BACKGROUND
the correlated mutations concept was introduced in the 90s  <cit>  and has been widely used for protein contacts prediction  <cit> .
the method is based on the assumption that interacting protein residues co-evolve, so that a mutation in one of the interacting counterparts is compensated by a mutation in the other.
therefore, it is possible to introduce an exchange matrix or other measures of similarity for each sequence position in a multiple sequence alignment and to use covariance  between two positions to predict if the residues at these positions may establish physical contact in 3d space, and develop contact maps.
several different similarity measures and algorithms have been implemented in the concept of correlated mutations  <cit> .
most exchange matrices are based either on physico-chemical properties of amino acids or on statistical data on the substitutions obtained from multiple sequence alignments  <cit> .
statistically it is clear that the distribution of distances between the residues at highly correlated positions is shifted towards lower values compared to the distance distribution of all residues.
this has been demonstrated in the study of correlated mutations for residues within one protein domain , for residues from different domains in multidomain proteins   <cit>  and in transmembrane proteins  <cit> .
at the same time, attempts to use the concept of correlated mutations to predict thermodynamically coupled residues have suggested that the method is successful only for residues in evolutionary constrained positions  <cit> .
the concept of correlated mutations has been intensively developed recently.
the implementation of neural nets into algorithms of contact predictions has allowed to substantially improve the accuracy of the methods in a number of studies  <cit> .
also the application of filtering procedures such as the similarity of sequences in a dataset and the number of sequences in multiple sequence alignments, introduction of weights for physico-chemical properties of the residue pairs and creation of sub-multiple sequence alignments were successfully used to increase a true positive ratio of contact predictions  <cit> .
nowadays, different correlated mutations based approaches yield predictions accuracies in the range of  <dig> – <dig>   <cit>  but they are still of little use in the ab initio prediction of protein structure  <cit> .
previously, we have shown that water-mediated interactions play an important role in protein interfaces  <cit> .
in particular, we observed that the interfacial residues interacting only through one water molecule  are more similar in terms of dynamic and energetic properties to residues in the core of proteins than to residues on the protein surface.
moreover, in our studies interfacial water molecules show significantly longer residence times than water molecules on the protein surface or in bulk solvent, and have been shown to give an indispensable energetic impact on complex formation  <cit> .
in other studies it has been demonstrated that inclusion of solvent term into the hamiltonian of protein systems has improved folding predictions compared to in vacuo folding models  <cit> .
also consideration of solvent explicitly in protein docking approaches has recently shown promising results  <cit> .
in addition, we have observed that water molecules in protein interfaces may contribute to the conservation of interactions by allowing more sequence variability in the interacting partners.
in particular, we have observed water-mediated interactions in protein complex interfaces that are not predicted by "dry" correlated mutations approaches  <cit> .
interestingly, in one of the recent studies on correlated mutations, protein contacts prediction has been shown to be more accurate for protein cores than for the whole protein  <cit> .
this could be partly explained by a higher conservation of residue contacts in protein cores, especially the hydrophobic ones  <cit>  and probably also by the fact that the participation of solvent in protein contacts is being ignored.
the goal of this study has been to analyze the impact of including solvent into the concept of correlated mutations.
for this purpose, we use a linear combination of predictions obtained by the use of two similarity matrices: a standard and widely used "dry" similarity matrix   <cit>  and a "wet" similarity matrix  derived from data on all water-mediated protein-protein interfacial interactions in the pdb  <cit> .
we compare the predictive results obtained with different combinations of these two similarity matrices in terms of number of correctly predicted contacts, accuracy, improvement ratio over random prediction for intradomain contacts and distributions of distances between residues in interdomain pairs.
our results show that, despite a partial interdependence of both wet and dry matrices, there is a clear trend pointing that a combination of these two matrices yields improved predictions over the single use of the dry matrix for both intra- and interdomain contacts.
the results obtained in this work underline the importance of water-mediated interactions in the description of protein-protein interactions, and that implementing combinations of "dry" and "wet" matrices could possibly improve the results obtained by correlated mutations-based approaches.
