BACKGROUND
protein-protein interactions are important events for regulating biological functions.
a network of protein-protein interactions is considered scale-free type, rather than random  <cit> , suggesting that from this network of protein-protein interactions we can find proteins interacting with various targets.
such proteins, referred to as hub proteins, are intrinsically disordered protein  <cit> , which is one of the problems associated with predicting protein complexes from the rigid-body docking approach because of protein flexibility.
in general, the rigid-body docking process is composed of four different steps: selection or building of molecular structures, docking for generating complex structures, refinement of models and finally the scoring or ranking step.
we adopted the ensemble docking approach for the rigid-body docking analysis after taking protein flexibility into consideration, because backbone flexibility, resulting from the large conformational changes in the protein structure, is one of the important factors of protein-protein interactions  <cit> .
a selection step is generally used to define conformational space in structure analysis.
there are different methods for generating multiple structures: molecular dynamics , normal-mode analysis , and 3-dimensional  structural data generated by nuclear magnetic resonance .
these methods are often combined to account for backbone and side-chain flexibilities  <cit> .
however, for rigid-body docking with large conformational changes, backbone flexibility should be considered before side-chain rearrangement.
multiple structures associated with rigid-body docking could provide the information required for taking the backbone flexibility into account in ensemble docking analysis  <cit> .
following this step, side-chain flexibility could be induced locally at the docking sites of the receptor and ligand complex using the soft-docking method of modifying the side-chain volumes  <cit> .
ensemble docking analysis can generate various complex structures based on the rigid-body data, and usually contains many false-positive structures.
therefore, another post-docking step could be added to the ensemble docking process to search for near-native structures.
cluster analysis is one such reliable approach available for use in this post-docking process.
in this case, it is necessary to use certain parameters that would specify similarity or distance between the complex structures.
there can also be issues in comparing structures using the root mean square distance .
global distance test  plot  <cit>  is a 2d diagram of the similarity of two molecular conformations based on searching sequence alignment regions with less rmsd, which is used for evaluating predicted structures in protein structure prediction contest, casp .
figure  <dig> is a gdt plot showing the differences in structure between the bound and unbound state of docking targets.
as shown in figure  <dig>  there are two types of conformational differences between the unbound and bound forms in protein-protein interaction.
one type shows less structural changes, which is typical of docking targets.
the others diverge even when small portions are aligned - in this particular case they are calmodulin  and some of the critical assessment of prediction interactions  targets.
it is, therefore, an important and a challenging problem to be able to predict the protein-protein interaction of a protein with large conformational changes.
to tackle the problem of docking in proteins with large conformational changes, the rmsd value, although still useful, often depends on the superposition step.
for comparing the structures of complexes generated from the docking process, we therefore introduced a protein interaction profile, called interaction fingerprints , which is composed of binary states of interacting amino acid residues, as a scale for measuring unique similarities between the complex structures.
ifp is useful for classifying or selecting representative complex structures, and has been previously used for analyzing the protein-ligand docking problems rather than analyzing the protein-protein interactions  <cit> .
ifp allows examination of the properties of protein-protein interactions simply by comparing the docking structures in terms of their interaction patterns, for example by using the tanimoto index   <cit> .
it is, therefore, possible that by using ifp one could select the near-native structures at the contact residue string level, rather than obtaining the exact complex structure at the cartesian coordinate level.
in the present study, we used an ensemble of native  and  <dig> different nmr-derived structures of calmodulin , which largely differed from the native structure as indicated by the dotted lines in figure  <dig>  for our analysis.
we then applied ifp to the protein-protein post-docking process for the reconstruction of various cam-ligand complexes.
ifps were obtained from the generated complex structures and were used for the cluster analysis.
subsequently, we analyzed relationship between the energy profile and structural similarity or distance using tc-ifp or rmsd in order to improve reliability of the overall process for proteins with large conformational changes.
we also briefly discussed about searching near-native groups by cluster analysis in terms of their energy scores.
