Currently there is a strong need for methods that help to obtain an accurate description of protein interfaces in order to be able to understand the principles that govern molecular recognition and protein function. Many of the recent efforts to computationally identify and characterize protein networks extract protein interaction information at atomic resolution from the PDB. However, they pay none or little attention to small protein ligands and solvent. They are key components and mediators of protein interactions and fundamental for a complete description of protein interfaces. Interactome profiling requires the development of computational tools to extract and analyze protein-protein, protein-ligand and detailed solvent interaction information from the PDB in an automatic and comparative fashion. Adding this information to the existing one on protein-protein interactions will allow us to better understand protein interaction networks and protein function.Structural Characterization Of Water, Ligands and Proteins) is a user-friendly and publicly accessible web-based relational database for detailed characterization and visualization of the PDB protein interfaces. The SCOWLP database includes proteins, peptidic-ligands and interface water molecules as descriptors of protein interfaces. It contains currently 74,907 protein interfaces and 2,093,976 residue-residue interactions formed by 60,664 structural units (protein domains and peptidic-ligands) and their interacting solvent.SCOWLP (The SCOWLP web-server allows detailed structural analysis and comparisons of protein interfaces at atomic level by text query of PDB codes and/or by navigating a SCOP-based tree. It includes a visualization tool to interactively display the interfaces and label interacting residues and interface solvent by atomic physicochemical properties. SCOWLP is automatically updated with every SCOP release..SCOWLP enriches substantially the description of protein interfaces by adding detailed interface information of peptidic-ligands and solvent to the existing protein-protein interaction databases. SCOWLP may be of interest to many structural bioinformaticians. It provides a platform for automatic global mapping of protein interfaces at atomic level, representing a useful tool for classification of protein interfaces, protein binding comparative studies, reconstruction of protein complexes and understanding protein networks. The web-server with the database and its additional summary tables used for our analysis are available at  One of the most interesting and important challenges in the so-called "Post-genomic Era" is the understanding of protein networks. Protein-protein interactions have been extensively investigated using a variety of methods , and manProtein interfaces have long been studied at protein chain and domain interface levels -12. FurtPeptidic-ligands and solvent mediate protein interactions and are fundamental components for a complete description of protein interfaces. Proteins can interact with peptides to perform their biological function. Besides, peptides have been used to mimic protein binding interfaces, and their complexes with proteins have been used to study protein binding affinity/specificity properties in a simplified way -25. For Most current methods do not provide an accurate description of protein interfaces, which is required to be able to establish the bases for understanding the principles that govern molecular recognition and protein function.Structural Characterization Of Water, Ligands and Proteins), a platform for complete and detailed characterization and visualization of protein interfaces. Our database includes all protein-interacting components of the PDB including peptides and solvent, which until now have been excluded from systematic protein interface analysis and databases. In our database all interface interactions are described at atom, residue and domain level by using interacting rules based on atomic physicochemical criteria. This complete characterization makes SCOWLP useful for comparative structural analysis of molecular interfaces. The web application allows the user to access all the atomic interaction information by querying the PDB or the SCOP hierarchy. All interface information characterized by different interaction descriptors can be interactively visualized by using a Jmol 3D applet [Here we present SCOWLP  for each structural unit of each PDB entry. Convex hull algorithms have been proved to reduce the computational time required for an interface calculation by both, reducing the search space to decrease the number of residues checked for the calculation and allowing distributed computations [Structural units with intersecting shapes and having at least one residue-residue interaction are considered interacting pairs , non water-mediated or direct (D), and mixed (M). Residues that only interact through water are defined as wet spots  nature: hydrophilic, hydrophobic; ii) contact type: main chain, side chain, mixed; iii) number of bridging water molecules. At residue level, we characterize the interactions by: i) nature: hydrophilic, hydrophobic, dual; ii) contact type: main chain, side chain, mixed; iii) number of bridging water molecules; iv) total number of atoms contacting. At structural unit level, we characterize the interactions by: i) contact volume; ii) surface area from convex hull surface; ii) number of interacting atoms/residues per unit; iv) type of interaction: intra-/inter-molecular. All interfacial interaction information is stored in the SCOWLP database  and SCOP Id code. All interfaces are also labelled by number of interactions  and number of interacting residues per binding partner. Each interaction is classified by type (side-/main-chain or both) and by number of bridging water molecules.Interface description table for interfacial solvent analysis. This table does not include homodimer interfaces because of their patchy, poorly packed and highly hydrated nature [This table stores interfaces of complexes at resolution ≤ 2.5 Å from the d nature . With thThis table can be used to rank superfamilies based on their content in water mediating interface interactions. For each interface, it contains the average of total interactions, all water-mediated interactions and the ratio from the percentage of water-mediated interactions at superfamily level.wet spots. In this table each family is represented by the complex with the highest number of wet spots, labelled with the total number of interacting residues and wet spots.This table can be used to rank the interfaces by number of wet spots. When the same PDB file contains a repeated interface of two binding partners, we select as a representative the one with more wet spots.This table can be used to monitor solvent variations in interfaces and compare them at family level. It contains interfaces sorted out by domain, and then by their respective ligands (protein or peptide). Because a protein-ligand interface can be found in different PDBs, we select the interfaces that appear more than once and contain We used MySQL and the Java programming language to generate and analyze the SCOWLP database. Interface calculations are performed on a 2.6 GHz Pentium IV in approximately 36 hours. SCOWLP is automatically updated with every SCOP release.SCOWLP database contains detailed information of protein interfaces including peptidic-ligands and solvent in the PDBs, and classifies protein interfaces by using specific physicochemical atomic criteria. The database can be accessed through a user-friendly web application.structural unit interactions we obtain by applying our interaction rules is reduced in comparison to PSIMAP . Besides, the superfamilies that have the higher occurrence of peptides are not necessarily those with higher domain-domain representation . By taking into account information about protein-peptide complexes SCOWLP contributes interfacial information of 8 SCOP superfamilies uniquely represented by protein-peptide complexes . In addition, it contributes with more than 50% of the interacting information in other superfamilies. Our results show the importance of including protein-peptide interfacial information in order to enrich considerably the description of protein interfaces.Poxvirus and Zinc finger) family. The twelve BTB/POZ complexes in the PDB present five domain-binding regions, two of them described by the protein-peptide complexes . Relating to the "only water-mediated" interactions, we observe from the Morphology table that 43 is the maximum number of wet spots found. Figures wet spots, may play an important role in the morphological description of protein interfaces (shape and size). Considering the solvent, a discontinuous surface formed by several small isolated patches changes to a bigger and rounded patch. These observations show that we can enrich the description of protein interfaces by considering interfacial solvent.All superfamiles of the Wet spots variations may be used as indicators in these cases. The Comparative table allows us to compare the interfaces of 127 families in 751 complexes based on wet spots variations.Although solvent molecules mediating protein interactions can be conserved in a protein family, variations may occur due to different facts: i) atomic resolution and/or quality of the structural data, ii) conformational changes upon ligand binding, iii) protein flexibility, iv) new interacting regions (e.g. loop insertions and deletions), v) residue mimicry. wet spots. Nevertheless, small variations of wet spots in complexes of the same family that do not present changes in total number of interactions can be used to locate residue mimicry cases (e.g. Lys+H2O≈Arg). Making use of this information may be very useful in analysis of protein interfacial evolution and in protein engineering/rational design when designing affinity and specificity of a protein for its ligands.Solvent molecules play an important role in the replacement of residues in protein interfaces. Sometimes the atomic resolution, the existence of different rotamers or even small differences in contact distances defining the interaction may influence the number of wet spots. This summary table gives a good overview over the interacting partners and interfacial variations at family level. By selecting any of the PDB IDs in this table, the user retrieves a list of all the interfaces of that PDB organized in two interactive tables: Interfaces and Interactions. We obtain the same tables querying SCOWLP by PDB ID  controls the interface display in the 3D viewer, allowing the user to highlight the residues forming part of each interface. The second panel .SCOWLP contains atomic interfacial information of all the PDB entries structured by the SCOP hierarchy. There are two ways to query our database: SCOP or PDB. The user can query SCOP by keywords, SCOP/PDB Ids, or by simply navigating the SCOP hierarchical tree Fig . When thB ID Fig . The "In scripts  to allowBy using SCOWLP, the user can achieve specific queries, SCOP family analysis, interface comparisons and a detailed 3D display of the atomic interaction data contained in PDBs.Detailed analysis of the interfacial information contained in the PDB is very useful to obtain more accurate descriptions of protein interfaces. We have created SCOWLP to have a platform for the characterization and 3D visualization of protein interfaces. SCOWLP enlarges the available information on protein-protein interactions by introducing 3,413 new protein-peptide interfaces and 435,086 additional water-mediated interactions. All interactions contained in SCOWLP are characterized and classified at physicochemical level instead of using general distance criteria. This allows a more appropriate definition and enhanced comparison of the interfaces contained in our database.wet spots can be used as indicators of interfacial solvent variations, being helpful in comparison of protein family interfaces, and perhaps guiding docking experiments.As the origin of specificity and affinity in molecular recognition can be partially explained in terms of solvent's contribution to the interaction, our database constitutes a very useful tool to facilitate rational ligand design. In particular SCOWLP may be of interest to many structural bioinformaticians, representing a useful tool for classification of protein interfaces, protein binding comparative studies, reconstruction of protein complexes and understanding protein networks.. The database and all summary tables used in this paper can be freely downloaded for independent studies.SCOWLP is available at JT developed the new methodology to create the SCOWLP database. JT and AD designed the web application. MS provided the PSIMAP software code. MTP coordinated and supervised the project.


