The study of functional subfamilies of protein domain families and the identification of the residues which determine substrate specificity is an important question in the analysis of protein domains. One way to address this question is the use of clustering methods for protein sequence data and approaches to predict functional residues based on such clusterings. The locations of putative functional residues in known protein structures provide insights into how different substrate specificities are reflected on the protein structure level.context-specific independence mixture model clustering framework which allows for the integration of experimental data. As these are usually known only for a few proteins, our algorithm implements a partially-supervised learning approach. We discover domain subfamilies and predict functional residues for four protein domain families: phosphatases, pyridoxal dependent decarboxylases, WW and SH3 domains to demonstrate the usefulness of our approach.We have developed an extension of the The partially-supervised clustering revealed biologically meaningful subfamilies even for highly heterogeneous domains and the predicted functional residues provide insights into the basis of the different substrate specificities. Protein families frequently can be divided into subfamilies of similar but distinct function. The study of these subfamilies and the residues which control the functional specificity is an important step in the analysis of these families.supervised problem, is the unsupervised setup, where subfamily assignments are unknown and have to be inferred from the data. In the unsupervised case, clustering approaches for protein data can be applied to obtain subfamilies from set of protein sequences. For protein subfamily clustering most methods rely on the construction of a phylogenetic tree. These methods can be further subdivided into pure clustering methods ) which are part of a loop surrounding the active site (Gln232) or involved in inter domain hydrogen binding (Leu33). To illustrate the kind of regularities in the alignment revealed by the position ranking, the top ten ranked positions in the phosphatase data are shown in Fig. Fig. pyridoxal_deC which had an unique annotation for the substrate specificity in the CATALYTIC ACTIVITY field of the corresponding SWISSPROT entries. This resulted in 35 sequences with glutamate specificity and 37 sequences with specificity for tyrosine. An alignment of these 72 sequences was obtained using Clustalw ) contains five primary binding interface positions, Tyr362, Phe364, Trp391, Pro406 and Phe409. These positions were listed at the very end of the ranking, within the last nine positions, and were found to be uninformative for the clustering. Thus, our method identified those sites, which are responsible for differences in binding between the family members. Complementary, it rejected those, which are of importance in all family members.Interestingly, other sites directly involved in the interaction yielded low scores. The SH3 domain of Sla1  with very strong  subgroup specific conservation. For details on the CSI mixture clustering on this data, refer to [In addition to domains described in detail above, we revisited a previously analyzed data set . The alirefer to .TreeDet server for being too short (fewer than 50 amino acids).Table Generally the CSI mixtures performed favorably. In the dehydrogenase data, there is a strong correlation between the clustering accuracy and the presence of the true specificity inducing residue in the predicted functional residues. For those methods with good clustering performance  Arg86 was correctly predicted to be functional, whereas the other method (ProteinKeys) did not . This underlined the importance of a high quality clustering as a necessary condition for the prediction of biologically valid functional residues.The ProteinKeys method achieved rather mediocre clustering accuracies. The main reason was the inflated number of clusters returned by the method. This suggests that the ProteinKeys method has the implicit assumption that the data contains a larger number of functional subgroups, which makes it less suited for the small, highly diverse data sets which are the focus of this work.seqboot program [proml and a consensus tree was generated by consense [To test whether this result was exemplary for other tree based methods, we calculated phylogenetic trees for the four analyzed domain families. Starting with the multiple alignments of the families, 100 bootstrap replicates were generated with the  program . Finallyconsense . The decDomains are the structural, evolutionary and functional building blocks of proteins. A domain based analysis is a fundamental step in predicting the function of a protein. Still, this step is hampered by the fact that different members of the same domain family can perform vastly differing functions. Here, we used a CSI mixture clustering method augmented with partially-supervised learning to assign domains to a functional subclasses. Additionally, our method predicts residues relevant for the specific function of a subclass. This information can directly be used in the experimental characterization of the protein. The application of our method to four domain families revealed a good classification and identified known functional residues even for the selected, highly challenging families .It is interesting to contrast the different effects of the extension to partially-supervised learning on these four data sets. For the WW and phosphatase domain data, the unsupervised clustering already yielded a very high quality clustering and as such it is not unsurprising that a minimal number of labels did not have a significant effect on the results.For the yeast SH3 domain data set, the addition of random labels actually caused more noise in the clustering than compared to the unsupervised case. There are a number of possible explanations for the apparent disadvantage the partially-supervised framework had on the SH3 data. First of all, the labels used for the 30 repetitions of the partially-supervised setup were chosen at random and therefore it is likely that samples which are outliers within their class were labeled. This can overemphasize these outliers in the characterization of the cluster center, especially if only few labels are available, as was the case in our setup. This effect of low quality labels having a detrimental effect on clustering performance has been described previously . From thIn addition to the functional grouping, our method also provides a ranking of sites important for each group. As exemplified by the SH3 domain, the method is able to distinguish 'core' position, which are functional in all family members, from those which are responsible for the sub-group specific function. This information could guide the further experimental characterization of the domain family.PyMix - the Python Mixture Package is freely available from . For future work it would be interesting to investigate additional data sets, especially protein sequences which arose from the kind of high-detail studies which yield data sets that do not lend themselves to study with tree-based methods. On the theoretical side it would be interesting to extend the framework with more complex notions of partial-supervision such as pair-wise positive and negative constraints between samples. Finally, we intend to devise additional improvements to the running time of the structure learning to optimize performance for the analysis of large scale data sets.The software we developed to carry out this analysis BG implemented the method and carried out the computations. JS assembled the data sets and performed the biological analyses. AS conceived the study and participated in its design and coordination. All authors wrote the manuscript and approved the final version.Supplementary figures. Figures of bootstrap trees for the various data sets.Click here for file


Deinococcus radiodurans R1 (Deira), resist ionizing radiation. Here, using the Database of Interacting Proteins (DIP) and the Protein Structural Interactome (PSI)-base server for PSI map, we have predicted novel interactions of orthologs of the 58 proteins under positive selection in Deira and other IRRB, but which are absent in IRSB. Among these, 18 domains and their interactomes have been identified in DNA checkpoint and repair; kinases pathways; energy and nucleotide metabolisms were the important biological processes that were found to be involved. This finding provides new clues to the cellular pathways that can to be important for ionizing-radiation resistance in Deira.Interactomes of proteins under positive selection from ionizing-radiation-resistant bacteria (IRRB) might be a part of the answer to the question as to how IRRB, particularly  This article was reviewed by Thiago Motta Venancio  and Arcady MushegianIonizing radiation-resistant bacteria (IRRB) are "non-spore forming bacteria" that can protect their cytosolic proteins from oxidation and tolerate many DNA double-strand breaks (DSBs) after exposure to high, acute ionizing radiation (doses greater than 1 kilogray (kGy) for a 90% reduction (D10) in the number of Colony Forming Units (CFUs)); moreover, they can resist prolonged desiccation,3, even Deinococcus radiodurans  has been a model for understanding many of the basic principles that govern resistance to ionizing radiation and tolerance of desiccation . An effifferent . Moreoveifferent . Databasifferent -11. Thermophilus . We desc[In this work, we focused on the 58 proteins that are under positive Darwinian selection in IRRB and that are absent in IRSB. To extract these 58 orthologs, we downloaded additional file . None ofDrosophila melanogaster, Saccharomyces cerevisiae). On the basis of the assumption that proteins from orthologous genes (COGs) have similar interactions or functions if they have a close similarity in their sequences [Deira ortholog accessions were submitted to the National Centre for Biotechnology Information (NCBI) gene-search engine to load UniProtKB/TrEMBL accessions and corresponding protein sequences. To determine their putative functions, we submitted the sequences to PSI-Blast (Position-Specific Iterated BLAST). The search was based on the non-redundant protein sequence database and the BLOSUM62 Matrix for scoring parameters. To predict the functions and interactomes of the 58 proteins, we initially used their orthologous sequences available at the Database of Interacting Proteins (DIP)  figure . The 58 equences , we searSubsequently, the sequences were submitted to the SAM_T08 server -19 for pFinally, we limited the interactome prediction to the SCOP domains using the Protein Structural Interactome (PSI)-Base data .Drosophila melanogaster, Saccharomyces cerevisiae) and available on several databases (DIP and