RESULTS
residue-solvent relations in proteins
independently of residue types, we calculated the average ratios between the number of residues found to be in contact with water and all residues in x-ray pdb structures.
a negligible difference was found between these ratios for interfaces and the whole protein .
the ratios by residue type  correlate with an adjusted squared correlation coefficient r <dig> =  <dig>   and there is also a clear trend of residue ratios distribution in interfaces, which relates to their hydrophilic properties.
this agrees with observations obtained from other datasets not including the whole pdb  <cit> .
the better correlation between the ratios and the hydrophilicity index for interfaces compared to the whole protein  could be explained by the fact that the whole protein includes many residues in the core that are not accessible to water.
this further supports the evidence that residue-solvent relations in protein interfaces are different from the ones in the proteins as a whole  <cit> .
relations between the dry and wet similarity matrices
both dry and wet similarity matrices are created in a way that each column or row is a vector, which coordinates correspond to the similarity between certain amino acid residue type and other residue types.
it is possible to define whether these vectors are interdependent for both matrices by application of linear regression analysis.
the data obtained and averaged for all types of residues are presented in table  <dig>  high degree of correlation is observed for some vectors, which correspond to hydrophilic residues  and for ile, leu, met, val, suggesting that these vectors in the matrices are close to be collinear in 20-dimensional space.
this can be explained by the properties of these residues.
in particular, hydrophilic residues interact by electrostatic forces through their polar atoms, and water mediation in this case can only change the electrostatic forces by introducing water dipoles oriented in a way to weaken the initial electric field.
for hydrophilic residues there is a correlation between hydrophilicity indexes and co-linearity of the corresponding vectors in the dry and wet matrices, which explains also relatively low co-linearity for tyr and thr residues in comparison to other hydrophilic residues .
direct and water-mediated interactions formed by main chains of ile, leu, met and val in interfaces have been previously shown to be especially important, whereas other residues that present no correlation have been shown to predominantly participate in side-chain interactions in interfaces  <cit> .
we conclude that the dry and wet similarity matrices contain partially interdependent information for some of amino acid residues, and the found similarities can be explained by the physico-chemical properties of these residues.
intradomain contacts prediction
our dataset for intradomain contacts prediction consisted of domains of  <dig> pfam protein families .
the lengths of the reference sequences varied from  <dig> to  <dig> residues.
initially we analyzed l, l/ <dig>  l/ <dig>  l/ <dig> and l/ <dig> best correlated contacts for each family .
the number of sequences considered for the multiple sequence alignments was in the range of  <dig> to  <dig> sequences.
previous studies have shown that accuracy  and improvement ratio over random prediction  decrease with the increase of the number of analyzed contacts  <cit> .
table  <dig> shows accuracy and improvement ratio over random prediction for α =  <dig>  , which corresponds to the average best accuracy obtained for different numbers of analyzed predicted contacts.
the results obtained for other α values followed the same trend .
independent of the number of analyzed contacts the best predictions in average did not correspond to α =  <dig>  the obtained values for accuracy and improvement ratio over random prediction are within the ranges obtained by other correlated mutations approaches  <cit> .
however, direct quantitative comparison of these methods is not appropriate because of their substantial differences in their residue contacts definitions.
in particular, some of these approaches utilize for contact definition  a chosen distance cut-off of 6– <dig> Å between atoms  <cit> , whereas we use physico-chemical properties of protein residues, which results in a ≤  <dig> Å cut-off  <cit> .
apdb id; bnumber of sequences; caverage sequences pairwise similarity ; dreference sequence length; erandom accuracy; faccuracy for optimal α; gimprovement ratio over random prediction for optimal α; hvalues for α = 0; iα corresponding to the highest accuracy; jα corresponding to the highest xd; kxd highest value.
l is the length of the reference sequence.