The characterization of protein interactions is essential for understanding biological systems. While genome-scale methods are available for identifying interacting proteins, they do not pinpoint the interacting motifs . Here, we develop and apply a method for delineating the interacting motifs of hub proteins . The method relies on the observation that proteins with common interaction partners tend to interact with these partners through a common interacting motif. The sole input for the method are binary protein interactions; neither sequence nor structure information is needed. The approach is evaluated by comparing the inferred interacting motifs with domain families defined for 368 proteins in the Structural Classification of Proteins (SCOP). The positive predictive value of the method for detecting proteins with common SCOP families is 75% at sensitivity of 10%. Most of the inferred interacting motifs were significantly associated with sequence patterns, which could be responsible for the common interactions. We find that yeast hubs with multiple interacting motifs are more likely to be essential than hubs with one or two interacting motifs, thus rationalizing the previously observed correlation between essentiality and the number of interacting partners of a protein. We also find that yeast hubs with multiple interacting motifs evolve slower than the average protein, contrary to the hubs with one or two interacting motifs. The proposed method will help us discover unknown interacting motifs and provide biological insights about protein hubs and their roles in interaction networks. Recent advances in experimental methods have produced a deluge of protein–protein interactions data. However, these methods do not supply information on which specific protein regions are physically in contact during the interactions. Identifying these regions (interfaces) is fundamental for scientific disciplines that require detailed characterizations of protein interactions. In this work, we present a computational method that identifies groups of proteins with similar interfaces. This is achieved by relying on the observation that proteins with common interaction partners tend to interact through similar interfaces. The proposed method retrieves protein interactions from public data repositories and groups proteins that share a sensible number of interacting partners. Proteins within the same group are then labeled with the same “interacting motif” identifier (iMotif). The evaluation performed using known protein domains and structural binding sites suggests that the method is better suited for proteins with multiple interacting partners (hubs). Using yeast data, we show that the cellular essentiality of a gene better correlates with the number of interacting motifs than with the absolute number of interactions. Protein–protein interactions play a central role in many cellular processes, ranging from signal transduction to formation of cellular macrostructures and cell cycle control –3. RecenProteins interact through a limited set of interface types ,10,11. TTraditionally, the description of protein interactions in terms of the interacting components has been based on protein structural domains , proteinRecently, several methods –25 have Here, our basic assumption is that proteins with overlapping sets of interacting partners tend to interact with the common partners through the same interacting motif, such as a domain, sequence segments, a binding site, or a set of residues. A similar assumption has been previously used to annotate protein sequences ,39–41. WTwo main objectives have been addressed in this work. The first objective was to demonstrate whether protein interactions alone can be used to infer interacting motifs. The positive predictive value of our method in detecting proteins with common SCOP families was 75% at sensitivity of 10%, and the Spearman correlation coefficient between the number of iMotifs assigned to proteins and the number of interfaces found by Kim et al.  was 0.57The basic assumption behind this work is that proteins with overlapping sets of interaction partners tend to interact with those partners through a common interacting motif. The validity of this assumption was tested on a nonredundant set of 368 proteins with known SCOP domains . Although SCOP does not classify proteins by their interfaces, SCOP domains were used as surrogates for iMotifs because protein interaction types can be defined by the domains in the interacting proteins .N) to be a good indicator of the probability of two proteins having a domain in the same SCOP family, especially for highly connected proteins . However, the likelihood of two proteins sharing a SCOP domain was lower by solely using yeast two-hybrid experimental data, a detection method that is more likely to contain binary protein interactions than other experimental methods  Figure B.N). Assigning an iMotif to a group of proteins simply establishes that they have a certain feature that allows them to interact with the same set of partners, without determining the size, sequence, or structure of that feature . Using an  of ∼20% . The posome data ,48 is liDomain–domain interactions can be predicted from the iMotif–iMotif interactions found by the method . We evaluated the accuracy of these predictions with respect to domain interactions in the PDB. Our method achieves a positive predictive value of ∼65% for ∼5% of the proteins in the test set , suggestN of 20, sr is 0.57 and p-value 0.01). The number of iMotifs assigned to proteins by our method tends to be higher than the number of binding sites defined by Kim et al. This might be attributed to two factors: (i) current structural data do not contain all possible protein–protein interactions, resulting in an underestimation of the number of binding sites assigned by the method in [Kim et al. used protein 3-D structures and binary protein interactions to make inferences about the number of binding interfaces of proteins . We testethod in , and , resulting on average in 8.6 iMotifs per hub. The percentage of hubs with one or two iMotifs was 46% . We studied the correlation between the number of iMotifs assigned to a hub and its number of interactions, finding no relationship between the two variables . A complete list of iMotif assignments for all hub proteins in PIANA is in Using an sr is 0.61 and p-value is 1.64 × 10−5) between the number of iMotifs in yeast hubs and the fraction of essential proteins .A common measure of evolutionary rate is the dN/dS ratio (the ratio of nonsynonymous to synonymous substitutions) . Kim et p-value) of a sequence pattern assigned to an iMotif depends on the occurrence of the pattern in the iMotif with respect the whole dataset. As shown on p-value cutoff of 10−8). A list with the best sequence pattern for each iMotif is provided in p-value cutoff of 10−8). Such a difference can be explained by the fact that many interactions are carried out by short sequence patches [Sequence patterns for each iMotif were generated using the PRATT program  .As indicated above, incompleteness in interaction data may result in artificially high numbers of iMotifs. This overestimation can be reduced by merging iMotifs with a common sequence pattern . Fusing iMotifs based on sequence pattern similarity decreased the average number of iMotifs per hub from 8.6 to 4.2. This reduction, in turn, increased the correlation between the number of binding sites from Kim et al.  and the We described, implemented, and evaluated a method that relies solely on binary protein interactions to identify interacting motifs (iMotifs) and their interactions. Our approach obtained high positive predictive value for identifying proteins with domains from the same SCOP family and predicting domain–domain interactions. We also analyzed hub proteins and their properties based on the number of iMotif assigned to them, obtaining similar findings to those in an independent approach that rely on protein structure information .N) will assign few iMotifs to reduced sets of proteins . On the other hand, using low N thresholds will assign the same iMotif to broad numbers of proteins . We showed that the method works better for highly connected proteins and using high values for N. Moreover, our approach is not limited to finding predefined classes of protein components and thus allows us to predict new types of interacting motifs. For example, an iMotif can be mapped to a predefined class  by examining the known classes assigned to proteins with that iMotif. Therefore, iMotifs that remain unmapped are likely candidates for unknown classes. Such predictions may prove useful for target selection in structural genomics.Recent estimates suggested that only one-fifth of interaction types are known . TherefoRelying solely on experimentally detected interactions affects the accuracy of our method. It has been shown that high-throughput experiments have limited reliability and that many of the detected interactions are probably not direct  or do not even exist  . HoweverThe combination of iMotif assignments with sequence search methods identified specific sequence patterns in iMotifs. We found that most iMotifs had a significant sequence pattern that was contained in most of the iMotif proteins. These patterns, which could be responsible for the iMotif proteins common interactions, could then be used to: (i) localize the iMotif in the protein sequence, (ii) assign iMotifs to proteins for which no interaction data is yet available, and (iii) predict interactions between proteins that contain patterns assigned to two interacting iMotifs., we observe different properties between hubs with multiple iMotifs (multi-iMotif) and hubs with one or two iMotifs (singlish-iMotif). In particular, we find that (i) multi-iMotif hubs are more likely than singlish-iMotif hubs to be essential for cell viability, and (ii) multi-iMotif hubs, in contrast to singlish-iMotif hubs, evolve slower than the average protein. Furthermore, we have also observed a correlation between the number of iMotifs of a hub and its essentiality for cell survival. The properties observed for hubs with respect to their number of iMotifs may reflect the difference between proteins with multiple simultaneously possible interactions (multi-iMotif hubs are probably involved in permanent complexes) and proteins with multiple exclusive interactions (for singlish-iMotif hubs involved in transient interactions). This is in agreement with the previous observation that interfaces of transient protein–protein interactions are less restricted in evolution than interfaces in permanent complexes [Our iMotif assignments are similar to those obtained using an independent approach, which relies not only on known protein–protein interactions, but also on protein structure information . In agreomplexes .Our results extend the findings and conclusions of Kim and co-workers  to protejp  is named “partners of jp”. In such a network, we define hubs as proteins with 20 or more partners. The average number of interactions per hub in our dataset was ∼49. PIANA builds the protein interaction network by retrieving partners for an initial set of proteins. To avoid a positive bias in the method evaluation, interactions inferred from 3-D structures were not used in this work.Protein–protein interactions from DIP 2006.01.16 , MIPS 20Protein domain assignments and classification were obtained from the SCOP release 1.69 . Here, dSaccharomyces Genome Deletion Project [A list of ORFs essential for the survival of the yeast cell was obtained from the  Project . The evo Project .The assignment of iMotifs to a set of proteins is carried out in a four-step procedure :First, build the protein interaction network.jp) interacts with those clusters that contain a partner of jp in the protein interaction network.Second, initialize a cluster interaction network  by assigning each protein of the protein interaction network to a different cluster. Each