PSIbase data).On the basis of the assumption that proteins from orthologous genes (Clusters of Orthologous Groups of proteins or COGs) have similar interactions or functions if they have a close similarity in their sequences , the intThe 58 protein sequences were compared to the dip20080918B.seq database (42562 sequences) from DIP using the Blast2 program. Among these, 44 proteins were found with their corresponding ortholog in the DIP-sequence database  that catalyses the reaction of L-Alanine degradation. This reaction generates the purine (NAD and NADP) involved in energy metabolism; or the radial spoke protein 2 (RSP2) (ligase) that has the function of conjugating proteins or DNA. RSPs have a direct link with RNA helicase DHH1 that is involved in recovery at the G1/S DNA-damage checkpoint. DR_A0338 is also involved in mitotic synthesis of damaged DNA through the enzyme CDC7 kinase. Kynureninase provides NAD for protein-modification processes involved in cell division and DNA-damage checkpoint. Other proteins with related pathways are involved in DNA stability and management were found. Thioredoxin, linked to DR_1271, has a direct interaction with DNA polymerase. It is implicated in the neutralisation of reactive oxygen species and in DNA polymerisation. DR_2074 functions to excise 3-methyladenine and 7-methylguanine from the damaged DNA polymer formed by alkylation lesions. It has a direct interaction with the DNA-repairing RAD23 superfamily of proteins that plays a central role both in proteosomal degradation of misfolded proteins and DNA repair. It is involved in DNA-excision repair by stabilising the xeroderma pigmentosum group C protein, thereby perhaps playing a role in DNA-damage recognition and/or in altering chromatin structure to allow access by damage-processing enzymes.For instance, another network is related to nucleotide metabolism and it includes two proteins, having 19 interactions. DR_1160 participates in purine metabolism, similar to DR_A0338. It has an oxidoreductase function, catalysing the oxidation of uric acid to 5-hydroxyisourate. DR_0505 has a d.114.1.1 domain that is implicated in nucleotide metabolism and transport. The second domain (d.159.1.2) belongs to the superfamily of metallodependent phosphatases and the family of DNA DSB-repair nucleases. Other networks are composed of proteins implicated in signal transduction and amino acid metabolism.Protein domains are regions, typically comprising 40–100 amino acids, within a protein molecule that show structural homology . A domaiIn the first step, the 58 protein sequences from Deira were submitted to the SAM_T08 server -19 for pAll domain-domain interactions are considered by the interface that belongs to the same protein and also to different proteins. Initially, the domains were grouped by the protein classes they belong to. They were subsequently matched using shell script with the psibase 1.71 data to find putative domain interactions. Thus, 9 networks implicated in different biological processes were found.Domain networks were then grouped by biological processes and metabolism pathways. Three of them are involved in the recognition of DNA and its binding with related proteins, in addition to the catabolism of ATP  is linked with the "Winged-helix" DNA-binding domain (a.4.5), DNA-repair protein MutS (a.113.1.2 and c.55.6.1), Cdc-48 domain (d.31.1.1), DNA polymerase (a.80.1.1 and d.131.1.2) N-terminal nucleophile aminohydrolases , and with ABC transport (f.22.1.1). These superfamilies are involved in a restricted range of critical DNA management.We found two network interactions of two proteins, which are common between the DIP and PSIbase databases (DR_A0178 and DR_A0129). Research results show that, in both cases, DR_A0178 belongs to a network of interactions involved in electron transport  and redox activity (molecular-based); and that DR_A0129 belongs to a network of interactions involved in metabolic process  and cationic transmembrane transporter activity. These results show the correlation of data between these two databases (DIP and PSIbase).), widely studied and has an important role in the radioresistance in Deira [The present discovery notes deals with use of combinatorial server for structural protein prediction and protein-protein interaction databases to deciphering the news clues about the radioresistance bacteria. Based of protein sequence from Deira, we predicted several cluster interacting proteins present on radioresistant bacteria but absent on radiosensible bacteria. This method allowed us to draw the pathways that maybe have a link with radioresistance machinery. Indeed we have predicted four proteins that have a direct involvement with the DNA repair . Two of them  have c.37.1 domains that belong to the ABC transporter ATPase domain-like family that can be found in the RecA protein , NCBI-NIH, Bethesda, Maryland, United StatesIn my first revision of this manuscript, I pointed several key problems with this study (see below). In addition to the problems emphasized in the first report, one of the most fundamental problems is that the study is almost completely based on the electronic transfer from eukaryotes to bacterium. The efficacy of such process was evaluated and shown to be reliable only for extremely high levels of sequence similarity ,24, whicThe text still requires English revision. In addition, there are several typographic errors that could have been solved by a spell-checker.Although the revised version of the manuscript is slightly better than the previous one, I think it is still below the journal's publication standards. Considering the issues outlined in my revisions, I do not support the publication of this manuscript.Response:Luscombe et al. indicated on his work that: Using interaction information from Saccharomyces cerevisiae,Caenorhabditis elegans, Drosophila melanogaster, and Helicobacter pylori, we find that protein-protein interactions can be transferred when a pair of proteins has a joint sequence identity >80% or a jointE-value <10(-70).We compared Deinococccus radiodurans protein sequence to the protein sequences from DIP  using Blast2 program . We seleIn the case of PSIMAP we used directly the SCOP domain references.Reviewer: In this manuscript, Mezhoud and colleagues aimed to identify protein-protein interactions that would be related to the ionizing radiation resistance in the bacteria Deinococcus radiodurans. The scientific problem and the way it was addressed in this study are not clearly stated. The authors should provide a minimal description about how the study has been conducted, rather than just citing other publications and databases.Response: The 58 orthologs sets were selected using The positive Darwinian philosophy that leads to fix the advantageous mutations and the fundamental process behind adaptive changes in genes [. The 58 orthologs sets were selected using DnaSP program . This program calculates the ratios of no synonymous to synonymous mutation rates (Ka/Ks) in protein coding genes. The Ka/Ks ratio measures the strength of selection [.in genes . The 58 election .Reviewer: The authors mentioned that the proteins are under positive selection and not present in IRSB bacteria. However, the results were basically obtained using orthology assessment and domain detection. Since these proteins are absent in most bacteria, what are the model organisms used to infer the interactome?Response: Radioresistance is surprisingly high in many organisms, in contrast to previously held views. For example, the study of environment, animals and plants around the Chernobyl accident area has revealed an unexpected survival of many species, despite the high radiation levels. A Brazilian study in a hill in the state of Minas Gerais which has high natural radiation levels from uranium deposits, has also shown many radioresistant insects, worms and plants. We cite the examples of Drosophyla and Sacharomyces.Drosophyla:  Radioresistance of a natural population of Drosophila willistoni living in a radioactive environment. Mutat Res. 1973 Sep;19(3):325–9.Sccharomyces: Reviewer: How complete are the interactomes in these model organisms? How complete is the database used?Response: We explained on the following, the strategy given by every server to complete their database.Searching the Database of interacting proteins on DIP: Currently protein-protein interactions are entered into the DIP only following publication in peer-reviewed journals. Entry is done manually by the curator, followed by automated tests that show the proteins and citations exist. Interactions are double-checked by a second curator and flagged accordingly in the database. DIP can be searched in a variety of ways. One can look for interactions involving a specific protein by entering its gene name or its accession code from GenBank, PIR or SWISS-PROT. More general searches can be performed for information such as organisms, protein superfamilies, keywords, experimental techniques or literature citations. Multiple fields can be searched simultaneously to narrow the query, and the use of wildcards and regular expressions is supported to further aid in searching. A search returns a list of protein-protein interactions, each hyperlinked to a DIP entry. Each resulting DIP entry reports information about the two interacting proteins, the protein domains and range of amino acids involved the curator, date of entry and updating and the articles describing the interaction, and the corresponding experiments. For example, a search on a single protein returns all of the interactions recorded in DIP in which that protein participates [.icipates .Searching the Database of interacting proteins on PSIBASE: PSIBASE, the Protein Structural Interactome Map, is a database of all the structurally observed interactions between protein domains of known three-dimensional structure in the PDB. It can be constructed using any reliable protein domain definition, where domains are defined as evolutionarily conserved structural and functional protein units. Here we use the domain definitions provided by SCOP , which uses structural and functional homology to manually define evolutionarily distinct protein domain families and superfamilies. Alternatively, other domain definitions  can be used [. be used .Reviewer: What means a "correct score in the DIP-sequence database"?Response: We will change this vocabulary. We compared le 58 protein sequences to the DIP protein sequence database using Blast2 program. We found 44 sequences with a e-value lower than e-5.Reviewer: All these questions should be discussed, along with the limitations involving the use of computationally predicted protein-protein interactions. This is particularly critical when analyzing proteins that may form completely different complexes due to their putative relationship with the IRRRB phenotype.Response: