Oryza sativa mitogen activated protein kinase kinase (MAPKK) and mitogen activated protein kinase (MAPK). The 3-dimentional (3-D) structures of five MAPKKs and eleven MAPKs were determined by homology modelling and were further used as input for docking studies. With the help of the results obtained from ZDOCK and RDOCK programmes, top six possible interacting MAPK proteins were predicted for each MAPKK. In order to assess the reliability of the computational prediction, yeast two-hybrid (Y2H) analyses were performed using rice MAPKKs and MAPKs. A direct comparison of Y2H assay and computational prediction of protein interaction was made. With the exception of one, all the other MAPKK-MAPK pairs identified by Y2H screens were among the top predictions by computational dockings. Although, not all the predicted interacting partners could show interaction in Y2H, yet, the harmony between the two approaches suggests that the computational predictions in the present work are reliable. Moreover, the present Y2H analyses per se provide interaction network among MAPKKs and MAPKs which would shed more light on MAPK signalling network in rice.Protein-protein interaction is one of the crucial ways to decipher the functions of proteins and to understand their role in complex pathways at cellular level. Such a protein-protein interaction network in many crop plants remains poorly defined owing largely to the involvement of high costs, requirement for state of the art laboratory, time and labour intensive techniques. Here, we employed computational docking using ZDOCK and RDOCK programmes to identify interaction network between members of  Arabidopsis thaliana while in the case of most of the crop plants the knowledge about these facets is still in its nascent stage.The genetic potential of improved crop cultivars to perform better in field conditions is often constrained by a wide spectrum of biotic and abiotic stresses. These factors directly and indirectly affect the growth and development of plants and thus ultimately result in lower production of food grains. In order to develop crop cultivars resistant/tolerant to an array of stresses, it is a prerequisite to understand how plants perceive and transduce the cues and generate responses to cope up with the stress in question. Protein kinases are crucial players, not only in regulating various stress responses but also in growth and development. Among the protein kinases, the highly conserved mitogen-activated protein kinase (MAPK) pathways in eukaryotes play pivotal role in basic cellular process, development, hormone biosynthesis/signalling, senescence, plant immunity as well as in producing responses to several stress conditions The MAPK cascades are composed of three main components, MAPKKK (MKKK/MEKK), MAPKK (MKK/MEK) and MAPK (MPK) which are activated through consecutive phosphorylations. Arabidopsis genome comprises 60–80 MAPKKK, 10 MAPKK and 20 MAPK genes A. thaliana, there is lack of such studies in crop plants. Surpisingly in rice very few MAPKKs-MAPKs interactions have been identified either through yeast two-hybrid (Y2H) or phosphorylation assays The available literature showing activation of downstream kinases by its upstream kinase module in response to specific stimuli seems to be quite complex as well as redundant. Discrepancy in the number of MAPKK and MAPK genes suggests that a single MAPKK is likely to activate multiple MAPKs. Further, an individual MAPK protein may serve as a target for multiple upstream MAPKK The advancements in the field of bioinformatics have given us efficient tools to understand several biological processes at molecular level. In recent times a significant progress has been made in computational modelling of protein structures and molecular docking, which holds a great promise in prediction of protein-protein interactions. Docking is the computational scheme that attempts to find the best matching between two molecules: a receptor and ligand In the present work, homology modelling approach was employed to determine 3D structure of rice MAPKKs and MAPKs. These 3D structures were further used as an input for protein-protein docking using ZDOCK and RDOCK programmes, to predict MAPKK-MAPK interactions. Simultaneously, Y2H analyses were used to study rice MAPKK-MAPK protein-protein interaction networks. A direct comparison of computational prediction and Y2H analyses of MAPKK and MAPK was made to assess the reliability of computational docking for prediction of protein-protein interactions.http://www.pdb.org/pdb/home/home.do) http://www.accelrys.com/dstdio) suite was used, which is a ClustalW hybrid for sequence alignment and modeler Homology modelling was performed as mentioned For protein–protein docking, the ZDOCK and RDOCK programs were used as mentioned in MAPKs and five of the six functional MAPKKs have already been cloned from the Pusa Basmati 1 cultivar of indica rice and these sequences are available in GenBank database OsMPKs and OsMKKs, using gene specific primers which had specific restriction enzyme recognition sequence as an adapter. Fourteen rice MAPKs, OsMPK3 (DQ826422), OsMPK4 (FJ621301), OsMPK6 (FJ621301), OsMPK7 (DQ826424), OsMPK14 (EU675864), OsMPK16-1 (EU779804), OsMPK16-2 (EU675865), OsMPK17-1 (EU675866), OsMPK17-2 (DQ826423), OsMPK20-1 (DQ826423), OsMPK20-2 (FJ907414), OsMPK20-3 (EU675869), OsMPK20-4 (DQ826425), OsMPK20-5 (EU675870), OsMPK21-2 (FJ621303) and five MAPKKs, OsMKK1 (EF529623.1), OsMKK3 (EF392366), OsMKK4 (JQ886088), OsMKK6 (DQ779790.1) and OsMKK10-2 (EF666056.1) were cloned in pGADT7 and pGBKT7 vectors (BD Biosciences). Rice MAPKs nomeclature accroding to Hamel et al. In our laboratory, all the sixteen rice http://www.accelrys.com/dstdio) suite, 3D structure of twelve rice MAPKs and five MAPKKs were determined.In order to understand the molecular interaction properties of a protein, it is a prerequisite to have the information about its 3D structure. However, in the absence of crystallographic structures for rice MAPKs and MAPKK, the homology modelling approach was employed to determine a reasonable 3D structure of these proteins based on the known structure of the template proteins. To select the template for homology modelling Psi Blast All the 3D models were refined with the help of loop refinement  and side chain refinement protocols. The modelled 3D structure of each of the eleven MAPKs and five MAPKKs have been shown . The DOPThe overall stereochemical quality of the modelled 3D structure of proteins were evaluated by using Ramachandran plot which is based on psi (Cα-C bond) and phi (N-Cα bond) angles of the protein and provides information about the number of amino acid residues present in allowed and disallowed regions. All the modelled proteins showed maximum residues in the most favoured region followed by allowed region and least in the generously allowed regions in the Ramachandran plot . Very feAdditionally reliability of the structures for docking studies was evaluated by employing the ‘Verify Protein protocol’ (profile 3-D method) for testing a preliminary protein structure based on experimental data. It depends on the principle, that a protein's structure must be compatible with its own sequence. The ‘Verify Protein’ was used to calculate local 3D-1D scores in a fixed length sliding window  and plotted against residue position. This reveals local regions of relatively high or low 3D-1D compatibility The docking of rice MAPKKs and MAPKs was performed following ZDOCK and RDOCK programs. ZDOCK is a docking program that predicts several protein complexes using Pairwise Shape Complementarity (PSC) of input protein structure The results obtained from docking studies involving OsMKK3 and eleven MAPK modules have been presented in Docking of OsMKK4 with eleven MAPKs was performed and output values are presented in For OsMKK5, top six interacting MAPKs were OsMPK20-5, OsMPK3, OsMPK17-1, OsMPK21-2, OsMPK20-3 and OsMPK6 . The besTop six predictions of OsMKK6 interacting OsMAPKs were OsMPK20-2, OsMPK16-1, OsMPK21-2, OsMPK20-5, OsMPK6 and OsMPK7 . Figure In case of OsMKK10-2, after the initial docking (ZDOCK), OsMKK10-2 could not yield successful RDOCK with any of the OsMAPKs. Nonetheless, in the absence of E_RDOCK score, the other next important ‘ZRANK score’ In Y2H analysis a total of 11 interactions were identified from 75 combinations of five MAPKKs and fifteen MAPKs. Each MAPKK was found to have at least one MAPK as interacting partner whereas, interestingly six MAPKs  were found not to be interacting with any of the MAPKKs. On the other hand, two MAPKs, OsMPK4 and OsMPK7 were found to have two distinct upstream MAPKKs as interacting proteins. Further, another MAPKK, OsMKK6 was found to interact with five different MAPKs.For initial screening MAPKKs were fused with GAL4 DNA binding domain  while MAPKs with the GAL4 activation domain . However, in case of OsMKK1, OsMKK1-pGBKT7 showed activation of reporter genes even in the presence of blank vector pGADT7 . Owing tOsMKK1, a group A MAPKK showed interaction with OsMPK4, a group B MAPK   showed interaction with OsMPK7, albeit weak in nature  if the homology based 3-D structures of proteins could be used for protein–protein docking in the absence of experimentally elucidated structure? 