The Human Immunodeficiency Virus (HIV) is one of the pathogens that cause the greatest global concern, with approximately 35 million people currently infected with HIV. Extensive HIV research has been performed, generating a large amount of HIV and host genomic data. However, no effective vaccine that protects the host from HIV infection is available and HIV is still spreading at an alarming rate, despite effective antiretroviral (ARV) treatment. In order to develop effective therapies, we need to expand our knowledge of the interaction between HIV and host proteins. In contrast to virus proteins, which often rapidly evolve drug resistance mutations, the host proteins are essentially invariant within all humans. Thus, if we can identify the host proteins needed for virus replication, such as those involved in transporting viral proteins to the cell surface, we have a chance of interrupting viral replication. There is no proteome resource that summarizes this interaction, making research on this subject a difficult enterprise. In order to fill this gap in knowledge, we curated a resource presents detailed annotation on the interaction between the HIV proteome and host proteins. Our resource was produced in collaboration with ViralZone and used manual curation techniques developed by UniProtKB/Swiss-Prot. Our new website also used previous annotations of the BioAfrica HIV-1 Proteome Resource, which has been accessed by approximately 10 000 unique users a year since its inception in 2005. The novel features include a dedicated new page for each HIV protein, a graphic display of its function and a section on its interaction with host proteins. Our new webpages also add information on the genomic location of each HIV protein and the position of ARV drug resistance mutations. Our improved BioAfrica HIV-1 Proteome Resource fills a gap in the current knowledge of biocuration.http://www.bioafrica.net/proteomics/HIVproteome.htmlDatabase URL:  The objective of the BioAfrica HIV-1 Proteomics Resource is to provide accurate and comprehensive information on Human Immunodeficiency Virus (HIV) proteins. The first version of the resource was published in 2005  in order to allow much of the knowledge produced by their independent projects to be synergized and presented in a new online section of the Swiss and South Africa academic websites. One of our main goals was to add information on ARV drug resistance and to generate new knowledge of the pathogen and host interaction. We also aimed to represent current understanding in an innovative and interactive way with graphical images and with links to relevant protein databases. We applied biocuration methods developed by UniProtKB/Swiss-Prot, which included manual extraction and structuring of information from the literature, manual verification of results from computational analyses, and mining and integration of large-scale datasets. The resource now includes protein structure and function, gene expression, post-transcriptional and translational modification, protease cleavage sites, drug resistance information and HIV and host protein interactions. In this biocuration paper, we present the upgrade of the BioAfrica HIV Proteome Resource and its synergetic interaction with ViralZone/SIB.The original BioAfrica HIV-1 Proteome Resource and ViralZone were used as a starting point for the biocuration process. A team of researchers from South Africa and Switzerland updated the information using manual curation in accordance with Swiss-Prot standards . This in(i) The General Overview contains information on the main function and localization of the protein in the HIV-1 replication cycle. This section describes: (a) the main function of the protein, (b) protein isoforms, (c) protein cleavage sites and when these exist, (d) localization of the protein within the virus and host cell and (e) additional information on protein function. The objective of this section is to allow users to access key information on the function of each HIV-1 protein as well as on key online resources. This was the last section to be curated as it summarizes information presented in the other sections of the webpage. It also provides key links to other online resources such as ViralZone  HIV repl(ii) The section on Protein Function and Host–Virus Protein Interactions was produced from a critical literature review of experimental and predicted host protein interaction for each HIV-1 protein. All interactions were manually verified for each protein sequence in UniProtKB. The interactions were summarized in graphical images that were created in Illustrator, following the standard process in ViralZone . One of (iii) The Genomic Location and Protein Sequence section presents the genomic coordinates and amino acid sequence for each HIV-1 protein. The genomic location is presented as a graphical image and was produced by Rega Subtyping Tool Version 3.0  The Protein Domains/Folds/Motifs section contains information from the analysis of the amino acid sequences of the reference protein by InterPro  and Proshttp://hivdb.stanford.edu). HIVDB also regularly provides an updated, in-depth, referenced summary of HIV drug resistance mutations. This is displayed in tables, with links to detailed information on how the mutations act on the HIV proteins in order to cause resistance. (http://bioafrica.mrc.ac.za/hivdb/pages/download/resistanceMutations_handout.pdf). This section contains a summary of the mechanism of action of the ARVs, which is followed by a table that highlights the drug resistance mutations and the ARVs affected. It was curated in collaboration with Stanford University HIV Drug Resistance Database (Stanford HIVdb) and links back to the respective pages on the Stanford HIVDB ((v) The HIV ARVs and Drug Resistance Mutations section is presented for the HIV-1 proteins that are targeted by ARVs. The section links information housed in ViralZone  and the rd HIVDB . We experd HIVDB .http://www.ncbi.nlm.nih.gov/structure), Uniprot (http://www.uniprot.org/), EMBL-EBI (http://www.ebi.ac.uk/Tools/dbfetch/), InterPro (http://www.ebi.ac.uk/interpro/), Pfam (http://pfam.xfam.org/), SCOP (http://supfam.org/SUPERFAMILY/index.html), BLOCKS (http://blocks.fhcrc.org/), Prosite (http://prosite.expasy.org/), ProtoNet (http://www.protonet.cs.huji.ac.il/index.php), ModBase (http://modbase.compbio.ucsf.edu/modbase-cgi/index.cgi) and the HIV-1 Human Protein Interaction DB at NCBI (http://www.ncbi.nlm.nih.gov/gene/155971).(vi) The Primary and Secondary Database Entries section provides links to related databases within each protein page of the HIV-1 Proteome Resource. All of the links are provided to the HIV-1 reference genome (HXB2). We have selected 15 well-curated databases to link to, including, among others, PDB/MMDB (http://www.uniprot.org/uniprot/P04608). The BioAfrica Proteome Resource is a part of http://www.bioafrica.net website. All pages from the previous version of the BioAfrica Proteome Resource are still online. However, we modified the html address to ‘proteomics2005’ (e.g. http://www.bioafrica.net//proteomics2005/POL-PRprot.html). This allows a comparison between the old and the newly upgraded resource (e.g. http://www.bioafrica.net/proteomics/POL-PRprot.html). In addition, Supplem entary Figure 1 shows the old and upgraded protease pages.All of the proteome pages end with a list of the referenced articles, which are linked to PubMed. All of the links provided are functional and a script has been developed to check them every quarter. All of the updated webpages in the resource kept the original Bioafrica HIV proteomics resource html links. The reason for that is that these webpages also receive many links. For example, all of the 22 webpages of the resource are cross-linked from Uniprot in their HIV-1 reference sequence (i.e. HXB2) annotation page  and their interaction with host–proteins. Each protein is also described in detail in the six sections of the manuscript, which mimic the sections of our online resource. We use the Env gp120/gp41 and Rev annotation pages as examples to present some of the results of the biocuration process. Other proteins pages are available at The HIV-1 genome is approximately 9.75 kb in length. It codes for only nine open reading frames (ORFs), including the Gag, Pol, Env, Tat, Rev, Nef, Vif, Vpr and Vpu genes. The HIV-1 genome is capable of producing 19 proteins via alternative splicing, alternative translation, ribosomal frameshift, alternative initiation and post-translational cleavages . Within http://bioafrica.net/proteomics/ENV-GP120prot.html and Each HIV-1 protein page starts with a general overview. For example, the main function of the gp120 protein is viral attachment. This protein is an external membrane glycoprotein. It is localized at the host cell plasma membrane and virion envelope . In addition, gp120 has been shown to interact with DC-SIGN/CD209 on the surface of dendritic cells to enhance virion transmission and infection. DC-SIGN also facilitates mucosal transmission by transporting HIV to lymphoid tissue  as a second example, we show the Rev-mediated export of unspliced or incompletely spliced viral RNA transcripts from the host nucleus to the cytoplasm, facilitated by various Rev-host protein interactions (Using the Rev protein page (ractions . Host–viractions  and B23 ractions  form a cractions . Followiractions . DDX3 (1ractions  and Sam6ractions  bind to ractions .http://www.uniprot.org/) pages. Together, the Protein Function and Host–Virus Protein Interactions section provides users with an illustrated description of the role of the HIV-1 protein within the virus life cycle as well as descriptions of host–virus protein interactions linked to relevant publications and resources. All interactions listed have been proved by dozens of experiments, which were manually curated from the literature. In addition, all host and virus proteins have been linked to their appropriate UniProt .As in the original version of the BioAfrica HIV-1 Proteomics Resource, the section on protein domains/folds/motifs includes information about the predicted motifs and structure of the protein as well as the protein functional domains . This sewww.bioafrica.net/proteomics/POL-PRprot.html), reverse transcriptase (http://www.bioafrica.net/proteomics/POL-RTprot.html), integrase (http://www.bioafrica.net/proteomics/POL-INprot.html) and envelope gp120/gp41 proteins (http://www.bioafrica.net/proteomics/ENVprot.html). For each of these protein pages, there is an additional section that provides users with an overview of the current ARVs. Information in this section also includes descriptions of the type and position of the mutation and relevant publications. Using Env as an example, there is only one fusion inhibitor that has been approved for HIV treatment, namely Enfuvirtide. This 36 amino acid polypeptide binds to the heptad repeat (HR) regions of the gp41 viral protein and engages in a coil-coil interaction. This interaction inhibits the fusion of viral and cellular membranes and thus prevents the entry and infection of HIV. There are six drug resistance mutations  that have been well described in the literature , protein domains/folds/motifs and host and virus protein-protein interactions. We believe that the easy access to well curated and current information will advance HIV drug resistance and HIV vaccine research and will provide a better understanding of the interaction between the host and the virus.Supplementary data are available at Database Online. the Swiss South African Joint Research Programme (SSJRP) research grant entitled "Swiss Prot / South Africa: Protein Bioinformatics Resource Development for Important Health- related Pathogens. The Swiss Federal Government through the State Secretariat for Education, Research and Innovation. Flagship grant from the Medical Research Council (MRC) of the Republic of South Africa (MRC-RFA-UFSP-01-2013/UKZN HIVEPI) the Wellcome Trust (082384/Z/07/Z) a Royal Society Newton Advanced Fellowship (T de Oliveira).Conflict of interest. None declared.