the value α =  <dig>  has been used.
we compared the dependences on α of: i) accuracy, ii) improvement ratio over random prediction, iii) number of correctly predicted contacts ; and, since our dataset is heterogeneous , we normalized these parameters by the corresponding values at α =  <dig> .
for the purpose of wet prediction ratio comparison at different values of α we found l/ <dig> to be the most appropriate number of contacts.
this choice is explained by the fact that the changes in prediction results influenced by α variation become hardly detectable if a smaller number of contacts  is considered for analysis since these changes are limited by low values of ctotal and, consequently, of correctly predicted contacts .
on the other hand, the increase of ctotal generally leads to decrease of prediction accuracy and to negligible differences in prediction results corresponding to different α values.
only in  <dig> out of the  <dig> families of our dataset best predictions correspond to α =  <dig> values .
maximum values for wet prediction ratio and relative xd  averaged for the whole dataset are obtained when α =  <dig>  and α =  <dig> .
this means that, for these values of α, introduction of the wet similarity matrix improves prediction by 20–30% on average.
noticeably, the high values of α ∈ { <dig>  20} still make the predictions on average better than by the single use of the dry matrix.
for optimal value α =  <dig> , absolute values of accuracy and improvement ratio over random prediction averaged for all  <dig> families increase by  <dig> % and  <dig> , respectively, in comparison to the single use of the dry similarity matrix.
for each family in the dataset there is an essentially higher increase of accuracy and improvement ratio over random prediction than on average.
in some families, wet prediction ratio is improved more than twice  and even  <dig>  times  when α >  <dig>  our results show a significant improvement  in predictions by the introduction of the wet similarity matrix in comparison to the single use of the dry matrix within a correlated mutations approach.
we observe that for sequence separations |i-j| >  <dig>   <dig>   <dig> our results follow the same trend.
the obtained results for α =  <dig>  for different number of contacts  are shown in table  <dig>  we observe that the best predictions correspond to α =  <dig>  and  <dig>  for most of sequence separation values and number of contacts.
wet prediction ratios for the whole range of analyzed α are presented in a figure in supplementary material .
in all cases, independently of sequence separation and number of contacts, the best predictions correspond to α >  <dig>
l is the length of the reference sequence.
r is improvement over random prediction.
the value α =  <dig>  has been used.
interdomain contacts prediction
the interdomain dataset used for our studies consisted of  <dig> different pairs of interacting domains .
from the analysis of the / <dig> predicted interdomain residue contacts  we observed that in  <dig> out of  <dig> cases best predictions in terms of xd were obtained when both the wet and dry matrices were used.
relative xd averaged for the whole dataset reaches a maximum value of  <dig>  at α =  <dig>  and then decreases with the further increase of α .
in one of the examples  the differences of distance distributions for different α values are dramatic .
in this case the xd value for predicted contacts at α =  <dig> and α =  <dig>  changes almost twice .
these results point out that the use of the wet similarity matrix might improve the statistic xd in comparison to the single use of the dry similarity matrix.
mg superoxide dismutase
apdb id of the reference structure; bnumber of sequences in the multiple sequence alignment; caverage percentage of sequences pairwise similarity; d, elengths of the reference sequences; fvalues for α = 0; gα value corresponding to the highest xd; hxd highest value.
dependence of relative average xd on α for interdomain contacts prediction  resembles the one obtained for intradomain prediction  but they differ in the optimal α and in the xd corresponding to the higher α values.
while in predictions of intradomain contacts all values of α >  <dig> lead to the improvement of contact predictions, in the case of interdomain contacts prediction the use of the wet similarity matrix yields higher xd than the dry alone when α ∈ { <dig> , <dig> }.
this might be due to the differences in distance distributions between the analyzed pairs of residues, which are closer to each other in the case of intradomain contacts.
nevertheless, introduction of the wet similarity matrix improves contact prediction compared to the single use of the dry similarity matrix for both intra- and interdomain contacts.
although there are still significant limitations for practical use of the correlated mutations approach for interdomain contacts prediction, also mentioned by other authors  <cit> , we believe that consideration of water by the use of "wet" similarity matrices could improve the results obtained by correlated mutations approaches.