Entry is done manually by the curator, followed by automated tests that show the proteins and citations exist. Interactions are double-checked by a second curator and flagged accordingly in the database (see the paragraphs "Searching the databases").Reviewer: The fact that these 58 proteins are under positive selection may provide clues about the differences between IRRB and IRSB. However, it is still premature to state that "... these orthologous sets decide the difference between IRRB and IRSB", as authors did in the background section.Response: OK we will change this sentence. Absolutely we are not sure that these ortholog sets make difference between IRRB and IRSB. This is only a prediction, based on a program to calculate "recognized" to reduce the workload of a full proteome of Deira to 58 proteins and database interactome reasonably established. There is still checking the genes of interest by an experimental study, which needs enough time consumed.Reviewer: The results could be better tied up. I had the general impression that protein annotations are just placed in different phrases, without a biological discussion on their possible roles in an orchestrated manner with their interacting partners. This is of key importance when discussing proteins under a systems perspective.Response: We restricted the discussion to some proteins or domains involved on DNA repair and not cited previously. This work is a discovery note using available protein interaction data. We don't have enough information to discuss the possible roles in an orchestrated manner. Nonetheless, we made changes on our manuscript. Our goal was to draw the clusters protein-protein interactions to locate the most interesting proteins which involved on DNA repair.Reviewer: Moreover, the conclusions section is poor in the present format and does not present any clearly novel insight in the differences between IRRB and IRSB. To exemplify my point, I took the phrase "Considering all the molecular processes that contribute to the radioresistant phenotype of radioresistant phenotype of Deira, it can be concluded that it is achieved by the interaction of various proteins.". This is an obvious outcome, since  every process regulated by proteins is achieved by interaction of various proteins.Response: The conclusion has been rewritten. We worked on proteins that exist only in IRRB. If there is an interest protein or domain that involved on DNA repair or the neutralization of reactive oxygen species then IRSB do not have.Reviewer: The results and conclusions sections of the abstract are also not very clear. I suggest some re-phrasing to better explain the analyses and their respective implications. Instead of mentioning very broad terms, such as "pathways involving kinases", some specific result should be briefly explained.Response: The abstract has been rewritten.Reviewer: For the reasons outlined here, I think the manuscript requires extensive modifications to meet the publication standards of Biology Direct. English revision is also strongly recommended in several parts of the text.Minor changesReviewer: I think citations should not be used in the abstract.Response: OKReviewer: In the Additional file Response: OK We added a column of the UniProtKB/TrEMBL accession.Reviewer: There is a problem in the design of the flowchart  more important  and evolResponse:OKReviewer:p. 6 top line and several other places. Avoid the slang – replace "blasted against" with "compared to". Ibid, line 3: what is "correct score"?Response:OKReviewer:p. 6 par. 2 and further. Essentially, there was no explicit assessment of the reliability of predicted interaction, i.e., all interactions recorded in DIP or PSIMAP, whether observed in any species or inferred, were treated as true, correct? Moreover, in the case of DIP, the interaction information was transferred from the orthologs in other species, but in the case of fold recognition and PSIMAP, also from paralogs?Response:Searching the Database of interacting proteins on DIP:Currently protein-protein interactions are entered into the DIP only following publication in peer-reviewed journals. Entry is done manually by the curator, followed by automated tests that show the proteins and citations exist. Interactions are double-checked by a second curator and flagged accordingly in the database.DIP can be searched in a variety of ways. One can look for interactions involving a specific protein by entering its gene name or its accession code from GenBank, PIR or SWISS-PROT. More general searches can be performed for information such as organisms, protein superfamilies, keywords, experimental techniques or literature citations. Multiple fields can be searched simultaneously to narrow the query, and the use of wildcards and regular expressions is supported to further aid in searching. A search returns a list of protein-protein interactions, each hyperlinked to a DIP entry. Each resulting DIP entry reports information about the two interacting proteins, the protein domains and range of amino acids involved, the curator, date of entry and updating and the articles describing the interaction, and the corresponding experiments. For example, a search on a single protein returns all of the interactions recorded in DIP in which that protein participates .Searching the Database of interacting proteins on PSIBASE:PDB. It can be constructed using any reliable protein domain definition, where domains are defined as evolutionarily conserved structural and functional protein units. Here we use the domain definitions provided by SCOP , which uses structural and functional homology to manually define evolutionarily distinct protein domain families and superfamilies. Alternatively, other domain definitions  can be used [PSIBASE, the Protein Structural Interactome Map, is a database of all the structurally observed interactions between protein domains of known three-dimensional structure in the  be used .PSIMAP ALGORITHMThe basic mechanism to check interactions between any two domains or proteins is the calculation of the Euclidean distance in order to see if they are within a certain distance threshold. PSIMAP checks every possible pair of structural domains in a protein to see if there are at least five residue contacts within a 5 Å distance (5-5 rule). The current PSIMAP protocol has three methods. They are the Full Atom Contact (FAC) PSIMAP, Sampled Atom Contact (SAC) PSIMAP and Bounding Box Contact (BBC) PSIMAP .Reviewer:p. 6–7: there is some biological rationalization of the observed interactions , but, despite the section title, I do not see any comparison to the IRSB and how they get by without these clusters.Response:We replaced "networks" by "clusters of interacting proteins"We are not sure that these ortholog sets make difference between IRRB and IRSB. This is only a prediction, based on a program to calculate "recognized" to reduce the workload of a full proteome of Deira to 58 proteins and database interactome reasonably established. There is still checking the genes of interest by an experimental study, which needs enough time consumed.We worked on proteins that exist only in IRRB. If there is an interest protein or domain that involved on DNA repair or the neutralization of reactive oxygen species then IRSB do not have.The list of the 58 Orthologs.Click here for fileSAM T_08 blast result: Best-scoring hits from combining t06 t04 t2k version. If the E-value is poor , then the model should be regarded as speculative [culative -18.Click here for fileDeinococcus radiodurans proteins with the DIP database .Blast result of 58 Click here for fileInteractomes of 9 proteins from the DIP database. Each protein is represented by a square (node), according to its identity in the DIP database. The 9 proteins of interest are represented by a bigger square. Interaction is represented by Edge. Protein-protein interaction data were extracted from the DIP database. Networks were grouped by biological processes. Groups were implicated respectively in signal transduction, DNA checkpoint in G1/S, DNA repair and nucleotide metabolism.Click here for fileInteractomes of the 9 proteins using the DIP database.Click here for fileInteractomes of the 58 proteins under positive selection in ionizing radiation-resistant bacteria (IRRB) but absent in all ionizing radiation-sensitive bacteria (IRSB). SCOP domains were represented by octagons. Union of octagons represent proteins with multiple domains. Protein-protein interactions were represented by edges. Data on interaction domains were extracted from the PSIBASE 1.71 database. Network interactions were grouped by biological processes. Groups were involved respectively in electron transport and oxidoreduction (A), DNA repair (B), Energy metabolism (C) and beta-lactam resistance (D).Click here for file


The traditional approach to studying complex biological networks is based on the identification of interactions between internal components of signaling or metabolic pathways. By comparison, little is known about interactions between higher order biological systems, such as biological pathways and processes.Linked Processes and the resultant network of biological processes, the Process Linkage Network (PLN).We propose a methodology for gleaning patterns of interactions between biological processes by analyzing protein-protein interactions, transcriptional co-expression and genetic interactions. At the heart of the methodology are the concept of We construct, catalogue, and analyze different types of PLNs derived from different data sources and different species. When applied to the Gene Ontology, many of the resulting links connect processes that are distant from each other in the hierarchy, even though the connection makes eminent sense biologically. Some others, however, carry an element of surprise and may reflect mechanisms that are unique to the organism under investigation. In this aspect our method complements the link structure between processes inherent in the Gene Ontology, which by its very nature is species-independent.As a practical application of the linkage of processes we demonstrate that it can be effectively used in protein function prediction, having the power to increase both the coverage and the accuracy of predictions, when carefully integrated into prediction methods.Our approach constitutes a promising new direction towards understanding the higher levels of organization of the cell as a system which should help current efforts to re-engineer ontologies and improve our ability to predict which proteins are involved in specific biological processes. The study of biological systems at different levels of organization is a rapidly emerging area of computational biology. The majority of research in this field has focused on partitioning genes into biological pathways or processes With the aim of helping to