Protein-DNA interactions are essential for many biological processes. However, the structural mechanisms underlying these interactions are not fully understood. DNA binding proteins can be classified into double-stranded DNA binding proteins (DSBs) and single-stranded DNA binding proteins (SSBs), and they take part in different biological functions. DSBs usually act as transcriptional factors to regulate the genes' expressions, while SSBs usually play roles in DNA replication, recombination, and repair, etc. Understanding the binding specificity of a DNA binding protein is helpful for the research of protein functions.In this paper, we investigated the differences between DSBs and SSBs on surface tunnels as well as the OB-fold domain information. We detected the largest clefts on the protein surfaces, to obtain several features to be used for distinguishing the potential interfaces between SSBs and DSBs, and compared its structure with each of the six OB-fold protein templates, and use the maximal alignment score TM-score as the OB-fold feature of the protein, based on which, we constructed the support vector machine (SVM) classification model to automatically distinguish these two kinds of proteins, with prediction accuracy of 87%,83% and 83% for HOLO-set, APO-set and Mixed-set respectively.We found that they have different ranges of tunnel lengths and tunnel curvatures; moreover, the alignment results with OB-fold templates have also found to be the discriminative feature of SSBs and DSBs. Experimental results on 10-fold cross validation indicate that the new feature set are effective to describe DNA binding proteins. The evaluation results on both bound (DNA-bound) and non-bound (DNA-free) proteins have shown the satisfactory performance of our method. The family of DNA binding proteins is able to recognize and bind to DNAs, and they play vital roles in many biological processes such as DNA replication, recombination, repair, transcription, translation, and maintenance of telomeres, and so on -4. ThereWith the development of biotechnology, a large amount of proteins have been sequenced. However, SSBs have shown to have little sequence conservation . Even DSThe surface of a protein is generally irregular, containing many clefts and grooves of varying shapes and sizes . PreviouResearch results have shown that although the sequences of different SSBs are very different, there are well-conserved elements in the structures. That is, most SSBs contain one or more OB (oligonucleotide/oligosaccharide binding) -fold domains ,15-18. AIn this paper, we aim to investigate the structural differences between collected SSBs and DSBs, and extract the structure-based features related to surface clefts and OB-folds, based on which, we construct a computational model that can automatically classify the DNA protein as a DSB or SSB by using the widely used support vector machine (SVM). The promising performance suggests that our method will be useful in the protein function annotation and refinement.http://dunbrack.fccc.edu/PISCES.php) [We first extracted the structures of all 3390 DNA binding proteins from PDB  according to their annotations, which contain 1039 DSBs , 158 SSBs  and 2193 unknowns. Then we use PISCES . For example, for protein 1A73, CAVER detects out 27 tunnels shown in Figure OB-fold is a small structural motif that was first characterized in 1992 in four proteins that bind either oligonucleotides or oligosaccharides . TypicalConsidering that OB-folds evolve into several variants though they are very conserved, we choose the chain A of six typical proteins  shown iFor an unknown protein, we use the protein structure alignment package TM-align  to compaIn this work, we used support vector machine (SVM) to build the classification model. The SVM classifiers were implemented using Matlab 2012a SVM package with the Gaussian Radial Basis Function (RBF) as a kernel.In order to evaluate the performance of the prediction results, we used several measures, including Accuracy, Sensitivity, Specificity, and F-measured and area under the receiver operating characteristic curve (AUC). Let TP (true positive) is the number of proteins correctly predicted as SSBs, FP  is the number of proteins incorrectly predicted as SSBs, TN (true negative) be the number of proteins correctly predicted as DSBs and FN  be the number of proteins incorrectly predicted as DSBs. The accuracy (ACC), sensitivity (SN), specificity (SP), F-measured (F1) and Matthews Correlation Coefficient (MCC) are defined as the following:We use 10-fold cross validation test to evaluate the classification performance. Because of the unbalance of different kinds of proteins, in each fold we iterate 15 times to randomly select the equal numbers of SSBs and DSBs into the train set by using down-sampling method, and use the voting strategy to assign the class label of the test protein. To the best of our knowledge, there is no computational method to distinguish SSBs from DSBs, therefore we also train the random classifier as the baseline in each test.By using CAVER3.0, we have detected 990 tunnels from HOLO set , and 1168 tunnels from APO set . According to the maximizing criterion described above, we selected one maximal tunnel for each protein. As a result, we finally got 37 tunnels for bound (DNA-bound) SSBs, 38 tunnels for unbound (DNA-free) SSBs, 154 tunnels for bound DSBs and 51 tunnels for unbound DSBs. Accordingly, we also got three feature values for each tunnel. By using TM-align, we aligned every protein with each of the six OB-fold templates shown in Figure This table shows the values of Pearson coefficient and P-value between tunnel length and curvature. The columns of Pearson coefficient and P-value correspond to the pairs of DSBs/SSBs in HOLO set and APO set, respectively.We have done the validation experiments on HOLO set and APO set by using one, two or three features to construct the classification models. The validation performances are shown in Table In many cases, it is easier to collect information on DNA binding proteins in the bound form than in unbound form, whereas we need to know whether an unknown unbound protein be SSB or DSB. Thus, we train the classifier on HOLO set and test it on APO set. The results are listed in Table In practice, we often found the available dataset include not only the bound form proteins, but also the unbound form proteins, whereas we need to know whether an unknown DNA binding protein be SSB or DSB. Thus, we have done the validation experiments on the mixed set by using one, two or three features construct the classification models. The results are listed in Table Despite many similar properties, dsDNA and ssDNA possess distinctive entities that are recognized differently by specialized dsDNA and ssDNA binding proteins, respectively. SSBs and DSBs binding interfaces are thus expected to differ in their geometrical features consistent with the different nature of dsDNA and ssDNA ,38,39. WThe protein surface features should also be useful for the analysis of other types of molecular interactions, such as protein-ligand, protein-RNA, and protein-protein complexes, and for the study of a variety of proteins, multiple binding sites or a specific family of proteins. These problems would require modelling interface surfaces of different characteristics such as compatibility, different sizes, and cooperatives between these surfaces, thus new surface features in addition to the solid angle may be needed.DSBs: double-stranded DNA binding proteins; SSBs: single-stranded DNA binding proteins; ssDNA: single-stranded DNA; dsDNA: double-stranded DNA; OB-fold: OB (oligonucleotide/oligosaccharide binding) -fold; ACC: accuracy; SN: sensitivity; SP: specificity; F1: F-measured; MCC: Matthews Correlation Coefficient. AUC: area under the receiver operating characteristic curve; TP: true positive; FP: false positive; TN: true negative; FN: false negative.The authors declare that they have no competing interests.W.W., J.L. contributed to the software design and testing. W.W. and X.Z. implemented the software. W.W. and J.L. wrote this paper. All authors read and approved the final manuscript.This file contains the complete list of PDB codes for DNA-binding proteins set.Click here for fileThis file describes the classified results of the unknown proteins by the mixed set classifier.Click here for file


LRRK2 was identified in 2004 as the causative protein product of the Parkinson’s disease locus designated PARK8. In the decade since then, genetic studies have revealed at least 6 dominant mutations in LRRK2 linked to Parkinson’s disease, alongside one associated with cancer. It is now well established that coding changes in LRRK2 are one of the most common causes of Parkinson’s. Genome-wide association studies (GWAs) have, more recently, reported single nucleotide polymorphisms (SNPs) around the LRRK2 locus to be associated with risk of developing sporadic Parkinson’s disease and inflammatory bowel disorder. The functional research that has followed these genetic breakthroughs has generated an extensive literature regarding LRRK2 pathophysiology; however, there is still no consensus as to the biological function of LRRK2. To provide insight into the aspects of cell biology that are consistently related to LRRK2 activity, we analysed the plethora of candidate LRRK2 interactors available through the BioGRID and IntAct data repositories. We then performed GO terms enrichment for the LRRK2 interactome. We found that, in two different enrichment portals, the LRRK2 interactome was associated with terms referring to transport, cellular organization, vesicles and the cytoskeleton. We also verified that 21 of the LRRK2 interactors are genetically linked to risk for Parkinson’s disease or inflammatory bowel disorder. The implications of these findings are discussed, with particular regard to potential novel areas of investigation. LRRK2 is the most frequently mutated gene in familial Parkinson’s disease (PD) (LRRK2 locus with increased risk of developing inflammatory bowel disorder (IBD)  is a large enzyme hosting kinase and GTPase functions surrounded by protein–protein interactions domains  , and hasase (PD) . There aer (IBD) . LRRK2 h leprosy , althoug leprosy . Furtherulations . The pro domains . It is n domains . The imp domains . None of domains . The reaunctions .In the past decade, a number of databases have been established to collect protein annotations from the published literature and high-throughput datasets . These rad hoc basis (>1200 PubMed entries as of 30th October 2014) and where the range of techniques and models can cloud interpretation, a bioinformatics approach using freely available, manually-curated protein–protein interactions (PPI) datasets provides a powerful tool to develop a picture of LRRK2 in the cell environment and infer functional implications.For a protein such as LRRK2, where the volume of functional literature exceeds the capacity of researchers to analyse on an in silico investigation of the LRRK2 interactome. Taking advantage of the manually curated datasets stored in the BioGRID and IntAct repositories  the same experiment was performed using differently tagged A and B interactors . To apply filter #2, the following cut-off was established: 2 entries with the same “Publication Identifier(s)” and “#ID(s) interactor A and ID(s) interactor B”, were considered duplicated  if “Interaction detection method(s)” AND “Host organism(s)” were identical.The IntAct database  was querAs a general note, protein fragments can be retrieved with the same UniProtKB identifier as for the entire protein  has the same identifier as the entire MAP1B protein (P46821)); protein isoforms are queried by the same UniProtKB identifier as for the principal isoform  has the same identifier as DVL2 (O14641)). For this reason no discrimination was made between protein fragments and the entire protein, nor between different protein isoforms and the principal isoform.BioGrid database  was querGO enrichment analysis has been performed by the use of 2 different web-based applications.http://bioinfo.vanderbilt.edu/webgestalt/ was used with the following parameters: Reference set for enrichment analysis: h.sapiens_genome; Enrichment: GO analysis; Datasets: biological process and cellular component. Analysis was performed using hypergeometric statistics and Bonferroni’s correction for multiple testing. The minimum number of genes was set at 2 and the significance level was set to retrieve the top10 hits. All the input proteins were mapped. GO enrichment  .  