Escherichia coli and Mycoplasma pneumoniae, yielding 443 and 116 heteromultimeric soluble protein complexes, respectively. We have coupled the results of these mass spectrometry-characterized protein complexes with the 285 “gold standard” protein complexes identified by EcoCyc. A comparison with databases of gene orthology, conservation, and essentiality identified proteins conserved or lost in complexes of other species. For instance, of 285 “gold standard” protein complexes in E. coli, less than 10% are fully conserved among a set of 7 distantly-related bacterial “model” species. Complex conservation follows one of three models: well-conserved complexes, complexes with a conserved core, and complexes with partial conservation but no conserved core. Expanding the comparison to 894 distinct bacterial genomes illustrates fractional conservation and the limits of co-conservation among components of protein complexes: just 14 out of 285 model protein complexes are perfectly conserved across 95% of the genomes used, yet we predict more than 180 may be partially conserved across at least half of the genomes. No clear relationship between gene essentiality and protein complex conservation is observed, as even poorly conserved complexes contain a significant number of essential proteins. Finally, we identify 183 complexes containing well-conserved components and uncharacterized proteins which will be interesting targets for future experimental studies.Large-scale analyses of protein complexes have recently become available for  E. coli or Mycoplasma pneumoniae, as well as gene orthology, to predict protein complexes across many species of bacteria. Surprisingly, the majority of protein complexes is not conserved, demonstrating an unexpected evolutionary flexibility. We also observe broader trends within protein complex conservation, especially in genome-reduced species with minimal sets of protein complexes.Though more than 20,000 binary protein-protein interactions have been published for a few well-studied bacterial species, the results rarely capture the full extent to which proteins take part in complexes. Here, we use experimentally-observed protein complexes from  Abundant genome sequencing revealed an astounding diversity among bacterial genomes. Even species that inhabit the same environment may only share a fraction of their genes. This raises the question how these organisms have adapted to their environments using only a limited number of genes. Here, we investigate the protein complements across bacterial genomes, how proteins are combined into protein complexes across species, and whether these complexes have been conserved across diverse branches on the prokaryotic tree of life.S. cerevisiae, S. pombe and E. coli made possible by systematic screens of genetic interactions and have found notable differences in their structure and content [Other studies have compared the interaction networks of  content ,2. Studi content . This mi content .Numerous studies of protein-protein interactions have revealed the organization of proteomes into networks of interactions as well as protein complexes. Systematic surveys of protein complexes exist for only a few bacterial species, namely E. coli [Mycoplasma pneumoniae [homologous protein complexes in other species. This is a particular challenge in prokaryotes as the genomes of most species are highly divergent from the few model species used here. However, E. coli and Mycoplasma provide two important paradigms: E. coli is a generalist that can live under a variety of conditions while Mycoplasma is a specialized parasite that requires host cells to grow. With ~4,300 and ~700 genes, respectively, they represent medium-sized as well as minimal genomes and thus medium and minimal diversity of protein complexes. E. coli ,5 and Myeumoniae . The listhe extent to which protein complexes are likely to be conserved across microbial species. Furthermore, we can evaluate the biological role of proteins and complexes of unknown function across many species.Few studies have investigated the evolution and diversity of protein complexes across a wide range of taxa. This is not surprising given that large-scale experimental data has only become available in recent years. In combination with a large number of completed genome sequences we can use this data to evaluate comparing sets of interactome data, including pure bioinformatics approaches [S. cerevisiae, S. pombe and E. coli [Existing studies proaches  have gen E. coli ,7,8. Met E. coli ,9 may alE. coli and M. pneumoniae) alleviates some of the bias inherent in using a single species as a universal model. Rather, observing which protein complexes and their components are present in two otherwise distinct species allows us to draw conclusions about how critical these components are to microbial life.In order to compare genomes and protein complexes across species, we couple the results of mass spectrometry-characterized protein complexes ,6 with dPseudomonas aeruginosa, may be conserved across 20 to 30 percent of all other bacteria, on average. The most minimal genomes, including those of Mycoplasma species, may share their orthologous groups (OGs) with 60 to 80 percent of other bacterial species, on average. These results are reasonable and expected: reduced genomes, by definition, have lost sequence space but have not lost the loci most crucial to bacterial life itself. Furthermore, though most genomes show an increasing fraction of paralogs being conserved as their size increases  containing 5 or more unique protein components . Note that some of the Hu et al. complexes appear to represent subsets of full complexes . Also, the EcoCyc set is partially redundant .In this study, we used the literature-curated set of EcoCyc u et al.  as a set[E. coli . The setr et al.  was alsoM. pneumoniae complexes with two or more unique members contain just those two unique proteins. The cross-species discrepancy may also result from methodology, though Kühner et al. suggest it is representative of authentic biological differences between the two species. M. pneumoniae contains fewer unique proteins than E. coli does and this difference limits the number of unique proteins seen in any single complex.The size of the complexes within the data set produced by Kühner et al. appears to differ in distribution from those characterized by Hu et al. . SpecifiM. pneumoniae complexes provide a better match for the EcoCyc complex than the Hu set does. Topoisomerase IV (EcoCyc: CPLX0–2424) has a good match in all three data sets though the representative Hu complex contains an additional protein. Lastly, RecBCD serves as an example of a good E. coli-specific match with no components present among the M. pneumoniae complexes.The exact protein complexes defined by each data set differ. Pairwise comparison of presence or absence of proteins in each complex is improved by mapping components to orthologous groups but few complexes appear to be present in an identical form across all three data sets. E. coli complexes but no match among the Kühner et al. M. pneumoniae complexes.In the aggregate, most EcoCyc complexes do not have reliable matches in the other experimental sets . Using aM. pneumoniae complexes serves as a rough model for the complexes most commonly found across bacterial species .The set of cies see  for the E. coli. The intersection of the two sets  of the potential complexes unless we also use the unique complexes of each set. We used these results as evidence that the data sets should be used as independent models rather than as an intersecting set: losing more than half of the potential model complexes simply due to inconsistencies across data sets may be too limiting for a broad cross-species comparision.The variability between the EcoCyc and Hu datasets has a direct impact on the usefulness of these complexomes as models for other bacterial species. In any case, the EcoCyc and Hu complex sets provide the most comprehensive complex set currently available for two sets  is indeeE. coli. (M. pneumoniae complexes were not used in this comparison.) These plots provide the median and interquartile range of protein complex conservation fractions in each species, using either EcoCyc or Hu et al. complexes as a model of the complex set. A comprehensive set of protein complexes has not been identified for any of these species as of yet. Following the results shown in P. aeruginosa  but shows a greater range of conservation across the three other species. The Hu complexes show lower complex conservation median values than EcoCyc for all but H. pylori and lower variability for all but P. aeruginosa. Here, the median values are not as useful as the conservation ranges: the distance between the highest and lowest values includes every possibility from 0 to 100% conservation using either model of E. coli complexes. We see that the two species most closely related to E. coli in this set—P. aeruginosa and C. crescentus—produce different median values and interquartile ranges between the sets across all protein complexes. Components of complexes in the two E. coli sets, used as models, are clearly