RESULTS
decoy structures were obtained from the rigid-body docking simulations using one of the  <dig> peptides  as a ligand and cam multi-structures generated from nmr analysis .
through one docking simulation round for each cam-ligand pair, we obtained  <dig> decoys.
for each peptide, two types of decoys,  <dig> decoys called as "a-decoys" and  <dig> decoys called as "b-decoys", were generated from  <dig> apo-cams and one bound state cam, respectively.
some complex structures were rejected in the process of side-chain rearrangement because of their unavailable arrangements, as detailed in the "protein docking process" of the methods section.
consequently, out of  <dig>  decoys for each peptide, the numbers of decoys used for post-docking analysis were  <dig> for pmca,  <dig> for camkk and  <dig> for cng.
interaction energy scores of decoys
in the case of cng, the b-decoy set showed lower mean values and larger standard deviations than the a-decoys.
in the case of camkk, distributions of the b-decoys were similar to those of the a-decoys.
however, the b-decoys showed larger deviations because of the existence of a low energy peak around - <dig> in the interaction energy score , which was completely divided at - <dig> and included  <dig> b-decoys.
in contrast to cng, pmca had more b-decoys with higher interaction energy scores than the a-decoys.
structural similarities between the decoys and the native structure
in order to analyze the structural landscape of decoys, it is important to investigate whether the near-native decoys could be discriminated from all decoys.
thus, we next investigated which method, tc-ifp or rmsd, is better able to discriminate the near-native decoys from all decoys.
figure  <dig> shows distributions of a-decoys , near-native b-decoys with rmsd less than  <dig>  angstrom , and other b-decoys .
areas i, ii, and iii indicate areas containing only b-decoys, overlap of b-decoys and a-decoys, and only a-decoys, respectively.
as shown in figure 4a, the b-decoys are distributed in the lower rmsd compared to the a-decoys, which are distributed in bell-shaped manner in the case of cng and camkk.
there were  <dig> and  <dig> of cng b-decoys, and  <dig> and  <dig> camkk b-decoys in areas i and ii, respectively.
b-decoys in area i of camkk have the lowest energy groups of around - <dig> energy scores, as previously seen in the inset of figure  <dig>  which could be divided from a-decoys.
in area i of cng, b-decoys have energy scores between - <dig> and - <dig>
in the case of tc-ifp , most a-decoys in all three cases were distributed around  <dig> , indicating that almost all a-decoys did not have any similarity with the native interactions.
on the other hand, b-decoys were distributed more broadly than a-decoys in the case of cng and camkk.
we found  <dig> and  <dig> cng b-decoys, and  <dig> and  <dig> camkk b-decoys in the tc-ifp areas i and ii, respectively, indicating that the b-decoys contained more near-native structures than the a-decoys.
b-decoys in the tc-ifp area i of cng were consisted of decoys with rmsd lower than  <dig>  angstrom and other b-decoys.
the latter b-decoys have slightly higher energy scores than the former decoys.
in camkk, b-decoys in area i consisted of portions of b-decoys with rmsd lower than  <dig>  angstrom and other b-decoys with energy scores between - <dig> and - <dig>  comparison of the tc-ifp and rmsd results indicate that for both cng and camkk, the tc-ifp method produced more b-decoys in area i than the rmsd method.
furthermore, the b-decoys, obtained using the tc-ifp method, could be better divided into groups than the rmsd method.
thus, it appears that the tc-ifp is a more desirable method than rmsd for discriminating b-decoys from other decoys.
in contrast to the cng and camkk b-decoys, the number of pmca b-decoys in areas i and ii, obtained using the rmsd method, were  <dig> and  <dig>  respectively, and those obtained using the tc-ifp method were  <dig> and  <dig>  respectively.
clearly, the tc-ifp method resulted less number of pmca b-decoys in area i than the cng and camkk b-decoys.
this might have resulted from the high-energy scores of b-decoys  and the smaller interacting surface of the cam-pmca complex than the respective cng and camkk complexes, as shown in figure  <dig>
to examine the difference between the tc-ifp and rmsd, we also paid attention to the b-decoys with low rmsd.