decipher this higher order connectivity we propose a new methodology for gleaning patterns of interaction between biological processes, manifested by a significantly enriched web of protein-protein interactions, transcriptional coordination or genetic interactions.Linked Processes and the resultant new network of biological processes, the Process Linkage Network (PLN), whose nodes correspond to biological process terms in the Gene Ontology (GO) database. Using this methodology and exploiting various experimental data and annotations, we are able to uncover different interactive and cooperative relationships between processes. Many of these linked terms are distant from each other in the GO-hierarchy, suggesting perhaps a need to revisit the philosophy to organize biological data as a single taxonomy.At the heart of the methodology described in the paper are the concept of PPI-PLN), transcriptional co-expression (expression-PLN) and genetic interactions (GI-PLN). An analysis of the different PLNs yields intriguing findings: Many of the processes that were found to be linked in the various networks are consistent with biological knowledge, while other links are suggestive of further research to elucidate their very existence and meaning. The process “protein ubiquitination” (GO:0016567), for example, is predicted to be PPI-linked to processes related to protein catabolism while it is expression-linked to processes related to rRNA-processing and GI-linked mainly to cell cycle related processes. A biological explanation for these links is offered in the We construct and analyze different types of PLNs based on physical protein-protein interactions . The prediction method and the statistical significance procedures are described in the a to node b, if process a is found to be linked to process b. The PPI-PLN constructed for S. cerevisiae contains 21,097 edges (links) connecting 1,161 processes (nodes), at a p-value of 0.001. The corresponding q-value (using the FDR correction) is 0.06. The characteristics of this network are given in the Supplement  and “chromatin modification” (GO:0016568), as one example. This link reflects the fact that chromatin and histone modification is utilized in the DNA damage response pathway Fifteen additional examples of PPI-linked processes are given in Some of the other PPI-links are less intuitive. For example, the PPI-link of “main pathways of carbohydrate metabolism” (GO:0006092) to the process “response to stress” (GO:0006950) (semantic similarity 0). This link is of particular interest, since it was recently reported that upon oxidative stress the regulator Stb5 shunts carbohydrate metabolism from glycolysis to the pentose phosphate pathway S. cerevisiae.Some links clearly enrich the universal GO structure with links that are unique to the organism at hand. For example, a link between “hyperosmotic response” (GO:0006972) and “regulation of MAPK activity” (GO:0043405)  is many times larger than the size expected at random see , indicatOne example of the different links can be found for the process “protein ubiquitination” (GO:0016567). This process is PPI-linked mainly to processes related to protein catabolism, while it is expression-linked mainly to processes related to rRNA-processing, and GI-linked mainly to cell cycle related processes. Given the role of ubiquitination in protein degradation, it is reasonable that proteins participating in the ubiquitination process will physically interact with proteins participating in the degradation processes and the regulation of this degradation. There are multiple lines of evidence explaining the expression-linkage between protein-ubiquitination and rRNA processing. At the highest level of resolution, transcription, translation and degradation are processes that are expected to be correlated in cell cycle gene expression data. At the more specific level the main ribosomal protein, S27a is synthesized as a C-terminal extension of ubiquitin. The synthesis of ribosomal proteins as extensions of ubiquitin promotes their incorporation into nascent ribosomes by a transient metabolic stabilization and is required for efficient ribosome biogenesis Although obtained from different underlying input graphs, all three networks show very similar characteristics in terms of the semantic similarities between the linked processes. S. cerevisiae genes with known biological process to participate in specific processes that are PPI-linked is 24% (we refer to a process as specific when at most 200 of the S. cerevisiae annotated genes participate in it). This probability increases to 59%, once it is known that the two genes were found to genetically interact with each other.We conclude this section with the observation that many of the genetically interacting gene pairs participate in PPI-linked processes. The chance for a pair of G1 is to be predicted, the algorithm takes into account each protein G2 which was judged to have something in common with G1 . If G2 is known to participate in process t1, then the probability of predicting G1 as annotated to t1 increases. Furthermore, such algorithms utilize the relations between the terms derived from the Gene Ontology: if process t1 is a descendant of process t2 in the ontology, then following the “true path rule” G2 is also annotated with t2. Consequently, the probability of predicting G1 as annotated to t2 is also increased. In view of the fact that process-links constitute additional relations between processes, we integrated them into functional prediction algorithms. We show here that this integration improves the prediction abilities of the algorithms.Functional annotation of currently unannotated proteins is a significant problem, since 35–55% of all newly sequenced genes have no current functional assignment. When a gene without known function is associated with some genes that have a function in common, then it is natural to hypothesize that the same function can be attributed to that gene. This principle is often referred to as “guilt by association”. Two of the most popular associations used for functional prediction, are PPI and transcriptional co-expression. Many algorithms based on PPI-data or expression-data have been proposed in the past few years D. melanogaster, with and without the integration of the PPI-linked processes . Both algorithms predict the function of a protein using the known functions of the proteins that are connected to it in the input graph. In the original MV algorithm (simple guilt-by-association), only neighbors that participate in the process under consideration serve as positive witnesses: for a particular GO term t, the algorithm sets the state of a protein to +1 if the protein is annotated with t and to −1 if the protein is annotated with a different function (in the same GO hierarchy). Our IMV algorithm integrates PPI-linked processes into the prediction by adjoining as positive witnesses also those neighbors that participate in processes that are PPI-linked to the predicted process: for a particular GO term t, the state of a protein is set to +1, if the protein is annotated with tor if the protein is annotated with t' and t' is linked to t. Otherwise the state of the annotated protein is set to −1. Both algorithms are fully described in the In order to evaluate the improvement in the prediction abilities of the standard guilt-by-association method, as described for protein-protein interaction networks The MV algorithm was able to make predictions for 551 processes. The IMV algorithm made those same predications, of course, but the integration of process linkage into the prediction algorithm, generated predictions for 121 additional GO-processes. When the average precision and average recall of the IMV method were computed for all 672 predictable terms, they were higher by 12% and 51% respectively. By taking the average only over 551 GO-terms, the average precision and average recall increased by 33% and 69% respectively.As an example of the heightened predictive power of the IMV algorithm observed in the cross-validation experiment consider the gene CG10701 which is known to have the annotation “larval development” . The genS. cerevisiae genes, using cell-cycle expression data Expression-linked processes can be similarly integrated into expression-based prediction procedures. The resulting functional prediction method for This integration also considerably increases the number of new predictions. The MV predicts 5,182 functional annotations for proteins that currently have no annotation, and 5,305 additional functional annotations for annotated proteins. The IMV predicts 9,246 functional annotations for proteins that currently lack one, and 18,437 additional functional annotations for proteins that are currently annotated see  section.et al. used various characteristics of pairs of genes in order to predict genetic interactions S. cerevisiae genes to genetically interact is significantly increased once it is known that the two genes participate in PPI-linked processes or expression-linked processes, by 2.4 and 1.9 times, respectively The probability increases three-fold if it is known that the processes are both PPI-linked and expression-linked , we hypothesize that biological ontologies will benefit from a complementary multi-scale network supported data structures that provide additional useful connections to organize complex multi-dimensional data.For now, the framework of linked processes described in this paper constitutes a promising new direction towards understanding the higher levels of organization of the cell as a system as well as improving our ability to predict which proteins are involved in specific biological processes at the lower levels.i to process term j to indicate that proteins annotated with term i tend to interact with proteins annotated with j to a greater extent than expected by chance. The statistical enrichment of j is computed with respect to the set of proteins that are connected to the proteins annotated with i in the input graph, excluding proteins that participate in both i and j. This exclusion prevents the creation of an abundance of hypothesized links that could result from proteins that have multiple annotations and interact with each other, or from proteins that are connected in the GO-hierarchy in a “is a” relation. However, a process i that is linked to a process j, where i “is a” j, remains of interest since it arises when proteins that are annotated with process i interact with many proteins that are annotated with process j but not with process i.The definition of linked biological processes is based on an input (undirected) graph whose nodes correspond to proteins and whose edges correspond to some form of functional connection between the proteins, such as protein-protein interaction, co-expression or genetic interaction. Such an annotated