cluster (containing one protein N). Thus, the similarity score between two clusters is their number of common partners in the cluster interaction network. Other similarity metrics were considered, but none outperformed the use of N , and that iMotif is assigned to all proteins within that cluster. iMotif–iMotif interactions are then derived from interactions in the cluster interaction network where both sides of the interaction have been labeled with an iMotif identifier.N = 2 common interactions). Thus, the iterative process will run for another iteration creating a new cluster (cluster 8) by fusing clusters 2, 3, and 4, which have two common interactions (step 3). Once the iterative process is finished, the method assigns iMotif identifiers to all proteins in clusters with more than one protein  (step 4). Moreover, iMotif–iMotif interactions are then derived from the cluster interaction network .For example , a prote−5, and the alignment had at least 30 residues, the shortest member of the pair was not selected). The final set contained 368 sequences  with at least five experimentally detected interactions, (ii) with at least 80% of their sequence covered by the domains defined in SCOP, and (iii) that did not introduce a redundancy bias in the evaluation  and their SCOP families were considered when counting positives and negatives. The positive predictive value is defined as the number of true positives over the total number of positives, and sensitivity is the number of true positives over the sum of true positives and false negatives. The positive predictive value and sensitivity were calculated with respect to the similarity score threshold used for stopping the clustering. We also define the applicability of the method as the percentage of proteins with at least one positive under a given threshold.For each group of protein sequences with a given iMotif, sequence signatures were generated using the PRATT program , a softwp-value) of the association between a sequence pattern and an iMotif was assessed using the binomial distribution, based on the occurrence of the pattern inside the iMotif with respect the whole dataset [M is the number of protein sequences within an iMotif, n represents the number of proteins within the iMotif that contain the sequence pattern, and p is the probability of finding a protein from the whole dataset that contains the same pattern .The significance .Hidden Markov Models from the Pfam-A database  were asssr). The assessment of whether two binomial samples of essentiality observations are significantly different was calculated using Fisher's test. The assessment of whether two non-Gaussian samples of evolutionary rate observations come from the same distribution was calculated using the Mann-Whitney U two-sided test. Correlations and differences in the observations were considered significant for p-values lower than 0.05. All tests were performed using the implementation provided by R [All correlations were measured using the Spearman rank correlation coefficient  N: the number of interaction partners that are common to ip and jp (long dashed line); 2) maxR: the ratio between N and the number of partners of the protein with more partners (bold line); 3) minR: the ratio between N and the number of partners of the protein with fewer partners (circles); 4) aveR: the average of metrics maxR and minR (dotted line). For each score obtained using the similarity metrics described above, the percentage of protein pairs within that score range is plotted. For example, we observed that using N as the similarity metric, 73% of proteins with 50–60 common interaction partners shared a SCOP domain.To measure the likelihood of two proteins (94 KB TIF)Click here for additional data file.Figure S2(A) Proteins that have more than 70 interactions are ignored when performing the analysis.(B) Only interactions from y2h are used.(94 KB TIF)Click here for additional data file.Figure S3(A) Superposition of the prothrombin and the pancreatic trypsin inhibitor structures (PDB IDs 1BTH and 2HPQ) shows an interaction through the SCOP family domain Eukaryotic proteases (in red).(B) The structure of the anionic trypsin II interaction with the pancreatic trypsin inhibitor (PDB ID 1BRB) also shows an interaction through the SCOP family domain Eukaryotic proteases (in red).(233 KB TIF)Click here for additional data file.Figure S4The positive predictive value, sensitivity, and applicability are plotted as a function of the number of common interacting partners threshold used for the clustering. The positive predictive value and sensitivity using a trivial approach are also shown (thin lines). The applicability for the trivial approach is ∼70%. Sensitivity is highly dependent on the group of proteins for which an iMotif–iMotif interaction can be predicted at a given threshold: if a protein with a prediction has a SCOP code with multiple interactions in the PDB, the sensitivity obtained can vary greatly from one threshold to another. Moreover, we compared our method with the trivial approach of creating putative lists of domain–domain interactions by assuming that all domain families of proteins in the test set interact with all domain families of their interaction partners. The positive predictive value for this trivial approach was 33%, which is below that of our method for thresholds higher than 15.(93 KB TIF)Click here for additional data file.Figure S5p-value > 0.05) for N thresholds higher than 22.Due to the limited number of iMotif assignments with stringent N thresholds, correlations become nonsignificant Click here for additional data file.Figure S6Three different significance cutoffs were used for finding Pfams associated with iMotifs: 10–5 (long dashed line), 10–8 (plain line), and 10–10 (short dashed line).(87 KB TIF)Click here for additional data file.Figure S7The procedure followed to remove redundancy was the same as the one used for creating the evaluation set. We observe a significant improvement for all metrics with respect to (95 KB TIF)Click here for additional data file.Figure S8(345 KB TIF)Click here for additional data file.Table S1In parentheses, the number of pairs with at least one domain within the same SCOP family is indicated. We observe that metrics such as Rmin outperform N at detecting a higher number of protein pairs with a domain within the same SCOP family, but this is done at the expense of decreasing the accuracy of the method.(63 KB PDF)Click here for additional data file.Table S2The number of common interaction partners (N) was set to 15. First column is the iMotif identifier. Second column is the number of proteins within the iMotif. Subsequent colums are the proteins within the iMotif. Proteins are identified using UniProt entry names and NCBI GI identifiers. In parentheses, “yes”, “no”, and “-” indicate whether the proteins had a domain within the same SCOP family.(251 KB TXT)Click here for additional data file.Table S3The number of common interaction partners (N) was set to 20. See legend on (2.4 MB TXT)Click here for additional data file.Table S4(38 KB TXT)Click here for additional data file.Table S5p-value of the pattern.First column is the iMotif identifier. Second column is the pattern in PROSITE format. Third column is the fraction of proteins within the iMotif that have the pattern. Fourth column is the (704 KB TXT)Click here for additional data file.Table S6Click here for additional data file.Proteins from the Test Set, using UniProt Accession Numbers(68 KB PDF)


The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for functional prediction. Knowledge of catalytic sites provides a valuable insight into protein function. Although many computational methods have been developed to predict catalytic residues and active sites, their accuracy remains low, with a significant number of false positives. In this paper, we present a novel method for the prediction of catalytic sites, using a carefully selected, supervised machine learning algorithm coupled with an optimal discriminative set of protein sequence conservation and structural properties.To determine the best machine learning algorithm, 26 classifiers in the WEKA software package were compared using a benchmarking dataset of 79 enzymes with 254 catalytic residues in a 10-fold cross-validation analysis. Each residue of the dataset was represented by a set of 24 residue properties previously shown to be of functional relevance, as well as a label {+1/-1} to indicate catalytic/non-catalytic residue. The best-performing algorithm was the Sequential Minimal Optimization (SMO) algorithm, which is a Support Vector Machine (SVM). The Wrapper Subset Selection algorithm further selected seven of the 24 attributes as an optimal subset of residue properties, with sequence conservation, catalytic propensities of amino acids, and relative position on protein surface being the most important features.The SMO algorithm with 7 selected attributes correctly predicted 228 of the 254 catalytic residues, with an overall predictive accuracy of more than 86%. Missing only 10.2% of the catalytic residues, the method captures the fundamental features of catalytic residues and can be used as a "catalytic residue filter" to facilitate experimental identification of catalytic residues for proteins with known structure but unknown function. The high-throughput genome projects have resulted in a rapid accumulation of predicted protein sequences for a large number of organisms. Researchers have begun to systematically tackle protein functions and complex regulatory processes by studying organisms on a global scale, from genomes and proteomes to metabolomes and interactomes. Meanwhile, structural genomics projects have generated a growing number of protein structures of unknown function. To fully realize the value of these high-throughput data requires better understanding of protein function. With experimentally-verified information on protein function lagging behind, computational methods are needed for functional prediction of proteins. In particular, knowledge of the location of catalytic residues provides valuable insight into the mechanisms of enzyme-catalyzed reactions.Many computational methods have been developed for predicting protein functions and functional residues involved in catalytic reactions, binding activities, and protein-protein interactions. Automated propagation of functional annotation from a protein with known function to homologous proteins is a well-established method for the assignment of protein function. However, reliable functional propagation generally requires a high degree of sequence similarity. For example, to transfer all four digits of an EC number at an error rate of below 10% needs at least 60% sequence identity .1L]; in another study  were compared using their default parameters and a benchmarking dataset of 79 enzymes with 254 catalytic residues. The performance of the algorithms was measured by the Matthews correlation coefficients (MCC) in a 10-fold cross-validation analysis using three balanced datasets generated from the benchmarking data, each with an equal number of non-catalytic residues randomly chosen from all non-catalytic residues of the benchmarking dataset. Each residue was represented by a set of 24 sequence and structural attributes and a label of {+1/-1} to indicate whether the residue is catalytic (+1) or not (-1).To determine the best machine learning algorithm for the predictive task, 26 classifiers currently available in the WEKA software package  werSequential Minimal Optimization (SMO) algorithm , which is a Support Vector Machine (SVM) , which consisted of 615 manually-curated catalytic residues from 178 enzymes [4L] was used as the benchmarking data, which included 79 enzymes and 254 catalytic residues. Protein members in PIRSF families are homologous (sharing common ancestry) and homeomorphic (sharing full-length sequence similarity with common domain architecture).The benchmarking dataset