CONCLUSIONS
this study is the first investigating the impact of inclusion of solvent into the concept of correlated mutations.
with this work we further demonstrate our previous observations that relations between solvent and protein residues in protein interfaces differ from those in the whole protein.
recent work on bond preferences in inter- versus intraprotein interactions highlights the different architecture of protein interfaces and their unique bond preferences  <cit> .
two similarity matrices have been used in this work: the mclachlan matrix as the dry similarity matrix and a wet similarity matrix derived by statistical analysis of the frequency of water contacts by residue type in protein interfaces in the whole pdb.
analysis of the dry and wet similarity matrices shows that they are interdependent for some residue types, which could be explained by physico-chemical properties of individual amino acid residues.
we analyze two datasets containing  <dig> domains and  <dig> domain pairs belonging to pfam families.
we sum the predictions obtained by the use of both matrices with different weight coefficients and find optimal combinations for best predictions.
our datasets are heterogeneous to propose one best weight value to be able to apply the optimized method to all domain families; however, the prediction of contacts obtained by the introduction of the wet similarity matrix is improved for most of the families in the datasets  as well as on average .
our analysis of solvent impact on contact prediction in proteins suggests that further development of the correlated mutations concept would benefit from taking into account solvent as an active participant in protein-protein interactions, which is usually overlooked in these studies.

 the correlated mutations concept was introduced in the 90s and has been widely used for protein contacts prediction. the method is based on the assumption that interacting protein residues co-evolve, so that a mutation in one of the interacting counterparts is compensated by a mutation in the other. therefore, it is possible to introduce an exchange matrix or other measures of similarity for each sequence position in a multiple sequence alignment and to use covariance between two positions to predict if the residues at these positions may establish physical contact in 3d space, and develop contact maps. several different similarity measures and algorithms have been implemented in the concept of correlated mutations. most exchange matrices are based either on physico-chemical properties of amino acids or on statistical data on the substitutions obtained from multiple sequence alignments. statistically it is clear that the distribution of distances between the residues at highly correlated positions is shifted towards lower values compared to the distance distribution of all residues. this has been demonstrated in the study of correlated mutations for residues within one protein domain, for residues from different domains in multidomain proteins and in transmembrane proteins. at the same time, attempts to use the concept of correlated mutations to predict thermodynamically coupled residues have suggested that the method is successful only for residues in evolutionary constrained positions. the concept of correlated mutations has been intensively developed recently. the implementation of neural nets into algorithms of contact predictions has allowed to substantially improve the accuracy of the methods in a number of studies. also the application of filtering procedures such as the similarity of sequences in a dataset and the number of sequences in multiple sequence alignments, introduction of weights for physico-chemical properties of the residue pairs and creation of sub-multiple sequence alignments were successfully used to increase a true positive ratio of contact predictions. nowadays, different correlated mutations based approaches yield predictions accuracies in the range of – but they are still of little use in the ab initio prediction of protein structure. previously, we have shown that water-mediated interactions play an important role in protein interfaces. in particular, we observed that the interfacial residues interacting only through one water molecule are more similar in terms of dynamic and energetic properties to residues in the core of proteins than to residues on the protein surface. moreover, in our studies interfacial water molecules show significantly longer residence times than water molecules on the protein surface or in bulk solvent, and have been shown to give an indispensable energetic impact on complex formation. in other studies it has been demonstrated that inclusion of solvent term into the hamiltonian of protein systems has improved folding predictions compared to in vacuo folding models. also consideration of solvent explicitly in protein docking approaches has recently shown promising results. in addition, we have observed that water molecules in protein interfaces may contribute to the conservation of interactions by allowing more sequence variability in the interacting partners. in particular, we have observed water-mediated interactions in protein complex interfaces that are not predicted by "dry" correlated