2) If the computational protein-protein docking approach be employed to predict protein-protein interactions? If yes, 3) how reliable such an approach could be?The in-silico approach to study protein-protein interactions, it is considered necessary to render experimental basis to the outcomes of the docking studies. To achieve the same, computational protein-protein docking was further confirmed by directed Y2H analyses in the present study. The homology modelling approach was used to design 3D structures of MAPKK and MAPKs. Such homology models have been successfully used by earlier workers for docking studies in-silico docking. Additionally, in case of OsMKK5, for which Y2H analysis could not be performed, the available literature furnishes evidence for the same. OsMKK5 was shown to activate OsMPK3 and OsMPK6 in-vitro Since there are very limited reports which use the per se. Indeed the performance of the docking is often reduced when homology modelled proteins are employed, owing to the uncertainty of the models adding to the intrinsic noise of the docking results Some of the the MAPKK-MAPK interactions identified by Y2H screens were not among the top most predictions by computational dockings. This could be due to inherent limitations of the use of homology models for docking or the docking process in-silico docking could not be identified in Y2H screen has recently been reported by phosphorylation assay in-silico protein-protein interaction predicted in the resent study  nt study . From thin-silico approach with good accuracy clearly suggest that this approach holds great promise in prediction of protein-protein interactions. Further, the accuracy of these predictions suggests that 3D structures of proteins generated by homology modelling could be used for docking studies, provided that the binding sites are well conserved. If the binding interface of the modelled protein is in its near native conformation then the protein–protein interactions can be predicted more precisely. It is therfore, not necessary to find the whole length of protein in its native conformation to proceed with protein-protein docking, which makes homology models suitable for prediction of protein-protein interaction.The prediction of interactions between rice MAPKKs and MAPKs by present in-vitro as well as in-planta validations. Therefore experimental evidences from Singh et al. in-silico predicted protein-protein interaction in the present study . Among the identified interactions OsMKK6-OsMPK4 interaction has also been reported earlier OsMKK3, the only member of group B MAPKK, was found to interact with two rice MAPKs (OsMPK7 and OsMPK14) both belonging to group C of MAPKs in-vivo and involved in phytoalexin biosynthesis in rice cell culture OsMKK4, a representative of group C MAPKK showed interaction with two MAPKs, OsMPK3 and OsMPK6. This is in agreement with another study where OsMKK4 was shown to activate OsMPK3 and OsMPK6 OsMKK10-2, the only member of group D MAPKKs, showed interaction with OsMPK7 albeit weak in nature. In contrast to this, in a previous study, OsMKK10-2 (OsMEK3) was found to interact with OsMPK6 (OsMPK1) Findings from the Y2H work showed that none of the rice MAPKKs included in the Y2H screen could interact with OsMPK16-2, OsMPK17-1, OsMPK17-2, OsMPK20-2, OsMPK20-5 and OsMPK21-1. Similarly, a Y2H study Comparison of MAPKKs and MAPKs orthologs between rice and Arabidopsis revealed a few new interacting modules. Changes in docking domain over the period of evolution could be accounted for these new interactions since docking domains are major determinants of the specificity of interactions between MAPKKs and MAPKs The present work apart from improving our understanding about the MAPK-MAPKK interaction network in rice, also shows that the computational approach could be employed to explore large scale protein-protein interactions.in-silico docking approach to predict protein-protein interactions between rice MAPKKs and MAPKs. To achieve the same, 3D structures of eleven MAPKs and five MAPKKs were predicted by homology modelling. ZDOCK and RDOCK docking programmes were used for computational dockings and top possible MAPKK-MAPK interacting pairs were predicted. Further to confirm the reliability of this approach, Y2H analysis was performed for conforming MAPKK-MAPK interactions. Except for one MAPKK, all the other interacting partners identified in Y2H assay were listed in the top possible interactions by computational dockings. The results suggest that 3D structure built by homology modelling could be used for docking studies. Since the top interacting pairs identified by docking in the present work could not be confirmed by Y2H assay in each case, this approach may be suitable mainly for narrowing down the interacting partners from several proteins to a fewer members. The protein-protein interaction study of MAPKKs and MAPKs gives a comprehensive interaction network for rice MAPKK and MAPK.The MAPK and MAPKK interaction network is crucial to understand the MAPK signalling pathways at cellular level. The present work represents an Figure S1Top docking poses of OsMKK4 with rice MAPKs. The best docking positions of OsMKK4 with each of the eleven rice MAPKs  are shown. OsMKK4 is represented as a wireframe pattern whereas all OsMAPKs as a solid ribbon form.(PDF)Click here for additional data file.Figure S2Top docking poses of OsMKK5 with rice MAPKs. The best docking positions of OsMKK5 with each of the eleven rice MAPKs  are shown. OsMKK5 is represented as a wireframe pattern whereas all OsMAPKs as a solid ribbon form.(PDF)Click here for additional data file.Figure S3Top docking poses of OsMKK6 with rice MAPKs. The best docking positions of OsMKK6 with each of the eleven rice MAPKs  are shown. OsMKK6 is represented as a wireframe pattern whereas all OsMAPKs as a solid ribbon form.(PDF)Click here for additional data file.Figure S4Yeast two-hybrid assay control experiment.OsMKK1, OsMKK3, OsMKK4, OsMKK6 and OsMKK10-2 cloned in pGBKT7 were co-transformed with blank pGADT7 to AH109. The co-transformants were selected on double drop out medium and later patched on quadruple drop out medium to check auto-activation of the reporter genes.(PDF)Click here for additional data file.Table S1Details of the proteins used as templates for homology modelling of rice MAPKKs and MAPKs.(PDF)Click here for additional data file.Table S2Rice MAPK and MAPKKs interactions predicted by in-silico docking and its validation as observed in the experimental evidences from current Y2H analyses and available literatures.(PDF)Click here for additional data file.


Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors. Proteins perform essential functions in biological systems through recognizing their protein partners and by forming permanent or transient protein complexes. Computational predictions of the protein-protein interaction (PPI) sites on protein surfaces can provide insights into the biological functions of the proteins at the proteomics level and into the sequence-function relationships critical in identifying key targets for therapeutics development. Works on PPI site prediction and analysis have been summarized in many recent reviews Protein-protein interactions have been perceived as a process driven in large part by hydrophobic interactions in the core interfaces and by polar interactions in the interface rims. The core interface regions are tightly packed as in protein interior with key residues that are mostly hydrophobic in nature  Computational algorithms have been developed for PPI site predictions. A large portion of these methods are based on information embedded in amino acid sequences and on evolutionary information derived from multiple sequence alignments of homologues in the sequence databases The three-dimensional arrangement of amino acid residues in the PPI sites determines the affinity and specificity of the protein interactions, and hence the complementarities of surface geometry and physicochemical nature of the PPI interfaces are expected to be critical determinants in PPIs. Following this rationale, Sacquin-Mora et al. employed a rigid-body, coarse-grain docking method to detect interfaces within a small dataset The successes of the current prediction methods, albeit limited in accuracy, have indicated that not only sequence and structural features of the query proteins are critical determinants for PPI sites, the physicochemical complementarities of the partner surfaces are also important factors in predicting the interface locations. But for most of the proteins, the complementarity information is unavailable without knowing the binding partners and the binding interfaces, which are the targets of the PPI site predictions in the first place. In this work, we circumvent the difficulty by simulating the binding surface physicochemical complementarity with three-dimensional probability density maps (PDMs), which were derived based on the distributions of non-covalent interacting atoms in protein interiors. The PDMs provide information of possible interacting atoms from the protein partners in the PPI interfaces, because the PPI interface cores share similar amino acid composition with protein interiors It has been well-established that geometrical and physicochemical complementarities are critical determinants in PPI interfaces i were simulated with the PDMs of non-covalent interacting atoms and were described with the 32 numerical features calculated with Equation (2) . The matrix element  in i,jA: one group of i,jA was calculated for the interacting atom type j around the surface atom type i in the known PPI sites on proteins in the S432 dataset and the other group was calculated for the same interacting atom type around the non-PPI site atom type i in the same dataset. The matrix elements showing decreasing p-value substantially less than the statistical threshold of 0.025 are colored in red with increasing depth. These U-test p-values reflect the significant statistical differences in the attributes calculated from the PDMs or surface geometry between the protein surface atoms in known PPI sites and the atoms outside known PPI sites.The U-test results shown in nd feature based on the surface atom local geometry for each of the 30 protein atom types can be used as effective attributes in training machine learning models for PPI site predictions. Machine learning algorithms ANN_BAGGING and SVM_BAGGING were trained for each of the 30 protein surface atom types with five-fold cross validation on the S432 dataset as described in the The results in http://ismblab.genomics.sinica.edu.tw/> benchmark >protein-protein.The PPI surface patches on protein surfaces were predicted by combining the machine learning predictions for each of the surface atoms. The activity (probability) outputs from the machine learning models were first converted into