was compiled from the CATRES  database  was5L], enzyme classification (EC number) [6L], and BLAST sequence similarity , enmilarity . The folThe 79 proteins (identified by the PDB code) were: 1a26, 1a4i, 1a4s, 1ab8, 1ae7, 1afw, 1ah7, 1akm, 1aop, 1apx, 1apy, 1aq2, 1aw8, lb3r, lb57, lb93, 1bo1, 1brm, 1bs4, 1btl, 1bzy, 1cd5, 1chd, 1ctt, 1d4a, 1daa, 1dae, 1db3, 1dbt, 1dco, 1diz, 1dj0, 1dnk, 1dnp, 1dqs, 1dzr, 1e2a, 1ef8, 1eyi, 1fua, 1gim, 1gpm, 1gpr, 1grc, 1hxq, 1iph, 1jdw, 1kas, 1kra, 1lba, 11xa, 1mbb, 1mek, 1mla, 1moq, 1mpy, 1nba, 1nsp, 1pfk, 1pjb, 1pnl, 1pud, 1qfe, 1smn, 1uae, 1ula, 1uok, 1uox, 1wgi, 1xva, 2acy, 2alr, 2bbk, 2cpo, 2hgs, 2jcw, 2pfl, 2plc, 3eca.For the initial analysis, each residue of the benchmarking dataset was represented as a vector with 24 residue property values and a label {+1/-1} to indicate the catalytic (+1) and non-catalytic (-1) residue. The list of properties was chosen based mostly on the work of Bartlett et al.  and otheAmino acids have different propensities to be catalytic . These pAA_identity: A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y- AA_type: The amino acids are grouped based on their chemico-physical properties into three types: charged , polar , and hydrophobic   for biologically active chain from PDB. Since atoms of the same residue can be in different clefts, the cleft number is the largest cleft for a given residue. CASTp numeration of the clefts starts with the smallest one first, so we reversed the numbering so that the largest cleft of the protein would be the first cleft of the protein. If a residue was not part of any cleft, the cleft number was assigned zero. The attributes include:Enzyme active sites are usually located in large and deep protein clefts . It was nearest_cleft_rank, nearest_cleft_SA_volume, nearest_cleft_SA_area: the three attributes for rank, solvent accessible volume and solvent accessible area are obtained directly from the output of the CASTp server.- nearest_cleft_distance: The distance to the nearest cleft was calculated as a minimal distance between any atom (except hydrogen) of the residue and any atom (except hydrogen) of the residues of the closest cleft. If a residue was in a cleft, the distance was assigned zero.- distance_to_3_largest_clefts was calculated as a minimal distance between any atom (except hydrogens) of the residue and any atom (except hydrogens) of the residues of the 3 largest clefts. If a residue was a part of the 3 largest clefts, the distance was assigned zero.- The majority of catalytic residues participate in hydrogen bonding through their main chain . Two attHB_main_chain_protein, HB_side_chain_protein: number of hydrogen bonds of the residue atoms from the main chain or side chain, respectively, with any other atom in the protein.- It was observed that about half of catalytic residues are localized in the coiled regions of the protein . The att2D_structure: the 2D structure of individual residues was based on the DSSP program, which assigns a single letter code  to represent different 2D structure types  with an open source issued under the GNU General Public License. The package provides a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from the user's own Java code. WEKA contains tools for data pre-processing, classification, regression, clustering, association rules and visualization, and is well suited for developing new machine learning schemes. In this study, all algorithms were trained using WEKA's default settings, except in the IBK algorithm where the parameter K was chosen to be 13 to maximize the algorithm's performance.The selections of the best-performing algorithm and an optimal set of properties for the selected algorithm were performed using WEKA . WEKA is a JAVA software package from the University of Waikato, New Zealand  witThe WEKA's implementation of SMO converts all nominal attributes into binary ones and normalizes all attributes by default. We used the default polynomial kernel function for the analysis with default parameters, such as the complexity parameter C = 1.0, exponent = 1.0.The performance of each algorithm was measured as an average value in a 10-fold cross-validation analysis, where each dataset was divided into 10 parts – 9 parts for model learning (training) and the remaining part for validation (testing). Four performance measures were used: Matthews Correlation Coefficient (MCC) , true poWhere Tp, Fp, Tn, Fn, P, N, The FP rate and TP rate can be used for comparison of the results with different positive-to-negative ratios, whereas accuracy and MCC are sensitive to dataset imbalance.2D, secondary; Å, angstrom; AA, amino acid; ABS, ABSolute; ADTree, Alternating Decision Tree; CASTp, Computed Atlas of Surface Topography of proteins; CDD, Conserved Domain Database; DSSP, Database of Secondary Structure in Proteins; EC, Enzyme Commission; ET, Evolutionary Trace; Fn (FN), False negatives; Fp (FP), False positives; HB, Hydrogen Bond; IB1, nearest-neighbor classifier; IBK, K-nearest neighbors classifier; J48, a pruned C4.5 decision tree; Jrip, a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER); LMT, Logistic Model Trees; LWL, Locally Weighted Learning algorithm; MCC, Matthews Correlation Coefficient; MolMol, MOLecule analysis and MOLecule display; NBTree, decision Tree with Naive Bayes classifiers at the leaves; NN, neural network; NNge, Nearest-Neighbor-like algorithm using non-nested generalized exemplars; OneR, One-Rule classifier; PART, a partial C4.5 decision tree algorithm; PDB, Protein Data Bank; PIR, Protein Information Resources Database; RBFNetwork, a normalized Gaussian radial basis function network; REL, relevant; REPTree, a decision tree learner; Ridor, RIpple-DOwn Rule learner; SA, solvent accessible; SAS, Solvent Accessible Surface; Scorecons, Score Conservation; SMO, Sequential Minimal Optimization; SVM, Support Vector Machine; Tn (TN), True negative; Tp (TP), True positive; WEKA, Waikato Environment for Knowledge Analysis.NP designed the analysis, developed the source code, conducted the study, and wrote the manuscript. CW coordinated the study, helped drafting the manuscript, and critically revised its content. All authors read and approve of the final manuscript.The Protein Data Bank (PDB)1L. Catalytic Residue Dataset database (CATRES)2L. Waikato Environment for Knowledge Analysis (WEKA)3L. PIRSF protein family database4L. Structural Classification of Proteins (SCOP)5L. Enzyme Nomenclature (EC)6L. The Scorecons server7L. The CASTp server, Computed Atlas of Surface Topography of proteins8L.


Protein-protein interactions have traditionally been studied on a small scale, using classical biochemical methods to investigate the proteins of interest. More recently large-scale methods, such as two-hybrid screens, have been utilised to survey extensive portions of genomes. Current high-throughput approaches have a relatively high rate of errors, whereas in-depth biochemical studies are too expensive and time-consuming to be practical for extensive studies. As a result, there are gaps in our knowledge of many key biological networks, for which computational approaches are particularly suitable.We constructed networks, or 'interactomes', of putative protein-protein interactions in the rat proteome – the rat being an organism extensively used for cancer studies. This was achieved by integrating experimental protein-protein interaction data from many species and translating this data into the reference frame of the rat. The putative rat protein interactions were given confidence scores based on their homology to proteins that have been experimentally observed to interact. The confidence score was furthermore weighted according to the extent of the experimental evidence, giving a higher weight to more frequently observed interactions. The scoring function was subsequently validated and networks constructed around key proteins, identified as being highly up- or down-regulated in rat cell lines of high metastatic potential. Using clustering methods on the networks, we have identified key protein communities involved in cancer metastasis.The protein network generation and subsequent network analysis used here, were shown to be useful for highlighting key proteins involved in metastasis. This approach, in conjunction with microarray expression data, can be extended to other species, thereby suggesting possible pathways around proteins of interest. Microarray experiments provide information about gene expression within the cells under study.Expression patterns can be uncovered from large-scale microarray data by systematically grouping genes with the help of clustering methods. Co-clustering of genes can indicate that the genes in question have a similar function or that they participate in the same cellular process ,2. NeverProtein network analysis is dependent on a reliable assignment of protein-protein interactions. Protein-protein interactions are commonly studied using biochemical methods, and several different experimental methods are currently in use. Two-hybrid screens have, to date, yielded the bulk of available data ,4; howevA number of computational methods, either based on sequence or structural features, have been developed to complement experimental approaches to predicting protein-protein interactions ,10. An iet al. [et al. [Alternative techniques to network construction have also been taken, see e.g. Cabusora et al. , where a [et al.  who conset al. [et al. [Previous studies have mapped expression data of different systems onto experimentally-based networks. Ideker et al.  used gen [et al.  made use [et al. , who comHere we have taken an extensive multi-genome approach, utilising a homology-based method for predicting interacting proteins  and furtIn order to demonstrate the utility of our predicted interactions, expression data on tumour progression resulting in rat sarcomas with high metastatic potential were mapped onto our interactome, creating protein networks around key proteins involved in the metastatic process.Rattus norvegicus) genome using the approach described in the methods section and summarised in Figure Networks of interacting proteins were constructed automatically for the entire rat , indicating that true interactions score higher than those whose association has not been confirmed by crystallography.We found that highly reliable interactions, identified by X-ray crystallography, score higher than those without crystallographic evidence, with median scores 128 and 16 respectively. This difference was significant according to a Moreover, as shown in Table n = 2038) and the average score for interactions between communities was 13.5 (n = 502). This is significant at a 95% confidence level (p = 3.1 × 10-30).Another way of estimating the quality of the scoring function is to look at proteins participating in the same cellular process and compare them with proteins that are not thought to interact directly in a pathway. We used a clique percolation method to identify 'communities' within the network that show high interconnectivity. This yielded 37 communities of tightly interconnected proteins that will be described later. One can assume that interactions within communities are more likely to be true than interactions between communities, i.e. higher scores would be expected for intra-community interactions . We founp = 0.001 at a 95% confidence level).Lastly, the protein interaction scores were examined in the context of cellular localisation. We assume that for true interactions, interacting proteins would co-localise in the same cellular compartment, at least during the time of interaction, and thus would expect predicted interactions between proteins in separate cellular compartments to be less reliable and receive a lower score. Localisation data from the Gene Ontology Consortium  were useMetastasis is a key event that is associated with a poor prognosis in cancer patients. Metastasising cancer cells have the ability to break away from the