conserved differently across bacterial species. A higher-resolution comparison is necessary to determine which complexes are highly-conserved.E. coli and Mycoplasma, we hypothesized that homologous complexes should be very similar, both in size and composition. However, this is not true: few complexes share even half of their components across the data sets  but also biological reasons (E. coli vs. Mycoplasma) for these differences.Although the size distribution is different in ata sets . The majTo get a more global yet more detailed picture of protein complex conservation, we compared conservation across 8 bacterial species, including the two species for which full protein complex sets exist. The EcoCyc complex set was used as a standard to which all other species were compared. succinate dehydrogenase components are essential in only 3 of the species shown. The four components of this complex (as defined by the default structure in E. coli) are present only in Pseudomonas aeruginosa and Caulobacter crescentus. Helicobacter pylori and B. subtilis encode 3 out of 4 components and the other 3 species appear to have lost the entire complex. Similarly, the Bam outer membrane protein assembly complex (EcoCyc: CPLX0–3933) shows partial essentiality across the complex in 4 species though its components are well conserved in only 3 species. This complex has a similarly patchy pattern of conservation, with any number from zero to all 5 components conserved. In the case of H. pylori Bam complex, what initially seems like a lack of conservation may be the result of component replacement by functionally similar proteins [1 ATP synthaseF is conserved in all species examined. These examples show that most complexes are less well conserved than their often important functions indicate .It is commonly assumed that highly conserved proteins must be important and thus should be essential in many cases. Interestingly, this is often not true . For exaproteins . By contE. coli complexes with at least one component present in M. pneumoniae. In this case, fraction of essentiality  is shown. E. coli complexes with at least one protein conserved in M. pneumoniae, though not necessarily present in a M. pneumoniae complex. A complete survey of all EcoCyc complexes across these species in terms of conservation and essentiality is provided in Mycoplasma species. The remaining complexes vary extensively in their degree and extent of conservation. A number of complexes are well conserved across E. coli, P. aeruginosa, C., crescentus, H. pylori, and B. subtilis but not S. sanguinis or the Mycoplasma . Overall, of the 176 EcoCyc complexes of 3 or more unique proteins  in all other species. This is a broad taxonomic range, so a more relaxed cutoff may be appropriate to predict a complex is conserved; even so, only 28 complexes contain at least 2/3 of the E. coli components across all species. Lowering the cutoff to at least half of the E. coli components still yields only 34 complexes. The lack of broad conservation is not, however, a matter of full complex presence or absence across species. Rather, many complex components appear to be conserved independently from other members of their complex. Similarly patchy conservation can be seen for essentiality  on the basis of its fractional conservation across all other species reveals groups of complexes with similar patterns of predicted conservation.E. coli complexes are, unsurprisingly, those most closely related to E. coli. Roughly a third of the complex set is conserved across all species with the minimal Rickettsia and Mycoplasma genomes, among others, serving as notable exceptions. The middle third shows the most difference in conservation between the Proteobacteria and all other species. The Lactobacillales show the most difference in conservation among these complexes, to the degree that they resemble Cyanobacteria more closely among this subset. The last third  have at least one component present in all 894 bacterial genomes in the set. None are perfectly conserved across all genomes but 14 complexes appear to be conserved across at least 95% of the genomes. If potential complex conservation is generously defined as conservation of at least half of the complex components, 3 EcoCyc complexes are potentially conserved across all 894 genomes, 25 are potentially conserved across 95% of the genomes, and 186 are potentially conserved across at least half of the genomes. Variance across the full set of complex conservation fractions is 0.189. Because conservation of these complexes follows the existing taxonomy well, some generally well-conserved complexes like RNA polymerases may be missing from entire genera.The species with the most overall conservation of the E. coli complexome  have at least one component present in all 894 bacterial genomes in the set. As with the EcoCyc complexes, none are perfectly conserved across all genomes but a single complex (complex 271) appears to be conserved across at least 95% of the genomes. Using the same 50% cutoff for potential complex conservation as used above, no Hu complexes appear to be conserved in all 894 genomes, 10 are potentially conserved across 95% of the genomes, and 182 are potentially conserved across at least half of the genomes. Though these Hu et al. complex values appear similar to those for the Ecocyc complexes, variance across the full set of Hu complex conservation fractions is 0.097, indicating less variability among the values than that seen for the EcoCyc complexes. This lesser variance can also be seen in the surprising consistency across taxonomic lines . As the number of paralogs for each gene was broadly defined using orthologous groups, these numbers are considered maximum possible values rather than specific counts of known paralogous regions. We observed an inverse trend between E. coli complexes vs. M. pneumoniae  and Kühner (Mycoplasma pneumoniae) complexes, respectively. Unlike in parts A and B, the complexes shown are experimental results rather than literature-defined complexes. Each complex contains at least one component of unknown or unclear function, whether in the context of the protein complex or broader cellular function. For instance, complex 66 from Hu et al.  complexes contain at least one component of unknown function. Of these, 109 Hu et al. E. coli complexes and 19 Kühner et al. M. pneumoniae complexes contain components highly conserved as essential proteins. The full list of experimental complexes with unknown components is available in Protein complexes are attractive targets for functional analysis, given that proteins are embedded in a functional context. This is especially true for proteins of unknown function that are part of a complex . Here, cu et al.  consistspathways . More thE. coli Sec translocation complex (EcoCyc: SEC-SECRETION-CPLX) are well-conserved across species from P. aeruginosa to M. genitalium. One of these components, SecA, has been found to be essential in all species focused on in this work with the exception of S. sanguinis; orthologs of this protein are present in all 894 bacterial genomes examined. The remaining 4 E. coli components are more variable in conservation across species. For instance, YajC is present in 727 out of the same 894 genomes. Strong selection pressure seems to avoid mutations that render the entire complex ineffectual. This may explain why we have observed a higher level of conservation for protein complex components than for proteins in general  are present in all species examined, though they do vary in essentiality. B. subtilis essentiality screens found no essential genes in ATP synthase, while those for M. pneumoniae found all but one component to be essential. Other complexes—predominantly those with transmembrane domains and/or transporter functions—are more variable in both conservation and essentiality, though they provide examples of how dispensable accessory proteins may be.Components of protein complexes are, on average, more likely to be present in other bacterial species than proteins not in complexes . This isE. coli may not be present in other species. The lipopolysaccharide transport complex (EcoCyc: CPLX0–7992) serves as an excellent example: all seven of the Lpt proteins in this complex have been found to be essential in E. coli though their conservation is limited to other Gram-negative species including C. crescentus and P. aeruginosa. We found that most transmembrane protein complexes follow this pattern. Interestingly, species with partial complex component conservation vs. E. coli may highlight situations in which core elements of a complex are conserved but have been modified to carry out other functions or adapted to special physiological circumstances. For example, 3 out of 4 of the succinate dehydrogenase complex (EcoCyc: SUCC-DEHASE) components in E. coli are also present in B. subtilis but not at all in S. sanguinis. This is an especially interesting example as two of the components, SdhC and SdhD, are inner membrane proteins, though only SdhC is present in the three-component B. subtilis succinate dehydrogenase. We conclude that membrane proteins and their