http://bst 2014)  http://wPD GWAs meta-analysis  was use http://www.cytoscape.org/)  and Cytoscape  .The IntAct database was queried for LRRK2 , and 542 unfiltered annotations were downloaded. After applying filter #1 to include only human interactions and filter #2 to remove multiple annotations with similar experimental details in the same publication , the total number of annotations dropped from 542 to 307. Of these 307 annotations, 278 described interactions between LRRK2 and different partners (heterologous interactions) and 29 annotations described LRRK2 self-interaction (homologous interaction).The BioGRID database was similarly queried, and 260 unfiltered annotations were downloaded. After applying filter #1 and filter #2 the total number of annotations dropped from 260 to 230, of which 194 were heterologous and 36 homologous interactions.via different PPI detection methods  or BioGRID (20 publications). For this reason the IntAct and the BioGRID datasets for LRRK2 heterologous interactions, after the previously described filtering, were merged to prepare the final dataset for heterologous interactions only (hereafter referred to as “merged dataset”). In the case of publications duplicated between IntAct and BioGRID only one set of annotations was moved to the merge dataset. Discrepancies were found for some of the shared records; in particular, the number of annotations was not necessarily consistent between IntAct and BioGRID, this has been previously reported  and is l methods , for a tThe 422 annotations in the merged dataset were scored based on the following:Low occurrence—the protein identifier was reported just once in the list of 422 annotations.Medium-low occurrence—the protein identifier was reported in 1 publication but with 2 or more experimental approaches.Medium-high occurrence—the protein identifier was reported in 2 or more publications but with the same experimental technique.High occurrence—the protein identifier was reported in 2 or more publications with 2 or more experimental approaches.This ranking system allowed us to classify the interactors as follows: 207 interactors were annotated only once; 24 LRRK2 interactors were reported in 1 publication only but with different methods; 13 LRRK2 interactors were reported in more than 1 publication but with the same detection method; finally, 25 LRRK2 interactors were reported in more than 1 publication and with different experimental techniques. Interactors annotated only once are detailed in http://www.uniprot.org/uniprot/). Twenty-two of the proteins could be assigned to 10 different families: heat shock protein 90 family; tubulin  family; argonaute family; small GTPase superfamily, Rho family; STE Ser/Thr protein kinase family; 14-3-3 family; TRAFAC class dynamin-like GTPase superfamily, dynamin/Fzo/YdjA family; DSH family, small GTPase superfamily, Rab family and actin family : Function 1The filtered list of 62 LRRK2 interactors, was also used for a second, independent enrichment analysis using the GO Consortium term enrichment service, supported by Panther . The anag is the number of terms enriched in the functional group G within the significance level N; n is the total number of enriched terms across all the functional groups within the significance level N. Results are shown in −16, 10−15, 10−14 and 10−12 were only composed of terms belonging to the groups of transport/localization, cell organization and membrane processes. Once these highly significant terms were taken aside, all the other GO term groups started to present their contributions toward the enrichment (in the significant levels from 10−11 to 10−7).Where The IntAct dataset contained 29 annotations for LRRK2 self-interactions (homologous interactions); BioGRID, a total of 36 homologous interactions. Only 7 publications were shared between the two datasets therefore, in the total of 31 publications reporting LRRK2 self-interaction, 77% of the informfation was captured independently by IntAct or BioGRID. Following the same procedure used for the heterologous interactions, a merged dataset was prepared, with a final count of 31 publications and 53 annotations for LRRK2 self-interaction, based on different experimental methodologies .The IntAct database (not BioGRID) reports details (when available) on the regions of the LRRK2 protein participating in protein interactions. Based on this information, a profile of the LRRK2 fragments/residues associated with LRRK2 self-interaction was prepared . As expeThe complete LRRK2 interactome comprised of 269 proteins (the filtered interactome combined with the interactors identified by 1 annotation only) was compared with data from PD and IBD GWAs. Since the associated SNP is only a marker for a genomic locus, not an indication of a specific gene, all the coding open reading frames (ORF), for a total of 156 ORFs in the interval ±200 Kbp from the PD associated SNPs, have been taken into account. The positive matches between GWAs hits and LRRK2 interactors were recorded . Four prin silico analysis to critically appraise the literature creating a synthesis of our understanding of the LRRK2 interactome. LRRK2 literature can be divided into two different blocks, the first one comprises functional publications that study LRRK2 without reporting PPI data, the second contains a smaller number of publications with details about physical interactions between LRRK2 and partners. This second set of publications is what is annotated in PPI databases. The critical collection of the LRRK2 interacting proteins can be used to generate the state of the art LRRK2 interactome. The interactome can be then analysed to rationalize the understanding of the existing functional literature, which describe the role of LRRK2 in a multitude of biological processes (not annotated in PPI databases). The result of such analysis can also suggest possible future wet-lab investigations.The implication of LRRK2 in different human diseases has made this protein the centre of interest for many research groups, leading to a large number of functional studies. The breadth of the LRRK2 literature is complemented by its depth, with a wide range of different approaches and techniques used in investigations. A PubMed search for LRRK2 retrieved 1257 publications on the 30th October 2014. As a consequence of this, LRRK2 is an excellent candidate for When searching for LRRK2 interactions, we found that the information stored in the IntAct and BioGRID databases overlapped only for around 50%; this is probably a consequence of the curator’s choice in paper selection and the database specialization. Moreover IntAct and BioGRID are part of the IMEX consortium and thus committed to minimize redundant annotations . Therefohttp://geneontology.org/faq/what-minimum-information-include-functional-analysis-paper). Our analysis included just 62 LRRK2 interactors, nevertheless we found statistically significant enrichment with both of the two algorithms we used. This may suggest that the group of proteins we used as input (the LRRK2 interactome) had a low level of background noise which therefore didn’t mask enrichment, and this was probably achieved by using a screened interactome composed only by interactors that have been confirmed in at least one replication study.The filtered list of 62 LRRK2 interactors  was used for enrichment analysis to identify specific biological processes (indicated by GO terms) that LRRK2 is likely to be associated with. These data can be used to infer the function of the LRRK2 interactome. Moreover, based on the assumption that any other protein annotated to these biological processes may interact (directly or indirectly) with LRRK2, these data guide the deduction of additional potential LRRK2 interacting proteins. Enrichment is performed by algorithms developed to recognize if a descriptor  is particularly enriched in the list of LRRK2 interactors in comparison with what is expected due to the recurrence of that term in a standard reference list of proteins or genes. This analysis has been performed using Panther  and WebGestalt, two freely available online platforms. Since WebGestalt and Panther apply different statistical approaches and use different versions of the GO annotation and ontology datasets they were used to provide 2 independent analyses. The results using both tools were, however, very similar, pointing towards a comparable functional interpretation of LRRK2 interactome; specifically, all the top 10 WebGestalt hits were also present in the Panther enriched list. It has to be noted that a number of 150 genes/proteins is generally suggested as minimum to obtain a significant enrichment  and transport/localization (72%) thus suggesting LRRK2’s interactome may regulate kinase functions in vesicle transport. Sixty percent of the LRRK2 interactors involved in the GO term group of cytoskeleton also contributed toward the enrichment of transport. Similarly, 79% of the interactors involved in the GO term group of vesicles were also annotated as involved in transport, thus suggesting that the LRRK2 interactome influences transport processes related with cytoskeleton and vesicles dynamics. These results are in accordance with recent functional literature that sees a role of LRRK2 in vesicle mediated processes like endocytosis , synaptip-value = 1.32e−11, 21% of LRRK2 interactors contributing to the enrichment of this term). This is a very specific GO term that in the ancestor GO chart is directly connected with cell surface signaling and immune response, and supports the recent findings that microglial Fc receptors have been associated with alpha-synuclein-induced pro-inflammatory signaling in PD  and possibly the developmental stage, so the outcome of wet lab research may depend on the cellular system used to infer interaction. Not only does this give a possible explanation for the number of different functions that are described for LRRK2 in different experimental models; it also suggests a possible reason for the plethora of different diseases LRRK2 is associated with and opens to discussions of a LRRK2-oriented therapy. If LRRK2 regulates different biological processes in different tissues, a hypothetical targeting of LRRK2 during disease may generate unexpected, and unwanted, side effects. It would be beneficial to analyse LRRK2 interactome according on the cell type in which the interaction was reported. Unfortunately at the moment the majority of the annotations come from in vitro studies and cancer cell lines; we do not have enough annotations from primary cultures and physiological model systems to perform cell type specific analysis as yet.In the light of these observations we propose the function of LRRK2 interactome to be associated with transport and trafficking, possibly regulating enzymatic events associated with cytoskeleton and vesicles. However, we recognize that LRRK2 probably has a role as a hub protein, thus controlling many different functions at a time, and this is likely to be one of the reasons why it has proved so challenging to reach a consensus on LRRK2 physiological function. Based on our results we suggest the role of LRRK2 can also be modulated depending on the cell type analysed .Rationale: (i) Protein Kinase Assay = Biochemical Activity (one of the redundant entries has been removed); (ii) Affinity Capture-Western (BioGRID) is split in Pull Down and Anti-tag Coimmunoprecipitation in IntAct, following the rule of keeping the record with maximum number of annotations, the record in IntAct has been preferred over the one in BioGRID. (B). Rationale: (i) Protein Kinase Assay = Biochemical Activity (one of the redundant entries has been removed). (C). Rationale: (i) Affinity Capture-Western = Affinity Chromatography Technology (one of the redundant entries has been removed). (D).Rationale : (i) Affinity Capture-Western = Anti-tag Coimmunoprecipitation (one of the redundant entries has been removed). (E). Rationale: (i) Affinity Capture-Western/MS = Pull Down (one of the redundant entries has been removed). (F). Rationale: Merged.Click here for additional data file.10.7717/peerj.778/supp-2Table S2It contains details regarding the 207 LRRK2 interactors that were annotated 1 time only.Click here for additional data file.10.7717/peerj.778/supp-3Table S3It contains details regarding grouping of the terms enriched in WebGestalt.Click here for additional data file.10.7717/peerj.778/supp-4Table S4It contains details regarding grouping of the terms enriched in Panther. BF, background frequency; SF, sample frequency.Click here for additional data file.10.7717/peerj.778/supp-5Table S5It contains specifications regarding the kinases in the LRRK2 interactome. Details are from the UniProt database (downloaded on the 9th January 2015).Click here for additional data file.10.7717/peerj.778/supp-6Data S1Results as obtained from the WebGestalt, GO terms enrichment analysis.Click here for additional data file.