prediction confidence levels so that surface atoms with high confidence level predictions can be clustered into surface patches as PPI sites see . Figure http://ismblab.genomics.sinica.edu.tw/> benchmark >protein-protein.Residues in the predicted PPI surface patches were predicted based on the atom-based PPI site predictions see  and wereThe distribution of prediction accuracy for proteins in the S432 and S142 dataset are shown in http://ismblab.genomics.sinica.edu.tw/> benchmark >protein-protein.http://ismblab.genomics.sinica.edu.tw/> benchmark >protein-protein. Furthermore, the prediction capacities of the predictors devised in this work have been compared with public domain servers using protein structures as input. The structures from the independent test set S58  calculated in Equation (3). The prediction confidence-attribute correlations are strongly dependent on the attribute type. As shown in the histogram, increasing prediction confidence levels are linearly and positively correlated with increasing values of the attributes derived from the aliphatic and aromatic carbons, suggesting that the PDM concentrations of these interacting atoms are greater around the protein surface atoms that are predicted to be in the PPI sites with high prediction confidence level. This is in good agreement with the notion that PPI interface cores are similar to protein interiors in hydrophobic amino acid composition, and thus are predicted with higher accuracy and confidence level. Attributes of hydrophilic atoms  and type 2 PPI sites are centered on a representative surface patch with more hydrophilic residues . Hydrophobic residues are Ala, Pro, Leu, Ile, Met, Cys, and Val; aromatic residues are Phe, Tyr, and Trp. The rest of the amino acid types are hydrophilic. Two datasets derived from S432, named DS_Type1 and DS_Type2, were generated with atoms labeled as positive for only type 1 PPI sites and type 2 PPI sites, respectively. Cross validation benchmark procedures as described above were applied to the two datasets. Taken together, the PPI sites in homo-oligomers are usually formed with large interface area with hydrophobic interface cores and hydrophilic peripheral areas. These PPI sites can be predicted with reasonable accuracy with the methodology developed in this work. As the PPI sites become smaller and more hydrophilic, as in the interfaces of some hetero-oligomers where hydrophobic cores become less prominent, the accuracy of the PPI site prediction deteriorates. In some of these interfaces, the rim regions make the dominant parts of the PPI sites and the interface cores become increasingly insignificant as the interface size decreases In summary, PPI sites on proteins of known structures can be predicted with accuracy to an extent based on the physicochemical complementarity derived from PDMs on protein surfaces. Although the PDMs, which describe the three-dimensional distributions of non-covalent interacting atoms on protein surface, were derived from protein structures, the physicochemical complementarity in PPI interfaces can be faithfully reproduced with the numerical features derived from the PDMs, indicating that protein folding and binding are governed by similar energetic principles. The predictions based on these PDM-recreated physiochemical complementarity features on protein surfaces are among the best in PPI site predictions with known protein structures. In particular, the hydrophobic cores of the PPI sites are more likely to be correctly predicted. As the PPI sites become smaller in size and less hydrophobic in amino acid composition, the prediction of these PPI sites became increasingly difficult. The difficulties could not be overcome by training the predictors with the subset of PPI sites characterized with more hydrophilic residues in the PPI sites. The PPI site predictions are nevertheless likely to be further improved with additional understanding of polar and water-mediated interactions in protein-protein recognitions.http://ismblab.genomics.sinica.edu.tw/> gallery.Probability density maps (PDM) constructed with protein non-covalent interacting atoms from known protein structures have been described previously i, the PDM values for interacting atom type j associated with the grids within 5 Å radius centered at the atom i were summed and associated with the center of the atom as i,jS:i,kr is the distance between atom i to a grid point k; k,jg is the PDM value of atom type j at grid point k.One machine learning model was trained for each of the 30 protein atom types .i,jS is defined in Equation (1); i,kd is the distance between atom i and atom i,nk; d is the distance between atom i and atom n. i,jA encodes complementarity information on interacting atom type j over a circular protein surface patch centered at atom i on the protein. The 32nd attribute for the atom i was the fraction of the space not occupied by the van der Waals volume of the protein in the 10 Å sphere centered at the atom i.The distance-weighted sum  associated with protein atom i as inputs for the machine learning algorithms were scaled between 0 and 1. Equation (3) shows the calculation of i,ja from i,jA (j = 1∼32):if i,jA > max,jM then i,ja = 1; otherwise, if i,jA < min,jM then i,ja = 0; otherwise,The attributes Three datasets were downloaded from the SPPIDER website In order to test the performance of the predictors devised in this work with other comparable predictors in the public domain, we downloaded protein complex structures released in 2011 from PDB website with the following criteria: 1) resolution is less than 3.0 Å, 2) chain length is greater than 100 amino acids, 3) entry has two subunits in biological ensemble, 4) entry does not have DNA, RNA, ligands, or modified residues, 5) there is no missing atom in the PDB files, and 6) pairwise sequence identity between any two proteins is less than 30%. The protein chains were further filtered to ensure none of them share greater than 30% sequence identity to proteins in S432, the training set used in this work as described in the previous paragraph. This set of 58 protein chains, denoted S58, was used as the test set for the comparison of prediction capabilities among different PPI site prediction servers.dSASA) upon complex formation by NACCESS software u,iSASA and c,iSASA are the SASA of atom i in the uncomplexed and complexed state, respectively. An atom i was defined as a PPI site atom when idSASA is greater than 0.All PDB chain records in the three datasets above were checked with PQS (protein quaternary structure) server sf, was used as the transfer function for the hidden and output layers of of the ANN network.The standard feed-forward back-propagation neural network As an alternative to the more common Levenberg-Marquardt back-propagation training algorithm The details of the standard SVM methodology implemented with LIBSVM package has been described previously Since non-binding atoms in the training set greatly outnumbered binding atoms, ordinary machine learning algorithms would produce learning biases without suitable treatment. The methodology included multiple predictors to produce an ensemble of prediction results http://ismblab.genomics.sinica.edu.tw/>Download. The attributes i,ja (j = 1∼31 for the 31 interacting atom types in j = 32 for the geometry attribute) associated with protein atom i for all proteins in the data sets S432, S142, S17a, S58 can be downloaded from the same web-server.The machine learning parameters can be downloaded from the web-server The prediction capabilities of the machine learning models were benchmarked by accuracy (Acc), precision (Pre), sensitivity (Sen), specificity (Spe), F-score, and Matthews correlation coefficient (MCC) Prediction activity (ANN_BAGGING) or probability (SVM_BAGGING) with value ranging from 0 to 1 from the output of the machine learning algorithm was normalized to prediction confidence level so that the prediction results from different machine learning models can be compared on a level ground. For each of the 30 protein atom types, the machine learning outputs from the validation sets were sorted into bins of interval 0.1. The prediction confidence level for each of the bins was calculated as the fraction of the true positives over the total number of predictions in the bin. In the end, lookup-tables for output-confidence relationships were constructed; the machine learning outputs can be converted to prediction confidence levels with these lookup tables. Five-fold cross validation was performed for each of the 30 protein atom types in the S432 dataset. Each dataset was randomly divided into 5 equal portions with similar distributions of positive and negative cases. One portion of the dataset was selected as test set, another one portion as validation set, and the rest as training set. The training set was used to train the models, and the validation set was used to optimize the prediction parameters so as to achieve the best predictive capability without over-fitting. The optimized models were then benchmarked by the test set. The process took turns to benchmark prediction accuracy on the 5 non-overlapping test sets with the predictors optimized with the corresponding training and validation set. The accuracy benchmarks were the averaged results from the 5-fold cross validation.For each of the predictors, an optimal threshold for the output activity value was determined with the validation set. Positive predictions have the output activity values greater than or equal to the threshold; the negative predictions have the output activity values smaller than the threshold. With these thresholds, the TP, TN, FP, and FN in Equations (6)∼(11) were determined and the accuracy benchmarks were calculated. The thresholds for the predictors of all 30 atom types were determined to optimize the MCC for the predictions with the validation set.Five predictors for each protein atom type were optimized after performing the 5-fold cross validation on the S432 dataset. The predictors which yielded the best testing performance were assessed in the