primary tumour and move to different organs, making the cancer more difficult to treat. Much is unknown about the molecular biology of metastasis, but it culminates in the cancerous cells acquiring several properties, such as increased motility and invasiveness. This involves a network of cascading protein-protein interactions which have to be unravelled if an effective treatment is to be developed.As a starting point, we used data from a microarray analysis of cell lines with different metastatic potentials (see Methods). We took the highest up- and down-regulated genes (≥ 4-fold up- or down-expression), and constructed networks around these, extending two generations from the starting point, i.e. initially including proteins that interact directly with the originating protein and then going on to include the proteins that interact with them. This subset of the rat interactome contained 10,628 interactions. We then performed a cluster analysis in order to highlight areas in the protein networks that are involved in the metastatic process. The clustering is based on a clique percolation method  that seeet al. [The community definition is based on the observation that a typical member in a community is linked to many, but not necessarily all other nodes in the community. In other words, a community can be regarded as a union of smaller, complete, fully-connected subgraphs that share nodes (see Methods section). Palla et al.  have shoet al. . Highly et al. .By applying the clustering method to our rat interactome we automatically identified 37 protein communities of highly interconnected proteins, containing 313 proteins involved in 1,094 interactions  and the receptor (Kdr), which play a principle role in tumour progression and angiogenesis  and whicThe figure shows the three ligands, Vegfa and Vegfc and Figf, at different levels of expression, all of which can bind to kinase insert domain protein receptor Kdr, a VEGF receptor, which in turn induces mitogenesis and differentation of vascular endothelial cells .The interaction between Kdr and Socs1, an SH2 domain-containing suppressor of cytokine signaling 1, is plausible as Kdr has a tyrosine protein kinase domain which in a mouse homologue has been shown to interact with Socs1 . FurtherThe proposed Ptpn11-Lck interaction is based on orthology to an interaction between Ptprc and Lck in mouse. Ptpn11 and Ptprc both have tyrosine specific protein phosphatase activity. Ptpn11 is phosphorylated by tyrosine protein kinases, contains two SH2 domains and therefore could be phosphorylated by Lck.Higher up the network are the insulin-like growth factor 1 and its receptor  which are highly implicated in different cancers -41. The Drosophila melanogaster [Another distinguishing feature in the network is the highly down-regulated protein tyrosine phosphatase (Ptpn13). It has been reported that a protein tyrosine phosphatase, Ptp61F, negatively regulates the JAK/STAT pathway in nogaster . Our netThe few examples shown here illustrate the value of the approach in terms of revealing potential pathways and interactions that play a part in cancer metastasis, but further experimental work will be needed to confirm the validity of these predictions.Extracting meaningful information from microarray expression data is often difficult, especially when looking at a complex process involving a large number of genes and unknown mechanisms. Clustering of genes may be of use when trying to find genes in a common pathway and genes with related function, but it often has limitations, such as in identifying negative feedback loops . FurtherExamining the distribution of up- and down-regulated proteins in the context of their neighbours, shows that this is indeed the case for the protein networks shown in Figure Expression data has previously been put into a network context, for example by incorporating gene ontology data  and protAlthough we believe the general approach is of value in protein network analysis, there remain some shortcomings. Most importantly, transient protein-protein interactions are unlikely to be captured by our approach. This is a direct consequence of transient not being as well documented as non-transient interactions. Moreover, the method cannot distinguish between true and false positives, for which there is limited experimental data – however, these problems will be alleviated as more high-throughput proteomic studies are completed.The system-level approach taken here, combining information on how proteins interact with each other and how genes are expressed, is a particularly appealing way to gain understanding of complex biological processes, such as metastasis. Although not discussed here in great detail several interesting groups of interactions, at the domain level, have been highlighted as potentially important players in the metastatic process. Further dissection of these is the subject of ongoing studies and consequently to be confirmed experimentally.This method of using homologous protein interaction data to infer protein-protein information could be particularly useful for proteins for which there is no definite binding partner information. Moreover the relative expression levels of neighbouring proteins may prove an important consideration, when protein networks are to be subsequently modulated in conjunction with disease analysis, for example by targeting the expression of a particular gene by short interfering RNA (siRNA) .The approaches described in this work are readily transferable to other species and cellular processes.In order to identify homologous interaction pairs for which there is experimental data, BLAST searches were run for the rat genome  against The experimental data arises from several methods – the most frequent are listed in Table S, was calculated for each putative interaction according to the following:The score, ai and bi, respectively, which have experimentally been shown to interact; n is the number of experiments linking protein ai to protein bi; and N is the total number of instances where the same pair of proteins is identified as interacting through different homologues. As mentioned in the Introduction, two-hybrid experiments are prone to giving false-positive results. Although most of the interactions created here are derived through yeast two-hybrid links, it has been shown that confidence is higher for interactions detected in multiple independent yeast two-hybrid experiments [where eriments . This fae ≤ 1 × 10-10). We distinguished biologically functional complexes  from transient ones , by applying a method proposed by Ofran and Rost [In order to test the scoring function, we created a subset of data from the RCSB Protein Data Bank  that speand Rost . This yiCellular localisation of proteins was obtained from the Gene Ontology Consortium . Each ofn = 100). Networks were expanded two generations out from the starting points using protein-protein interactions whose S-score value was 10 or higher. The resulting 10,628 interactions were then analysed using CFinder [k-cliques) in the networks and then identifies 'communities' by carrying out standard component analysis of the clique-clique overlap. In this context, the variable k is defined as the number of nodes in the subgraph and a k-clique community is defined as the union of all k-cliques that can be reached from each other through a series of adjacent k-cliques, where cliques sharing k - 1 nodes are defined as adjacent. Table k-values. Thirty-seven communities were identified for k = 4, i.e. setting the subgraph size threshold to a minimum of four. Selecting the k-value is a balancing act; the higher the k-value, the smaller and more internally connected the communities become, but less connection is observed between communities. The k-value was selected after observing that at k = 4, reasonably large communities were formed. Proteins which shared sequence identity higher than 40% within each community, were merged together such that they appeared as a single nodes on the protein map. These merged nodes inherited all the interactions from the individual proteins before the merging process. This was done to correct for any possible redundancies caused by our homology-based method for predicting protein interactions. There was negligible change in the protein networks as a result of this.Rat genes that were overexpressed or underexpressed more than four-fold were used as starting points  primer. After second-strand synthesis, in vitro transcription was performed with biotinylated UTP and CTP (Enzo Diagnostics), resulting in approximately 100-fold amplification of RNA. A complete description of the procedures is included in The Paterson Institute's Affymetrix GeneChip systems protocols [To investigate genes that may be important in the development of metastases, we used a rat sarcoma model in which the cell populations K2, T15, A297 and A311 have 0, 40, 90 and 100% incidence of metastasis, respectively. We performed Affymetrix microarray analysis on the four cell populations and the primary tumours that formed when the cells were injected subcutaneously into rats. All experiments were performed in triplicate, using Affymetrix rat 230A GeneChip oligonucleotide arrays . Total Rrotocols .μg fragmented cDNA before overnight hybridisation, arrays were washed and stained with streptavidin-phycoerythrin, and scanned on an Affymetrix GeneChip scanner. The procedure is further described in The Paterson Institute's RNA Hybridisation protocols [The target cRNA generated from each sample was processed as per the manufacturer's recommendation using an Affymetrix GeneChip Instrument System . Brieflyrotocols . The medPFJ constructed and analysed the protein networks and drafted the manuscript. TC carried out the microarray analysis on metastatic rat cell lines. PAB initiated the construction of the interactome and DZ its integration with the microarray data. PAB and DZ coordinated and participated in discussions and the preparation of the manuscript. All authors have read and approved the final manuscript.Metastasis-related protein communities. List of the proteins identified by the clique analysis.Click here for fileMicroarray data. The microarray expression data used in this study.Click here for file