mutations approaches. interestingly, in one of the recent studies on correlated mutations, protein contacts prediction has been shown to be more accurate for protein cores than for the whole protein. this could be partly explained by a higher conservation of residue contacts in protein cores, especially the hydrophobic ones and probably also by the fact that the participation of solvent in protein contacts is being ignored. the goal of this study has been to analyze the impact of including solvent into the concept of correlated mutations. for this purpose, we use a linear combination of predictions obtained by the use of two similarity matrices: a standard and widely used "dry" similarity matrix and a "wet" similarity matrix derived from data on all water-mediated protein-protein interfacial interactions in the pdb. we compare the predictive results obtained with different combinations of these two similarity matrices in terms of number of correctly predicted contacts, accuracy, improvement ratio over random prediction for intradomain contacts and distributions of distances between residues in interdomain pairs. our results show that, despite a partial interdependence of both wet and dry matrices, there is a clear trend pointing that a combination of these two matrices yields improved predictions over the single use of the dry matrix for both intra- and interdomain contacts. the results obtained in this work underline the importance of water-mediated interactions in the description of protein-protein interactions, and that implementing combinations of "dry" and "wet" matrices could possibly improve the results obtained by correlated mutations-based approaches.  residue-solvent relations in proteins independently of residue types, we calculated the average ratios between the number of residues found to be in contact with water and all residues in x-ray pdb structures. a negligible difference was found between these ratios for interfaces and the whole protein. the ratios by residue type correlate with an adjusted squared correlation coefficient r = and there is also a clear trend of residue ratios distribution in interfaces, which relates to their hydrophilic properties. this agrees with observations obtained from other datasets not including the whole pdb. the better correlation between the ratios and the hydrophilicity index for interfaces compared to the whole protein could be explained by the fact that the whole protein includes many residues in the core that are not accessible to water. this further supports the evidence that residue-solvent relations in protein interfaces are different from the ones in the proteins as a whole. relations between the dry and wet similarity matrices both dry and wet similarity matrices are created in a way that each column or row is a vector, which coordinates correspond to the similarity between certain amino acid residue type and other residue types. it is possible to define whether these vectors are interdependent for both matrices by application of linear regression analysis. the data obtained and averaged for all types of residues are presented in table high degree of correlation is observed for some vectors, which correspond to hydrophilic residues and for ile, leu, met, val, suggesting that these vectors in the matrices are close to be collinear in 20-dimensional space. this can be explained by the properties of these residues. in particular, hydrophilic residues interact by electrostatic forces through their polar atoms, and water mediation in this case can only change the electrostatic forces by introducing water dipoles oriented in a way to weaken the initial electric field. for hydrophilic residues there is a correlation between hydrophilicity indexes and co-linearity of the corresponding vectors in the dry and wet matrices, which explains also relatively low co-linearity for tyr and thr residues in comparison to other hydrophilic residues. direct and water-mediated interactions formed by main chains of ile, leu, met and val in interfaces have been previously shown to be especially important, whereas other residues that present no correlation have been shown to predominantly participate in side-chain interactions in interfaces. we conclude that the dry and wet similarity matrices contain partially interdependent information for some of amino acid residues, and the found similarities can be explained by the physico-chemical properties of these residues. intradomain contacts prediction our dataset for intradomain contacts prediction consisted of domains of pfam protein families. the lengths of the reference sequences varied from to residues. initially we analyzed l, l/ l/ l/ and l/ best correlated contacts for each family. the number of sequences considered for the multiple sequence alignments was in the range of to sequences. previous studies have shown that accuracy and improvement ratio over random prediction decrease with the increase of the number of analyzed contacts. table shows accuracy and improvement ratio over random prediction for α =, which corresponds to the average best accuracy obtained for different numbers of analyzed predicted contacts. the results obtained for other α values followed the same trend. independent of