three types of distributions were found in tc-ifp:  only area i type, all  <dig> b-decoys with low rmsd in cng are found in area i;  almost area i type,  <dig> out of  <dig> b-decoys with low rmsd in camkk are in area ii; and  only area ii type, all  <dig> b-decoys with low rmsd in pmca are in area ii.
thus, in the case of globular cognate structure of cam , b-decoys with low rmsd values were found in an area of tc-ifp that scored higher similarity than the a-decoys.
cluster analysis
cluster analysis is useful for choosing near-native decoys without prior knowledge of native structures.
using a certain threshold, decoys could be classified into groups composed of similar decoys.
uniformity of every group could then be investigated by using interaction energy scores.
when groups with more uniformity of interaction energy scores were obtained, the used parameters were considered to be better suited for the cluster analysis.
we, therefore, used the unweighted pair group method with arithmetic mean  to construct hierarchical tree from the results obtained by tc-ifp and rmsd analyses.
after generating a dendrogram, groups of decoys obtained using tc-ifp or rmsd were divided according to their similarities.
figure 5a shows two examples of deviations of interaction energy scores of groups of decoys generated from camkk; results from the tc-ifp analysis is derived from  <dig>  threshold and results from the rmsd analysis is derived from  <dig>  angstroms threshold.
in this case, result obtained from the tc-ifp analysis appears to be better than that from the rmsd analysis because magnitudes of the error bars are smaller in the tc-ifp than in the rmsd.
next, we used  <dig> ,  <dig>  and  <dig>  as thresholds of tc-ifp and  <dig> ,  <dig> ,  <dig>  and  <dig>  angstroms as thresholds of rmsd, respectively.
figure 5b shows the standard deviations of interaction energy scores for every group with error bars; clearly, for each parameter, the deviations become large as the threshold was increased.
in the case of cng, deviations and error bars were almost of similar magnitudes irrespective of whether tc-ifp or rmsd was used for the analysis.
on the other hand, in the cases of camkk and pmca, deviations and error bars obtained using tc-ifp were of smaller magnitudes than those obtained using rmsd.
these results suggest that in terms of interaction energy scores we could obtain more uniform groups by using tc-ifp than by rmsd.
discussion
we introduced ifp for describing interaction profiles by taking protein flexibility into consideration.
in the case of the globular cognate structure of cam , b-decoys scored higher similarities to the native complex than the a-decoys derived by the tc-ifp analysis .
we also used the cluster analysis to determine the abilities of tc-ifp and rmsd in dividing the decoys into groups of similar decoys according to the tc-ifp or rmsd threshold.
we obtained more uniform groups in terms of interaction energy scores by tc-ifp than by rmsd .
these results indicated that ifp can be used as one of the profiles of decoys for determining similarities among themselves in terms of interacting amino acids.
we then tried to discriminate the near-native clusters without the knowledge of native complex structure.
for this purpose, decoys were classified into groups of similar decoys by cluster analysis, which was slightly different from the cluster analysis described in the results section.
in this analysis,  <dig> decoys, derived from the structural data of each cam-ligand pair, were divided into  <dig> groups by using the statistical computing "r" software.
because we used  <dig> cam structures, we therefore had  <dig>  groups for each cam-ligand complex.
accordingly, using both ifp and rmsd, we found near-native clusters , having most number of interacting residues identical to the native complex or having highest similarities  to the native complex, for each ligand except for pmca.
in the case of cng, we could discriminate the near-native groups in both tc-ifp and rmsd by searching for groups with lower energy scores.
the number of decoys in the near-native group was the second largest and the largest among the b-decoy groups obtained by tc-ifp and rmsd analyses, respectively.
similarly, in the case of camkk, the near-native groups obtained by tc-ifp analysis could be discriminated as a group with the lowest energy score among all groups, which includes the set of b-decoys with the lowest energy scores .
however, the number of decoys in the near-native groups was not so large.
on the other hand, for camkk, the near-native group obtained by rmsd analysis consisted of the most number of b-decoys, although the energy score was lowest only among the b-decoy groups.