complexes are particularly malleable, given their role in signaling and transport which reflects adaptations to specific environments and the nutrients present in them.Some protein complexes with essential functions in E. coli) appear to code for a greater fraction of highly-conserved protein complexes. This conservation is evident in comparisons of the Mycoplasma pneumoniae protein complexes. In an examination of these protein complex components across more than 800 bacterial genomes, we found that species such as M. pneumoniae offers a better model of the protein complexes most critical to bacterial life. Protein complexes observed in M. pneumoniae may not only have retained a core set of functions but also utilized a higher degree of multifunctionality among its metabolic enzymes [Smaller and more reduced bacterial genomes  only appears to be present in K-12 E. coli but also in single species of Shewanella and Photobacterium. This toxin-antitoxin system has a role in bacteriophage resistance in E. coli [Buchnera species) yet is poorly-conserved across most of the species traditionally considered Gram positive. A subset of complexes, including CPLX0–1163 (HslVU protease) and ABC-56-CPLX , fit a strict co-conservation model: these complexes are almost always present in their full form rather than as a fraction of the E. coli model complex. These complexes are exceptions rather than the rule. Using E. coli as a model, few complexes are conserved perfectly across a wide range of species; in fact, most complexes are fractionally conserved.Variable conservation of some complexes is visible even among the  E. coli  but it ihttp://github.com/caufieldjh/spicednog). Statistical analysis and clustering was performed using R package vegan [All data management was performed using in-house Python scripts  for which whole-genome gene essentiality data was selected for in-depth analysis. This species set is referred to as the focused set. Lists of all protein-coding loci for each species were obtained using the respective full proteome sets from UniProt  and areA set of 894 species, referred to as the large set, was also prepared using every bacterial species present in eggNOG v.3 and in the NCBI Taxonomy database . The treEach locus in each genome was assigned to a single orthologous group (OG) as in eggNOG v.3 , such thThe observed distributions of essential genes among those coding for protein complex components were obtained using protein complex sets ,6,13, eg2(Observed/Expected) values are plotted in A random model was created for the purpose of comparing the data set to background noise. The random model, meant to represent a collection of randomly sized complexes, was populated by proteins that have been randomly assigned essential status. The complex sizes were randomly assigned a value from three to ten. Each complex was then assigned protein values of either essential or non-essential status. The probability of being essential was determined by the overall percent of essential genes within the organism, while the random model size is equal to the maximum of the species and dataset being compared. This random model was then put through the same binning process as the observed data. The mean of each bin was obtained after 10,000 replications. This results in s bins of a size that is no longer equal to the actual data set but demonstrates an appropriate background noise level for comparison purposes. The logThe general scheme for data analysis was as follows: (1) A list of all orthologous groups (OG) was produced for each of 894 bacterial species found in the large set defined above. (2) Presence or absence of each OG was determined for all species. (3) Repeated OGs were removed from each list and step 2 was repeated. (4) The list from step 1 was used to map OGs to the components of three sets of protein complexes. The complexes were compared to search for cross-data set complex matches. Gene essentiality was also mapped to each OG in a species-dependent basis. (5) A list of 8 taxonomically-divergent species was selected and used to define fractional conservation and fractional essentiality of each protein complex.Complex size was defined as the number of unique proteins isolated from a complex; i.e. a complex may contain 3 unique OGs but 4 distinct protein components, yielding a complex size of 4. For each complex, the presence of each OG within the complex was assayed in the full proteome sets of the seven other representative species. The resulting binary presence/absence values were combined to produce a value for the percent complex conservation. This value intentionally disregards any gene context similarity  and simply expresses the fraction of complex components which a specific genome may code for. When a target proteome did contain a specified complex component, the number of paralogs of the component-coding gene was determined as the number of proteins in the list mapping to the same OG. While further verification, may be necessary to define any of these protein-coding genes as true paralogs, we simply used the OGs  as determined by eggNOG.OGs were used as the basis of comparison for similarity between data sets. essentiality values using published assays specific to the species listed above. These values were used to define the essentiality fraction of each potentially conserved complex, i.e. an E. coli complex for which 80% of the components appear to be conserved in M. pneumoniae but only 60% of the components may be essential in the latter species.All protein complex components were also assigned binary http://www.genome.jp/kegg/genome.html, [average locus conservation for a particular genome, CAAL(g), was calculated as:CL is the number of genomes in which the locus is present, L(g) is the number of loci in the genome, N is the total number of genomes, and m is the percentage of loci mapped by eggNOG v.3. Values are adjusted using the fraction of loci actually mapped so unmapped loci lower the effective conservation.A broader comparison was prepared using the list of 894 species as defined above. Genome sizes for each species were retrieved from the KEGG GENOME Database . For eaaverage OG conservation for a particular genome, CAAO(g), was calculated as:CL is the number of genomes in which the locus is present, O(g) is the number of unique OGs in the genome, N is the total number of genomes, and m is the percentage of loci mapped by eggNOG v.3.An identical list of values, but with repeated OGs reduced to a single occurrence, was averaged to produce average OG conservation. This modification removes the effect of counting loci more than once when they share OGs, as may happen when two or more loci are paralogous. Adjusted N = 943, though Species/strains were sorted by genome size and compared to the average conservation values. For the set of all bacterial genomes, E. coli complex) and the target genome over the size of the model complex. Heatmaps were prepared using the R heatmap.2 function in the gplots package. Randomized models of the large set heatmaps  and the large set. A cladogram of all species in the set was prepared using the Interactive Tree of Life  projectheatmaps  Figs. re package ) and theS1 FigAn extended version of (PDF)Click here for additional data file.S2 FigAn extended version of (PDF)Click here for additional data file.S3 Fig(A) Proteins in E. coli complexes [(B) By contrast, in M. pneumoniae complexes [E. coli complexes as defined by Hu et al. were compared to the full M. pneumoniae proteome while M. pneumoniae complexes were compared to the full E. coli proteome; all cross-species comparison are done using predicted orthologs as described in Materials and Methods.omplexes  tend to omplexes  more con(PDF)Click here for additional data file.S4 FigE. coli literature-curated Ecocyc (A) and experimentally-observed Hu et al. (B) complexes. However, this is not true in reduced genomes such as that of Mycoplasma pneumoniae(C). Each node represents a single protein complex with relative size corresponding to the size of the complex in number of components. Fraction of essentiality and average paralogy are calculated as described in Materials and Methods. Data from Hu et al. [(D) An example complex from each of the three data sets is shown. These complexes are not identical in composition but have similar components. Each complex, as defined by a single data set, may offer an incomplete set of protein components and may overlook the impact of paralogy.More essential protein complexes tend to have fewer components with paralogs, at least in u et al.  (A), Ecou et al.  (B) and u et al.  (C). (D)(PDF)Click here for additional data file.S5 FigThis figure expands upon that in (PDF)Click here for additional data file.S6 FigUsing the Hu et al. set of protein complexes as a model, each column is a single complex from the set and each row is a distinct bacterial genome. 310 complexes and 894 genomes are shown in total. Genome order corresponds to a cladogram produced using NCBI taxonomy and an Interactive Tree of Life  tree. C(PDF)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.S11 Table(XLSX)Click here for additional data file.