independent test with S142, S17a, and S58 dataset.A protein-protein binding site was predicted by a cluster of surface atoms predicted as positive cases with high prediction confidence level. Protein surface atoms in PPI sites with prediction confidence level greater than 60% were used as cluster centers to include neighboring surface atoms within radius of 11 Å. Within each of the surface patches, all the surface atoms with the confidence level for positive prediction greater than 20% were included in the tentative patch of atoms as a PPI site. If the pairwise distance of any two seeds was within 10 Å, the two corresponding patches were merged as one patch. The parameters were optimized for residue-based prediction accuracy with the validation set.uSASA>0) included in the atom-based binding patch were considered as positive residues of the residue-based patch. Similarly, actual PPI sites at the residue level were defined by patches of positive residues, each of which has more than 30% of the surface atoms (uSASA>0 in the uncomplexed structure) on the residue defined as PPI atoms ). This definition enabled the comparison of prediction results with actual binding sites at the residue level. The percentage parameter was optimized for residue-based prediction accuracy with the validation set.To facilitate comparison of this work with previous methods predicting binding sites at the residue level, a heuristic procedure was used to transform the atom-based binding site predictions as described in the previous paragraph into binding site predictions at the residue level: only the residues with more than 30% of the surface atoms  CPU is around 50 minutes with single thread and around 23 minutes with two threads. The following procedures for generating input attributes and for predicting with machine learning models take less than 20 seconds.ranksum in MATLAB (http://www.mathworks.com/help/toolbox/stats/ranksum.html).Mann-Whitney U-test is a non-parametric statistical method to test whether two groups of numerical values come from identical continuous distributions of equal medians – increasing p-value indicates decreasing difference of the two distributions and p-value of 1 indicates that the two distributions are statistically indistinguishable. The Mann-Whitney U-tests were carried out with the statistic tool http://ismblab.genomics.sinica.edu.tw/. All the benchmark results can also be accessed in interactive graphic presentations from the same web address above.Predictions can be submitted to the webserver Figure S1Probability density maps and encoded features of human vascular endothelial growth factor A (VEGF). Structure of VEGF is extracted from PDB ID 2FJG chain V and W. Number 1 to 31 in each cell of the table corresponds to each of the interacting atom types defined in i,ja values (Equation (3) in the main text). Solvent inaccessible atoms are colored in gray. Interactive 3-D graphic presentation of the PDMs can be viewed from the web server http://ismblab.genomics.sinica.edu.tw/> gallery.(DOCX)Click here for additional data file.Figure S2min,jM (in square symbols) and max,jM (in diamond symbols) against the 32 attribute types. The maximum and minimum i,jA values were derived from each protein in S432 and the medians of the maximum  and the minimum  are plotted against the attribute index. These values were used for normalization of i,jA (Equation (3) in the main text).(DOCX)Click here for additional data file.Figure S3Lookup charts converting output activity (probability) from the corresponding machine learning predictor to prediction confidence level. For each of the 30 protein atom types, the machine learning outputs from the validation sets were sorted into bins of interval 0.1. The confidence level of each of the bins was calculated as the fraction of true positive over the total number of predictions in the bin. The panels (a) and (b) are derived from ANN_BAGGING and SVM_BAGGING predictions respectively. In each of the panel, two sets of curves are shown; one set for the prediction confidence level described as above ; the other set for the negative prediction confidence. The sum of the positive prediction confidence level and the negative prediction confidence level equals to one.(DOCX)Click here for additional data file.Table S1A filter system used to eliminate non-interacting atomic pairs based on the work by McConkey et al. with modifications. During the construction of the PDMs, only the atom pairs with the matrix value less than −0.1 were included in the probability density maps. The atom pairs for which the matrix value colored in red were not included for PDM constructions.(DOCX)Click here for additional data file.Table S2Five-fold cross validation of ANN_BAGGING prediction accuracy benchmarks on the S432 dataset. The dataset, the 5-fold cross validation, and the benchmark measurements have been described in the main text. Matthews correlation coefficient (MCC), F-score(Fsc), Accuracy(Acc), Precision(Pre), Sensitivity(Sen) and Specificity(Spe) are shown in Equations (6)∼(11) in the main text. TP, FP, TN, and FN are true positive, false positive, true negative, and false negative respectively. The ratio of the number of predicted positive atoms against actual number of binding atoms for each protein is also listed. C1∼C4 represent PPI sites in each of the test proteins; different protein has different number of PPI sites. In these columns, the number of the predicted true positive atoms is shown over the actual number of atoms involving in the PPI site. Interactive examination of the prediction results for each of the proteins in the S432 dataset can be accessed from the web server: http://ismblab.genomics.sinica.edu.tw/> benchmark >protein-protein.(DOCX)Click here for additional data file.Table S3Five-fold cross validation of SVM_BAGGING prediction accuracy benchmarks on the S432 dataset. The dataset, the 5-fold cross validation, and the benchmark measurements have been described in the main text. Matthews correlation coefficient (MCC), F-score(Fsc), Accuracy(Acc), Precision(Pre), Sensitivity(Sen) and Specificity(Spe) are shown in Equations (6)∼(11) in the main text. TP, FP, TN, and FN are true positive, false positive, true negative, and false negative respectively. The ratio of the number of predicted positive atoms against actual number of binding atoms for each protein is also listed. C1∼C4 represent PPI sites in each of the test proteins; different protein has different number of PPI sites. In these columns, the number of the predicted true positive atoms is shown over the actual number of atoms involving in the PPI site. Interactive examination of the prediction results for each of the proteins in the S432 dataset can be accessed from the web server: http://ismblab.genomics.sinica.edu.tw/> benchmark >protein-protein.(DOCX)Click here for additional data file.Table S4Independent test of ANN_BAGGING prediction accuracy benchmarks on the S142 dataset. The dataset and the benchmark measurements have been described in the main text. Matthews correlation coefficient (MCC), F-score(Fsc), Accuracy(Acc), Precision(Pre), Sensitivity(Sen) and Specificity(Spe) are shown in Equations (6)∼(11) in the main text. TP, FP, TN, and FN are true positive, false positive, true negative, and false negative respectively. The ratio of the number of predicted positive atoms against actual number of binding atoms for each protein is also listed. C1∼C2 represent PPI sites in each of the test proteins. In these columns, the number of the predicted true positive atoms is shown over the actual number of atoms involving in the PPI site. In the annotation column, complex type (homo or hetero-oligomer) and the secondary structure element (SSE) in the PPI sites are listed for each protein. Interactive examination of the prediction results for each of the proteins in the S142 dataset can be accessed from the web server: http://ismblab.genomics.sinica.edu.tw/> benchmark >protein-protein.(DOCX)Click here for additional data file.Table S5Independent test of ANN_BAGGING prediction accuracy benchmarks on the S17a dataset. The dataset and the benchmark measurements have been described in the main text. Matthews correlation coefficient (MCC), F-score(Fsc), Accuracy(Acc), Precision(Pre), Sensitivity(Sen) and Specificity(Spe) are shown in Equations (6)∼(11) in the main text. TP, FP, TN, and FN are true positive, false positive, true negative, and false negative respectively. The ratio of the number of predicted positive atoms against actual number of binding atoms for each protein is also listed. C1∼C2 represent PPI sites in each of the test proteins. In these columns, the number of the predicted true positive atoms is shown over the actual number of atoms involving in the PPI site. Interactive examination of the prediction results for each of the proteins in the S17a dataset can be accessed from the web server: http://ismblab.genomics.sinica.edu.tw/> benchmark >protein-protein.(DOCX)Click here for additional data file.Table S6Independent test of ANN_BAGGING prediction accuracy benchmarks on the S58 dataset. The dataset and the benchmark measurements have been described in the main text. Matthews correlation coefficient (MCC), F-score(Fsc), Accuracy(Acc), Precision(Pre), Sensitivity(Sen) and Specificity(Spe) are shown in Equations (6)∼(11) in the main text. TP, FP, TN, and FN are true positive, false positive, true negative, and false negative respectively. The ratio of the number of predicted positive atoms against actual number of binding atoms for each protein is also listed. C1∼C2 represent PPI sites in each of the test proteins. In these columns, the number of the predicted true positive atoms is shown over the actual number of atoms involving in the PPI site. Interactive examination of the prediction results for each of the proteins in the S58 dataset can be accessed from the web server: http://ismblab.genomics.sinica.edu.tw/> benchmark >protein-protein.(DOCX)Click here for additional data file.Text S1Supplemental methods.(DOCX)Click here for additional data file.