based on the energy scores we could discriminate the near-native groups in the case of cam-cng both by tc-ifp and rmsd.
however, in the case of cam-camkk, energy score could not be the index for searching the near-native groups by rmsd, indicating that the rmsd method is unable to classify the decoys with the lowest energy scores .
we, therefore, compared the interaction frequencies of near-native clusters.
as shown in figure  <dig>  more frequently interacting residues were found on the surface of cam in the near-native clusters obtained by rmsd than those found in the native structure and also than those in the near-native clusters obtained by ifp.
this result suggests that by using the ifp method one could identify interactions similar to the native interactions.
therefore, the cluster analysis in combination with ifp, rather than with rmsd, appears to be a much better approach in predicting the near-native decoys without any prior structural knowledge.
in the case of the cam-camkk complex, we obtained a set of desired near-native decoys with lowest interaction energy  and high tc-ifps .
these results are explained by the structure of the cam-camkk complex, which is different from those of the cam-cng and cam-pmca complexes.
the globular cognate structure of cam in the cam-camkk complex differed only slightly from that of the cam-cng complex, indicating that the cavity of the cam-camkk complex is narrower than that of the cam-cng complex, as shown in figure  <dig>  in contrast to the simple alpha helical structure of the cng and pmca ligand peptides, the camkk ligand peptide has an additional loop region in the c-terminal end.
these structural features of the ligand have contributed in obtaining a set of near-native decoys for the cam-camkk complex.
in context with the shape of the ligand peptide, it is noteworthy that we found cam-cng b-decoys in which the cng was bound to the cam cavity in an inverse manner as compared to the native complex; this inverse binding of cng might have contributed to the broad distribution of the cam-cng b-decoys .
in any general docking problem, any ligand with simple alpha helix-like pseudo-symmetrical shape might have similar binding characteristic of cng.
in the case of cam-pmca, near-native decoys could be discriminated clearly from the native complexes using rmsd.
on the other hand, in the case of tc-ifp, less number of near-native decoys  and none of the near-native groups  were obtained for the cam-pmca complex.
this is in contrast to the cam-cng and cam-camkk complexes, in both of which the number of interacting residues are similar  <cit> .
pmca interacts with cam using about half the number residues than those involved in cam-cng or cam-camkk interactions  <cit> , because it interacts only with the c-terminal lobe of cam.
in a general docking problem, this type of interaction is normally involved in "solvent-accessible ligand" and "open and shallow form of receptor" cases.
that the number of near-native decoys for the cam-pmca complex was found to be less by our analysis, could be related to the fact that the decoys were obtained not only from the extended cognate structure of cam , but also from some unbound states of cam .
therefore, in order to obtain better near-native decoys following the post-docking process, we need to further improve on the ifp method in terms of physicochemical approach, and also need to develop a combination method of ifp and rmsd.
although we could discriminate near-native decoys in cam-cng and camkk, diversity of decoys were also obtained using this method.
to further develop the ifp method for solving the docking problem of proteins with large conformational changing, we need to generate better ensemble of bound structures by using md or nma methods for the cross-docking analysis.
CONCLUSIONS
in the present study, ifp was introduced for describing interaction profiles, which could be used for comparing different structures of decoys by taking protein flexibility into consideration.
in cluster analysis, ifp could clearly discriminate the near-native structures better than rmsd in terms of energy scores.
we also showed that a combined method that included ifp was useful for protein-protein docking analysis.
we believe that this method, in combination with other docking software, such as rosettadock software that optimizes both side-chains and backbones, could be applied to other protein complexes deposited in the protein structure databases.