input graph is often referred to as a functional linkage graph Here is the formal definition:Ni be the set of nodes that are annotated with process i (and possibly with other processes as well).Let NBi be the set of nodes that are connected to at least one node that is annotated with process i but that are not annotated with process i themselves:Let i but that are not annotated with i themselves, a number of proteins equal to or bigger than the number of proteins that were found actually to be annotated with process j, |NBi∩Nj| isWith these definitions, the probability of finding at random, among the set of proteins that are connected to at least one node that is annotated with process i is linked to process j, if process j is statistically enriched in NBi, in the sense that P<threshold. The threshold was set at 0.001, in order to filter out false positives at a level that is satisfactory according to a straightforward FDR calculation and statistically significant according to two randomization procedures (see next section).We will say that process An illustration of the determination of a link and a discussion of some of its properties are given in the Supplement .PPI-linked processes those linked processes obtained when the input graph is a PPI-network.We call We computed 3 sets of PPI-linked terms. Since the linkage of processes is defined using a network in which all nodes are annotated, we excluded the unannotated nodes and the induced edges, to create a fully annotated subnetwork.S. cerevisiae, obtained from the Database of Interacting Proteins (DIP) http://dip.doe-mbi.ucla.edu. This network currently contains 4,765 nodes (proteins) connected by 15,518 edges (interactions). The proteins in this network were annotated by mapping biological process annotations for S. cerevisiae  from the GO consortium The first set resulted from the PPI data for D. melanogasterD. melanogaster  from the GO consortium.The second set was determined from the PPI data for H. sapiens, obtained from OPHID http://ophid.utoronto.ca/ophid/. This network currently contains 9,098 nodes connected by 41,376 edges. The proteins in this network were annotated by mapping biological process annotations for H. sapiens  from the GO consortium.The third was based on the PPI data for t, was annotated also with all processes more general than t .Since the GO annotations form a directed acyclic graph (DAG), many of the annotations of the gene products are implicit. Therefore in our analysis every protein annotated with process S. cerevisiae network contains 3,429 nodes connected by 12,406 edges (0.1% of the possible edges), the D. melanogaster network contains 4,187 nodes connected by 7,754 edges (0.044% of the possible edges) and the H. sapiens network contains 5,601 nodes connected by 21,702 edges (0.07% of the possible edges).After the exclusion of the unannotated proteins, the S. cerevisiae network scaffold. In the first type of random annotation, named random1, the original sets of protein annotations were randomly redistributed among the proteins. This can be viewed equivalently as the procedure of assigning proteins randomly to the vertices of the given PPI-network. For the second type of random annotation, named random2, a new PPI-network was constructed by placing an edge between two proteins with probability equal to the fraction of edges in the original graph. The results of the comparisons between the obtained numbers of edges are summarized in S. cerevisiae PPI-linked network. Note that the two random annotation procedures yield very similar distributions of p-values.To assess statistical significance we compiled a histogram of pairs of linked processes, corresponding to two types of random annotations for the proteins derived from the expression-linked processes if they are linked when the input graph is based on expression data. This graph, often referred to as a relevance network We call a pair of processes S. cerevisiae, we used a data-set comprising the expression levels of 5595 of the predicted S. cerevisiae genes, in 42 different stages of the cell-cycle http://genome-www.stanford.edu/cellcycle/data/rawdata. The relevance network contained an edge between two genes if the absolute Pearson correlation between their expression profiles is at least 0.85; all singleton nodes were deleted. Next, the genes in this network were annotated by mapping biological process annotations for S. cerevisiae  from the GO consortium t, was also annotated with all terms that are more general than t, and all unannotated nodes were deleted, to create a fully annotated sub-network. The resulting network consists of 997 nodes connected by 4,168 edges (0.42% of the possible edges).To construct the relevance network for GI-linked processes those linked processes obtained when the input graph is constructed on the basis of genetic interactions. A GI-network was constructed for S. cerevisiae. In this network nodes represent genes and edges represent genetic interactions.We call S. cerevisiae ., was obtained from BioGRID http://www.thebiogrid.org. The biological process annotations for S. cerevisiae  from the GO consortium were used to annotate the genes.The list of genetic interactions for The resulting network contained 2,904 nodes and 21,942 edges (0.52% of the possible edges).In some instances of the present analyses, we generate a multiplicity of hypotheses. We compute the FDR  as follows:R denote the number of hypotheses rejected by a procedure.Let V denote the number of true null hypotheses erroneously rejected (type I error).Let q denotes V/R when R > 0 and 0 otherwise.V is the number of expected false-positive predicted links between GO-terms  and R is the number of predicted links at the current p-value threshold.In our context, S. cerevisiae, 1,347 are expected to be false discoveries . Therefore, calculation of the FDR q-value for a p-value of 0.001 yields a q-value of 0.06, or 6% .Of the 21,097 directed edges of the Process Interaction Network constructed for the We use the measurement suggested by Lin i)P. ancestort is the most specific common ancestor-term of it and jt (the one with the least number of genes annotated to it).Where This similarity measure has the following desirable properties:To determine the statistical significance of the size of the intersection between three sets of linked process, we computed the p-value of obtaining an intersection of this size or larger when three sets of the given sizes are drawn at random.et al. where a separate network is constructed for each GO term19. For a particular GO term t, the algorithm sets the state of a protein to +1 if the protein is annotated with t and to −1 if the protein is annotated with a different function (in the same GO hierarchy). Next, the algorithm repeatedly selects an unannotated protein i, representing a hypothetical protein, and sets its state is to −1 or +1 using the following activation rulein is the number of neighbors of protein i, js is the state of neighbor j, and θ is an “activation threshold”. The right hand side of this equation computes the sum of the states of the neighbors of node i and sets the state of node i to +1 if the sum is The specific implementation we use relies on the procedure described in Karaoz t to all hypothetical proteins whose state was set to +1.Finally, the algorithm assigns the putative annotation The algorithm is applied only to those GO-terms for which the cross validation procedure yielded at least one positive prediction.t, the state of a protein is set to +1, if the protein is annotated with tor if the protein is annotated with t'andt' is linked to t. Otherwise the state of the annotated protein is set to −1. Next, the hypothetical proteins are assigned a state of +1 or −1 in the same way as before. Finally, the putative annotation t is assigned to each hypothetical protein whose state was set to +1 provided at least one of the neighbors of the hypothetical protein is annotated with t. The activation threshold was selected for each GO-term separately, as described for the simple guilt-by-association procedure. Again, predictions were made only for those GO-terms for which at least one positive prediction was generated in the cross-validation test.Similarly to the original algorithm, a distinct network is constructed for each GO term. For a particular GO term it, three measurements, precision ip, recall ir and F-measurement if were computed. These measurements are based on counting true positives (iTP), the number of genes correctly assigned; false positives (iFP), the number of genes incorrectly assigned; false negatives (iFN) the number of genes incorrectly not assigned. The precision and recall for term it are defined as follows:The prediction performances of the different procedures were measured by cross-validation. Separately for each GO-process term i = 2* ri* pi/(ri+pi)f.F-measurement, the harmonic mean of the precision and the recall is defined as follows: T be a set of GO-processes. The overall precision, recall and F-measurement are then:Let Text S1An illustration of link determination(0.03 MB DOC)Click here for additional data file.Text S2Degree distribution in the S. cerevisiae PPI-PLN(0.03 MB DOC)Click here for additional data file.Text S3Comparison of PPI-linkage, expression-linkage and GI-linkage of processes in the yeast Saccharomyces cerevisiae.(0.03 MB DOC)Click here for additional data file.Text S4Genetic interactions are more likely to bridge redundant or complementary processes: The differences between the approaches.(0.02 MB DOC)Click here for additional data file.Figure S1A) The yeast PPI-subnetwork consisting of only those genes that are annotated with “regulation of MAPK activity” (yellow nodes) or “hyperosmotic response” (black nodes), and their neighbors. B,C) In order to test whether there is an edge linking term t1 to term t2, one needs to consider the genes that are annotated with t1 (green nodes), those of their neighbors that are not annotated with t1 (blue nodes), the genes that are annotated with t2 but are not annotated with t1 (red nodes) and the intersection of the latter two (purple nodes). B) The test for an edge linking “hyperosmotic response” to “regulation of MAPK activity” is positive. C) The test for an edge linking “regulation of MAPK activity” to “hyperosmotic response” is negative.(0.43 MB TIF)Click here for additional data file.Figure S2Complementary Cumulative Distribution of the in- and out-degrees in the PLN obtained for yeast. A) In-degree; B) Out-degree.(0.17 MB TIF)Click here for additional data file.Figure S3Venn diagram representation of the sizes of the intersections between the sets of pairs of PPI-linked, expression-linked and GI-linked processes. Each set contains only linkages between processes that appear in all three types of linkage networks.(0.14 MB TIF)Click here for additional data file.


