on related proteins and cannot be applied to proteins with no known relatives. another limitation of these methods is the lack of a well annotated set of functional sites to use as benchmark for validating their predictions. experimental findings and theoretical considerations suggest that residues involved in function often contribute unfavorably to the native state stability. we examine the possibility of systematically exploiting this intrinsic property to identify functional sites using an original procedure that detects destabilizing regions in protein structures. in addition, to relate destabilizing regions to known functional sites, a novel benchmark consisting of a diverse set of hand-curated protein functional sites is derived.  a procedure for detecting clusters of destabilizing residues in protein structures is presented. individual residue contributions to protein stability are evaluated using detailed atomic models and a force-field successfully applied in computational protein design. the most destabilizing residues, and some of their closest neighbours, are clustered into destabilizing regions following a rigorous protocol. our procedure is applied to high quality apo-structures of unrelated proteins. the biologically relevant binding sites of these proteins were annotated using all available information, including structural data and literature curation, resulting in the largest hand-curated dataset of binding sites in proteins available to date. comparing the destabilizing regions with the annotated binding sites in these proteins, we find that the overlap is on average limited, but significantly better than random. results depend on the type of bound ligand. significant overlap is obtained for most polysaccharide- and small ligand-binding sites, whereas no overlap is observed for most nucleic acid binding sites. these differences are rationalised in terms of the geometry and energetics of the binding site.  we find that although destabilizing regions as detected here can in general not be used to predict binding sites in protein structures, they can provide useful information, particularly on the location of functional sites that bind polysaccharides and small ligands. this information can be exploited in methods for predicting function in protein structures with no known relatives. our publicly available benchmark of hand-curated functional sites in proteins should help other workers derive and validate new prediction methods. 