 protein-protein interactions are important events for regulating biological functions. a network of protein-protein interactions is considered scale-free type, rather than random, suggesting that from this network of protein-protein interactions we can find proteins interacting with various targets. such proteins, referred to as hub proteins, are intrinsically disordered protein, which is one of the problems associated with predicting protein complexes from the rigid-body docking approach because of protein flexibility. in general, the rigid-body docking process is composed of four different steps: selection or building of molecular structures, docking for generating complex structures, refinement of models and finally the scoring or ranking step. we adopted the ensemble docking approach for the rigid-body docking analysis after taking protein flexibility into consideration, because backbone flexibility, resulting from the large conformational changes in the protein structure, is one of the important factors of protein-protein interactions. a selection step is generally used to define conformational space in structure analysis. there are different methods for generating multiple structures: molecular dynamics, normal-mode analysis, and 3-dimensional structural data generated by nuclear magnetic resonance. these methods are often combined to account for backbone and side-chain flexibilities. however, for rigid-body docking with large conformational changes, backbone flexibility should be considered before side-chain rearrangement. multiple structures associated with rigid-body docking could provide the information required for taking the backbone flexibility into account in ensemble docking analysis. following this step, side-chain flexibility could be induced locally at the docking sites of the receptor and ligand complex using the soft-docking method of modifying the side-chain volumes. ensemble docking analysis can generate various complex structures based on the rigid-body data, and usually contains many false-positive structures. therefore, another post-docking step could be added to the ensemble docking process to search for near-native structures. cluster analysis is one such reliable approach available for use in this post-docking process. in this case, it is necessary to use certain parameters that would specify similarity or distance between the complex structures. there can also be issues in comparing structures using the root mean square distance. global distance test plot is a 2d diagram of the similarity of two molecular conformations based on searching sequence alignment regions with less rmsd, which is used for evaluating predicted structures in protein structure prediction contest, casp. figure is a gdt plot showing the differences in structure between the bound and unbound state of docking targets. as shown in figure there are two types of conformational differences between the unbound and bound forms in protein-protein interaction. one type shows less structural changes, which is typical of docking targets. the others diverge even when small portions are aligned - in this particular case they are calmodulin and some of the critical assessment of prediction interactions targets. it is, therefore, an important and a challenging problem to be able to predict the protein-protein interaction of a protein with large conformational changes. to tackle the problem of docking in proteins with large conformational changes, the rmsd value, although still useful, often depends on the superposition step. for comparing the structures of complexes generated from the docking process, we therefore introduced a protein interaction profile, called interaction fingerprints, which is composed of binary states of interacting amino acid residues, as a scale for measuring unique similarities between the complex structures. ifp is useful for classifying or selecting representative complex structures, and has been previously used for analyzing the protein-ligand docking problems rather than analyzing the protein-protein interactions. ifp allows examination of the properties of protein-protein interactions simply by comparing the docking structures in terms of their interaction patterns, for example by using the tanimoto index. it is, therefore, possible that by using ifp one could select the near-native structures at the contact residue string level, rather than obtaining the exact complex structure at the cartesian coordinate level. in the present study, we used an ensemble of native and different nmr-derived structures of calmodulin, which largely differed from the native structure as indicated by the dotted lines in figure for our analysis. we then applied ifp to the protein-protein post-docking process for the reconstruction of various cam-ligand complexes. ifps were obtained from the generated complex structures and were used for the cluster analysis. subsequently, we analyzed relationship between the energy profile and structural similarity or distance using tc-ifp or rmsd in order to improve reliability of the overall process for proteins with large conformational changes. we also briefly discussed about searching near-native groups by cluster analysis in terms of their energy scores.  