Saccharomyces cerevisiae, Homo sapiens, and Helicobacter pylori, we obtained good results of average accuracies of 93.8%, 97.96%, and 89.47%, which are much better than in previous studies. Extensive validations have also been explored to evaluate the performance of the Rotation Forest ensemble classifier with the state-of-the-art Support Vector Machine classifier. These promising results indicate that the proposed method might play a complementary role for future proteomics research.Protein-Protein Interactions (PPIs) play a vital role in most cellular processes. Although many efforts have been devoted to detecting protein interactions by high-throughput experiments, these methods are obviously expensive and tedious. Targeting these inevitable disadvantages, this study develops a novel computational method to predict PPIs using information on protein sequences, which is highly efficient and accurate. The improvement mainly comes from the use of the Rotation Forest (RF) classifier and the Local Phase Quantization (LPQ) descriptor from the Physicochemical Property Response (PR) Matrix of protein amino acids. When performed on three PPI datasets including  As a necessary component of all organisms, proteins are involved in most processes of living cells. Because proteins usually function in pairs, knowledge of protein interactions can provide great insights into more biological functions ,2. As a Many computational methods have been developed for predicting PPIs. They are based on different data sources, such as gene fusion, sequence conservation among interacting proteins, gene neighborhood, literature mining knowledge, phylogenetic profiles, and combining interaction information from various data sources . Howeveret al. T with size N × 1 that holds the value 1 or −1 to differentiate whether the protein pair is interacting or not. A value of 1 represents PPI and −1 non-PPI. Denote K as the number of subsets of feature set F and L as the number of the decision trees in a Rotation Forest. An individual decision tree is denoted as iD. Note that the parameters K and L must be set in advance.Assume a matrix iD is as follows:The training procedure for an individual decision tree classifier K subsets from the feature set F at random. Note that each subset must hold M = n/K features, and fill with zero vectors if the last subset has less than M features.Step 1: Select jth feature subset to ijF for training classifier iD, and let ijX be a dataset of features in ijF. Then, denote a new set ijC, which is composed of the coefficients of principal components, M× 1.Step 2: Set the Cij, and generate a sparse rotation matrix Ri, as follows:Step 3: Organize iR and build F. Finally, iD.It is essential to rearrange L decision trees, when a given test sample x is as input, each decision tree iD assigns the probability x has correlation. Then, an average probability After training x to the class with the highest confidence.Finally, assign Saccharomyces cerevisiae, Homo sapiens, and Helicobacter pylori datasets are explored to evaluate the prediction performance of the proposed method. The validation results show that our proposed model can achieve better performance than the existing methods. The improvement of our method mainly comes from the use of the Rotation Forest (RF) classifier and the Local Phase Quantization (LPQ) descriptor from the Physicochemical Property Response Matrix (PR). Therefore, the proposed method can be used to guide related experimental validations and as a supplementary tool to proteomics research.Predicting interactions between protein pairs is of great importance to understand the molecular basis of complex cellular processes. This article reports a novel computational method for predicting protein-protein interactions solely using the protein sequence. Three large, real public PPI data sets including


Aims. To optimize and verify the regulatory pathway of p42.3 in the pathogenesis of gastric carcinoma (GC) by intelligent algorithm. Methods. Bioinformatics methods were used to analyze the features of structural domain in p42.3 protein. Proteins with the same domains and similar functions to p42.3 were screened out for reference. The possible regulatory pathway of p42.3 was established by integrating the acting pathways of these proteins. Then, the similarity between the reference proteins and p42.3 protein was figured out by multiparameter weighted summation method. The calculation result was taken as the prior probability of the initial node in Bayesian network. Besides, the probability of occurrence in different pathways was calculated by conditional probability formula, and the one with the maximum probability was regarded as the most possible pathway of p42.3. Finally, molecular biological experiments were conducted to prove it. Results. In Bayesian network of p42.3, probability of the acting pathway “S100A11→RAGE→P38→MAPK→Microtubule-associated protein→Spindle protein→Centromere protein→Cell proliferation” was the biggest, and it was also validated by biological experiments. Conclusions. The possibly important role of p42.3 in the occurrence of gastric carcinoma was verified by theoretical analysis and preliminary test, helping in studying the relationship between p42.3 and gastric carcinoma. The occurrence and development of gastric carcinoma is a multifactor, multistage, and multistep process . A largeAccording to our early study, p42.3 may be involved in the regulatory pathway in the occurrence and development of gastric carcinoma and the regulatory pathway is as follows: Ras→Raf-1→MEK→MAPK kinase→MAPK→microtubule-associated protein→spindle protein→centromere protein→cell proliferation . Neverth2 cell culture box at 37°C.Gastric carcinoma cell lines BGC823, MGC803, SGC7901, AGS, N87, and GES1 were provided by Beijing Cancer Hospital  and were cultivated in DMEM culture media with 5% fetal bovine serum in a 5% CO2  [After obtaining the amino acid sequence of p42.3 (GenBank: NP_848543) from NCBI database, the spatial structure of protein was predicted by the threading prediction tool Phyre London) . Then, rMultiparameter weighted sum method was put to use in calculating the similarity of reference protein and p42.3. First select several parameters in which the two proteins have similarity to calculate the degree of similarity of each parameter, and then add the weight trained by artificial neural network. Finally, obtain the degree of similarity after a weighted summation.According to the literature data, the following nine parameters of protein similarity were selected: protein spatial structure, the number of atoms inside the molecule, the number of amino acids in each protein, the species of amino acids, the location of element P and element S in the protein molecule, and the proportion of the number of atoms C, N, and O in the protein molecule –16.Before calculating similarity values, the coordinates of each atom in the protein structure file (pdb file) were determined and Euclidean coordinates were used as spatial coordinates, with the geometrical center of the protein as the origin. The distance from each atom to the origin was then calculated. According to these distances, the protein was divided into layers and the structure similarity of two proteins in corresponding layers was analyzed by stratified analysis. It was found that the distances between most of the atoms of p42.3 protein and the origin were in the range of 0~80 nm and a small portion of the distance were within 80~100 nm, and also, very few of them were above 100 nm. Therefore, based on the length of radius, p42.3 protein was divided into 10 layers from the center to outer edge. The distances of each layer were as follows: the first layer 0~10 nm; the second layer 10~20 nm; the third layer 20~30 nm; the fourth layer 30~40 nm; the fifth layer 40~50 nm; the sixth layer 50~60 nm; the seventh layer 60~70 nm; the eighth layer 70~80 nm; the ninth layer 80~100 nm; and the tenth layer beyond 100 nm. The number of atoms in each layer was counted for each of the proteins being compared and stored in array vector data 1 and data 2, respectively. The similarity in atom numbers in each layer was then compared using the formula: sim = 1 − (|data 1 − data 2|/data 1), wherein sim represents a ten-dimensional vector that has stored the similarity of each layer.wi = ((li1/n1) + (li2/n2))/2, i = 1,2,…, 10, where n1 is the total number of atoms of the first protein, n2 is the total number of atoms of the second protein, while li1 and li2 are the number of atoms in the ith layer in protein 1 and protein 2, respectively. Thus, the spatial structure similarity of the two proteins was obtained.Weights were then added to the similarity of each layer and the overall density similarity was calculated by the weighted summation method. It is reasonable to suppose that the layers that contain the most atoms will be more likely to determine properties of the protein. Based on this assumption, the more atoms the layer owns, the higher the weight of this layer is, so the proportion of the atoms number in each layer determined the weight of this layer. Of course, it is maybe different in every layer for two proteins, so the average would be taken. Hence, each layer was weighted as the following formula: n1 and n2, respectively, and then the formula used to calculate the similarity in atom numbers was sima = 1 − (|n1 − n2|/n1). Likewise, the similarity of the number of amino acids and its types could be also obtained.Similarity algorithms of the three parameters were alike. The number of atoms and amino acids, and the number of amino acid types in the two proteins were calculated by textread function in MATLAB software. Then, the number of atoms and the number and type of amino acid can be read from the pdb file of the two proteins. The total number of atoms of the two proteins was recorded as n1 − n2|/n1). In addition, in protein molecules, the number of elements P and S was usually small, but they both play crucial roles in the function of protein. While in p42.3 protein, there was only one S atom and no P atoms. Therefore, it is obviously not scientific to calculate the degree of similarity according to the number of atoms of the two elements. Instead, similarity of the location between atoms P and S was set as the criteria for calculation. In this algorithm, it was assumed that if the two elements P and S were in the same layer, the similarity was regarded as 1.0; if they were in adjacent layers, the similarity was 0.8; otherwise, the similarity was 0. Therefore, the similarity parameter of each element in proteins was achieved.This study was mainly to analyze elements C, N, O, P, and S. Firstly, the proportion of the number of C, N, and O to the total number of atoms in each protein was calculated. Then, the similarity was calculated among C, N, and O in accordance with the formula: sim_element = 1 −  artificial neural network for training; thus, the weights of each parameter Qi had been achieved , number of atoms in the protein, number and type of amino acids, number and proportion of C, N, and O atoms [Therefore, for each pair of proteins, their overall similarity can be calculated by formula: In condition of cellular proliferation, the reference protein set obtained by the similarity calculation was screened out. Then, with a reference protein as the starting point and cell proliferation as the ending point, the acting pathway and node of each reference protein were collected. There are crosses between different pathways, thus constituting a regulatory network . In the U = {X1, X2,…, Xn}. Bayesian networks can be symbolized by the element pair B = , where G is a DAG in which the nodes represent random variables X1, X2,…, Xn. It can symbolize gene expression vectors in expression profiling data, while θ represents the conditional probability of each variable. DAG showed the independent relation under the following conditions. It was the Markov assumption; each variable Xi was independent of its nonchild node in the prerequisite that it was the parent node in G. Based on the assumption of independence, the Bayesian network G had only one joint probability distribution for set U was P = ∏i=1nP  (Xi∣Pa(Xi)), where Pa(Xi) symbolizes the parent node of Xi. In order to determine the joint probability above, all the conditional probabilities in this formula need to be confirmed.Bayesian networks are Directed Acyclic Graphs (DAGs), which describe the joint probability distribution of a finite set of variables In the Bayesian network in this paper, after obtaining the probability of occurrence in each node and pathway, the Bayes theorem was used to inverse the probability of protein in acting their roles in each node, thus finding the highest possible regulatory pathway of p42.3 protein.