Annotation of protein sequences of eukaryotic organisms is crucial for the understanding of their function in the cell. Manual annotation is still by far the most accurate way to correctly predict genes. The classification of protein sequences, their phylogenetic relation and the assignment of function involves information from various sources. This often leads to a collection of heterogeneous data, which is hard to track. Cytoskeletal and motor proteins consist of large and diverse superfamilies comprising up to several dozen members per organism. Up to date there is no integrated tool available to assist in the manual large-scale comparative genomic analysis of protein families.Pfarao  is a database driven online working environment for the analysis of manually annotated protein sequences and their relationship. Currently, the system can store and interrelate a wide range of information about protein sequences, species, phylogenetic relations and sequencing projects as well as links to literature and domain predictions. Sequences can be imported from multiple sequence alignments that are generated during the annotation process. A web interface allows to conveniently browse the database and to compile tabular and graphical summaries of its content.We implemented a protein sequence-centric web application to store, organize, interrelate, and present heterogeneous data that is generated in manual genome annotation and comparative genomics. The application has been developed for the analysis of cytoskeletal and motor proteins (CyMoBase) but can easily be adapted for any protein. The sucss rates ; and theManual annotation is still by far the most accurate and successful way to achieve correct predictions of genes. This process is best done using the possibilities of comparative genomics and multiple sequence alignments. Because a majority of the proteins are not characterized and their functions are largely unknown, the initial process involves categorizing these predicted proteins into subsets of proteins or protein families based on homology, presence of various functional domains and motifs, as well as similarity to well characterized proteins from other species.Thus, when working with collections of protein-sequences from different species and sources, one quickly accumulates large amounts of heterogeneous data: Protein and DNA sequences, their identifiers in different databases, references to literature, information about species including taxonomy, and links to online resources like sequencing projects. Since data that can be retrieved from public databases is often incomplete or incorrect it is very desirable to be able to combine manually edited with automatically generated content. In addition, there is often misleading and contradicting data, especially concerning the nomenclature and classification of proteins, that needs to be tracked and commented.Cytoskeletal and motor proteins have extensively been studied in the past. They are involved in diverse processes like cell division , cellulaPfarao is a database driven web application that was written to assist researchers investigating structure, function and phylogeny of proteins. It has been developed for the analysis of cytoskeletal and motor proteins (CyMoBase), but can be adapted to any type of protein. It stores, organizes, interrelates, presents, and analyzes data of various sources. Additionally, it triggers external prediction programs, so that manually entered and automatically generated data is always synchronized.The system is running on UNIX (OS X and Linux) systems. The database management system is PostgreSQL . As web We implemented a service-oriented mechanism that starts specific scripts, when records in the database are added or updated. In this case, a PostgreSQL trigger starts a PL/Ruby script , which oThe automation scripts for parsing BLAST  and HMMEThe web pages are generated as XML . By relating a position in the alignment to the positions in a set of protein sequences it is possible to retrieve homologous stretches from different sequences. In addition there are fields for sequence classification and nomenclature, comments, legacy names, information about the completeness of the sequence, its potential to be a pseudo-gene, and links to records in NCBI's nucleotide and protein databases . The comSeveral versions can be assigned to each sequence so changes and corrections can be tracked as more information becomes available. Furthermore, there are links to tables containing automatically generated protein domain predictions (see Automated processes).Species are defined by a set of names. There are fields for the scientific name of a species, the species abbreviation as used to identify database sequences, and common names. As some species are known by different scientific names, fields containing alternatively used names are also included. To account for the different usage of the scientific names, all possible names are listed and linked to the corresponding reference record wherever species are listed or used for selection via the interface. A comment field may contain general information about the corresponding species, the specific strain used, or common and divergent features compared to closely related organisms. The taxonomy field is converted automatically into a hierarchical representation of the taxa. (see Automated processes)Proteins are stored with their name and abbreviation as used in the database. Furthermore, classes of a certain protein can be grouped and categorized according to aspects like cellular function or localization. The project table includes information about the sequencing centres including type of data and completeness. Publications can be related either to a sequence to provide additional links to biological information or to a sequencing project.Data entry is done using the iiwi system  allowinThe database can trigger external programs upon insertion or update of certain fields in the database tables by contacting the delegation server program, which can in turn write computed data to the database Fig. . When a Files containing protein sequences in FASTA-format can be imported into the database to update existing or insert new records in the sequence table Fig. . A naminThe requirements for Pfarao can be summarized as follows: The key component of the database is the protein sequence that is obtained by manual annotation of genome and EST data with the help of a multiple sequence alignment. The sequence needs to be connected to data that allows the useful interpretation of the results concerning its biological function, and it needs to be linked with primary databases like GenBank or PubMed. To be useful for the specific protein community, whose members are expected to work in all biological and medical sub disciplines, the information of the database has to be presented in the most comprehensible way.Great attention has been paid to a versatile yet easy to use web interface. We think that accessibility and high quality representation is key to a productive usage of the system.Data can be entered and edited using a series of forms and lists. Relations are represented as pull-down menus.Pfarao encompasses a live web front end that is generated from the content of the database at each request and thus always reflects the current data, eliminating the need for manual updates. To browse the content of the database, the user selects a set of proteins and protein classes, and is then guided to refine the selection by choosing a set of specific taxa or species. Taxa and species can either be selected from tables containing specific subsets, or from a tree representation of the taxa and species that is generated to match the protein and protein class selection. Taxa and species can be browsed and selected by expanding/collapsing and including/excluding subsections of the tree, or by using shortcuts or auto-completion fields Fig. . We consUpon confirmation of the selection of protein and species, the system compiles a list of all sequences matching the specified criteria and presents it as a list grouped by species in taxonomic order. Additional data about the species like alternative names, links to sequencing centres and publications, as well as detailed information about the sequences including publications, comments, domain organization, and the sequence data, can selectively be shown or hidden Fig. .The system provides an integrated BLASTP  search aPfarao provides a solid platform for additional features and significant future developments of the system are underway. The front end will be extended to allow the graphical representation and fast browsing of large alignments of selected sequences that will be of great value for mutational studies. The interface is also intended to support the generation of phylogenetic trees for a user-defined set of sequences. These extensions will increase the transparency of the manual annotation process, as the user will be able to look at the two basic sources of information about protein sequence relations. It is also planned to incorporate the corresponding DNA data and to track the various alternative splice forms of the proteins.Pfarao has initially been developed for cytoskeletal and motor proteins but can easily been adapted to any protein. The database for cytoskeletal and motor proteins is called CyMoBase . Our curHere, we introduce a web application for the analysis of proteins from manual annotation and their relationship. The major motivation for this work was to provide an integrated environment that organizes and relates all relevant information and presents it using a high quality interface. Pfarao is a tool that allows the researcher to constantly monitor the state of the work without having to manually aggregate data from a range of sources. It has been developed for the analysis of cytoskeletal and motor proteins (CyMoBase) but can easily be customized for any type of protein..CyMoBase can be accessed at Due to the technologies used, it requires Firefox version 1.5 or greater with cookies and JavaScript enabled. Other browsers do not have the required feature set or do not comply with the standards of the W3C . The datID Identification NumberSVG Scalable Vector GraphicsXHTML Extensible HyperText Markup LanguageXML Extensible Markup LanguageXML-RPC Extensible Markup Language – Remote Procedure CallMK specified the requirements from a users perspective, defined the rules for data handling and participated in the design of the interface. He collected all the data and evaluated every function of the system. FO carried out the implementation of the system, designed the database scheme and did the technical design and the programming. Both authors wrote and approved the final manuscript.The file contains the detailed database schema.Click here for file


With the rapid increase in the number of new protein structures, the need for In this paper we present a unified framework for protein structure classification and identification of novel protein structures. The framework consists of a set of components for comparing, classifying, and clustering protein structures. These components allow us to accurately classify proteins into known folds, to detect new protein folds, and to provide a way of clustering the new folds. In our evaluation with SCOP 1.69, our method correctly classifies 86.0%, 87.7%, and 90.5% of new domains at family, superfamily, and fold levels. Furthermore, for protein domains that belong to new domain families, our method is able to produce clusters that closely correspond to the new families in SCOP 1.69. As a result, our method can also be used to suggest new classification groups that contain novel folds.We have developed a method called proCC for automatically classifying and clustering domains. The method is effective in classifying new domains and suggesting new domain families, and it is also very efficient. A web site offering access to proCC is freely available at  As a reWe recognize that the manual and semi-automated methods used in SCOP and CATH produce classification hierarchies that are of high quality, and automated methods are unlikely to incorporate the nuanced judgment that an experienced biologist brings to the classification task. Nevertheless, automated methods, if they are highly accurate, can provide a valuable complementary approach in producing classification hierarchies. With the rapid increase in the number of protein structures, automated methods can (and currently do) play an important role as a pre-processing step for producing manually-tuned classification hierarchies.Recognizing this need, several automatic domain classification methods -9 have rexisting classes, the effectiveness of these methods in detecting new classes is modest. Of the existing classification tools, both SGM [An important issue in automatic protein classification is the ability of a tool to detect new classes (i.e. detecting novel folds). Detecting such new classes is important as novel domain structures are constantly found in newly determined protein structures, and the information about new classes can be effectively used to better understand the new structures and can also be used to assist humans in organizing the new structures into the next version of the classification hierarchy. While many of these existing protein classification methods are very good at classifying new domains into both SGM  and SCOPboth SGM  can be uproCC – an automatic, accurate, and efficient classification framework, which consists of three components. Given an unclassified query domain, a structure comparison component employs an index-based method to quickly find domains with similar