decoy structures were obtained from the rigid-body docking simulations using one of the peptides as a ligand and cam multi-structures generated from nmr analysis. through one docking simulation round for each cam-ligand pair, we obtained decoys. for each peptide, two types of decoys, decoys called as "a-decoys" and decoys called as "b-decoys", were generated from apo-cams and one bound state cam, respectively. some complex structures were rejected in the process of side-chain rearrangement because of their unavailable arrangements, as detailed in the "protein docking process" of the methods section. consequently, out of decoys for each peptide, the numbers of decoys used for post-docking analysis were for pmca, for camkk and for cng. interaction energy scores of decoys in the case of cng, the b-decoy set showed lower mean values and larger standard deviations than the a-decoys. in the case of camkk, distributions of the b-decoys were similar to those of the a-decoys. however, the b-decoys showed larger deviations because of the existence of a low energy peak around - in the interaction energy score, which was completely divided at - and included b-decoys. in contrast to cng, pmca had more b-decoys with higher interaction energy scores than the a-decoys. structural similarities between the decoys and the native structure in order to analyze the structural landscape of decoys, it is important to investigate whether the near-native decoys could be discriminated from all decoys. thus, we next investigated which method, tc-ifp or rmsd, is better able to discriminate the near-native decoys from all decoys. figure shows distributions of a-decoys, near-native b-decoys with rmsd less than angstrom, and other b-decoys. areas i, ii, and iii indicate areas containing only b-decoys, overlap of b-decoys and a-decoys, and only a-decoys, respectively. as shown in figure 4a, the b-decoys are distributed in the lower rmsd compared to the a-decoys, which are distributed in bell-shaped manner in the case of cng and camkk. there were and of cng b-decoys, and and camkk b-decoys in areas i and ii, respectively. b-decoys in area i of camkk have the lowest energy groups of around - energy scores, as previously seen in the inset of figure which could be divided from a-decoys. in area i of cng, b-decoys have energy scores between - and - in the case of tc-ifp, most a-decoys in all three cases were distributed around, indicating that almost all a-decoys did not have any similarity with the native interactions. on the other hand, b-decoys were distributed more broadly than a-decoys in the case of cng and camkk. we found and cng b-decoys, and and camkk b-decoys in the tc-ifp areas i and ii, respectively, indicating that the b-decoys contained more near-native structures than the a-decoys. b-decoys in the tc-ifp area i of cng were consisted of decoys with rmsd lower than angstrom and other b-decoys. the latter b-decoys have slightly higher energy scores than the former decoys. in camkk, b-decoys in area i consisted of portions of b-decoys with rmsd lower than angstrom and other b-decoys with energy scores between - and - comparison of the tc-ifp and rmsd results indicate that for both cng and camkk, the tc-ifp method produced more b-decoys in area i than the rmsd method. furthermore, the b-decoys, obtained using the tc-ifp method, could be better divided into groups than the rmsd method. thus, it appears that the tc-ifp is a more desirable method than rmsd for discriminating b-decoys from other decoys. in contrast to the cng and camkk b-decoys, the number of pmca b-decoys in areas i and ii, obtained using the rmsd method, were and respectively, and those obtained using the tc-ifp method were and respectively. clearly, the tc-ifp method resulted less number of pmca b-decoys in area i than the cng and camkk b-decoys. this might have resulted from the high-energy scores of b-decoys and the smaller interacting surface of the cam-pmca complex than the respective cng and camkk complexes, as shown in figure to examine the difference between the tc-ifp and rmsd, we also paid attention to the b-decoys with low rmsd. three types of distributions were found in tc-ifp: only area i type, all b-decoys with low rmsd in cng are found in area i; almost area i type, out of b-decoys with low rmsd in camkk are in area ii; and only area ii type, all b-decoys with low rmsd in pmca are in area ii. thus, in the case of globular cognate structure of cam, b-decoys with low rmsd values were found in an area of tc-ifp that scored higher similarity than the a-decoys. cluster analysis cluster analysis is useful for choosing near-native decoys without prior knowledge of native structures. using a certain threshold, decoys could be classified into groups composed of similar decoys. uniformity of every group could then be investigated by using interaction energy scores. when groups with more uniformity of interaction energy scores were obtained, the used parameters were considered to be better suited for the cluster analysis. we, therefore, used the unweighted pair group method with arithmetic mean to construct hierarchical tree from the results obtained by tc-ifp and rmsd analyses. after generating a dendrogram, groups of decoys obtained using tc-ifp or rmsd were divided according to their