β-actin, the RT-PCR was used to amplify, respectively, . After PCR products were detected by agarose gel electrophoresis, the expressions of various proteins in different cell lines were compared.After obtaining the highest possible acting pathway of p42.3 protein through calculation and prediction, some basic biological experiments were carried out for initial validation. To begin with, Trizol  method was used for extraction of the total mRNA in the six cell lines: BGC823, MGC803, SGC7901, AGS, N87, and GES1. Through reverse transcription, cDNA was synthesized . According to the gene sequence of the reference proteins and p42.3, the primers were designed. The sequence of the primers was shown in http://www.sbg.bio.ic.ac.uk/~3dligandsite/, Imperial College London) [The spatial structure of protein was predicted by the threading prediction tool Phyre. A three-dimensional ligand-binding model of the characteristic of p42.3 in EF-hand region was predicted by using 3DLigandSite ( London) . The metThe similarity algorithm of protein was compared by the similarity of nine parameters mentioned above. The MATLAB software  was used for programming. The similarity of the reference protein and p42.3 was calculated. After screening by “cell proliferation,” the results were displayed in Cell proliferation was set as the restrictive condition, and the acting pathways and nodes of different reference proteins were worked out by literature collection. With different acting pathways crossing, the regulatory network was thus formed, shown in The similarity of the reference proteins and p42.3 was treated as prior probability of the initial parent nodes. According to formula , the proBased on the analysis of the spatial structure of p42.3 and Bayesian regulatory network, expressions of S100A11  and S100A2 (the protein with the shortest negative acting path) in gastric carcinoma cell lines were examined, respectively, for preliminary valediction of the correlation of p42.3 and S100A11. The results showed that when p42.3 showed normal expression, both S100A11 and S100A2 had shown expressions. In The occurrence and development of gastric carcinoma involve changes in the structure and expression of multiple related genes . In part2+ chelate ring [α helices (most commonly two or four) to form a braided structure [p42.3 expression is dependent on mitosis and is expressed at low levels or not at all in normal gastric mucosa but is highly expressed in gastric carcinoma tissues. It has the effect of promoting cellular proliferation and tumor metastasis . Changesate ring . In all ate ring , 39. Protructure . Many prtructure . The mostructure . By a setructure . Genes' The S100 proteins are a group of calcium-binding proteins with low molecular weight (10–12 kDa). Its amino acid sequence is highly conserved in vertebrates . S100 prCurrently, there are various ways to compare protein structures, each with their own advantages and disadvantages . By analHere, the ligand-binding model of the EF-hand structure of p42.3 was successfully predicted. Meanwhile, a Bayesian network using the corresponding mathematical algorithm was constructed and optimized to predict the most likely pathway. On the other hand, molecular biology experiments indicated that p42.3 and S100A11 may be with the commonplace in character, and this provided a hypothesis for us to conduct further research. In a word, our findings provide important research directions for exploring the mechanism of action of p42.3 in gastric cancer.


Motivation: Similarity-based methods have been widely used in order to infer the properties of genes and gene products containing little or no experimental annotation. New approaches that overcome the limitations of methods that rely solely upon sequence similarity are attracting increased attention. One of these novel approaches is to use the organization of the structural domains in proteins.Results: We propose a method for the automatic annotation of protein sequences in the UniProt Knowledgebase (UniProtKB) by comparing their domain architectures, classifying proteins based on the similarities and propagating functional annotation. The performance of this method was measured through a cross-validation analysis using the Gene Ontology (GO) annotation of a sub-set of UniProtKB/Swiss-Prot. The results demonstrate the effectiveness of this approach in detecting functional similarity with an average F-score: 0.85. We applied the method on nearly 55.3 million uncharacterized proteins in UniProtKB/TrEMBL resulted in 44 818 178 GO term predictions for 12 172 114 proteins. 22% of these predictions were for 2 812 016 previously non-annotated protein entries indicating the significance of the value added by this approach.Availability and implementation: The results of the method are available at: ftp://ftp.ebi.ac.uk/pub/contrib/martin/DAAC/.Contact:tdogan@ebi.ac.ukSupplementary information:Supplementary data are available at Bioinformatics online. The reduction in the cost of sequencing has led to the accumulation of a vast amount of data in biological databases. These data are stored in public repositories such as the UniProt Knowledgebase  for protDiscovery of functional properties for proteins is a key step in biomedical research, yet experimental identification of proteins is still a quite laborious and expensive task. This has led to many similarity-based computational methods being developed to infer the unknown properties of proteins based on their similarity to experimentally annotated proteins. The most widely used approach is sequence alignment . A signiNevertheless, different approaches have been tried lately for the prediction of protein properties, to augment the performance of sequence methods. One approach is to exploit information on the physicochemical properties of the amino acids in the protein sequence to infer subcellular localization  or by usMany of the methods using evolutionary conserved sub-sequences focus on protein domains. These are the structural building blocks in proteins that are able to function and fold independently from the rest of the protein . There aOne view in the field of protein function inference states that the function of a protein is not simply the sum of the functions of the independent domains it contains, but rather is a unique property emerging from the contribution of all of the structural blocks synergistically . This haet al. (2007). Although the methodology used varies greatly between the different studies, most of them try to predict the pairwise similarities/homologies between proteins  procedure for the automatic annotation of uncharacterized proteins in UniProtKB based upon domain architectural similarity to manually reviewed sequences in the UniProtKB/Swiss-Prot. Four attributes are incorporated into the measurement of domain architectural similarity: domain content, order, position and recurrence. The proposed method incorporates domain annotation from InterPro in order to obtain comprehensive domain information coverage for the proteins.This study is the first that we are aware of to use DA comparison and classification in the automatic functional annotation of large protein sets. The proposed method also brings new approaches to the field by (i) employing InterPro as the domain annotation source, (ii) the use of multi-label classification technique to annotate proteins with multiple functions in one run and to be able to optimize the parameters for each functional term independently and (iii) application of domain weights during the alignment step to direct the procedure to the optimal solution. Multi-label classification is a technique used to classify each sample into one or more classes. This method is frequently employed to analyze examples with multiple attributes as opposed to the traditional single-label or binary classification  from the different member databases; resulting in 7518 domain, 18 218 family, 277 repeat and 847 site type InterPro entries.In order to generate the DAs, v.49 release files were downloaded from the InterPro database. DAs were generated using the InterPro domain type hits to UniProtKB proteins. A DA is composed of the linear arrangement of domains in the protein sequence from the N to C terminus. In addition, non-annotated regions in the sequences longer than 30 amino acids are annotated with hypothetical ‘GAP’ domains in DAs. The reason behind is that these regions may contain domains that are yet to be identified. Two sample proteins from UniProtKB (P77334 and P64826) and their DAs are shown in DA alignment is the pairwise optimal alignment between two proteins using the linear arrangement of their domains instead of amino acids. The Needleman-Wunsch Global Sequence Alignment algorithm  is the ctN is the total number of proteins in the set and dN is the number of proteins containing domain d. To reduce the computational burden, scoring matrices are generated on the fly during the alignment, only using the domains in the test protein pair. The substitution values for each domain pair in the raw scoring matrix are multiplied by the inverse domain frequency values of both domains in the corresponding pair prior to the alignment to obtain the final scoring matrix. An example pairwise DA alignment is shown in 2Gop represents gap opening penalty for the second protein, I is the mismatch score between the corresponding InterPro domains and M is the match score for the corresponding InterPro domain. Any matches between an actual domain and a GAP domain or between two GAP domains are mildly negatively scored with gap opening penalties, instead of mismatches, since there is no knowledge about the information hidden in these regions. The finalized DA similarity (between 0 and 1) is obtained by normalizing the alignment score using the self-alignment scores of the two DAs. Thus, alignment scores from various DA pairs become comparable with each other. The DA similarity between two protein 1 and 2 is:1,2S is the DA alignment score, 1,2Smn is the minimum alignment score that could be obtained from these two proteins and 1Sself is the self-alignment score of protein 1. The minimum alignment score is calculated as if the two proteins have no common domains. This is done by the placement of gaps in sequence 1 equal in number to the number of domains in sequence 2, followed by the placement of gaps in sequence 2 equal in number to the number of domains in sequence 1; and calculating the total negative score for this alignment. Placing gaps is favored over mismatches here due to the values of the selected penalties. The reason for incorporating the minimum alignment score in the equation is to compensate for negative alignment scores that would otherwise result in negative DA similarity values.Domain hits are weighted before the alignment procedure in order to reduce the contribution of promiscuous domains  to the final similarity measure. Inverse domain frequency is a measure of how frequently a domain appears in different proteins. Highly frequent domains appearing in various non-related proteins are less informative compared with the rarely occurring domains (The reference data for GO term prediction was composed of the DAs for the protein entries in the UniProtKB/Swiss-Prot (v2014_11) and the associated GO