Leishmania donovani and Leishmania infantum protozoan parasites. A significant number of infections take a fatal course. Drug therapy is available but still costly and parasites resistant to first line drugs are observed. Despite many years of trial no commercial vaccine is available to date. However, development of a cost effective, needle-independent vaccine remains a high priority. Reverse vaccinology has attracted much attention since the term has been coined and the approach tested by Rappuoli and colleagues. This in silico selection of antigens from genomic and proteomic data sets was also adapted to aim at developing an anti-Leishmania vaccine. Here, an analysis of the efforts is attempted and the challenges to be overcome by these endeavors are discussed. Strategies that led to successful identification of antigens will be illustrated. Furthermore, these efforts are viewed in the context of anticipated modes of action of effective anti-Leishmania immune responses to highlight possible advantages and shortcomings.Visceral leishmaniasis is a neglected infectious disease caused primarily by  Leishmania-infections  also known as Kala azar is a prioritized objective in global efforts directed toward improving the situation for people at risk of and patients suffering from fections , 2. The fections . Therapyfections . Vaccinefections .There is encouraging evidence that vaccination against VL should be possible. Immunity is thought to depend on a protective cellular immune response requiring CD4 as well as CD8 T cells that activate leishmanicidal mechanisms in host phagocytes , 7 sinceLeishmania-antigens were fit for purpose.For human use, subunit vaccines based on selected parasite antigens, however, would offer a more defined and more stable alternative . But, main silico. Selection proceeds through an algorithm that is developed “backwards” starting from a known or anticipated mode of action of the vaccine. This has been impressively successful for the development of novel anti-Meningococcal Serotype B vaccines because (a) the mode of action was known and allowed to develop a straight forward in vitro screening assay based on lysis-mediating antibodies and (b) this assay was scalable and had high throughput capacity  or surface exposure and hydrolytic release.5 candidate epitopes. To reduce this number, a stringent but arbitrary threshold of the binding score to MHC was introduced resulting in 250 candidate peptides. In step 2 of the process, these candidates were further analyzed using different T cell epitope prediction algorithms. A set of 78 epitopes was predicted by all or nearly all software. In step 3, the 78 epitopes were compared to the predicted proteomes of putative hosts based on mouse and human genome data, the rationale being to reduce the risk of inducing autoimmune reactions. In this step, it was considered satisfactory that none of the selected peptide-epitopes had >80% identity with a host protein. However, there were peptides with lower identity, i.e., with up to 9 of 11 amino acids identical. Step 4 checked for conservation of the candidate protein containing the epitope(s) in different Leishmania spp. and other kinetoplastids. The authors noted that their algorithm did not identify any of the known, experimentally validated candidates. These failed the arbitrarily set stringent threshold for the MHC-binding score in step 1.The most puristic Reverse Vaccinology algorithms to identify candidate vaccine antigens adapted for leishmaniasis proceeded stepwise from genome to T cell epitope prediction , 30. ForAn alternative algorithm based on the same idea of vaccine mode of action was developed by John et al. . In thisWhile both of these approaches identified potentially immunogenic epitopes [in fact immunogenicity was demonstrated in the case of Ref. ], the faThe working hypothesis that the success of a vaccine to prevent or treat VL in humans will rely on the induction of CD4 and CD8 T cells is valid. However, individual steps in the algorithms aiming at antigen identification need to be scrutinized on the one hand for the validity of underlying concepts and logic and on the other hand for their effectiveness as selecting filters. Since the abovementioned studies offer recent examples, I shall follow steps as proposed in their algorithms for illustration.5 candidates. Thus, every ORF is likely to encode more than one candidate hence the filter lacks efficiency. An arbitrary threshold as introduced can seemingly provide filtering capacity but will quickly become too stringent since in the said example it excluded all experimentally identified candidates. The next step involved selection based on T cell epitope predicting algorithms. This filtering is highly error prone and probably superfluous as the T cell receptor is an explorative, adaptive molecule that can recognize epitope variants . Similarly, if we agree that both CD4 and CD8 cells are relevant for protection, we can base our analysis on the number of MHC class I molecules expressed per antigen-presenting cell (~105 molecules) since this is thought to be lower than the number of class II molecules, hence can be considered the limiting peptide receptor species. To illustrate the next steps, I will base my arguments on a data set published by our group. We aimed at identifying the relative abundance of proteins in amastigotes of L. mexicana based on a label free method that deduces a protein abundance index (emPAI) for each protein in a data set  into an algorithm of Reverse Vaccinology. If we accept that in principle each parasite protein contains functional MHC I and II binding peptides and, thus, potential T cell epitopes, we may simply base our estimates on the number of protein molecules per parasite . The expected value of MHC–peptide complexes for each protein in the data set can be plotted in an ordered way according to protein abundance, which produces an S-shaped curve . Thus, the copy number per cell of proteins detected in current proteomic analyses ranges from a few million to a couple of 100 molecules. MHC–peptide complex formation, however, ultimately follows the law of mass, hence abundant molecules have a greater chance of becoming processed and ensuing peptides bound to the MHC-binding groove. The simplest version of predicting the chance of a protein to be successful in this respect is to calculate an expected value for how often this may be the case if 10e Figure . For cane Figure . This thThe presented approach is easily expanded or adapted to additional proteomic data sets when information on relative protein abundance becomes available. It reveals not only the likely reason why most experimentally studied antigens were immuno- and antigenic but also defines a large number of additional candidates. In contrast, the majority of the candidates predicted purely by bioinformatics , 30 wereLeishmania donovani infection (L. donovani than suggested by the data derived from L. mexicana that were used here for illustration. Consistent with the latter idea, the same γ-glutamylcysteine synthetase-based vaccines were less effective against L. mexicana (Of course an algorithm as presented above, that integrates protein abundance to derive the set of likely immunogenic and hence vaccine candidate proteins, is simplistic. But, its advantages are its practical value and high flexibility since any change in parameters can be easily accommodated. Changing parameters will essentially only re-position the threshold value for the effective number of MHC–peptide complexes. For example, the threshold may change if dynamic sampling of the peptide pool by recycling MHC is integrated over the time of an infection cycle. In this case, peptide off-rates from MHC–peptide complexes may be a valuable, bioinformatically accessible factor to improve the algorithm. It has been shown that kinetic stability of MHC–peptide complexes is probably the single most important determinant that defines immunodominant T cell epitopes . Furthernfection , 78. Altmexicana . UnfortuIn summary, developing an algorithm to adapt Reverse Vaccinology for the identification of antigens for anti-VL vaccine should include as a first step quantitative aspects of protein expression and incorporate the growing resource of proteomic data sets. On its own, however, this approach still leaves one with some 500 candidates. Selection against epitopes with homology to host proteins is certainly advisable but one should be aware of its limitations and the gargantuan dimension of its unknowns due to the fact that T cells recognize a sequence space . If adopThe author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Most cellular