structures. Then, based on these results, a classification component assigns the query to an existing class label, or marks the query as unclassified to indicate that the query domain is potentially a new fold. Finally, a clustering component takes all domains marked as unclassified and runs a clustering method to detect potentially novel folds.In this paper, we present Collectively, these components provide a unified and automated protein domain classification tool. To demonstrate the capabilities of our methods, we have tested our method to predict the classification for new domains in SCOP 1.69 based on prior knowledge of the previous version of SCOP (version 1.67). Our experimental results show that the precision of our method is 86.0%, 87.7%, and 90.5% at the family, superfamily, and fold levels. We also compare our method with SGM and SCOPmap, and show that our methods are about 15–19% more accurate than SGM and comparable to SCOPmap. However, SCOPmap only classifies at the superfamily and the fold levels, whereas our tool also provides a classification at the the family level. More significantly for new fold detection, the predications made by proCC is 20% better than SCOPmap. Our experimental evaluation also shows that our method produces clusters which closely correspond to the new families in SCOP 1.69.In this section, we present results measuring the effectiveness of our classification methods. For the empirical evaluation, we employed the experimental strategies used in previous studies ,9: namelSCOP 1.67 and 1.69 contain 65122 and 70859 domains, which are grouped into 2630 and 2845 families respectively. However, in our evaluation theoretical domains and domains with less than 3 SSEs are excluded. After these exclusions, we end up with 58456 and 63745 domains in SCOP 1.67 and 1.69 respectively. Our database is the set of 58456 domains in SCOP 1.67, and our query set is the 5289 newly added domains in SCOP 1.69. We used the ASTRAL Compendium  for the SVMlight[class to refer to a class in the classification scheme.Our implementation is written in C++, and uses the LEDA 3.2R package for the maximum bipartite graph matching, and the SVMlight package.To measure the effectiveness of our classification method, we compare the predicted classification label  with the actual label in SCOP 1.69 using the following metrics:Overall precision = (CC + UN)/(TE + TN)Classification error ratio = CI/(CC + CI)New class detection ratio = UN/TNunclassified. UE is the number of domains which should have been classified into existing classes, but which are marked as unclassified by our method.  TE represents the total number of domains in common classes in SCOP 1.67 and SCOP 1.69, and TN represents the total number of domains in new classes in SCOP 1.69.In the above equations, CC is the number of correctly classified domains, and CI is the number of incorrectly classified domains. UN represents the number of domains of new structures which are not in existing classes and therefore are correctly marked as Overall precision measures how many proteins are correctly classified or correctly labeled as unclassified. The classification error ratio measures how many errors are made when query domains are assigned actual labels. A new class detection ratio measures how effectively a method can detect domains that are in new classification classes.The results for this experiment are shown in Table A number of methods have previously been proposed for automatic classification ,7-9. In Therefore, in this section, we compare our method with SGM  and SCOPBefore presenting the results with SGM, we note that the performance of SGM can change depending on adjustable parameters, such as the distance ratio cutoff in SGM. We experimented with a variety of parameter settings for SGM and found that settings that increase the new class detection ratio (or decrease the classification error ratio), degrade the overall precision. To select a reasonable baseline for comparison, we picked parameter values for SGM which produce a new class detection ratio similar to our method. With this method, we end up with distance ratio cutoff values of 1.22, 1.23, and 1.23 at the family, superfamily, and fold levels respectively.The results comparing SGM and proCC for the 5289 new domains in SCOP 1.69 are shown in Table We have also evaluated proCC, and compared it with SGM, using CATH . We used CATH 2.0 and CATH 2.4 as the database and query domains. The overall precision of the SGM method in classifying CATH is 93.9%, 94.5%, 94.7%, and 97.1% at the H, T, A, and C levels, whereas the overall precision of our method is 94.1%, 95.6%, 95.6% and 97.2% at the H, T, A, and C levels. Compared to SCOP, both methods generate more accurate results with CATH. However, the higher precision with CATH is expected since CATH uses a broader definition of fold, i.e. there are fewer folds in the CATH classification compared to SCOP .In addition, we have also compared the sensitivity and specificity of proCC with SGM and plotted standard ROC curves. These results are presented in the In this section, we present results comparing SCOPmap and our proCC method. In comparison with SCOPmap, we note that SCOPmap takes as input a query protein chain, identifies domains by aligning the query protein chain to sequences and structures in its database, and assigns a classification label to each identified domain. On the other hand, the input to proCC is a domain rather than a protein chain. So for comparison with SCOPmap, we first ran a domain prediction method with query protein chains to identify the domain boundaries. Then, we ran our classification method on the identified domains. For domain boundary prediction, we used the SSEP-domain method , which wWe compared SCOPmap and proCC using 2773 new single domain chains in SCOP 1.69. For this experiment, multi-domain chains are excluded, due to the difficulty in measuring effectiveness objectively .. Therefore, we compared our method with SCOPmap based on the latest result posted on the SCOPmap ftp site.Initially, we attempted to run SCOPmap on the 2773 chains. However, running SCOPmap on these 2773 chains takes an enormous amount of computational resource requiring approximately 2–3 hours to process each individual chain . Due to this high computation cost, new proteins are typically classified using large clusters and classification results are posted at Finally, while our method can predict the family, superfamily, and fold labels, SCOPmap primarily predicts the superfamily label, and only predicts the fold label for queries that it cannot assign a superfamily label. SCOPmap never predicts a family label. Since the main classification prediction made by SCOPmap is at the superfamily level, for this evaluation we compared the classification effectiveness only at this level. The results of this evaluation are shown in Table By examining column 5 in Table From column 6 in Table With respect to computation time . In addition, for the clustering at the different SCOP levels, different threshold values are needed. For this experiment, we set the threshold value to 0.4, 0.32, and 0.3 for the family, superfamily, and fold levels respectively, based on the observation that more than 90% of correctly classified proteins have a similarity score above these values with their nearest structure neighbor in the same SCOP family, superfamily, and fold.From the query set of 5289 domains, our classification method labels 934 domains as To measure the capability of the automated method in identifying novel SCOP families, we compared the automatically produced clusters with the family level classes in SCOP 1.69. The 934 unclassified domains are spread across 320 families in SCOP 1.69. For these domains, the automated method produced 358 clusters. To check the agreement between SCOP and the automatically generated clusters, we generated class labels for the clusters based on the most common family label in a cluster. Based on this class label assignment, each SCOP family is paired with one or zero cluster having the same class label. When more than one cluster maps to the same SCOP family, we count only the assignment of one of the automatically generated cluster; this cluster is the one in which the number of domains that correctly match the SCOP family label is highest amongst the set of clusters that also have the same SCOP family label. We then counted the number of common clusters/families that were "correctly" mapped, and found that there are 301 common clusters between the two classifications. Then, for each correctly mapped cluster, we counted the number of actual domains in the cluster that had the same label as the corresponding SCOP family. This total is 822, which is 88% of the total number of unclassified domains.Using the same method, we also computed the clustering effectiveness at the superfamily and fold levels. These results are shown in Table In Table C is a cluster in the set of MCL clusters S is a family in the set of SCOP families N is the total number of domains in In the above equation, Using this measure, the cluster purity of the MCL clusters is 0.96, 0.95, and 0.96 at the SCOP family, superfamily, and fold levels respectively. This high cluster purity value shows that our clustering method produces clusters that have a high degree of agreement with the SCOP classes. An example of automatically clustered novel SCOP families is shown in Figure In general, existing protein classification methods have focused on classifying new domains into existing classification hierarchies. However, it has been observed that in SCOP previously classified domains are often rearranged in subsequent releases, as new structures sometimes reveal more relationship amongst new and existing domains . TherefoOne way of approaching this problem is to perform an all-to-all comparison with existing and new domains, and then generate clusters using a clustering method. For instance, if the introduction of a new domain provides evidence connecting previously unrelated domains, a cluster that consist of these domains can be found, suggesting some potential rearrangements involving these domains.O(n2) comparisons). Our structure comparison method (see structure comparison section in Methods), is very efficient and could be a suitable choice for this task.In performing this task, along with clustering techniques, an efficient and accurate structure comparison method is crucial since one has to compare each pair of structures  [unclassified if the classification module cannot assign a class label with enough confidence. Finally, for the domains that are labeled as unclassified by the previous step, a clustering module identifies cluster boundaries as a way of suggesting groups of domains that are potentially in novel folds. Each of these three steps is described in more detail below.Our proCC method consists of a pipeline of the following three modules: structure comparison, structure classification, and clustering. Given a new query protein domain, the structure comparison module finds the top ne (SVM) . This seCα atoms, as is done in DALI [k domains that are similar to a given domain, the following steps are performed in order.The protein domain structure comparison module employs an index structure to rapidly find structures that are similar to the query. The basic unit for comparing structure similarity is a triplet of secondary structure elements (SSEs). Comparing protein structures using SSEs has been used before ,22,23 as in DALI  and CE [ in DALI . In addi1. Each domain in the database is decomposed into a set of SSE triplets. A 10-dimensional vector is used to represent the SSE triplet, and an index is constructed over all the SSE triplets in the database. The query protein domain is also decomposed into SSE triplets.2. For each SSE triplet in the query, matching SSE triplets are retrieved using the index. Based on the hits from this index probe, a similarity score between the matching triplets is calculated.k scoring targets are returned as the result of the search.3. For each target domain in the database, a weighted bipartite graph is generated based on the SSE triplet matching results. For each target graph, a maximum weighted bipartite graph matching algorithm is run to compute an overall similarity score between the query and the target. Finally, the top Each of these three steps is described in detail in the following three subsections.k is the number of such domains in the database. Therefore, the value for k varies depending on the query structure and is automatically determined by our method.We note that our method finds all the domains in the database that have at least one or more SSE triplet matches to the query, and Si, its type, denoted as Ti, is either an α helix or a β strand. For a concise representation, loops and turns are excluded. The length of the SSE, denoted as Li, is the number of residues contributing to the formation of that SSE. The direction vector, denoted as Vi, is a unit vector, Vi = (Xs- Xe)/||Xs- Xe||, where Xs and Xe represent the two end points of the SSE. Xs and Xe are calculated using the following equations defined in [We model each protein domain as a set of SSEs, and represent each SSE using its associated type, length, and a direction vector. Given a SSE fined in .