the number of analyzed contacts the best predictions in average did not correspond to α = the obtained values for accuracy and improvement ratio over random prediction are within the ranges obtained by other correlated mutations approaches. however, direct quantitative comparison of these methods is not appropriate because of their substantial differences in their residue contacts definitions. in particular, some of these approaches utilize for contact definition a chosen distance cut-off of 6– Å between atoms, whereas we use physico-chemical properties of protein residues, which results in a ≤ Å cut-off. apdb id; bnumber of sequences; caverage sequences pairwise similarity; dreference sequence length; erandom accuracy; faccuracy for optimal α; gimprovement ratio over random prediction for optimal α; hvalues for α = 0; iα corresponding to the highest accuracy; jα corresponding to the highest xd; kxd highest value. l is the length of the reference sequence. the value α = has been used. we compared the dependences on α of: i) accuracy, ii) improvement ratio over random prediction, iii) number of correctly predicted contacts; and, since our dataset is heterogeneous, we normalized these parameters by the corresponding values at α =. for the purpose of wet prediction ratio comparison at different values of α we found l/ to be the most appropriate number of contacts. this choice is explained by the fact that the changes in prediction results influenced by α variation become hardly detectable if a smaller number of contacts is considered for analysis since these changes are limited by low values of ctotal and, consequently, of correctly predicted contacts. on the other hand, the increase of ctotal generally leads to decrease of prediction accuracy and to negligible differences in prediction results corresponding to different α values. only in out of the families of our dataset best predictions correspond to α = values. maximum values for wet prediction ratio and relative xd averaged for the whole dataset are obtained when α = and α =. this means that, for these values of α, introduction of the wet similarity matrix improves prediction by 20–30% on average. noticeably, the high values of α ∈ { 20} still make the predictions on average better than by the single use of the dry matrix. for optimal value α =, absolute values of accuracy and improvement ratio over random prediction averaged for all families increase by % and, respectively, in comparison to the single use of the dry similarity matrix. for each family in the dataset there is an essentially higher increase of accuracy and improvement ratio over random prediction than on average. in some families, wet prediction ratio is improved more than twice and even times when α > our results show a significant improvement in predictions by the introduction of the wet similarity matrix in comparison to the single use of the dry matrix within a correlated mutations approach. we observe that for sequence separations |i-j| > our results follow the same trend. the obtained results for α = for different number of contacts are shown in table we observe that the best predictions correspond to α = and for most of sequence separation values and number of contacts. wet prediction ratios for the whole range of analyzed α are presented in a figure in supplementary material. in all cases, independently of sequence separation and number of contacts, the best predictions correspond to α > l is the length of the reference sequence. r is improvement over random prediction. the value α = has been used. interdomain contacts prediction the interdomain dataset used for our studies consisted of different pairs of interacting domains. from the analysis of the / predicted interdomain residue contacts we observed that in out of cases best predictions in terms of xd were obtained when both the wet and dry matrices were used. relative xd averaged for the whole dataset reaches a maximum value of at α = and then decreases with the further increase of α. in one of the examples the differences of distance distributions for different α values are dramatic. in this case the xd value for predicted contacts at α = and α = changes almost twice. these results point out that the use of the wet similarity matrix might improve the statistic xd in comparison to the single use of the dry similarity matrix. mg superoxide dismutase apdb id of the reference structure; bnumber of sequences in the multiple sequence alignment; caverage percentage of sequences pairwise similarity; d, elengths of the reference sequences; fvalues for α = 0; gα value corresponding to the highest xd; hxd highest value. dependence of relative average xd on α for interdomain contacts prediction resembles the one obtained for intradomain prediction but they differ in the optimal α and in the xd corresponding to the higher α values. while in predictions of intradomain contacts all values of α > lead to the improvement of contact predictions, in the case of interdomain contacts prediction the use of the wet similarity matrix yields higher xd than the dry alone when α ∈ {, }. this might be due to the differences in distance distributions between the analyzed pairs of residues, which are closer to each other in the case of intradomain contacts. nevertheless, introduction of the wet similarity matrix improves contact prediction compared to the single use of the dry similarity matrix for both intra- and interdomain contacts. although there are still significant limitations for practical use of the correlated mutations approach for interdomain contacts prediction, also mentioned by other authors, we believe that consideration of water by the use of "wet" similarity matrices could improve the results obtained by correlated mutations approaches.  