proteins function as part of stable protein complexes. We recently showed that around 38% of proteins associate with mRNAs that encode interacting proteins, reflecting the cotranslational formation of the complex between the bait protein and the nascent peptides encoded by the interacting mRNAs. Here we hypothesise that these cotranslational protein-mRNA associations can be used to predict protein-protein interactions.eti1 mRNA, which codes for a protein of unknown function and uninformative sequence. Based on this protein-mRNA association, we predicted that the Exo2 and Eti1 protein are part of the same complex, and confirmed this hypothesis by coimmunoprecipitation and colocalization of the proteins. Similarly, we show that the cotranslational interaction between the Sty1 MAP kinase and the cip2 mRNA, which encodes an RNA-binding protein, predicts a complex between Sty1 and Cip2.We found that the fission yeast Exo2 protein, which encodes an exonuclease of the XRN1 family, coimmunoprecipitates with the Our results demonstrate that cotranslational protein-mRNA associations can be used to identify new components of protein complexes. To address this question we recently used a method called RIP-chip  to identify mRNAs associated with specific proteins. We found that ~38% of a set of proteins with varied structures and functions interacted with mRNAs known or predicted to encode proteins that interact with the bait proteins. We also discovered that these associations required the presence of the protein encoded by the mRNA as well as active translation. These results indicated that the interaction between the bait protein and the protein encoded by the associated mRNA takes place cotranslationally . The immunoprecipitated protein is associated with the nascent peptide together with the mRNA that is being translated, allowing the identification of the nascent peptide through its mRNA . As hypothesised, we found that the two proteins coimmunoprecipitate in vitro and colocalise in vivo. This work demonstrates that genome-wide protein-mRNA interaction datasets can be used to predict protein-protein associations.Although most cellular proteins are part of protein complexes, little is known about how the formation of protein complexes occurs  Figure A. This aip2 mRNA, which epyp2, which encodes a protein phosphatase known to physically interact with Sty1[cip2, which codes for an RNA-binding protein that regulates the response to oxidative stress[cip2 mRNA requires active translation of cip2, as it is disrupted when translation is inhibited using puromycin and when the cip2 initiation codon is mutated[cip2 mRNA, and thus that both proteins are part of the same complex . SPAC12G12.09 encodes a predicted protein of 977 amino acids that lacks any known domain and does not display homology to any other protein. We named this gene eti1, for exo two interacting (see below). To confirm that these interactions reflected cotranslational assembly, we incubated cells expressing Exo2-TAP with puromycin to disrupt active translation, and used them for RIp-chip experiments. This treatment led to a complete loss of interaction between Exo2 and both ppo1 and eti1 mRNAs  in vegetatively growing cells  and of S phase initiation (cdc18)[Our results demonstrate that cotranslational mRNA-protein interactions detected using RIP-chip can be used to predict protein-protein associations. The nature and behaviour of the proteins we identified suggest that the interactions may have biological importance. For example, there is evidence that Sty1 regulates the phosphorylation of Cip2: Cip2 is phosphorylated in response to oxidative stress, and this phosphorylation does not take place in  mutants. Our resStandard methods were used for growth and manipulation of fission yeast cells. All strThe authors declare that they have no competing interests.JM and CD designed the study, performed the data analysis and wrote the paper. All experiments were carried out by CD. Both authors read and approved the final manuscript.Microarray data: The data sets supporting the results of this article are available in the ArrayExpress repository with accession number E-MTAB-1856. Additional supporting data includes four figures (Additional file Click here for file


Most protein complex detection methods utilize unsupervised techniques to cluster densely connected nodes in a protein-protein interaction (PPI) network, in spite of the fact that many true complexes are not dense subgraphs. Supervised methods have been proposed recently, but they do not answer why a group of proteins are predicted as a complex, and they have not investigated how to detect new complexes of one species by training the model on the PPI data of another species. We propose a novel supervised method to address these issues. The key idea is to discover emerging patterns (EPs), a type of contrast pattern, which can clearly distinguish true complexes from random subgraphs in a PPI network. An integrative score of EPs is defined to measure how likely a subgraph of proteins can form a complex. New complexes thus can grow from our seed proteins by iteratively updating this score. The performance of our method is tested on eight benchmark PPI datasets and compared with seven unsupervised methods, two supervised and one semi-supervised methods under five standards to assess the quality of the predicted complexes. The results show that in most cases our method achieved a better performance, sometimes significantly. A protein complex is a group of proteins that interact one another for specific biological activities3Over the last decade, more than 20 computational methods have been proposed as complementary tools to laboratory-based techniques for detecting protein complexes from protein-protein interaction (PPI) networks. By these methods, a PPI network is represented as a undirected graph, where the nodes stand for proteins and the edges stand for protein interactions. Given a PPI network, these methods search for protein complexes by detecting densely connected subgraphs in the network. These clustering methods can be roughly grouped into two categories1419However, true complexes are not always dense subgraphs. Sometimes they can be very sparse. Supervised learning methods have been recently developed to detect complexes by combining other informative properties of true complexes. For example, SCI-BNmeanClusteringCoeff ≤ 0.3, 1.0 < varDegreeCorrelation ≤ 2.80} is an emerging pattern in the Collinsand the degree correlation variance is between 1.0 and 2.80, then 98.2% of non-complexes (random subgraphs) exhibit this pattern, while only 6% of true complex subgraphs contain this pattern. If a new subgraph contains this EP, then it is much more likely to be a non-complex instead of a new complex. We can see that the characteristics of complexes involved in an EP is not restricted to the density property only. Rather, EPs combine novel properties of complexes to answer why a subgraph can be predicted as a complex or not. Usually, we group a set of EPs to make the prediction more reliable. Emerging patterns (EPs)EPs have been previously applied to deal with gene expression bioinformatics problems293031The prediction performance of ClusterEPs is compared with supervised, semi-supervised and unsupervised learning methods. Comparing with the two supervised learning methods SCI-BNcomposite score, which is a more comprehensive performance measurement widely used in the field. These unsupervised clustering methods under our comparison are: MCODE14As the unsupervised approach has been intensively explored in the literature, we choose to compare ClusterEPs with seven representative clustering methods. The evaluation is on five large-scale yeast PPI datasets under three standard scores, and the quality of predicted complexes is assessed with reference to the true complexes from the MIPS complex catalogue databaseDetection of new human complexes through the model learned from the complexes of yeast PPI networks is a novel attempt by this study. We trained our prediction model using the complexes from the six yeast PPI networks, and made predictions to discover new human complexes from human PPI networks. Again, ClusterEPs achieved a better performance in comparison with the other prediction methods.Case studies are also performed to further illustrate the detailed predictive capability of ClusterEPs. It is observed that only ClusterEPs can make a complete and accurate prediction for the challenging case of the RNA polymerase I complex that consists of 14 proteins. ClusterEPs is also able to detect the RecQ helicase-Topo III complex, a small complex of only three