Protein-DNA recognition underlies fundamental biological processes ranging from transcription to replication and modification. Herein, we present a computational study of the sequence modulation of internal dynamic properties and of intraprotein networks of aminoacid interactions that determine the stability and specificity of protein-DNA complexes.To this aim, we apply novel theoretical approaches to analyze the dynamics and energetics of biological systems starting from MD trajectories. As model system, we chose different sequences of Zinc Fingers (ZF) of the Zif268 family bound with different sequences of DNA. The complexes differ for their experimental stability properties, but share the same overall 3 D structure and do not undergo structural modifications during the simulations. The results of our analysis suggest that the energy landscape for DNA binding may be populated by dynamically different states, even in the absence of major conformational changes. Energetic couplings between residues change in response to protein and/or DNA sequence variations thus modulating the selectivity of recognition and the relative importance of different regions for binding.The results show differences in the organization of the intra-protein energy-networks responsible for the stabilization of the protein conformations recognizing and binding DNA. These, in turn, are reflected into different modulation of the ZF's internal dynamics. The results also show a correlation between energetic and dynamic properties of the different proteins and their specificity/selectivity for DNA sequences. Finally, a dynamic and energetic model for the recognition of DNA by Zinc Fingers is proposed. Protein-DNA recognition mechanisms underlie the functioning and regulation of several cellular processes ranging from transcription to replication, modification and restriction. Consequently, it is not surprising that questions on how to achieve a detailed molecular understanding of these phenomena have emerged since the first X-ray structures of complexes appeared.One of the central problems involves the understanding of how a certain binding protein efficiently selects a specific target sequence from a large number of possible sites . InitialHowever, despite significant progress at the experimental and theoretical level, the molecular determinants of the events at the basis of protein-DNA recognition have not been fully characterized.In this study, we apply all-atom, explicit solvent Molecular Dynamics (MD) simulations to protein-DNA complexes that show the same overall 3-Dimensional (3D) structures but differ for point mutations in either the protein or the DNA. Experimental data show that these sequence-differences have an impact on the affinity and specificity in recognition. Our goal here is to study the applicability of novel theoretical/computational approaches to map the principal energetic interactions and internal dynamic properties of complexes to investigate the determinants of stability, selectivity and specificity of different mutants with the same 3 D organization for selected DNA sequences.As a model system we chose the Zinc Finger (ZF) proteins of the Zif268 family ,25. ZincZinc Fingers of Zif268 belong to the C2H2 family (where Zn is coordinated by two Cys and two His residues) and are characterized by a modular structure featuring three repeated domains ,25. EachAnalyses of X-ray data of the Zif268-DNA complexes revealed that residues at the four specific positions, -1, 2, 3 and 6 (numbering with respect to the start of the α-helix) in helix 1 make most of the contacts to the DNA stretch ,25. To eFigure S1, visualizing the high identity of the sequences and their differences.These experimental structures of different complexes were used as starting points for multiple copies of MD simulations. Three simulations were thus started from each crystal structure with different sets of random initial velocities. We thus obtained a total of 60 ns simulations for each complex in set of seven different systems, resulting in a total of 420 ns of MD simulation in explicit solvent. The complexes, their pdb names, the different aminoacid motifs on the helix and the respective DNA sequences that they bind are reported in Table Analysis of the trajectories of each complex was then carried out to map the dynamic residue-residue coordination and rigidity distribution, the principal energetic interactions and the differences in their profiles.The results showed differences in terms of the organization of the intra-protein energy-networks responsible for the selection and stabilization of the protein conformations recognizing DNA, and consequently for the specificity. Moreover, sequence differences in the protein and DNA-mutations are reflected into different modulation of the ZF's internal dynamics. The results also showed a correlation between energetic and dynamic properties of the different proteins and their selectivity/specificity for DNA sequences. Finally, we propose a dynamic and energetic model for the recognition of DNA by Zinc Fingers that may be useful to improve our understanding of the physico-chemical bases of protein-DNA recognition mechanisms.Figure S2). The calculation of average RMSD values obtained by comparing all the structures visited by each trajectory with all the structures visited by the other trajectories also yielded an average value of 0.2nm, showing high degrees of structural similarity among the different complexes. The overall conservation of structural properties was also apparent in the secondary structure analysis. No major variation could be detected, indicating the absence of large conformational changes or folding-unfolding events.The time evolution of each protein's atom-positional Root Mean Square Deviation (RMSD) from the initial X-ray structures was evaluated after combining the data from the three independent trajectories for each complex, as described in Materials and Methods. In all cases, RMSD values stabilized around 0.2 nm, showing the substantial stability of the complexes in the simulation conditions  were carried out on the trajectories of the complexes. ED identifies relevant low-energy displacements of groups of residues and emphasizes the amplitude and direction of dominant protein motions by projecting the trajectories on a subset of the principal eigenvalues and eigenvectors of the residue pair covariance matrix calculated from MD ,30. UsinThe residue-based Root Mean Square Fluctuations (RMSF) were calculated by projecting each trajectory on the essential subspace defined by the eigenvectors responsible for 90% of the total variance Figure . DiffereDifferential fluctuations can also be noticed in the analysis of RADR sequences. Consistently with the previous observation, the proteins with higher fluctuations display better DNA-binding properties in their respective complexes (1A1I and 1A1J), compared to 1A1K. Figure .The trend of flexible protein and rigid DNA was also conserved also in the REDR sequences Figure .The picture emerging from the analysis of these data is that, within each single group of ZF, higher dynamics appears to be correlated with lower Kd values. In order to gain more quantitative insights into how protein flexibility determines specificity in general, and not limited to a single protein sequence, we defined a measure of global protein flexibility and explored their correlations with the value of the dissociation constant Kd Table .vide infra). It is to be noted that the direct linear correlation of Kd values with calculated parameters yields comparable correlation values, in all cases. Interestingly, the calculation of correlation between log(Kd) and the RMSF flexibility parameter yielded a correlation coefficient of -0.92. When considering also the fluctuations of the DNA stretch in the calculation, the correlation coefficient resulted -0.88  value. The log of Kd was chosen to calculate the correlations, as this quantity is directly related to differences in free energy or entropy between different complexes  favors the selection of the fine-tuned interaction motifs necessary to stabilize a certain complex.Differential flexibility plays a key role in helping different proteins select different, and relatively rigid, DNA targets ,32. AnalTo gain more insights into these points, we applied a novel method for the analysis of the dynamic connectivity within a protein. Our approach aims at the quantitative description of the degree of internal coordination between residue pairs in the presence of dynamics.Rij, describe how residue pairs are dynamically connected: high Rij values are due to low distance fluctuations and therefore detect residue pairs characterized by high dynamical coordination. On the other hand low Rij values describe poorly correlated moving pairs. Coordination between neighbouring residues may be a simple consequence of local interactions, while strong coordination between residues located at high distances sheds light on long-range correlations. In this model, the lower the distance fluctuation between two residues, the better their coordination. Groups of locally highly coordinated residues identify protein's rigid sub-structures that may have specific functional roles.Internal dynamic coordination is recapitulated by means of the ICRM  matrix . According to the definitions presented in Materials and Methods, the elements of the matrix, The representative matrices for each complex are displayed in Figure Dynamic coordination and rigidity (flexibility) can be unequally distributed among different secondary structures within each protein, and this distribution may change when considering different systems.To investigate these aspects and extract the principal features of internal dynamics, the matrices were simplified through eigenvector decomposition. The resulting main eigenvalue can be considered as a general parameter related to the overall degree of dynamic connectivity within a protein, while the corresponding eigenvector reports on the role of each residue in defining the global dynamic properties of the matrix. In this context, the principal eigenvector provides a direct and compact way to combine the linear sequence description with information on global dynamic properties. Higher values of the component indicate a higher contribution of the residue to the global rigidity (coordination) of the complex Figure .In this frame of thought, the components of the principal eigenvector weighted by the respective eigenvalue were calculated for each complex. Figure Overall, these data suggest that ZF proteins may exploit flexibility-rigidity modulation to facilitate DNA recognition and binding. The affinity for a DNA stretch may thus be linked to differences in the underlying dynamics as a function of small changes in the sequence and of the identity of the binding partner. These considerations corroborate the hypotheses of Elrod-Ericksson and co-workers who postulated flexibility as a necessary property for a protein to bind and optimally adapt to the relatively rigid DNA structure .N residues, the matrix of average non-bonded interactions between pairs of residues is built from an MD trajectory. All the matrices with average energy values and corresponding error bars are provided in Additional files N components of the eigenvector associated with the lowest eigenvalue  was shown to single out those residues (hot sites) behaving as strongly interacting and possible stabilizing centers. The eigenvector associated with the main eigenvalue provides a compact description of the participation of each aminoacid to the global stabilization of a structure or a complex. The properties of this first eigenvector  ultimately depend on the sequence.Next, we set out to calculate the principal energetic interactions