annotation  taken from the UniProt-GOA database  of the test protein to the GO term class is calculated. For the cases where this similarity exceeds the class-specific similarity threshold (YT), the method classifies the test protein into the corresponding class and the term is given as a prediction for the query protein. The procedure is carried out using all training classifiers, with a multi-label classification approach. A query protein can be classified to more than one class and thus has multiple labels.Considering the application step: classification of proteins is carried out with the help of the reference data and the pairwise DA alignment similarity values. Following the alignment of a query protein’s DA to all members of a class , the meaCurrently DAs are generated for all UniProtKB records at each release and stored in the UniProt Domain Architecture Database. Supplementary Information). A cross-validation experiment was carried out in order to observe the performance of DAAC on data with known labels  out of 13 826 tested terms with a mean recall: 0.84, precision: 0.89 and F-score: 0.85, and the high-performance GO term set was composed of 536 molecular function, 82 cellular component and 160 biological process terms. Only the predictions for these 778 terms are considered during application on UniProtKB/TrEMBL. In The performance of the method was calculated using the statistical measures explained in the Supplementary Fig. S2). The mean AUC for this run was 0.8 ± 0.10  and only 293 GO terms were marked as high performance classes. Omitting the domain weights resulted in a slight reduction in performance with 706 GO terms marked as high performance classes and a mean AUC of 0.87 ± 0.10 . The results indicate that both including imaginary GAP domains in DAs and weighting domains had a positive effect on the performance of the method in cross-validation. Performance comparison within different GO categories is discussed in the Supplementary Information.In order to observe the effect of adding imaginary GAP domains in DAs and the effect of weighting the domains on the performance, the cross-validation experiment was repeated without making these adjustments. The dark and light gray curves (red and yellow curves in online version) in Up to this point the performance of the method has been measured in terms of classifiers (GO terms). Another important topic here is testing the system in terms of input samples (proteins). Exploration of DAAC performance on proteins with varying complexity is one way to observe if the method fails on test samples with certain attributes. The number of domains on a protein can be employed to measure the complexity. In order to observe how the method performs with changing number of domains on proteins, we divided the cross-validation results by the number of domains contained in each protein. The method is run on UniProtKB/TrEMBL to annotate nearly 55.3 million uncharacterized protein entries in this database. The statistics of this run are shown in As shown in In order to comment on the biological relevance of the results and the value added by the method, we would like to discuss two interesting example cases here. The first example case: GO:0004653  is a molecular function category term that is associated with two very similar DAs by the DAAC method as shown in The second example case: GO:0042813 (Wnt-activated receptor activity) is a molecular function category term that is associated with seven DAs . DAAC enables the association of DAs with functional terms (each represented by a unique class) and the fast annotation of non-annotated proteins bearing the same or similar architectures. The employment of multi-label classification enables a protein (and its DA) to be a member of more than one class and thus have more than one functional annotation. Establishing independent classifiers for each GO term provides us with the ability to select different parameters for each class. With this approach we were able to optimize the class-specific DA similarity thresholds.In this article, we have proposed DAAC: a novel approach in the field of automatic functional annotation of protein sequences with the alignment and the classification of DAs. The proposed method is distinguished from conventional approaches in three main aspects: (i) the use of DAs as the basis of a similarity measure between proteins to propagate GO annotation; (ii) the employment of multi-label classification where each class represents a unique GO term, thus enabling the optimization of the parameters for each term independently and (iii) the use of InterPro as the domain resource in order to increase the coverage of domain annotation on the proteins . Next, the method was applied to nearly 55.3 million protein entries in UniProtKB/TrEMBL to obtain GO annotation for the whole database. This analysis resulted in 44 818 178 GO term predictions for 12 172 114 proteins, 2 812 016 of which were previously non-annotated. The results show that the proposed approach is effective and has the potential to identify functional relationships, especially between multi-domain proteins. Next, we plan to integrate the DAAC method into the UniProt automatic annotation production pipeline to enrich the automatic functional annotation of UniProtKB/TrEMBL. We also plan to extend the DAAC approach to the automatic annotation of EC numbers, UniProtKB keywords, UniProtKB comments, recommended protein names and subcellular locations.


In particular, ncRNA-protein interactions play an important role in post-transcriptional gene regulation like mRNA localization, mRNA stabilization, poly-adenylation, splicing and translation. The experimental methods to solve RNA-protein interaction prediction problem remain expensive and time-consuming. Here, we present the RPI-Pred (RNA-protein interaction predictor), a new support-vector machine-based method, to predict protein-RNA interaction pairs, based on both the sequences and structures. The results show that RPI-Pred can correctly predict RNA-protein interaction pairs with ∼94% prediction accuracy when using sequence and experimentally determined protein and RNA structures, and with ∼83% when using sequences and predicted protein and RNA structures. Further, our proposed method RPI-Pred was superior to other existing ones by predicting more experimentally validated ncRNA-protein interaction pairs from different organisms. Motivated by the improved performance of RPI-Pred, we further applied our method for reliable construction of ncRNA-protein interaction networks. The RPI-Pred is publicly available at:  RNA-protein interactions (RPI) play a crucial role in fundamental cellular processes, such as human diseases , viral ret al.  of successfully identified RNA binding proteins (RBP) in the human genome ,26, we set al.  introducet al. . Wang et  . The PBsin silico-based biological network on the predicted results in step 2. Step 1 includes various processes, such as construction of the training data set, removal of redundant RNA-protein pairs, feature extraction from sequences and structures in the training data set and development of the ‘RPI-Pred’ model. Step 2 includes the feature extraction in terms of primary sequence and predicted structure for given protein and/or ncRNA and ncRPI prediction using RPI-Pred. Step 3 consists of construction of interaction networks based on RPI-Pred interaction predictions. More detailed descriptions for each step are given below.Figure To develop the RPI-Pred method, first, we build a non-redundant training data set of RPI complexes by parsing the Nucleic Acid Database (NDB)  and the The procedure for constructing the training data set, included removing redundant protein/RNA pairs through sequence similarity criteria, is as follows. For instance, the RPI complex with the PDB id ‘1a9n’ has four protein chains  and two RNA chains , respectively. We obtained the possible interaction pairs from PRIDB as A-Q, B-Q, C-R and D-R. Then the homologous RNA-protein pairs (i.e. similar protein chains interacting with similar RNA chains) were removed by searching the sequence similarity between protein (RNA) sequences. In this study, we used EMBOSS needle program  with theThe above selected non-redundant pairs were further tested for atomic interactions with a distance threshold (3.40 Å). This distance threshold helped to strengthen positive pairs in the training data set by including only strongly interacting RPI pairs. Different thresholds have been used to distinguish the binding RNA-protein pairs from non-binding ones ,38,39,47As a result, we obtained a training data set, namely, RPI1807, with 1807 positive pairs (consisting of 1807 protein and 1078 RNA chains) and 1436 negative pairs (with 1436 protein and 493 RNA chains). The positive and negative pairs of RPI1807 are shown in Supplementary Table S1.et al.  or theoretical predictions, of both proteins and RNAs in our RPI-Pred. A protein 3D structure can be represented by 16-letter 1D structural fragments, called PBs . The PDBWe used the 3DNA suite  to extraThe sequence and structural features of protein and RNA used in this work were represented as follows. The protein sequence of 20 amino acids were classified into 7 groups  according to their dipole moments and side chain volume: {A,G,V}, {I,L,F,P}, {Y,M,T,S}, {H,N,Q,W}, {R,K}, {D,E} and {C}. Then, we combined these 7-letter sequence features with the 16-letter PB structure representations, resulting in 112 (7 × 16) possible combinations. The normalized frequencies of 112 combinations formed the 112 protein vectors. Similarly, RNA sequence and RSS representations resulted in 4 × 5 possible combinations , and normalized frequencies of these 20 combinations resulted in 20 RNA vectors. The labels of sequence and structural features obtained from the proteins and RNA are given in Supplementary Table S2. In summary, to construct the RPI-Pred method, we used the sequence and structure features of 132 vectors, in which the first 112 vectors represented proteins and the remaining 20 vectors represented RNAs.The SVM approach is a popular supervised machine learning technique used for many classification and regression problems . Here, wSince many proteins and RNAs have not been experimentally solved, we must use theoretical approaches to predict their structures. Many research groups have proposed PB prediction methods ,54. In tLikewise, RSS can also be predicted with available RNA structure prediction methods –64. Heretp and tn denote the number of correctly predicted positive and negative pairs, respectively, and fp and fn denote the number of wrongly predicted positive and negative pairs, respectively. The area under curve (AUC) of the receiver operation characteristic curve was calculated using a 10-fold CV. The AUC ranges from 0 to 1, with 1 indicating the best prediction.The performance of RPI-Pred was evaluated using 10-fold cross-validation (10-fold CV) approach. To perform this test, the training data set was divided into 10 subsets of equal size. Each subset was used for testing, while the remaining nine subsets were used for training. This process was repeated 10 times to cover all possibilities. Finally, we recorded the average performance over all 10 testing subsets. We evaluated the prediction performance by using Precision (PRE), Recall (REC), F-Score (FSC) and Accuracy (ACC), defined as follows:We built the RPI-Pred method for identifying binding partners of proteins or RNAs. In this section, we tested the performance of RPI-Pred on different test data sets, including RPI1807, RPI2241 and RPI369, and compared with previous methods. We also applied the RPI-Pred method to a large ncRPI data set and the predicted results were compared with other existing approaches.The performance of our RPI-Pred method was evaluated using the 10-fold CV on RPI1807, RPI2241 and RPI369 data sets. The experimentally validated structures were extracted from PDB database . The perSimilarly, the performance of RPI-Pred was evaluated using the positive and