proteins. It is highlighted here, because it is a not-well-separated subgraph, connecting to a large number of proteins outside the complex. GO analysis on the novel protein complexes predicted by our method suggests that there are strong evidences to support our prediction for these currently unknown complexes.http://lightning.med.monash.edu/ClusterEPs/. This website also provides a hyperlink pointing to a web server for a wider research community to use this prediction method.The algorithm has been implemented and a software version of ClusterEPs is downloadable at website This section is organized into five parts. The first part presents our comparison results with the two supervised learning methods: the Bayesian network (SCI-BN) modelet al. proposed another supervised learning method RMet al. used the published results to compare the performance between RM and SCI-BN. Unfortunately, the RM program is also not available to this work, we decided to compare their published results with ours.SCI-BNSCI-BN and RM used two independent sets of true complexes as the positive class of data. One is the MIPSThe PPI data set for the test is the well-known DIP data setWe conducted experiments using MIPS as the positive training set and TAP06 as the test set and vice versa. There are a total of 1579 proteins from the complexes stored in MIPS and TAP06. Taking the same way as SCI-BN and RM did, we first extracted a PPI graph containing these proteins and their interactions from the DIP network, and then tested ClusterEPs on this PPI graph. Same measures including Precision, Recall and F1 (see Methods) were then applied to assess the performance. The results are shown in NNet al.14et al.Recently, Nepusz ClusterEPs was first tested on the five yeast PPI networks when SGD was applied as the positive training data. It is noteworthy that none of the eight algorithms achieved an overall best performance across all the datasets. However, except ClusterONE, our ClusterEPs algorithm achieved a consistently better composite score than the other six algorithms on four datasets. As the negative data (random subgraphs) were generated by randomly selecting nodes in the PPI networks, we ran ClusterEPs 20 times on two personal computers to calculate the average performance. The standard deviation of the 20 runs was very small for each assessment score .This work was also aimed to detect novel human protein complexes from human PPI networks through a prediction model constructed using yeast complexes and PPI data. Yeast PPI networks and complexes have been deeply explored by many research teams. Our idea is to learn the basic principles of PPI networks and characteristics of protein complexes from yeast PPI and complex data, and then apply the constructed prediction model to human PPI data to transfer the basic principles for the prediction of novel human protein complexes which has been less intensively studied recently.We used all the true yeast complexes from MIPS and SGD (MIPS + SGD) as the positive training data. The negative data were randomly generated from individual or all-combined of the six yeast PPI networks. Then emerging patterns were discovered from these positive and negative data. The prediction model was then applied to the human PPI network named HPRD (the Human Protein Reference Database Release 9)http://www.cancer-systemsbiology.org/dataandsoftware.htm). Physical relations represent complexes that play an important role in cell signaling. To the best of our knowledge, this network has been considered as the largest manually curated human signaling network according to the literature42434445The prediction model constructed on the CombinedYeast datasets using all the true yeast complexes from MIPS and SGD as the positive training data was also applied to a large human signaling network (HSN) to detect new human complexes. HSN (Version 6) contains 6305 proteins and 62937 relations including activation, inhibition and physical interactions . The merged network contains 92252 distinct interactions after removing those duplicates. Then, we predicted human complexes from this network through the model constructed on CombinedYeast using complexes from MIPS and SGD.To assess the quality of the predicted human complexes, they are compared against the gold standard database CORUM HSN + HPRD is a very large network, it may contain some noise interactions. Similarly as preprocessing HPRD, we also used TCSS to filter out those interactions having a biological process score less than 0.5. The processed PPI network is named HSNHPRD_F which contains 38091 interactions. We ran our algorithm and ClusterONE on HSNHPRD_F to identify new human complexes. Experimental results  show thaThis section first presents detailed case studies on three non-overlapping yeast complexes (i.e. complexes that are separated one another in the yeast PPI networks), a pair of overlapping yeast complexes, and a human protein complex. The three non-overlapping complexes are: the RNA polymerase I complex (large size), RecQ helicase-Topo III complex , and the DASH complex. The two overlapping complexes are the pair of the RSC and the SWI/SNF complexes. The DASH complex and the two overlapping complexes have been previously used as case study examples by ClusterONEThe RNA polymerase I complex is a transcription complex (MIPS identifier 510.10), comprising 14 proteins. The Collins PPI network contains the subgraph of this complex. The RecQ helicase-Topo III complex is a small complex in the SGD database, comprising 3 proteins. 24The DASH complex has been examined as a case study byThe RSC and the SWI/SNF complexes are a pair of overlapping complexes which were also investigated byThe ubiquitin E3 ligase complex (complexe id:1162) is a human complex recorded at the CORUM database. The topological structure of this complex is like a five-pointed star shape  in the HSimilar to other supervised learning methods for protein complex prediction, ClusterEPs constructed the negative examples of data by randomly selecting subgraphs. However, this did not seem to have effect on these case studies. We ran ClusterEPs 20 times on two different personal computers, each run ended up with the same prediction results for these case study complexes.currently unknown true complexes.−3). All the 10 genes in YeastComplex-2 are enriched in 56 GO terms that are mostly related to histone deacetylation, protein amino acid deacetylation, positive regulation of transcription, positive regulation of metabolic process, positive regulation of biosynthetic process, negative regulation of cellular process, or post-translation protein modification . All the 16 proteins in YeastComplex-3 are enriched in 19 GO terms that are mostly related to transcription from RNA polymerase III promoter, RNA biosynthetic process, RNA metabolic process, or biosynthetic process . All the 9 proteins in HumanComplex-1 are enriched in 32 GO terms that are mostly related to positive regulation of transcription, positive regulation of RNA metabolic process, positive regulation of gene expression, positive regulation of metabolic process, positive regulation of biological process, regulation of biosynthetic process, regulation of metabolic process, regulation of cellular process, and biological regulation .We conducted gene ontology (GO) enrichment analysis for these predicted complexes by using BINGOThese GO analysis results suggest that the proteins in each of these subgraphs are strongly correlated with each other in terms of the enriched GO terms. These subgraphs represent biologically interpretable complexes that are currently not characterized but are worth further experimental investigations. Functional information of proteins such as biological processes, molecular functions and cellular components are useful to decide whether two proteins should be classified into or not into the same complex9A very recent studyFor the other proteins in HumanComplex-1, their functional analysis and results are even more interesting. As discussed byDue to the fact that gold standard protein complex sets are incomplete54Human PPIs are more noisy and less complete than yeast PPIs. The most recent version of HPRD (Release 9) has collected 39240 interactions, containing a high proportion of human PPIs that are present in other databasesHuman PPIs contain a larger number of small complexes compared with yeast PPIs. The CORUM database contains 72.3% (1333 of 1843) of small human complexes (less than five proteins each). HSPC contains 64.3% (400 of 622) of small human complexes. However, MIPS contains 34.9% (68 of 195) of yeast complexes, while SGD contains 61.6% (199 of 323) of yeast complexes. As