The experimental identification of hot spots is however a lengthy and costly process, and thus there is an interest in computational tools that can complement and guide experimental efforts.It is well established that only a portion of residues that mediate protein-protein interactions (PPIs), the so-called Presaging Critical Residues in Protein interfaces-Web server (http://www.bioinsilico.org/PCRPi), a web server that implements a recently described and highly accurate computational tool designed to predict critical residues in protein interfaces: PCRPi. PRCPi depends on the integration of structural, energetic, and evolutionary-based measures by using Bayesian Networks (BNs).Here, we present W has been designed to provide an easy and convenient access to the broad scientific community. Predictions are readily available for download or presented in a web page that includes among other information links to relevant files, sequence information, and a Jmol applet to visualize and analyze the predictions in the context of the protein structure.PCRPi- Cellular tasks require highly precise and regulated communication between proteins. Whether a protein is part of a metabolic pathway, an intermediate signalling effector, part of the transcription machinery, or a component of the cytoskeleton -just to mention some examples- requires proteins to act as complexes rather than as isolated units. Thus, protein-protein interactions (PPIs) are ubiquitous in Biology and therefore offer an enormous potential for the discovery of novel therapeutic agents able to modulate PPIs.2The analysis of protein complexes for which tertiary structure is known, has shown that protein interfaces are large, typically between 1500–2000 Anghot spot, of the protein interface. The concept of hot spots in protein interfaces originates from the pioneering work of Clackson and Wells Part of the recent successes in the modulation of PPIs using small molecules has been possible by direct targeting of the important region, or The experimental identification of hot spots in protein interfaces by Alanine scanning W is a straightforward procedure. On the submission web page .Prior to prediction, structures undergo a set of quality checks. If atoms present alternative locations or rotamers, only the first occurring rotamer is kept. Also, if residues have insertion codes, the distance with neighboring residues is calculated and discarded if structurally equivalent. Side-chains with missing atoms are re-constructed using Scrwl 4.0 W features two types of BNs, a naïve and expert, that can be trained using two different datasets: Ab+ and Ab− . This job identifier can be used to check the status of the submission  and to retrieve the results by typing it in the ‘Job ID’ field at the submission web page.PCRPi- and Ab− . More inJobs are handled by a queuing system and, if not competing jobs, typically take few minutes to be completed; larger protein complexes featuring large or multiple interfaces can take up to one hour. The most time consuming is the estimation of the binding free energy, which for large interfaces and protein complexes requires intensive and long computational times, and the sequence search and calculation of sequence profiles for evolutionary-based measures.W returns a list of interface residues sorted by probability (submitting a task); a modified version of the input coordinates where the B-factor field has been substituted by a value that is equal to the prediction probability times 100  applet, see next); and a log file that records the entire prediction process and that can be examined if errors are reported.PCRPI-Other elements that are shown in the results web page is the mapping of predictions on the protein sequence and a Jmol applet that allows the visualization of the structure of the complex and the mapping of the predictions. The Jmol applet includes a clickable list of protein chains and residues sorted by probability , panel EW may fail to provide a prediction. The main reason is usually when the coordinates file contains only one protein chain or if more that one, these do not interact, i.e. no atomic interactions between protein chains. In this case, interface(s) cannot be located and therefore the program fails. More rarely, there can be errors along the prediction process, e.g. problems during free energy calculations or errors when deriving evolutionary-based measures, e.g. PSI-BLAST Occasionally, PCRPI-http://www.bioinsilico.org/PCRPi. Besides the option of submitting tasks to the server, users can browser an extensive documentation, have access to related resources available online, and download the benchmark and training datasets.PCRPi-W server is freely available upon registration to the scientific community at Several are the features that characterize the residues that are part of a hot spot and these have been exploited in the past for prediction purposes. These features can be broadly grouped in three categories depending on nature of the data. Hot spots can be predicted by energy, structural, and evolutionary-based (e.g. sequence conservation) analysis. Although these features are useful, it was shown that, individually, cannot unambiguously define hot spots The performance of PCRPi was benchmarked in two independent datasets W is implemented on an Apache server running on a Red Hat® enterprise linux-based operating system. The server is interfaced with a CGI Perl and Javascript coded web interface. PCRPI-W modules and accessory scripts are coded in Perl, Fortran, and C++ respectively. Databases required by the server, namely, PDB PCRPI-


Subcellular localization information is one of the key features to protein function research. Locating to a specific subcellular compartment is essential for a protein to function efficiently. Proteins which have multiple localizations will provide more clues. This kind of proteins may take a high proportion, even more than 35%.We have developed a database of proteins with multiple subcellular localizations, designated DBMLoc. The initial release contains 10470 multiple subcellular localization-annotated entries. Annotations are collected from primary protein databases, specific subcellular localization databases and literature texts. All the protein entries are cross-referenced to GO annotations and SwissProt. Protein-protein interactions are also annotated. They are classified into 12 large subcellular localization categories based on GO hierarchical architecture and original annotations. Download, search and sequence BLAST tools are also available on the website..DBMLoc is a protein database which collects proteins with more than one subcellular localization annotation. It is freely accessed at  Knowledge of subcellular localization is crucial to understanding protein function and biological process. During translation or later, proteins will be transported into different compartments such as cytoplasm, membrane system, mitochondrion, etc., or may be secreted out of the cell. Locating to a specific subcellular compartment is essential for a protein to function efficiently. High-throughput experimental approaches like immuno-localization, tagged However, a lot of proteins have more than one subcellular localization annotations. These proteins may simultaneously locate or move between different cellular compartments, for example, transcription factors and signaling pathway transduction factors. Proteins may play different roles in biological process when they are in different subcellular localizations. For these proteins, single subcellular localization annotation will lose some important information. Usually these proteins have more important biological functions. Their localization annotations will provide more valuable clues to researchers. These proteins are quite common, accounting for about 39% of all organellar proteins in mouse liver. HoweverThe DBMLoc database is mainly developed from primary protein databases (Swiss-Prot/TrEMBL), availaProteins that interact with each other tend to share the same subcellular localizations, so we annotate the protein with interaction data collected from DIP, MINT2424 and BIN1: Number of the localizations shared by its interaction proteins' subcellular localizations.N2: Number of protein's subcellular localizations.Q85FL3' record means that the protein entry is linked to SWISS-PROT database Q85FL3 entry. More detailed description of the format can be found on the web page.Finally, with some literature annotated proteins added, 10470 protein entries are integrated into DBMLoc database. The downloadable DBMLoc database and non-redundant sub-datasets are released as plain text files. The format is similar to that of Swiss-Prot data file. Each line in the file is one record of an entry in the 'KEY VALUE' format. The cross-reference records begin with a 'CX' key. Each of the value data contains one cross-reference record in the 'Reference Database: Reference ID' format, for example, the 'CX SWISS-PROT: We provide free download of the database, organism specific sub-datasets and taxonomy-categorized files for all the education and research users. Users can search the database with DBMLoc identity, cross-referenced database identity or protein name. Figures The initial release contains 10470 multiple subcellular localization-annotated protein entries. Non-redundant protein data sets with sequence similarity less than 90% and 25% are also generated by BLAST. Table Various databases' annotations integrated together in DBMLoc database might be false annotations or conflicts. So, we will pay more attention to the quality of data in the future development. More experimental data and other available information, like experimental method and post-translation modification, will be integrated to the database. The database will be updated regularly as new version of Swiss-Prot is available. Besides, more web services and analysis tools will be developed.DBMLoc is a specific database aimed at multiple localization annotated proteins. Proteins are cross-referenced to NCBI taxonomy, Gene Ontology and original database. Proteins that interact with each other tend to share the same subcellular localizations. So, protein-protein interaction information is also integrated into the database. A quality score is derived from protein-protein interactions. These data will be valuable to help experimental and computational biologists understand and analyze biological function.DBMLoc home page: License: The database is freely available.GO: Gene Ontology.SZ and XX designed and constructed the database. SZ drafted the manuscript. JS and YZ participated in data curation. ZS supervised the project. All authors read and approved the final manuscript.