similarities. figure 5a shows two examples of deviations of interaction energy scores of groups of decoys generated from camkk; results from the tc-ifp analysis is derived from threshold and results from the rmsd analysis is derived from angstroms threshold. in this case, result obtained from the tc-ifp analysis appears to be better than that from the rmsd analysis because magnitudes of the error bars are smaller in the tc-ifp than in the rmsd. next, we used, and as thresholds of tc-ifp and,, and angstroms as thresholds of rmsd, respectively. figure 5b shows the standard deviations of interaction energy scores for every group with error bars; clearly, for each parameter, the deviations become large as the threshold was increased. in the case of cng, deviations and error bars were almost of similar magnitudes irrespective of whether tc-ifp or rmsd was used for the analysis. on the other hand, in the cases of camkk and pmca, deviations and error bars obtained using tc-ifp were of smaller magnitudes than those obtained using rmsd. these results suggest that in terms of interaction energy scores we could obtain more uniform groups by using tc-ifp than by rmsd. discussion we introduced ifp for describing interaction profiles by taking protein flexibility into consideration. in the case of the globular cognate structure of cam, b-decoys scored higher similarities to the native complex than the a-decoys derived by the tc-ifp analysis. we also used the cluster analysis to determine the abilities of tc-ifp and rmsd in dividing the decoys into groups of similar decoys according to the tc-ifp or rmsd threshold. we obtained more uniform groups in terms of interaction energy scores by tc-ifp than by rmsd. these results indicated that ifp can be used as one of the profiles of decoys for determining similarities among themselves in terms of interacting amino acids. we then tried to discriminate the near-native clusters without the knowledge of native complex structure. for this purpose, decoys were classified into groups of similar decoys by cluster analysis, which was slightly different from the cluster analysis described in the results section. in this analysis, decoys, derived from the structural data of each cam-ligand pair, were divided into groups by using the statistical computing "r" software. because we used cam structures, we therefore had groups for each cam-ligand complex. accordingly, using both ifp and rmsd, we found near-native clusters, having most number of interacting residues identical to the native complex or having highest similarities to the native complex, for each ligand except for pmca. in the case of cng, we could discriminate the near-native groups in both tc-ifp and rmsd by searching for groups with lower energy scores. the number of decoys in the near-native group was the second largest and the largest among the b-decoy groups obtained by tc-ifp and rmsd analyses, respectively. similarly, in the case of camkk, the near-native groups obtained by tc-ifp analysis could be discriminated as a group with the lowest energy score among all groups, which includes the set of b-decoys with the lowest energy scores. however, the number of decoys in the near-native groups was not so large. on the other hand, for camkk, the near-native group obtained by rmsd analysis consisted of the most number of b-decoys, although the energy score was lowest only among the b-decoy groups. based on the energy scores we could discriminate the near-native groups in the case of cam-cng both by tc-ifp and rmsd. however, in the case of cam-camkk, energy score could not be the index for searching the near-native groups by rmsd, indicating that the rmsd method is unable to classify the decoys with the lowest energy scores. we, therefore, compared the interaction frequencies of near-native clusters. as shown in figure more frequently interacting residues were found on the surface of cam in the near-native clusters obtained by rmsd than those found in the native structure and also than those in the near-native clusters obtained by ifp. this result suggests that by using the ifp method one could identify interactions similar to the native interactions. therefore, the cluster analysis in combination with ifp, rather than with rmsd, appears to be a much better approach in predicting the near-native decoys without any prior structural knowledge. in the case of the cam-camkk complex, we obtained a set of desired near-native decoys with lowest interaction energy and high tc-ifps. these results are explained by the structure of the cam-camkk complex, which is different from those of the cam-cng and cam-pmca complexes. the globular cognate structure of cam in the cam-camkk complex differed only slightly from that of the cam-cng complex, indicating that the cavity of the cam-camkk complex is narrower than that of the cam-cng complex, as shown in figure in contrast to the simple alpha helical structure of the cng and pmca ligand peptides, the camkk ligand peptide has an additional loop region in the c-terminal end. these structural features of the ligand have contributed in obtaining a set of near-native decoys for the cam-camkk complex. in context with the shape of the ligand peptide, it is noteworthy that we found cam-cng b-decoys in which the