α helix, Xs and Xe are calculated as:For an Xs = (0.74Xi + Xi+1 + Xi+2 + 0.74Xi+3)/3.48Xe = (0.74Xj + Xj-1 + Xj-2 + 0.74Xj_3)/3.48Xi and Xj represent the beginning and ending residues of the SSE.where β strand, Xs and Xe are calculated as:For a Xs = (Xi + Xi+1)/2Xe = (Xj + Xj-1)/2Si, Sj and Sk, the triplet containing these three SSE contains the following information:Since we are interested in indexing SSE triplets, we use the above representation of a single SSE to develop a representation for an SSE triplet. Given three SSEs, Ti, Tj, Tk.• SSE types: Li, Lj, Lk.• SSE lengths: θij, θik, θjk where θij is the angle formed by Si and Sj and it is calculated as: cos-1(Vi·Vj) mod 180. • Angles between each pair of SSEs: Dij, Dik, Djk where Dij is the average of the minimum distances between residues in Si and Sj. To calculate Dij the smaller SSE (between Si and Sj) is selected. Then, the minimum distances from every residue in Si to every residue in Sj are calculated (Si ≤ Sj) and the average of these minimum distances is used as the SSE distance Dij. Intuitively, this measure aims to concisely capture the distance between two SSEs. The index search (described below) will use these distances to remove pairs of SSEs that have very different inter-SSE distances.• Distances between each pair of SSEs: The information describing an SSE triplet is encoded into a compact 10-dimensional vector, which serves as the actual representation of the SSE triplet in an index. This 10-dimensional vector is:TC, Xi, Yi, Xj, Yj, Xk, Yk, Djk, Dik, Dij) in a 2D Euclidean space where Xi = Licosθjk and Yi = Lisinθjk. This transformation to a 2-D Euclidean coordinate allows us to use a conventional spatial index for efficiently locating close neighbors. The last three values, Djk , Dik, and Dij, are the pairwise SSE distance values as defined before.In this vector representation, amongst the three SSEs, the all inter-SSE distances less than 20 Å. This cutoff value is based on a similar cutoff that is used in DALI [The 10-dimensional vector representation serves as the key for indexing the SSE triplets. For a given protein domain, rather than inserting an index entry for every SSE triplet, we only insert SSE triplets that have  in DALI . For the in DALI .In our method, a SSE triplet is used as a basic search unit. While different cardinality for SSE can also be considered (for example a quadruplet or a pair instead of a triplet), the SSE cardinality directly affects the efficiency and the sensitivity of the searches. Using a SSE pair increases the sensitivity of searches, but degrades the search efficiency, especially when searching a large database of domains. Using a SSE quadruplet is more efficient, but may be too conservative in detecting distantly related structures, such as domains in the superfamily or fold levels. We use a SSE triplet to strike a balance between sensitivity and search efficiency. We also note that the use of SSE triplet has been made for similar reasons in previous works .target triplets in the database that are "similar" to the query triplet. Similarity between a query triplet and a target triplet is defined using the scoring model described below.To match a query against a database of proteins, we first decompose the query into all SSE triplets with inter-SSE distances less than 20 Å. We then probe the index with each query triplet and retrieve Tq in a query and Tt in a target (database), the SSE triplet similarity, denoted by SCtriplet is computed using the following equation.Given two matching triplets, Si and Sjwhere SCpair = exp(-(r/20)2), li = min.In the above equation, the first term measures the distance deviation between two SSE pairs. The second term de-emphasizes the significance of matches between two distant SSE pairs, since "distant SSE pairs are abundant and less discriminate" . The lasO(N2) where N is the number of residues).We note that our scoring equation has a strong similarity to the DALI scoring model. In the DALI model, a similarity score is calculated using all pairwise residue distances, whereas in our model the basic unit of comparison is an SSE rather than individual residues. The scoring using the SSE uses only the information in the index, and is computationally much faster than the scoring function used in DALI , we use an index search to find all database triplets that are similar to the query triplet. For each database triplet, we then compute the similarity score with the query triplet using the Tq and a target triplet Tt, which are defined as below,The index probe retrieves all matching entries using the following criteria: Given a query triplet t is a match for Tq when the following conditions are met.TTt are within in a small distance (sin(θ) × Tq. In our implementation, θ is set to 30°. The final condition checks if the distance between each matching SSE pair is within a small threshold, dε. As in the DALI scoring model [β-strand pair, 4 Å for an α-helix and β-strand pair, and 5 Å for an α helix pair. We note that these cutoff values are higher than the ones used in the DALI model as we are matching SSEs in a triplet, rather than just individual pair without considering a triplet configuration (as is done in DALI). The original DALI cutoffs would be too strict for matching SSE triplets.The first condition checks to ensure that the two SSE triplets have the same SSE types and the same order for the SSEs. The second condition checks to see if the three SSEs in ng model , the exaSCtriplet). Next, we need to assemble these triplet hits into matches for the entire protein domain. For this step, we construct a weighted bipartite graph for every target protein domain that has some triplet matches. In each graph, nodes on one side of the bipartite graph represent triplets in the query and nodes on the other side represent triplets in the database entry. An edge between two nodes indicates that the two triplets were matched by the previous step, and the weight of the edge represents the SCtriplet score. A maximum weighted bipartite graph matching algorithm is run on this graph to produce an injective (one-to-one) mapping from the query SSE triplets to the triplets in the target. Then, using this mapping, an overall structure similarity score is computed as:The previous step produces matching target triplets in the database for every triplet in the query, and the associated matching score  produces a normalized similarity score between 0 and 1, which represents how similar the query protein is to a target, compared to itself. The normalized score is reported as the final structure similarity score.In the equations above, Existing automatic classification methods ,7-9 emplunknown and/or possibly new when it cannot classify proteins with high confidence. To detect the boundary between classification and non-classification, it uses an inter to intra cluster distance ratio, based on the observation that "chains that are equidistant to several clusters are hard to classify and chains that are far away from any known clusters are probably new folds" [relatively closer to a specific cluster than to the remaining clusters. However, it cannot be used to detect whether a protein is absolutely close to a specific cluster.To resolve this problem, the SGM method , which ew folds" . The disOur classification method is also based on the nearest neighbor classification, and adopts the same observation as is used in SGM to detect unknown and/or possibly new folds. However, our method improves classification accuracy by using additional measures and a more sophisticated class boundary detection method, namely an SVM . FurtherF1), a relative similarity ratio (F2), and the nearest cluster classification label (C1). This information is collected using the following procedure:Our method for assigning a class label uses three pieces of information, namely: an absolute similarity ratio  and SCnorm.Then, we pick the top structure, F1 and F2 as: F1 = SCnorm and F2 = SCnorm/SCnorm. Finally, we return the values F1, F2 and C1.Then we use these scores to compute F1 and F2 values indicate that the query is structurally similar to its nearest neighbor, and is also relatively closer to its nearest neighbor than to any other domain, which in turn suggests a high confidence in the assignment of the classification label. On the other hand, low F1 and F2 values imply that the domain is not particularly similar to any existing domains, which suggests that the domain is potentially a new fold.Intuitively, high F1 and F2 scores. If the SCOP label of a query protein domain is the same as its nearest neighbor's SCOP label, the query with its F1 and F2 is used as a positive example, otherwise, it is used as a negative example in the training set for the SVM. The resulting training dataset is shown in Figure To automate the classification process, we need a classification decision model which defines clear boundaries between classification and non-classification. Using SCOP as the gold standard, we generated a classification decision model that reflects the rules used for creating new folds in SCOP. In order to create such classification decision model, we used a support vector machine (SVM) to capture nonlinear classification decision boundaries in SCOP. As a training set for the decision model, we picked SCOP version 1.65 and version 1.67 and trained the model as follows: Using domains in SCOP 1.67 as the queries, and domains in SCOP 1.65 as the database, we perform structure comparison using the method described in the structure comparison section. For each query, we calculate the unclassified. For queries that the SVM determines can be classified, the label of the nearest-neighbor in the database is used as the predicted class label.The classification label assignment step simply uses the trained SVM to determine if a query should be labeled as Our classification method takes the approach of assigning an "unclassified" label to protein domains that have novel folds or have subjective and fuzzy classification boundaries. Assigning an actual class label to such domains often requires additional biological information and manual interpretation ,29. Sinc.A web site offering access for protein classification is freely available at YJ carried out the design, implementation, and evaluation of proCC, and drafted the manuscript. JP conceived of the study, participated in its design and evaluation, and helped to draft the manuscript. All authors read and approved the final manuscript.roccurves. In this supplementary material, we present results evaluating the structure comparison methods using ROC curves.Click here for file