negative pairs of RPI2241 and RPI369 data sets. The positive pairs were directly adopted from RPI2241 and RPI369 data sets and their corresponding negative pairs were generated by following the steps reported in . Then, tWe compared the performance of the RPI-Pred with Muppirala's method  on the Ret al.'s results as RPI2241-SVM, RPI369-SVM, RPI2241-RF and RPI369-RF, based on the two classifiers, SVM and RF, and the used training databases.We compared our RPI-Pred results obtained from RPI2241 and RP1369 data sets  using AUC, PRE, REC, FSC and ACC measurements. The comparison results are shown in Table As shown in Table et al.  structures. To perform this analysis, we used the RPI-model constructed based on the RPI1807 data set and tested within the same data set. We observed a prediction accuracy of ∼83%. For the remaining measurements AUC, PRE, REC and FSC, the performance was 0.89, 0.79, 0.94 and 0.86, respectively. Compare with the results obtained using known structures as reported earlier . In particular, the prediction accuracy (ACC) decreased by nearly 10% compared with the performance of RPI-Pred on RPI1807 data set with known structures. We can observe similar decreases in the other performance measures. Expectedly, precision was significantly reduced when using predicted structures, while there was little or no impact on precision. Since, there is no experimental structural features were available for the rest of our test data sets  we used the predicted PBs and RSS in order to perform the RPI prediction.Although most of the DNA transcripts are ncRNAs, very few have known functions. The ncRNA function can be predicted by identifying the different interacting partners, such as DNA, RNA and protein. It is currently believed that ncRNAs interact with proteins and then perform their regulatory functions, such as chromatin remodeling, to enhance or suppress gene expression –20. TherCaenorhabditis elegans, Drosophila melanogaster, Escherichia coli, Homo sapiens, Mus musculus and Saccharomyces cerevisiae. The RPI-Pred performances for the above six model organisms were given in Table et al.'s four classifiers (RPI369-NB (62%), RPI369-NB (77%), RPI2241-ENB (66%) and RPI2241-ENB (79%) results on the RPI367 data set. Our RPI-Pred method outperformed with a prediction accuracy of 89% (328 out of 367 pairs were correctly predicted), and none of Wang et al.'s classifiers performed at more than 80% accuracy , RPI369-NB (77%), RPI2241-ENB (66%) and RPI2241-ENB (79%), our RPI-Pred result showed greater improvement in prediction accuracy by 27%, 22%, 23% and 10%, respectively. Especially, our RPI-Pred predicted more ncRPI pairs  than each of Wang et al.'s classifiers.Our RPI-Pred method was first tested using small RPI367 data set , consistaccuracy . When coOur next ncRPI prediction analysis was performed on a larger data set used by Muppirala , which cFinally, we tested the ability of our RPI-Pred method to predict ncRPI pairs in the currently available NPInter database (version 2.0). NPInter database  is the oC. elegans, D. melanogaster and E. Coli with the prediction accuracies of ∼78%, ∼77% and ∼76%, respectively. The RPI-Pred prediction accuracies for H. sapiens, M. musculus and S. cerevisiae were ∼89%, ∼97% and ∼82%, respectively. Table Among the tested 10 412 positive ncRPI pairs, our RPI-Pred method correctly predicted 9335 interaction pairs with the accuracy of ∼90%. The RPI-Pred method predicted fewer ncRNA-protein pairs for D. melanogaster, E. coli, H. sapiens and M. musculus). Most of the cases, these incorrect predictions were observed for same protein that interacts with different ncRNAs. Among 21 false-negative ncRNA-protein pairsin D. melanogaster, 16 involved 3 proteins . Similarly, among 48 incorrect predictions in E. coli, 18 involved 3 proteins . The RPI-Pred method also failed to predict the pairs involving in two proteins (P19338 and P62312) in H. Sapiens. In the above few mentioned protein-RNA interactions, one protein interacts with multiple ncRNAs. This is due to wrongly predicted protein structures. In this proposed approach, our RPI-Pred method uses the features extracted from predicted structures of proteins and ncRNAs. Hence, our RPI-Pred prediction performance will be strongly influenced by the protein or RNA structure prediction approaches.We further analyzed incorrect the prediction results of some specific complexes in each organism. We found a few cases in four organisms  and very few interactions were predicted as true negatives (288 out of 10 412). The other measurements PRE, REC and FSC were observed as 0.50, 0.98 and 0.66, respectively. We also observed the similar trend in the analysis of RPI2241-SVM results. The performance of RPI2241-SVM was ∼49% with more true positives (9682 out of 10 412 positive pairs were predicted) and fewer true negatives (730 out of 10 412 negative pairs were predicted). The other measurements PRE, REC and FSC were observed as 0.50, 0.93 and 0.65, respectively.Similarly, we analyzed the results obtained by RPI369-RF and RPI369-SVM models. Table in silico construction of ncRPI networks. Nacher and Araki , and RNA hubs (one RNA interacting with multiple proteins). The in silico-based network construction helps to understand how many interactions were correctly predicted by our RPI-Pred method in the same protein or RNA hubs, and the reliability of our model in deriving new ncRPI and constructing new biological networks.In Figure Lots of the important fundamental cellular processes are medicated by protein and RNA interactions (RPIs); therefore, the study of RPI is valuable for the understanding of their functions. In the recent years, the high-throughput sequencing methods have led to the discovery of enormous amount of ncRNAs, which also interact with protein and regulate gene expression. Hence, it is very important to understand their function by studying the correct interaction partners. However, the experimental methods to determine correct interacting partner(s) for ncRNA are expensive and labor-intensive. In this case, computational approaches were highly relied to predict the interacting partner for ncRNA molecules. To our knowledge, very few studies have been reported for RPI prediction and none of the methods was considered the high-order protein and RNA structures, which are known to be vital to their functions.in silico construction of ncRNA-protein networks. In addition, the proposed RPI-Pred method can also be extended to determine the binding partners (RNAs) for other types of proteins, such as transcription factors, which are able to interact with both DNA and RNA RPI data sets, and the results indicated that the proposed RPI-Pred was able to identify (nc)RPI with higher accuracy, when compared with other existing approaches. Therefore, our method is reliable to be applied to identify the binding partner(s) either for a protein or RNA. We further applied the method to  and RNA . A web sSupplementary Data are available at NAR Online.


Oryza sativa L.) and little is known about their interactors.Heat shock proteins (Hsps) perform a fundamental role in protecting plants against abiotic stresses. Although researchers have made great efforts on the functional analysis of individual family members, Hsps have not been fully characterized in rice  and Rice Gene Expression Profile (RGEP), and one online tool named Protein-Protein Interaction Predictor (PPIP), were constructed and could be accessed at The online version of this article (doi:10.1186/1471-2164-15-344) contains supplementary material, which is available to authorized users. Plants have evolved a spectrum of molecular programs to adapt to environmental stresses. To survive, plants undergo dramatic changes in physiological and molecular mechanisms . For insArabidopsis, at least 19 genes encoding sHsps, 16 chaperonins, 18 genes encoding Hsp70s, seven Hsp90s, and four Hsp100/ClpBs have been identified through genome-wide analysis [Hsps can be classified into five major categories based on molecular mass: small heat shock protein (sHsp) family, chaperonin (Hsp60/GroEL) family, 70-kDa heat shock protein (Hsp70/DnaK) family, Hsp90 family and Hsp100/ClpB family . In Arabanalysis –9. Rice analysis –14, howeFurthermore, detailed studies have established that the overexpression of Hsp70 genes enhanced the plant’s tolerance to environmental stresses –17. TranMany computational approaches have been proposed to predict protein-protein interactions. In terms of test dataset types, these approaches can be grouped into three classes: sequence-oriented methods –22, geneTo carry out more reliable functional analysis, we first conducted a genome-wide screening for the true Hsps in rice using integration of orthology and expression association data. Then, we used interolog- and expression profile-based methods to identify Hsp70s interactors in rice response to abiotic stresses. Through mining the signal behind their interactors, we further investigated the pattern of binding sites and the interaction network of Hsp70s in response to abiotic stresses.Four sets of gene expression data from rice seedlings exposed to drought, salt, cold and heat treatment were collected Table  from theThen, we identified heat-responsive (HR) probe-sets and estimated the global gene-gene pairwise relationships. In this study, we applied boxplots , 33 to iHsps screening in the rice proteome consisted of three steps. First, 41 candidate protein sequences, which were annotated as Hsps and contained the characteristic domains . We found that the absolute PCC of 1,072 PPIs related to Hsp70s, including 430 interactors, were greater than 0.90 .First, the co-localization method was applied to assess the Hsp70 PPIs. This method is based on the principle that interacting proteins are more likely to localize to the same cellular compartment than randomized pairs . The subp-value < 0.001)  Figure .Figure 6The above assessments provided strong support for the reliability of the Hsp70 interactors predicted in this paper. Therefore, we used these interactors as the positive dataset, and constructed a negative dataset composed of 10,158 proteins that were less likely to interact with Hsp70s. Since binding sites tend to occur more frequently in interacting proteins than in non-interacting proteins , we sougp-value lower than 0.05 in the 10 replicas , Rice Annotation Project Locus (RAP ID) and Affymetrix Rice Genome Probe-set (Affymetrix ID)  and Rice Gene Expression Profile (RGEP), and one online tool, named Protein-Protein Interaction Predictor (PPIP). The RiceHsps was built to store and show our predicted results in this paper. The RGEP was constructed to store the integrated gene expression data for rice subjected to abiotic stresses, including drought, salt, cold and high temperature. It also provided a function for identifier conversion among ) Figure . The too) Figure . These oUsing a combination of orthology and expression association data, we identified 27 heat shock proteins, including 12 sHsps, 6 Hsp70s, 3 Hsp60s, 3 Hsp90s and 3 Hsp100/ClpBs. Using an orthology-based strategy, Sarkar et al. (2009) identified 23 sHsps in rice , 12 of wInvestigating the binding sites of Hsp70s will provide insight into the activity of those proteins and improve our ability to predict the potential risks of a particular mutation. In this study, we identified 13 domains and 28 motifs that occurred more frequently in the positive dataset than in the negative dataset, suggesting that these sequences are potential target sites for Hsp70s in rice. The results were partially supported by biochemical experiments conducted in previous studies. For instance, our results showed that the J-domain  of DnaJ/Hsp40 was the binding site for DnaK/Hsp70. By point mutation analysis, Wall et al. (1994) demonstrated that the J-domain interacted with DnaK and regulated DnaK activity . Suh et Therefore, the results of this study provided useful clues for experimental biologists in further analyzing the function of Hsp70s.The Hsp70s network was shown in