cng was bound to the cam cavity in an inverse manner as compared to the native complex; this inverse binding of cng might have contributed to the broad distribution of the cam-cng b-decoys. in any general docking problem, any ligand with simple alpha helix-like pseudo-symmetrical shape might have similar binding characteristic of cng. in the case of cam-pmca, near-native decoys could be discriminated clearly from the native complexes using rmsd. on the other hand, in the case of tc-ifp, less number of near-native decoys and none of the near-native groups were obtained for the cam-pmca complex. this is in contrast to the cam-cng and cam-camkk complexes, in both of which the number of interacting residues are similar. pmca interacts with cam using about half the number residues than those involved in cam-cng or cam-camkk interactions, because it interacts only with the c-terminal lobe of cam. in a general docking problem, this type of interaction is normally involved in "solvent-accessible ligand" and "open and shallow form of receptor" cases. that the number of near-native decoys for the cam-pmca complex was found to be less by our analysis, could be related to the fact that the decoys were obtained not only from the extended cognate structure of cam, but also from some unbound states of cam. therefore, in order to obtain better near-native decoys following the post-docking process, we need to further improve on the ifp method in terms of physicochemical approach, and also need to develop a combination method of ifp and rmsd. although we could discriminate near-native decoys in cam-cng and camkk, diversity of decoys were also obtained using this method. to further develop the ifp method for solving the docking problem of proteins with large conformational changing, we need to generate better ensemble of bound structures by using md or nma methods for the cross-docking analysis.  in the present study, ifp was introduced for describing interaction profiles, which could be used for comparing different structures of decoys by taking protein flexibility into consideration. in cluster analysis, ifp could clearly discriminate the near-native structures better than rmsd in terms of energy scores. we also showed that a combined method that included ifp was useful for protein-protein docking analysis. we believe that this method, in combination with other docking software, such as rosettadock software that optimizes both side-chains and backbones, could be applied to other protein complexes deposited in the protein structure databases. 
protein-protein docking for proteins with large conformational changes was analyzed by using interaction fingerprints, one of the scales for measuring similarities among complex structures, utilized especially for searching near-native protein-ligand or protein-protein complex structures. here, we have proposed a combined method for analyzing protein-protein docking by taking large conformational changes into consideration. for each ligand, cam-ligand complexes were generated in the docking step and the relationship between their energy profiles and structural similarities to the native complex were analyzed using interaction fingerprint and rmsd. this combined method consists of ensemble soft docking with multiple protein structures, refinement of complexes, and cluster analysis using interaction fingerprints and energy profiles. near-native clusters were obtained in the case of cng and camkk. to test for the applicability of this combined method, various cam-ligand complexes were reconstructed from the nmr structures of unbound cam. the interaction fingerprint method discriminated near-native structures better than the rmsd method in cluster analysis.
 protein-protein docking for proteins with large conformational changes was analyzed by using interaction fingerprints, one of the scales for measuring similarities among complex structures, utilized especially for searching near-native protein-ligand or protein-protein complex structures. here, we have proposed a combined method for analyzing protein-protein docking by taking large conformational changes into consideration. this combined method consists of ensemble soft docking with multiple protein structures, refinement of complexes, and cluster analysis using interaction fingerprints and energy profiles.  to test for the applicability of this combined method, various cam-ligand complexes were reconstructed from the nmr structures of unbound cam. for the purpose of reconstruction, we used three known cam-ligands, namely, the cam-binding peptides of cyclic nucleotide gateway, cam kinase kinase and the plasma membrane ca2+ atpase pump, and thirty-one structurally diverse cam conformations. for each ligand, cam-ligand complexes were generated in the docking step and the relationship between their energy profiles and structural similarities to the native complex were analyzed using interaction fingerprint and rmsd. near-native clusters were obtained in the case of cng and camkk.  the interaction fingerprint method discriminated near-native structures better than the rmsd method in cluster analysis. we showed that a combined method that includes the interaction fingerprint is very useful for protein-protein docking analysis of certain cases. 