aforementioned, small complexes are more susceptible to noise (missing or spurious interactions), and accordingly it is a big challenge to detect small complexes accurately.Some human complexes are very big. The maximum size (number of proteins in a complex) of human complexes in CORUM is 142, and the number is 105 in HSPC. However, the maximum size of yeast complexes in MIPS is 95, and it is 55 in SGD. These complexes consist of some small complexes, so it is difficult to recover these complexes.There are many paralogous proteins that have similar and overlapping functions with human PPIs. For example, the proteins AKT1, AKT2 and AKT3 are present in human PPIs, but yeast PPIs have only one AKT. These paralogous proteins have similar and overlapping functions and are involved in multiple complexes. Thus, it remains a big challenge using the sequence or functional information only to identify which protein belongs to which complex.There are many proteins that match many UniProtKB human IDs in human PPIs. For example, the UniProtKB human IDs of the protein HLA-A include P30443, P01892, P04439, P13746, P30447, P05534, P18462, P30450, P30512 and P16188. However, yeast proteins have unique SGD gene IDs. Thus, it remains another difficult challenge as to how to filter out false interactions in order to improve the quality of predicted human PPIs.There remain significant challenges for the detection of human complexes from human PPI networks for the following reasons and facts:ClusterONEV denotes the set of proteins and E denotes the set of protein interactions. The degree of v ∈ V is defined as the number of its direct neighbors. Let C =  be a subgraph in the PPI network. The average degree avedeg(C) of C is given byA PPI network Ec| denotes the number of edges in Ec and |Vc| denotes the number of vertices in C. The density den(C) of C is given bywhere |C in The neighboring nodes of The three major steps of ClusterEPs: Step 1 uses a feature vector to represent the positive class of data (the true complexes) and the negative class of data (the random subgraphs); Step 2 discovers EPs that can distinguish the properties between the complex class and the random subgraph class; Step 3 uses an EP-based scoring method to discover currently unknown protein complexes or re-discover currently known complexes.C of a PPI graph C into a feature vector. This feature vector consists of 22 features. Feature-1 is named Node Size (the number of nodes in the subgraph C); Feature-2 is named Graph Density (the density of the subgraph C). The remaining 20 features are organised into six groups: Degree Statistics, Degree Correlation Statistics, Clustering Coefficient Statistics, Topological Coefficients, First Eigenvalues, and Protein Weight/Size Statistics.  from D1to D2is an itemset X that satisfies the following conditions:andany proper subset of X does not satisfy condition 1.whererepresents the occurrence frequency of X in dataset D1. is similarly defined.D1 to D2 is simply called an NEP of D2. The contrast pattern tree algorithmD.NEPs have strong predictive power and noise tolerance. An NEP from EP(D) to denote the set of NEPs discovered from D . Assuming G is a subgraph of Ins(G) to denote the feature vector comprising of the 22 discretized feature values of G. An aggregate score for G in terms of Dp is defined asFor a PPI network e ⊆ Ins(G) means e is contained by Ins(G). Similarly, an aggregate score for G in terms of Dn is defined aswhere G, score favors a positive label. The larger score is, the more likely G belongs to the class of true complexes. On the other hand, score favors a negative label for G. The number of NEPs for the two classes can greatly affect the final decision. If Dp has many more NEPs than Dn, score would be often larger than score, even when G actually belongs to the negative class. Therefore, we normalize the two scores for making a fair decision.To label the subgraph Dp, we calculate the score using Equation Dp| number of scores as a base score for Dp, denoted by base_score(Dp). The normalized score for G in terms of Dp is given byFor each instance of Dn, base_score, can be similarly defined, which is given byThe base score for G is defined asA clustering score for f(G) > 1/2 means norm_score > norm_score; it implies that G matches more properties of currently known complexes. f(G) = 1 means norm_score = 0; it implies that G does not match any property of the random subgraphs. Thus, a larger f(G) suggests that G is more likely to be a protein complex.G in f(G). As the number of possible subgraphs in We search for those subgraphs Using this heuristic method, we first select a seed protein as the starting cluster, and then add proteins one-by-one from its neighbor nodes to grow the cluster. More specifically, we select the protein with the highest degree as the first seed protein from the input PPI network v0. Step 1: Denote the initial cluster as C = {v0}; Step 2: Compute f(C) and avedeg(C). Let V = C; Step 3: For each vertex v in its neighborhood edgeNumv,C) ≥ f(C) and avedeg(C′) > avedeg(C) then let C = C′. Step 4: If C ≠ V, go back to Step 2. Otherwise, if f(C) > 1/2, then a subgraph C is constructed which is predicted to be a true complex.The steps of the heuristic search are detailed below. Suppose the search starts from v to expand the current subgraph C under the two strong conditions: f(G) > 0.5), while the second condition guarantees that a protein complex should have the highest average degree compared with all its ancestor subgraphs.We note that the search process uses C1 and C2 are two clusters, the overlapping score ωC1 and C2 is defined asAs proposed byω meets a certain threshold, then they will be merged as a subgraph when The DIP databaseWe used five large-scale yeast PPI datasets to compare the performance of our methods with unsupervised clustering methods. These five PPI datasets are: the Gavin datasetWe used the above six yeast PPI datasets as the training data to detect human complexes from human PPI networks. The Human Protein Reference Database (HPRD)S denote the set of standard protein complexes (true complexes), and P denote the set of predicted protein complexes by a method. The set of true complexes detected by predicted complexes is defined as St = {s | ω ≥ 0.25, s ∈ S, p ∈ P}, and the set of predicted complexes matching true complexes is defined as Sp = {p | ω ≥ 0.25, s ∈ S, p ∈ P}. Precision and Recall are then given by |Sp|/|P| and |St|/|S|, respectively, where |S| and |P| denotes the numbers of standard complexes and predicted complexes, respectively. The F1-measure is defined as 2*|Sp|/|St|.Three independent quality measures are used to assess the performance of supervised learning methods, i.e. Precision, Recall and F1-measure. Let Recall measures the fraction of true complexes matched by at least one predicted complex. It is also denoted as the fraction of matched complexes in the literatureet al. proposed a measure based on the maximum matching ratio (MMR), which is based on the maximal one-to-one mapping between predicted and the standard complexess ∈ S and predicted protein complex candidate p ∈ P, if ω > 0, then s and p are connected by an edge with the weight equal to ω.For each standard protein complex subEdges is chosen from the bipartite graph such that these edges satisfy the two following conditions:s ∈ S and p ∈ P, s and p are incident on at most one of the selected edges.For each The sum of the weights of the selected edges is maximal.A subset of edges A predicted complex and a gold standard one are often matched in part. A gold standard complex may match with more than one predicted complexes, and vice versa. These facts make the comparison difficultS and P is the sum of the weights of the selected edges in subEdges, divided by |S|.Then, the MMR between Acc)Acc is the geometrical mean of the clustering-wise sensitivity Sen and the clustering-wise positive predictive value PPV. Suppose S = {s1, s2, ···, si, ···, sn} contains n standard protein complexes, and P = {p1, p2, ···, pj, ···, pm} contains m predicted protein complexes, where si and pj respectively denotes the ith standard protein complex and the jth predicted protein complex, respectively. Let T be a matrix with n rows and m columns, row i corresponds to si and column j corresponds to pj. Tij indicates the number of proteins found both in si and pj. Sen is defined asAnother independent measure is the geometric accuracy  is given bywhere, |Then,How to cite this article: Liu, Q. et al. Using contrast patterns between true complexes and random subgraphs in PPI networks to predict unknown protein complexes. Sci. Rep.6, 21223; doi: 10.1038/srep21223 (2016).