that are involved in the stabilization of each complex, and are ultimately responsible for specificity. To this end, we applied the energy decomposition approach -39. ThisThus, the approximated energy obtained after the matrix decomposition  accounts for the main attractive interactions that stabilize a certain state.i and j in a system interact with an energy 1 is the first eigenvalue and wi1 is the component of the associated first eigenvector contributed by residue i. The summation over all possible residue-pairs of the energetic couplings provides the effective approximation to the stabilization energy. The contribution of a specific residue i to the overall effective stabilization energy is thus the product of wi1 with all other wj.1According to our approximation, any two residues In this framework, it is possible to calculate the effective approximation to the total stabilization energy as a sum of intraprotein (or protein-protein), protein-DNA and intra-DNA (or DNA-DNA) stabilization energy, as reported in Table Analysis of the results shows that the highest contributions to the global stabilization of the complexes are provided by DNA-DNA  and protein-DNA interactions (35%).These properties are reflected at the level of the components of the first eigenvectors derived from the decompositions of the energy matrices for each system. In all cases, the components due to the DNA bases show dominant values with respect to the protein residues, with little apparent modulation of the energy component profile that can be ascribed to different protein sequences Figure . DNA-DNAThe contributions involving intraprotein residue-residue pair interactions account for about 5% of the total stabilization energy. Strikingly, the correlation between the contributions to the stabilization energy due only to intraprotein residue-residue interactions and the dissociation constant (log(Kd)) yielded a value as high as 0.85 Figure , indicatFrom the physico-chemical point of view, this result indicates that the internal energetic distribution of the protein reorganizes specifically in response to the presence of a certain DNA stretch and in response to specific sequence mutations.Consistently with what reported for the dynamic characterization of the complexes, analyses of the matrices of intraprotein residue-residue energy couplings showed specific patterns of modulation of the internal interaction networks Figure , reflectSummarizing, the results of our energy analysis suggest that recognition and binding properties are linked to a specific distribution of the stabilization energy. As expected, the global stabilization of the complex is mainly due to electrostatic interactions bringing the protein and DNA together. Modulation of affinity for a specific DNA sequence is further regulated through a redistribution of the stabilizing intraprotein interactions that strongly depends on the protein sequence.The energy contributions we calculate with the energy decomposition method are to be considered as the effective energies approximating the free energy of binding in a situation where the unbound state is set to a common reference state (ensemble of unbound states) in which the non-bonded energy is equal for all sequences. Given the high degree of similarities of the sequences, this is a viable hypothesis, and was already shown to hold for the study of folding-unfolding of related proteins .In this paper, we have concentrated on protein-DNA recognition, using MD simulations to investigate the global mechanisms by which Zinc Fingers bind to and modulate their affinities for given oligonucleotide sequences. DNA-binding proteins are in general able to efficiently find their binding sites, amongst large numbers of alternative genomic sequences ,40,41. CIn order to evaluate these contributions and shed light on the molecular details underlying protein-DNA recognition and specificity, we have applied novel methods to the analysis of simulation data. To this end, we have extended our analysis of signal transduction ,43 and pThe results of our analysis, based on the statistics obtained from the combination of multiple trajectories for each system, show quantitative correlations between the degree of protein flexibility and intraprotein residue-residue coupling energies with Kd values for the protein-DNA complexes under study.The emerging picture is that selectivity and specificity are strictly correlated to lower rigidity and higher conformational entropy of the protein. In particular, the loop regions connecting different secondary structures emerge as the least coordinated and rigid motifs in the proteins showing the highest affinities. The possibility to visit a higher number of conformational states on the binding energy landscape may represent an advantage for the protein in the adaptation to the rigid-body like structure of DNA, and would allow the search for the best possible set of stabilizing interactions. Moreover, our results show that sequence variations in helix 1 determine long-range effects altering the dynamics of helices 2 and 3, suggesting a cooperative perturbation of the conformational dynamics that extends beyond the point of mutation and influences the dynamics of the whole protein in the complex.From the energetic point of view, the application of the energy decomposition method to the trajectories of the different complexes revealed that the modulation of specific intraprotein interaction networks in response to the presence of a certain DNA-stretch quantitatively impacts on the binding affinities. Interestingly, the proteins with higher affinities  are characterized by a more spread distribution of stabilizing residues, distributed all along the sequence. A diffuse network of strongly interacting residues once more suggests the importance of cooperative effects in determining complex stabilization and specificity.These observations are consistent with the recent observations by Miyazono et al . The autOverall, our calculations and models suggest that both flexibility (i.e. entropic factors) and energy modulation  contribute to the affinity and selectivity of the Zinc Fingers examined here for their target DNA sequences. The two factors are strictly interconnected: the distribution and modulation of dominant interactions reverberates in the corresponding dynamics of the complexes. Indeed, from the thermodynamic point of view, Kd is related to the complexation reaction free energy (ΔG), which is ultimately determined by the combination of internal energy and entropy. The good correlations with Kd obtained for intraprotein energy, entropy and flexibility reflect this point.From the mechanistic point of view, our calculations suggest that binding to DNA and selection of a certain sequence can be part of a hierarchical process: at the first level, electrostatic interactions due to the charged nature of the oligonucleotide stretch and of the binding site on the protein contribute to stabilize the complex. These interactions are typically long-range and would not allow a fine-tuned discrimination of sequences. Interestingly, all variations at the dynamic and/or energetic level do not involve any significant collective DNA-protein deformations, in line with what was already highlighted by Lavery and coworkers ,32.Specificity and selectivity, in turn, can be achieved through the modulation of interactions of differential intensity among specific subsets of residues. In other words, specific energetic interaction patterns throughout the protein structure can determine the accessibility of different sub-sets of conformations that allow the protein to optimally adapt to the structure and sequence of the target DNA.Our analyses of collective properties allowed us to appreciate the pervasive effects of perturbations in one limited region of the protein on the global dynamic and energetic determinants of recognition and binding.Overall, the results of our MD simulations and analyses have suggested that the energy landscape for DNA binding may be populated by dynamically different states, even in the absence of major conformational changes. Energetic couplings between residues may change in response to sequence variations thus modulating the importance of different regions for binding, and the consequent dynamics of protein-DNA complex formation.in silico mutations and evaluation of the dynamic and energetic properties with the methods presented here, would allow to select protein sequences with dynamic and energetic profiles that have maximal similarity with the ones of known proteins with high affinity and specificity.From the applicative point of view , given tAll MD simulations were performed using the AMBER 9.0 package  with theIn the Zif268 protein, each of the three zinc ions is coordinated to two cysteines and two histidines, one ion for each helix. The zinc was modeled as covalently bound to the ligands, and the parameters used to describe the metal, its charge and bonding properties were the ones developed by Merz and coworkers .In our work, 7 protein-DNA complexes were considered: they differ, with respect of the aminoacids placed on residues 16, 18, 19, 22 and of the nucleotides placed on the position 8, 9, 10, 11 and their complementary . After this, each system was solvated. Multiple solute equilibration processes were performed, in order to reorganize the water molecules (100 steps steepest descent + 50 ps equilibration dynamics at constant P and T = 100K).After the solute equilibration, another system minimization was performed ; afterwards, the temperature of the system was slowly brought to the desired value of 300K in 3 steps, with subsequent 100 ps NVT equilibration processes at 100K, 200K and 300K).Finally, a last 100 ps equilibration NPT process was performed. From this final structure a set of 3 different 20 ns MD simulations was performed for each system. Three different sets of initial velocities obtained from a Maxwellian velocity distribution at the desired temperature of 300K were used to yield three different production runs for each complex.Overall, this protocol resulted in 3 runs for each of the seven complexes studies, providing a total of 420 ns of simulation time. All the analyses were carried out on the combined trajectories obtained by concatenating each of the 3 runs for each complex, after eliminating the first 5 ns of each trajectory to allow for equilibration.Cij.Covariance matrices were built by averaging motions of Cα atoms of the aminoacids and of the C1 atoms of the deoxyribose ring of the nucleotides, deviating from the mean structure, with the latter calculated over the trajectory. The essential directions of correlated motions during dynamics were then calculated by means of the Essential Dynamics method , or prinThe covariance matrix was also used to calculate the entropic content of the complexes using the Schlitter's approach .In order to provide a simple and sequence-related one-dimensional descriptor of the contribution of each residue in defining the dynamic connectivity within the protein, we introduced an analysis based upon signal propagation; a concept originally developed based on elastic network models , and theijk represents the instant value of the k-th cartesian component of the distance between Cα of residues i and j and yields the fluctuation of the distance component when averaged over the trajectory. We set Rij = 0 on the diagonal and for nearest-neighbours to avoid divergence. In this way, the ICRM matrix describes how residue pairs are dynamically connected: high Rij values are due to low distance fluctuations and therefore detect residue pairs characterized by high dynamical coordination. On the other hand low Rij values describe poorly correlated moving pairs . Coordination between neighbouring residues may be a trivial