Figure Our results showed that the small GTPase Ran LOC_Os01g42530), importin α  and importin β (LOC_Os05g28510) could bind to Hsp70s. Hsp70 and importin β were previously identified as Ran-interacting proteins (Rips) [g42530, iImportin α recognizes the nuclear localization signal (NLS) of nuclear proteins in the cytoplasm, forming a stable complex termed the nuclear pore-targeting complex (PTAC) , 51. ImpIn vitro studies indicated that Hsp70 might assist in transporting fumaratehydratase between the cytosol and mitochondria [The results of this study revealed that Hsp70s interacted with enolase LOC_Os09g20820), fumaratehydratase (LOC_Os03g21950), malate dehydrogenase  and citrate synthase (LOC_Os02g10070), which were constructed in sub-network B , Hsp90 , SKP1 (LOC_Os09g36830) and MAPK6 (LOC_Os06g06090), as shown in Figure in vitro studies have indicated that SGT1 can interact with SKP1 and link it to the Hsp90 co-chaperone complexes [Previous reports have shown that Hsp90 and two co-chaperone-like molecules, RAR1 and SGT1, performed a key role in effector-triggered immunity (ETI), the second line of the plant defense system , 65, 66.omplexes . Furtheromplexes , suggestomplexes .Sub-network D showed that Hsp70s might interact with FtsH families  , CBL-interacting protein kinase (CIPK), osmotic stress/abscisic acid-activated protein kinase (SAPK) and wall-associated kinase (WAK) family members. Furthermore, our results showed that the expression level of approximately 81% of those protein kinases (159 out of 197) had a strong negative correlation (PCC < −0.90) with that of Hsp70s. This was consistent with previous studies. Hsp70s were reported to directly interact with PKC, ASK/MAP3K and CDK [In this study, we found that nearly 46% of the Hsp70 interactors (197 out of 430) contained protein kinase domains, including protein kinase C (PKC), protein kinase A (PKA), apoptosis signal-regulating kinase/mitogen-activated protein kinase kinasekinase (ASK/MAP3K), mitogen-activated protein kinase kinase (MAP2K), mitogen-activated protein kinase (MAPK), cyclin-dependent kinase (CDK), Ca and CDK , 74, 75, and CDK , 74–76.  and CDK . Therefohttp://bioinformatics.fafu.edu.cn/.By integrating orthology and functional association data, we identified 27 Hsps in rice, including 12 sHsps, 6 Hsp70s, 3 Hsp60s, 3 Hsp90s and 3 Hsp100/ClpBs. Then, using Hsp70s as a case study, we identified 430 interactors of Hsp70s in rice by combining interolog- and expression profile-based methods. According to the interactors of Hsp70s, we investigated the potential binding sites of Hsp70s, and analyzed the interacting network of Hsp70s in rice. Finally, we constructed two online databases and one tool, which could be accessed at http://rice.plantbiology.msu.edu/) [Rice proteome sequences were obtained from the Rice Genome Annotation Project  .http://dip.doe-mbi.ucla.edu/dip/).Eight hundred and thirty-seven experimentally verified protein-protein interaction (PPI) pairs related to Hsps in yeast . All data were obtained using the same microarray platform  and rice seedling samples (Table The impute package (version 1.22.0) , 79 in BBoxplot , 33 in RThe bootstrap method  was usedhttp://www.ricechip.org/). R software was used to calculate the PCC values between expression data of each candidate Hsp and HR gene probe-set.Rice candidate Hsps were selected from the Uniprot database. These sequences satisfied the following criteria: 1) they possessed the conserved domains of Hsps   against nterolog , 21, corFor each PPI predicted by interolog, we determined the absolute value of PCC between the corresponding gene expression data. R software was used to calculate the PCC values. Generally, the PCC values ranged from −1 to 1. A value of 1 indicated that the gene expression level of protein A would increase as that of protein B increased. In contrast, a value of −1 implied that the gene expression level of protein A would decrease as that of protein B increased. A value of 0 implied that there was no linear correlation between the expressions of these two genes. If the absolute value was less than 0.90, the PPI was filtered out.In addition, t-test was utilized to evaluate whether the paired PCC value was significantly greater or less than 0.Subcellular localization information of proteins in rice was obtained from WoLF PSORT . In addihttp://bioinfo.cau.edu.cn/agriGO/download.php) [The GO annotations of proteins in rice were downloaded from agriGO (oad.php) . FurtherNon-interactors of Hsp70s were used as negative controls. These proteins were collected from the rice proteome, and satisfied the following conditions: first, they could not interact with Hsp70s, based on the interolog prediction; and second, the absolute PCC value between the expression level of the non-interactor and that of any Hsp70 should be less than 0.40.http://pfam.sanger.ac.uk/) [The domain information of rice proteins was obtained from Pfam (.ac.uk/) . Because.ac.uk/)  and HMME.ac.uk/)  locally.http://prosite.expasy.org/) [The motif annotations of proteins in rice were acquired from PROSITE (sy.org/) . The Scasy.org/)  was downp-value to measure the significance level.A one-tailed Fisher’s exact test was used to detect the over-represented domains and motifs among the Hsp70s interactors in rice compared with the negative interactors. For each domain or motif annotation, a 2 × 2 contingency table was constructed, as shown in Additional file p-value into the corresponding adjusted p-value based on the BH method [p-value was used to determine the potential binding sites. A cutoff value of 0.05 was used in this work.To limit the false-positive error rate associated with multiple statistical tests, R software was further used to alter each H method . Ultimathttp://bioinfo.cau.edu.cn/agriGO/). For each GO term, all parent nodes were retrieved according to the archive of the GO database, and the minimum distance from the root (depth) was determined. Only terms beyond the fourth depth were considered. After that, fisher’s exact test was conducted to reveal the over-represented GO terms in the opposite dataset, and the BH method was used to control the false discovery rate (FDR). The Hsp70 network was generated using Cytoscape (http://www.cytoscape.org/)[The GO information of the predicted Hsp70 interactors in rice was obtained from agriGO (ape.org/).http://teethgrinder.co.uk/open-flash-chart/) and Google Chart Tools (https://developers.google.com/chart/).The web tools and rice Hsps database were constructed on a LAMP  platform. RGEP visualization was developed using two types of open source software, Open Flash Chart Additional file 1: Table S1: Domains for heat shock protein query in Uniprot database. Supplemental Data 1A. Predicted PPIs ofHsp70s in rice based on the interolog and gene expression-based methods. Supplemental Data 1B. Predicted interactors of Hsp70s. (XLS 297 KB)Additional file 2: Supplemental Data 2A. Domains overrepresented among interactors of Hsp70s. Supplemental Data 2B. Motifs overrepresented among interactors of Hsp70s. (XLS 28 KB)Additional file 3: Supplemental Data 3A. Enriched GO terms among interactors with expression levels positively correlated with Hsp70s. Supplemental Data 3B. Enriched GO terms among interactorswith expression levels negatively correlated with Hsp70s. (XLS 32 KB)Additional file 4: Figure S2. Gene expression profile of Hsp70s, enolase, fumaratehydratase, malate dehydrogenase and citrate synthase in response to abiotic stresses. Figure S3. Gene expression profile of Hsp70s, Racs, Hsp90s, SKP1 in response to abiotic stresses. Figure S4. Gene expression profile of Hsp70s and FtsH proteins in response to abiotic stresses. (DOC 80 KB)Additional file 5: Figure S1: Gene expression profile of Hsp70s, Ran and importin proteins in response to abiotic stresses.


From brain to the World Wide Web, information-processing networks share common scale invariant properties. Here, we reveal the existence of neural-like networks at a molecular scale within the ribosome. We show that with their extensions, ribosomal proteins form complex assortative interaction networks through which they communicate through tiny interfaces. The analysis of the crystal structures of 50S eubacterial particles reveals that most of these interfaces involve key phylogenetically conserved residues. The systematic observation of interactions between basic and aromatic amino acids at the interfaces and along the extension provides new structural insights that may contribute to decipher the molecular mechanisms of signal transmission within or between the ribosomal proteins. Similar to neurons interacting through “molecular synapses”, ribosomal proteins form a network that suggest an analogy with a simple molecular brain in which the “sensory-proteins” innervate the functional ribosomal sites, while the “inter-proteins” interconnect them into circuits suitable to process the information flow that circulates during protein synthesis. It is likely that these circuits have evolved to coordinate both the complex macromolecular motions and the binding of the multiple factors during translation. This opens new perspectives on nanoscale information transfer and processing. Ribosomes are large ribonucleoprotein particles that catalyse the mRNA-directed protein synthesis23689The extensions that are observed in most of the ribosomal proteins systematically participate in networks with a great diversity of protein-protein interactions . ReciproSecondly, our analysis shows that these networks present an interesting similarity with information processing networks . In the E. coli, D. radiodurans and T. thermophilus) crystallized in different functional translation steps, initiation, elongation and termination with various factors and antibioticsThirdly, to decipher the underlying molecular mechanisms of information transmission within these networks, all the inter-protein contacts of the eubacterial large subunit have been systematically analysed. This network indeed provides the opportunity to compare and analyse the highest number of equivalent structures from three eubacterial species (2) to the largest , contain conserved residues that are systematically involved in one and most frequently several intra- or inter-molecular basic-aromatic amino acid interactions2) are further stabilised by additional intermolecular contacts such as hydrophobic interactions and hydrogen bonds that are usually observed in classical dimers. The major finding of this analysis is that the smallest interfaces display a “necessary minimum” that is also shared by larger ones: the conserved aromatic-basic amino acid interactions. Too tiny to be rationalized in terms of dimer stabilisation, these highly conserved interfaces have probably been selected during evolution to play a specific role in inter-protein communication. They reveal the strictly necessary interacting residues to ensure information transfer from a protein to another. Indeed, it has been experimentally shown that cation-π interactions mediate inter-domain communication in proteinsRemarkably, except uL16-bL27, all 50S protein-protein interfaces, from the smallest . The tables for the conversion between the old and the new system are provided in pymolAll the structures of eukaryotic, eubacterial and archaeal ribosomes deposited in the protein data bank (PDB)circoset al.mfinder provided by U. AlonScripts have been written for systematically mapping the protein-protein interactions within the ribosomal subunit for display by the program ftp://ftp.ncbi.nih.gov/pub/wolf/_suppl/ribo. The aligned sequences have been visualized and analysed with the program Jalviewpymol scripts for their three-dimensional representation and zappo colouring  50S and 70S subunits whose resolution is greater than 3.5 Å  and the careful comparison of the density maps, our analysis has revealed that in most cases, the conformation “x” .The structures of each interacting protein pairs have been analysed in all the available PDB crystallographic structures of three species .