The Structural Descriptor Database (SDDB) is a web-based tool that predicts the function of proteins and functional site positions based on the structural properties of related protein families. Structural alignments and functional residues of a known protein set (defined as the training set) are used to build special Hidden Markov Models (HMM) called HMM descriptors. SDDB uses previously calculated and stored HMM descriptors for predicting active sites, binding residues, and protein function. The database integrates biologically relevant data filtered from several databases such as PDB, PDBSUM, CSA and SCOP. It accepts queries in fasta format and predicts functional residue positions, protein-ligand interactions, and protein function, based on the SCOP database.To assess the SDDB performance, we used different data sets. The Trypsion-like Serine protease data set assessed how well SDDB predicts functional sites when curated data is available. The SCOP family data set was used to analyze SDDB performance by using training data extracted from PDBSUM (binding sites) and from CSA (active sites). The ATP-binding experiment was used to compare our approach with the most current method. For all evaluations, significant improvements were obtained with SDDB.SDDB performed better when trusty training data was available. SDDB worked better in predicting active sites rather than binding sites because the former are more conserved than the latter. Nevertheless, by using our prediction method we obtained results with precision above 70%. In the post-genomic era, functional identification of proteins and their interactions became an essential step for the understanding of the molecular machinery of the cell. Advances in structural genomics and proteomics -3 are prProtein-ligand interactions can be solved by assessing the co-crystallized ligand structure. Nowadays, there are several applications for this purpose. One such application is LigBase , a 3D liIn order to predict functional sites in proteins with unknown function, the PROSITE database  uses a mStructural Descriptor DataBase (SDDB) that uses our prediction method and allow the detection of functional sites, biological activity, and of ligand interactions in query sequences.Herein, we present a method based on hidden Markov models (HMM) that uses functional sites of known ligand-protein interactions to predict functional sites in query sequences. Our method was based on the study done by Henschel et al , with so. In this website, query sequences can be scored by using a library of HMM descriptors. The SCOP classification, functional residue positions, and ligand binding interactions are considered in order to indicate the sequence most likely function. Also, SDDB contains links to all related databases, which allows the user to have immediate access to additional information on the protein of interest.The SDDB was built with information from several databases, namely the PDB , SCOP 2, PDB-SUMIn order to validate our prediction method, we performed experiments using three different data sets. We started by assessing one specific family, namely the Trypsion-like Serine proteases. Next, we assessed the remaining SCOP families. Finally, we compared our results with those on the ATP-binding study done by Henschel and colleagues . In the In our first experiment, we only considered SCOP sequences from the Trypsion-like Serine protease family. We selected a total of 39 proteins. Sequences were separated into 8 groups, according to ligand specificity. These groups are Chymotrypsin, Elastase, Granzyme, Thrombin, Urokinase, Trypsin, Plasminogen Activator, and a hybrid group formed by Procarboxypeptidase A-S6 subunit III, Granzyme K, and Neuropsin. Table We performed cross validation by applying the leaving one out method , in eachTable The second experiment was performed considering the SCOP families selected for this study (see Method section for election criterion), with the exception of the Trypsion-like Serine protease family. A total of 1,901 families were selected. Similarly as in the first experiment, we performed cross validation by applying the leave one out test . SequencThe SCOP families experiment achieved precision of 84% and recall of 70.8%, in predicting the active site. For binding site prediction, precision and recall were 78% and 57%, respectively. As observed in the first experiment, our methodology obtained better results in predicting active sites rather than in predicting binding sites. Indeed, active site residues are more conserved than binding residues. Therefore, we applied different levels of tolerance when defining an alignment column into a binding or active site. For instance, we set as binding site columns those that presented more than 50% of their binding residues aligned, whereas for active sites columns we required 80% of active residues aligned. These parameter settings narrowed the active site predictions because they decreased the number of false positives that could be incorporated in the training data, which in turn increases precision. The recall also increases because fewer cases of false negatives are incorporated in the active site column.We assessed the classification with respect to the SCOP database, and found that all proteins were correctly classified into the SCOP class, fold, super family, and family. Around 96% of the proteins were classified into their correct domain.We compared our results with those obtained by Henschel and colleagues . For thaTo evaluate the performance of HMM descriptors, we built precision-recall curves for our first and second experiments. Figure With this study we aimed to develop a web-based tool that allowed the use of a computational method for the prediction of functional sites and protein function. For that, we created a prediction method based on the study of Henschel and colleagues  in whichOur web-accessible database is a user friendly interface that allows users to analyze their own data. After setting the parameters, users can submit their protein sequences. Each protein is then analyzed and results are sent by email. Each sequence submitted is scanned against a library of HMM descriptors and results are displayed in two levels. In the first level of results, it shows all predicted functional residues and protein interactions with its ligands. In the second, it shows the predicted functional residues for each interacting ligand, individually. HMMs and alignments are available for download in the result page.To validate our prediction method, we performed experiments that achieved significant precision-recall ranges. Our approach is accurate in determining protein-ligand interactions and interaction-residues positions. Therefore, SDDB is useful for functional annotation and for predicting functional residues. Knowing the positions of functional residues may provide insights into the biological activity of the protein and reveal new targets for drug development.The web-based tool was developed in Java version 1.5 and is now served through a Tomcat apache web server running on a Linux operating system. The web-site was built with servlets, JSP, and java scripts technologies. To store our data, we built a relational database manager using Postgres, version 8.2. The SDDB was populated with data extracted from public databases such as SCOP, version 1.71, PDB , PDBSUM  and CSA version 2.2.5. PDB is a repository for 3D structural data of proteins widely used in structural proteomic analysis. SCOP is a manually inspected database of protein folds that is particularly interesting to our study because it describes structural and evolutionary relationships between proteins, including all entries in PDB. PDBSUM is a database that provides, for each PDB file, a summary of the key structural features, including protein-ligand interaction and binding sites positions. Finally, CSA catalogs catalytic residues in enzymes of 3D structures. Based on the family level of SCOP, we extracted and classified the majority of protein-ligand interactions found in the PDB. HMMs were built from structural alignment derived from 3DCOFFEE, using HMMER package. This stage is the so-called training phase (see below). The Inference phase is the step in which query sequences are submitted to SDDB tool.In this phase data was fed into the SDDB to be later used for inference. Initially, we used other databases to obtain the necessary data such as SCOP families, PDB structure files, ligands, and functional ligand residues. Next, structural alignments were built from the SCOP protein group using 3DCOFFEE. Finally, HMMER was performed to build HMM descriptors from both structural alignments and from functional ligand residues.T was not in the range , where M is the average size of the sequences in its respective SCOP family. This step is necessary because some of the SCOP family sequences greatly vary in size, which results in alignments with many gaps. As a result, HMMs built from a gapped alignment do not recognize homologous proteins well. With this initial data processing we obtained 1,902 families with 70,215 sequences.Initially, we selected all proteins in each SCOP family, except for those proteins without either ligand interactions or shared ligand interactions. Also, we removed the proteins whose size Next, selected sequences were separated in groups according to the SCOP family level. For each sequence, we extracted from PDB its three-dimensional structure required for the structural alignment. From PDBSUM, we extracted the set of ligands that interact with each sequence and for each ligand we also extracted its protein binding sites. Figure f be an arbitrary SCOP family. All proteins of f make up the core structural group of this family, namely SGf . The alignment of SGf proteins was called Af and it was used as the starting point for the construction of the HMM descriptor that represents the f, called by HMMf. A family f is made up of a set of proteins P = {p1,...,pQ}, where Q is the number of sequences within an f. Each protein pi ∈ P interacts with a set of ligands L = {L1,...,Ln} downloaded from the PDBSUM. Each family f was divided into groups of proteins that interact with the same ligand. Then, a group was created only by proteins of f that interact with ligand Li. This group was called SGfLi . Each SGfLi was defined as the side structural group. The alignment of SGfLi proteins was called AfLi. This alignment was the starting point for the construction of the HMM descriptors, namely HMMfLi. An HMM descriptor was built using both the MSA (Af or AfLi) and the alignment-columns, which corresponded to the residues of the functional sites (see below). Figure Let p within the alignment has a set of amino acids that form, in regard to its interaction with a specific ligand, its functional sites, i.e. the ligand determines the set of functional sites of that protein. Because each protein p in AfLi has a set of functional sites associated to the Li interaction, we directly applied the algorithm for the identification of the functional sites. On the other hand, the Af alignment requires knowing the cumulative sites for all involved proteins, before applying the algorithm for the identification of functional sites. The cumulative sites of a protein p were calculated by combining sets of functional sites of the protein p with each of its ligands. Thus, if a protein p has interactions with ligands L = {L1,...,Lj}, a set called pLk includes the positions of the functional sites that are associated to the interaction between p and Lk, where k ≤ j. As a result, the cumulative sites of a protein p that has the set of ligands L is created by pL = pL1 ∪... ∪pLj.The building of HMM descriptors required both the entire MSA and the columns referring to the amino acid alignment of the functional sites. Each protein A, there is a array B, with all elected columns of A, that represent functional site positions in A. Therefore, Bf contained functional sites of proteins of f and BfLi contained functional sites of a subset of proteins of f that interacts with the Li ligand.After identifying the cumulative functional sites, we applied the algorithm described below. Each column in an MSA is individually assessed. Whenever 50% of the sequences contained a binding residue in that column, the column was marked as a binding site of the alignment. If 80% of the sequences contained an active residue at that column, then this column was marked as an active site of the alignment. Therefore, for each alignment SGf of a arbitrary SCOP family denoted by f, we built a HMM from Af and Bf. We also built an HMM for each alignment in SGfL, where L is the set of ligands that interact with proteins of the f. Next, we built an HMM for each protein in Af and AfLi as follows. Each alignment contains a P = {p1,...,pn} protein subset, where each pi is a protein of the f, since i ≤ n ≤ Q. For each pi an HMM was built from its alignment with the consensus-sequence of the HMM that contains pi. The consensus-sequence is obtained from the alignment used to build the HMM, considering the most frequent amino acids in each column. These HMMs, called HMM classifications, were built to aid the SCOP classification of proteins of unknown function. Figure f1. First, the consensus-sequence  is extracted from pi in f1 , is aligned with consensus-sequencef1 through hmmalign program of HMMER package. This alignment is called Acspi, where pi ∈ p1,...,pQ} and Q is the number of sequences in f1. Each Acspi results in the building of The HMM descriptors were built using an HMMER package. Given a p is submitted to the HMMf, where f represents the families selected by the user. Let p by HMMf. If θf, p is accepted, i.e, it is likely that p is a member of the f. The θf is the threshold necessary in order for p to belong to f. When p is accepted, the cumulative functional site positions and the p protein function are predicted, followed by p scoring by all HMMfLi, where Li ∈ L and L is the set of all ligands that interact with proteins of f. Similarly, if p is accepted, where p by HMMfLi, and p interacting with Li. When p is accepted, the functional sites of interaction between p and Li are predicted.After establishing and characterizing the training phase, the web-tool can then be used for predicting functional sites and protein function of user query sequences. Users can submit sequences in fasta format to the web site. Each sequence Bs1 that represents a binding site position in the alignment mapped to the match state M2. Similarity, the column labeled As1, which represents an active site position in the alignment, mapped to the match state M8. Note that the column labeled Bs3 mapped an insert state I15. Hence all columns in the alignment were represented by one HMM state. When a protein p is scored by an HMM, the Viterbi algorithm , which describes the probability of observing a protein p within HMMf. The Viterbi algorithm give us both Pr[p|HMMf] and π, where π is the best path of p through HMMf states. If π is given by M1M2I3...Mn, then it means that a1 was recognized by the match state M1, a2 was recognized by the M2, a3 was recognized by the insert state I3, until an was recognized by Mn. Therefore, if M2 is a state that represents a binding site, it is likely that a2 in p is a binding site residue.The alignment-columns containing functional sites were mapped to the HMM states, i.e, either match or insert states, as shown in figure lgorithm  providess figure , so we wp protein to an HMM descriptor and verifying that its e-value is smaller than θf, it is necessary to determine the following parameters for the SCOP classification: class, fold, super family, family, and domain. We annotated p by comparing it with all HMM classifications (see section HMM construction) and by associating to p the same classification of pi, where pi is the protein most similar to p, and which belongs to the HMM classification. For instance, suppose that some protein p has been scored by p has been classified with e-value p is scored by each pi ∈ {p1,...,pQ} and Q is the number of sequences in f1. Each p. Suppose p receives the SCOP classification of p1.After submitting a JSB performed the studies and participated in writing the manuscript. JHF participated in the design of the study and ATRV participated in the design and coordination of the study, and in writing the manuscript. All authors read and approved the final manuscript.