consequence of local interactions, while strong coordination between residues located at high distances sheds light on long range correlations. Hence, to summarize, the lower the distance fluctuation between two residues, the better they are coordinated and behaving like two points of a rigid body. Groups of locally highly coordinated residues identify protein's rigid sub-structures, such as secondary structure elements. On the other hand, residue pairs at long distance having low distance fluctuations can be due to mutually coordinated protein sub-domains possibly related to long range correlations.Where ΔxThe energy decomposition method (EDM) -39 aims Herein, the structures sampled every 1 ns for each complex were taken into account and the average non-bonded interaction energy matrix was computed from averaging non-bonded pair interactions over all the protein structures saved. The solvent is directly taken into account using the GBSA method  in the cLet us indicate with M the non-bonded interaction energy matrix without the diagonal elements, namely without the self-interaction terms. This matrix can be diagonalized and expressed in terms of its eigenvalues and eigenvectors:where:N is the sum of the aminoacids and nucleotides in the complex;- k is the k-th eigenvalue;- λWik is the i-th component of the k-th eigenvector;- 1 is the lowest eigenvalue and, from now on, we will refer to the first eigenvector as the eigenvector corresponding to the eigenvalue λ1.Eigenvalues and eigenvectors are usually labeled following an increasing order. Therefore, λThe total non-bonded energy is defined as:1W1 is much larger than λkWk for k≠1, the sum over i and j of Mij is dominated by the contribution due to the first eigenvalue and eigenvector, such that the total non-bonded energy can be approximated by:with t. This threshold is chosen equal to the squared component of a normalized "flat eigenvector" . This corresponds to a case in which each residue equally contributes to the structural stability and, therefore, the threshold t is equal to 1/N, where N is the number of the eigenvector components.As mentioned above, the eigenvector associated with the lowest eigenvalue is used to identify the most stabilizing aminoacids. In particular, considering its squared components as the weights of the corresponding residues in the structural stabilization, we can define "hot spots" those residues with a weight higher than a threshold ZF: Zinc Fingers; MD: Molecular Dynamics; ICRM Matrix: Internal Coordination and Rigidity Matrix.All authors contributed to the research presented. EM, RT performed research, Analyzed Data, Interpreted Data. MC was critically involved in developing research. GM, GC designed research, methods, wrote the paper. All authors read and approved the final manuscript.This work was supported by a grant from AIRC  to GC, from the Fondazione Cariplo grant Nr. 2008.2198, and from the Italian Ministry of Health GR-2007 programme.Sequence alignment and structural time evolution of proteins in simulations. Figure S1: The sequence alignment of the protein residues, made with Clustal-W. Figure S2.The time dependent RMSD evolution of all trajectories over the combined molecular dynamic trajectories.Click here for fileAverage pair interaction energies for 1A1F Complex and their standard deviations.Click here for fileAverage pair interaction energies for 1A1G Complex and their standard deviations.Click here for fileAverage pair interaction energies for 1A1I Complex and their standard deviations.Click here for fileAverage pair interaction energies for 1A1J Complex and their standard deviations.Click here for fileAverage pair interaction energies for 1A1K Complex and their standard deviations.Click here for fileAverage pair interaction energies for 1A1L Complex and their standard deviations.Click here for fileAverage pair interaction energies for 1AAY Complex and their standard deviations.Click here for file


Protein functional annotation consists in associating proteins with textual descriptors elucidating their biological roles. The bulk of annotation is done via automated procedures that ultimately rely on annotation transfer. Despite a large number of existing protein annotation procedures the ever growing protein space is never completely annotated. One of the facets of annotation incompleteness derives from annotation uncertainty. Often when protein function cannot be predicted with enough specificity it is instead conservatively annotated with more generic terms. In a scenario of protein families or functionally related (or even dissimilar) sets this leads to a more difficult task of using annotations to compare the extent of functional relatedness among all family or set members. However, we postulate that identifying sub-sets of functionally coherent proteins annotated at a very specific level, can help the annotation extension of other incompletely annotated proteins within the same family or functionally related set. As an example we analyse the status of annotation of a set of CAZy families belonging to the Polysaccharide Lyase class. We show that through the use of visualization methods and semantic similarity based metrics it is possible to identify families and respective annotation terms within them that are suitable for possible annotation extension. Based on our analysis we then propose a semi-automatic methodology leading to the extension of single annotation terms within these partially annotated protein sets or families. The continuous development of high-throughput methodologies for biological molecule sequencing has led to an increase in the amount of raw biological data in need of further processing. The sequencing of a new biological molecule is normally followed by a functional annotation process that aims to provide functional descriptors elucidating its biological role. Functional annotations can be derived from either experimental determination or prediction. Generically, given supporting evidence, functional descriptors are assigned (with varying degrees of confidence) to their corresponding biomolecules. In fact, a functional annotation can be represented as the pair of a biomolecule (identifier) and corresponding functional descriptor.Among biomolecules, proteins are of particular interest given their participation in practically every process occurring within living cells. Their functions can range from structural or mechanical support to the catalysis of vital metabolic biochemical reactions. Furthermore, their functional specification is very broad and can range from descriptors on general participation in biological processes, such as responses to oxidative stress, up to more specific descriptors, such as catalysis of particular biochemical reactions. It would be desirable to determine protein function via accurate and comprehensive chemical characterizations, if possible by experimental assessment, however, this process is expensive and time consuming. Instead, the most commonplace approach is the use of any of the several function prediction methodologies, relying on techniques ranging from sequence homology detection to text mining of the scientific literature. Most of these methodologies also rely heavily on computational power and can range from partial to full automation, thus enabling them to handle the barrage of biological sequence data currently being made available.protein families. Within a family each protein shares homology with all the other proteins, i.e., it descends from a common ancestor and usually retains significant sequence similarity. In turn that often  translates into similar three-dimensional structures and functions. Although sequence similarity alone is not sufficient to conclude protein homology, it nevertheless provides a reasonable cornerstone for many sequence alignment methods. Similarly, homology also does not guarantee functional similarity among proteins but provides a good starting point and is commonly used in several functional annotation methods. Hence, it is typically advantageous to group proteins into homologous families because of the potentially shared functions.Proteins are commonly grouped into evolutionarily related groups known as functional similarity. However, caution must be exerted during the interpretation of annotation similarities since there are still issues that GO inherently does not solve, for instance, annotation bias and annotation incompleteness.The emergence of biological ontologies and most notably the Gene Ontology (GO) , where these processes take place are described by the cellular component aspect of the ontology. On the other hand, each protein can, independent of the surrounding environment, perform catalytic or binding elementary molecular activities thus being described by the molecular function aspect of the ontology. Structurally each ontology aspect is organized as a Directed Acyclic Graph (DAG), where each node represents a term and edges represent a relationship between those terms. Each term is identified by an alphanumeric code  and its textual descriptors, including its name, definition, and synonyms if available. Currently, the existing relationships between GO terms can be of three types: is_a, part_of and regulates. While is_a and part_of relations are only established within each individual ontology aspect, regulates relations can occur across aspects.The GO consortium provides a structured and controlled vocabulary for the description of molecular phenomena in which proteins (and or gene products) are involved. Within each GO aspect the biological phenomena are described at different levels, thus this vocabulary is divided into three orthogonal ontology aspects that describe gene products in terms of their associated biological processes, cellular components and molecular functions , which is an acronym identifying the type of evidence that supports that annotation, e.g., the IDA code (Inferred by Direct Assay) is assigned to annotations that are supported by that type of experiment.Proteins and other gene products are not actually part of GO which includes only terms that describe them. Nevertheless, the GO Consortium, via the Gene Ontology Annotation (GOA) project , automatic annotation and the hybrid approach, semi-automatic annotation. Despite manual annotations produced by expert curators typically being of an higher quality level, this annotation approach does not scale up to the output of the high-throughput sequencing projects. Therefore, the bulk of protein annotations are produced via automated procedures. These typically rely on methods for transferring annotation terms from previously annotated protein sources to other unannotated (or incompletely annotated) proteins.cellular component, molecular function, and biological process. However, GO does not solve all annotation issues and even introduces new ones. The GO ontology aspects themselves are a product of mostly manual curation and their growth is linked to research bias, thus some parts of the ontology are more developed (have terms for more specific functions) than others  proteins. Thus, the typical workflow of an automatic annotation system includes a first stage where potential functional peers are identified. A second stage then involves the actual annotation transfer where functional terms are extracted from the functional peers and associated to the previously incompletely annotated or unannotated proteins.Typically, the automatic protein annotation systems do not actually produce The automatic procedures used for protein annotation can be divided into sequence-based approaches and structure-based approaches. Although three dimensional structure of proteins is generally more conserved than its sequence, the wider availability of sequence data over structural data allows for potentially