this study is the first investigating the impact of inclusion of solvent into the concept of correlated mutations. with this work we further demonstrate our previous observations that relations between solvent and protein residues in protein interfaces differ from those in the whole protein. recent work on bond preferences in inter- versus intraprotein interactions highlights the different architecture of protein interfaces and their unique bond preferences. two similarity matrices have been used in this work: the mclachlan matrix as the dry similarity matrix and a wet similarity matrix derived by statistical analysis of the frequency of water contacts by residue type in protein interfaces in the whole pdb. analysis of the dry and wet similarity matrices shows that they are interdependent for some residue types, which could be explained by physico-chemical properties of individual amino acid residues. we analyze two datasets containing domains and domain pairs belonging to pfam families. we sum the predictions obtained by the use of both matrices with different weight coefficients and find optimal combinations for best predictions. our datasets are heterogeneous to propose one best weight value to be able to apply the optimized method to all domain families; however, the prediction of contacts obtained by the introduction of the wet similarity matrix is improved for most of the families in the datasets as well as on average. our analysis of solvent impact on contact prediction in proteins suggests that further development of the correlated mutations concept would benefit from taking into account solvent as an active participant in protein-protein interactions, which is usually overlooked in these studies. 
previously, we have shown that water-mediated interactions play an important role in protein interfaces. we have observed that current "dry" correlated mutations approaches might not properly predict certain interactions in protein interfaces due to the fact that they are water-mediated. our analysis, despite the complexity of its possible general applicability, opens up that the consideration of water may have an impact on the improvement of the contact predictions obtained by correlated mutations approaches. we analyze two datasets containing domains and domain pairs from pfam and compare the results obtained by using a combination of both matrices. for this purpose we use linear combinations of the predictions obtained by the application of two different similarity matrices: a standard "dry" similarity matrix and a "wet" similarity matrix derived from all water-mediated protein interfacial interactions in the pdb. we find that for both intra- and interdomain contacts predictions the introduction of a combination of a "wet" and a "dry" similarity matrix improves the predictions in comparison to the "dry" one alone. the correlated mutations concept is based on the assumption that interacting protein residues coevolve, so that a mutation in one of the interacting counterparts is compensated by a mutation in the other. approaches based on this concept have been widely used for protein contacts prediction since the 90s.
 the correlated mutations concept is based on the assumption that interacting protein residues coevolve, so that a mutation in one of the interacting counterparts is compensated by a mutation in the other. approaches based on this concept have been widely used for protein contacts prediction since the 90s. previously, we have shown that water-mediated interactions play an important role in protein interfaces. we have observed that current "dry" correlated mutations approaches might not properly predict certain interactions in protein interfaces due to the fact that they are water-mediated.  the goal of this study has been to analyze the impact of including solvent into the concept of correlated mutations. for this purpose we use linear combinations of the predictions obtained by the application of two different similarity matrices: a standard "dry" similarity matrix and a "wet" similarity matrix derived from all water-mediated protein interfacial interactions in the pdb. we analyze two datasets containing domains and domain pairs from pfam and compare the results obtained by using a combination of both matrices. we find that for both intra- and interdomain contacts predictions the introduction of a combination of a "wet" and a "dry" similarity matrix improves the predictions in comparison to the "dry" one alone.  our analysis, despite the complexity of its possible general applicability, opens up that the consideration of water may have an impact on the improvement of the contact predictions obtained by correlated mutations approaches. 