It has long been known that small regions of proteins tend to fold independently and are then stabilized by interactions between these distinct subunits or modules. Such units, also known as autonomous folding units (AFUs) or"foldons" play a key role in protein folding. A knowledge of such early folding units has diverse applications in protein engineering as well as in developing an understanding of the protein folding process. Such AFUs can also be used as model systems in order to study the structural organization of proteins.In an earlier work, we had utilized a global network partitioning algorithm to identify modules in proteins. We had shown that these modules correlate well with AFUs. In this work, we have developed a webserver, GANDivAWeb, to identify early folding units or "foldons" in networks using the algorithm described earlier. The website has three functionalities: (a) It is able to display information on the modularity of a database of 1420 proteins used in the original work, (b) It can take as input an uploaded PDB file, identify the modules using the GANDivA algorithm and email the results back to the user and (c) It can take as input an uploaded PDB file and a results file ) and display the results using the embedded viewer. The results include the module decomposition of the protein, plots of cartoon representations of the protein colored by module identity and connectivity as well as contour plots of the hydrophobicity and relative accessible surface area (RASA) distributions.We believe that the GANDivAWeb server, will be a useful tool for scientists interested in the phenomena of protein folding as well as in protein engineering. Our tool not only provides a knowledge of the AFUs through a natural graph partitioning approach but is also able to identify residues that are critical during folding. It is our intention to use this tool to study the topological determinants of protein folding by analyzing the topological changes in proteins over the unfolding/folding pathways. It is recognized that small regions of proteins tend to fold independently and are then stabilized by interactions between these distinct subunits or modules. The dissection of proteins into structurally independent and functionally distinct subunits led to the idea that proteins can be considered as collections of smaller units such as domains . Differeet al. [M is given byIn an earlier work , we had et al.  results Nm is the number of modules, L is the number of links in the network, l is the number of links between nodes in module s and d is the sum of the degrees of the node in module s. In doing so, this allows the representation of the residues of the protein in terms of their intra-module degree, z and participation coefficient, P., which are given bywhere κi is the number of links of residue i to other residues in its module si, κ over all residues in module sj, κ in module sj, κis is the number of links of node i to nodes in module s and ki is the total degree of node i.where P/z characterization, pointing to a common topological design of all protein structures. This invariant representation, applied to different protein systems enabled us to identify the possible functional role of high P/z residues during the folding process. Effectively, this invariance is a cartographic representation of the contact network for proteins and is represented by the plot of the residues in the P - z space. Since it is identical for all the proteins, it does not embed any structural peculiarities or information for separating between different protein folds [We demonstrated that the labeling of residues in terms of these invariants, allowed for information rich representations of the studied proteins as well as to sketch a new way to link sequence, structure and the dynamical properties of proteins. We discovered a strong invariant character of protein molecules in terms of in folds ,12.We also observed that the modules identified using the procedure outlined above correlated well with early folding units or "foldons" and thus a knowledge of the modules existing in a given protein can help to identify residues that are critical for folding.P/z| values tend to be protected during transition state and hence are fixed early in the folding process.A significant use for the modules identified using our methodology is for the development of algorithms for protein 3D structure determination. In addition knowing the modules for a protein can help in the understanding of the folding pathway for that protein since residues with high |The modules can also be used for engineering new enzymes which is typically carried out by building a chimera of multiple proteins by cutting and pasting sequences from the respective proteins. A knowledge of the modules can guide the cuts in order to obtain chimeras that can fold in-vitro. In addition models of such early folding units can be invaluable in understanding the biochemical pathways of diseases that are known to be pathological through partially folded forms of proteins leading to the development of therapeutics.The GANDivAWeb webserver is based on the GANDivA (Genetic Algorithm-based Network modularity DetectIVe Algorithm) algorithm which is an implementation of the Guimera algorithm  that use1. It displays the results from the application of the modularity algorithm GANDivA on a set of 1420 single-chain, globular proteins that were used as the basis for the work done in .2. The main functionality of the website however, is the ability to process an uploaded protein structure file (in PDB format) using GANDivA and to send the results back to the user. Since GANDivA is a stochastic algorithm, the algorithm is run a number of times and the best results, as denoted by the largest modularity score obtained, is emailed back to the user. The user can set certain parameters like the maximum number of modules to be determined, the number of times to run the algorithm, the number of generations for the genetic algorithm as well as the number of generations for the fitness score to remain constant before it is assumed to have converged to a solution. An algorithm that takes into account the size of the protein (number of amino acid residues) as well as the number of jobs in the queue for the cluster, notifies the user about the expected time required for the completion of the job. The results emailed back to the user are made up of the following parts:z), inter-module participation coefficient (P), P/z, |P/z|. The second column indicates the module to which each residue belongs. In addition to the results file, five different figures are generated.• A results file that contains the details of the modular decomposition of the protein. The results file 1(A) contains the modularity score in the first line. This is succeeded by the following columns: residue number, module number, intra-module connectivity  and hydrophobicities respectively for each residue followed by partitioning the P - z space into an 8 × 10 grid and then calculating the mean value of the RASA and hydrophobicities in each bin. The mean value for each cell of the 8 × 10 grid was then plotted as a contour plot. Significant differences have been observed between these distributions for native and decoy proteins, with native distributions showing more structure (inclusive of two clearly defined regions where hydrophobic/low RASA residues are embedded).• The fourth  and view the results in the embedded JMol viewer. For both functionalities 1 and 3, the user can choose to view the protein colored by the modules that the residues belong to or by the |Figure P/z| valued residues are identified. These residues have been shown [The GANDivAWeb server has been designed to partition any given PDB structure into modules. The modules thus obtained correlate well with autonomous folding units. Additionally, high |en shown  to correWe believe that the GANDivAWeb server will be of immense use to scientists interested in the phenomena of protein folding and those studying the architectural and structural organization of proteins.Additionally, the site will also be useful for scientists interested in the engineering of novel enzymes by providing them with a modularized view of the protein.The webserver can be found at AK wrote the main algorithm and wrote the paper. TL designed and implemented the webserver and also helped in the writing of the paper. MT was in charge of the overall project.