greater annotation coverage with the former. Still, proteins with similar sequences typically possess evolutionary proximity, and to some extent, function conservation thus providing good approximations. In a similar sense, structure-based approaches can also compare protein structures in order to obtain similarity scores, but further details on structure-based approaches are out of the scope of this topic  threshold. In order to handle the possibility of annotation of short sequence matches with low e-values filtering by minimal alignment length (hsp-length) is allowed. An alternative to querying unannotated sequences against databases of annotated sequences, is to query them instead against known recurring patterns of motifs known to be associated with particular functions. This is the so-called sequence motif-based methodology where an annotation system uses either the patterns, rules and profiles of PROSITE  are coded nearby in multiple genomes, (the gene neighborhood method); (ii) are components of an operon in prokaryotes, (the gene cluster method); (iii) can be fused into a single protein in some organisms, (the Rosetta stone method); (iv) are regularly both present or both absent within genomes, (the phylogenetic profiles method)  which are then used to assign GO term annotation to the previously unannotated sequences.Prediction and assignment of protein function is seldom done in a deterministic way. While some general functions can be assigned deterministically to sequences, as protein function specificity rises the uncertainty of predicting an exact assignment does also. Thus, following the identification of functional peers it is common for annotation systems to employ an additional stage where term selection and transfer occurs. A confidence measure is usually associated with these term transfers, which often derives directly from probabilistic features from either statistical or machine learning methods employed for term selection, or alternatively, arbitrary empirical confidence measures from rule-based term selection methods. The methodologies used at the annotation transfer stage can be roughly grouped into three types: rule-based transfer, statistical transfer and machine learning transfer. One example of a rule-based methodology for annotation transfer occurs in the previously mentioned Blast2GO annotation system. There, for each candidate GO term, the highest similarity weighted by their ECO is considered. In addition the level of abstraction is also considered through the use of a rule counting the total number of GO terms unified at a given node weighted by a user set factor that controls the possibility and strength of abstraction. In the end, the annotation rule will only transfer the lowest terms in each branch that surpass an user defined threshold  is the probability of annotation of term t. Consequently, terms annotating many proteins will score a low IC, while specific terms annotating only a few proteins will score an high IC. Additionally, the IC values can be normalized in order to provide a more intuitive meaning.In Equation 1 GO-based semantic similarity for proteins is given by the comparison of the sets of GO terms annotating each protein being compared within each GO ontology aspect. Two main approaches, pairwise and groupwise  can be quantitatively measured resorting to commonly used statistical tests for this effect, such as the Fisher exact test, the Chi-squared test, the Hypergeometric distribution and Binomial distribution.p-values which for SEA is done for each term in a list of pre-selected genes deemed of interest, whereas GSEA needs no pre-selection and has experimental values integrated directly into p-value calculation. On the other hand MEA is similar to SEA but additionally factors term-term and gene-gene relations into the p-value calculations. that describes the families of structurally-related catalytic and carbohydrate-binding modules  of enzymes that degrade, modify, or create glycosidic bonds  are a group of enzymes that cleave uronic acid-containing polysaccharide chains via a β-elimination mechanism to generate an unsaturated hexenuronic acid residue and a new reducing end. Within the CAZy database these enzymes are classified into families and subfamilies based on amino acid sequence similarities, intended to reflect their structural features  when all terms are shared by all proteins in a given family. It should be noted that when applying this metric to sets of families of multifunctional proteins misinterpretations can be made if the multiple functions are not evenly shared and annotated within the protein set or family being measured.Fundamentally, the GOscore metric is an indicator of the maximum lyase activity term. Considering their low IC (0.202) the functional information provided by these families is therefore also of little informative value.The incompleteness of annotation over a given protein space may lead to erroneous interpretations regarding functional coherence of that space. As mentioned previously we applied two annotation metrics, GOscore and GOoccurence to the PL families of the CAZy database. The results for both metrics are shown in Table oligogalacturonide lyase activity. Instead those 7 proteins are only annotated with lyase activity, an ancestor term of oligogalacturonide lyase activity. So, in this family, despite being mono-specific, it provides a clear case of annotation incompleteness that could lead to misinterpretations if we were to rely on coherence metrics alone. On the other hand, these annotations could be potentially extended to the oligogalacturonide lyase activity term. For instance, using all the proteins annotated to this term to create multiple sequence alignments, and subsequently creating position-specific scoring matrices, hidden Markov models or others statistical models these could be used to find matches on the 7 incompletly annotated proteins.In turn, family PL22, despite appearing to be mono-specific, nonetheless has a GOoccurrence score of 0.880. This is in fact due to the penalization inflicted by 7 out of 29 proteins annotated proteins not being annotated with the most specific term pectate lyase activity term. However, this otherwise coherent annotation is broken by 6 additional terms that annotate the family heterogeneously to a much lesser extent (only up to 2 proteins per term). Thus, here can be seen that the multi-functional nature of proteins can greatly affect the GOoccurrence metric. However, given the context of the PL enzyme class in which the PL3 family is inserted, if we were only to consider annotation terms that are children of lyase activity then we would obtain a considerable GOoccurrence improvement to a score of 0.798 (data not shown) for this particular family. The annotation terms that would be discarded, in this case, are clearly the product of secondary functional modules in the proteins that do not contribute to the global functional characterization of the family. Hence, their removal when accounting for family functional coherence is appropriate for this particular case. Regardless of any analitical assertion over their biological value, their low annotation count does not lend additional statistical support. That can be further confirmed through the use of enrichment analysis on this family and using the Benjamini-Yekutieli .Another example, the PL3 family, despite having a similar GOscore (0.593), conversely has a rather low GOoccurrence score (0.306). In addition, by looking at the distribution of annotation terms within this family we can discover that all of its 228 proteins are annotated to the molecular_function to which all the 43 sequences are annotated. It is important to notice that in the graph all indirect or inherited annotations are represented by unlabelled white nodes while direct annotations are represented by gray GO term-named nodes. It should also be noted that the direction of edges on the depicted graph in Figure is_a relations that hold between the molecular function aspect terms, and edge direction points from the outer leaf terms converging into a common root node, making the foundations of the true path rule that states that “the pathway from a child term all the way up to its top-level parent(s) must always be true”  is relatively low, therefore indicative of a differentially low informative value. According to the term enrichment corrected p-values, the term carbohydrate binding has the greater statistical significance . However, intuitively it can be seen that this term, despite being biologically relevant, does not provide a great information increment, since it has the third lowest IC value in Table carbon-oxygen lyase activity, acting on polysaccharides ranks second in terms of significance but its IC is also only slightly higher than the one for carbohydrate binding. It is actually the third ranked term for statistical significance, carboxypeptidase activity that has the greatest IC even though it is not even a descendant of lyase activity. Both calcium ion binding and catalytic activity fall below the previously chosen threshold of significance. The former can be explained by the fact that it has only one annotation occurrence, and is most likely not relevant for the PL4 family functional profile. The lack of significance of the latter term is explained by its ubiquitousness both within the CAZy families and the GOA annotation corpus which in turn also reflects itself as a low IC.InterPro . In order to avoid erroneous interpretations on heterogeneous protein sets or families (in terms of annotation specificity), functional comparisons are usually done at conservative levels. This means that by comparing families at conservative annotation levels we would also be comparing terms with lower Resources such as the CAZy database provide high-quality classifications of segments of the protein space into functionally related families. These kind of protein families present themselves as an opportunity and a knowledgebase from which we can benefit in order to provide annotation extension methodologies. Considering that any given protein family is a functionally related set of sequences, then the heterogeneity of annotation specificity can be explored within each family. Thus, sub-sets of homogeneous annotation in a family can be used to produce classifiers which can potentially extend other proteins within the same family that are under-annotated. This proposed methodology should be regarded as a generic approach guided at mitigating some of the current issues with annotation incompleteness, and despite not being suitable for all annotation incompleteness states it should allow for an increased extension of annotation over the ever increasing protein space. It should be particularly useful when applied in tandem with protein families from databases like CAZy where proteins despite being grouped together into functionally close families they still do not focus on functional annotation. It should be noted that the coherence metrics presented here are only to illustrate typical annotation baseline patterns and are not intended to be used in fully automated procedures or to address issues like the measuring of coherence in sets of multifunctional proteins on their own. However, customized metrics derived from groupwise semantic similarity measures can be implemented for each specific knowledge domain under study in order to automate most of the procedure in the suggested methodology.Portuguese Fundacão para a Ciência e Tecnologia (www.fct.pt/) through the financial support of the SPNet project (PTDC/EIA-EIA/119119/2010), the SOMER project (PTDC/EIA-EIA/119119/2010) and the PhD Grant ref. SFRH/BD/48035/2008 and through the LASIGE multi-annual support.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.