Non-coding RNAs (ncRNAs) play crucial roles in many biological processes, such as post-transcription of gene regulation. ncRNAs mainly function through interaction with RNA binding proteins (RBPs). To understand the function of a ncRNA, a fundamental step is to identify which protein is involved into its interaction. Therefore it is promising to computationally predict RBPs, where the major challenge is that the interaction pattern or motif is difficult to be found.In this study, we propose a computational method IPMiner (Interaction Pattern Miner) to predict ncRNA-protein interactions from sequences, which makes use of deep learning and further improves its performance using stacked ensembling. One of the IPMiner’s typical merits is that it is able to mine the hidden sequential interaction patterns from sequence composition features of protein and RNA sequences using stacked autoencoder, and then the learned hidden features are fed into random forest models. Finally, stacked ensembling is used to integrate different predictors to further improve the prediction performance. The experimental results indicate that IPMiner achieves superior performance on the tested lncRNA-protein interaction dataset with an accuracy of 0.891, sensitivity of 0.939, specificity of 0.831, precision of 0.945 and Matthews correlation coefficient of 0.784, respectively. We further comprehensively investigate IPMiner on other RNA-protein interaction datasets, which yields better performance than the state-of-the-art methods, and the performance has an increase of over 20 % on some tested benchmarked datasets. In addition, we further apply IPMiner for large-scale prediction of ncRNA-protein network, that achieves promising prediction performance.http://www.csbio.sjtu.edu.cn/bioinf/IPMiner.By integrating deep neural network and stacked ensembling, from simple sequence composition features, IPMiner can automatically learn high-level abstraction features, which had strong discriminant ability for RNA-protein detection. IPMiner achieved high performance on our constructed lncRNA-protein benchmark dataset and other RNA-protein datasets. IPMiner tool is available at The online version of this article (doi:10.1186/s12864-016-2931-8) contains supplementary material, which is available to authorized users. Recently non-coding RNA (ncRNA) have received enormous attention within the field of RNA biology. ncRNAs play crucial roles in different biological processes, and their dysregulations have been associated with many human diseases –4. ThousThere have been many promising progresses for large-scale RNA-binding protein detection, e.g. reviewed extensively in , such asHere, we aim to computationally predict interaction partner between RNAs and proteins, which has attracted plenty of research efforts in past years , 20–22. Many challenges still remain in this new area. First, in the above studies, their extracted features for proteins and RNAs were hand-crafted. As an example, in , the autSecond, previous studies mainly extracted information from observed sequences –16, but Deep learning provides a powerful solution for this kind of problems, it consists of model architectures with multiple layers of neural network –28, whicIn this study, we propose a fully sequence-based method, IPMiner, to predict ncRNA-protein interaction using deep learning. First, it extracts raw sequence composition features from RNA and protein sequences, then applies stacked autoencoder to extract hidden high-level features , which aThe experiments on our constructed lncRNA-protein benchmark dataset from Protein Data Bank (PDB)  demonstrhttp://pridb.gdcb.iastate.edu/RPISeq//download.php) [In this study, we proposed IPMiner Fig. , stackedoad.php)  and RPI1oad.php) . lncPro To investigate the impact of different network architectures on IPMiner’s performance, we also designed two different network architectures with fully connected layer:1) Sep-256-128-64: It has two separate (Sep) stacked networks. One is for proteins, the other one is for RNAs, their inputs are protein sequence features and RNA sequence features, respectively. And the last hidden layer is the concatenation of the two sub-networks. The hidden layers for two stacked sub-networks are both 256-128-64. Here 256-128-64 means that the number of neurons for 3 hidden layers in stacked autoencoder are 256, 128, and 64, respectively.2) Con-256-128-128, The raw input is concatenation (Con) of protein and RNA sequence features, which connects to one stacked networks. The hidden layers for stacked autoencoder is 256-128-128, whose 3 hidden layers have 256, 128, and 64 neurons, respectively.From Table Different network architectures were trained on our constructed dataset using the different number of neurons in the hidden layers. The results shown in Additional file We first tested IPMiner on our own constructed lncRNA-protien interaction dataset RPI488. The ROC curve shown in the Fig. We further compared IPMiner with lncPro on RPI488, lncPro yielded an accuracy of 0.870, sensitivity of 0.900, specificity of 0.827, precision of 0.910 and MCC of 0.740, which was a little worse than IPMiner, and a little better than PRISeq-RF  with lower sequence similarity, which both have RNA sequence similarity cut-off 80 %, but different protein sequence similarity cut-off . As indicated in Additional file In IPMiner, we applied stacked ensembling strategy to integrate different predictors, here we compared it with general averaging ensembling . As shown in Fig. To test the reliability and robustness of IPMiner, we also compared it with other sequence-based methods RPISeq and lncPro on other published ncRNA-protein and RNA-protein datasets. In study , the autAs shown in Table On the other hand, the above results indicated that individual predictors can perform differently on different datasets, and no single predictors can surpass others in all datasets. So IPMiner applied stacked ensembing to integrate different predictors. As indicated in Table In addition, we also compared IPMiner with lncPro on these 3 datasets as shown in Table For RPI369 and RPI2241, it is worth mentioning that RPISeq-RF performed worse on our constructed dataset than  on theirTo evaluate our proposed method on other different sources of RNA-protein interaction datasets, we downloaded another two larger non-structure-based experimental datasets: One is NPInter2.0, consisting of 10412 experimentally verified ncRNA-protein pairs from 6 model organisms . The othAs shown in Table In addition, IPMiner was still superior to all single predictors on both datasets, it achieved the high accuracy of 0.952, 0.945 on NPInter2.0, RPI13254 respectively. Especially for RPI13254 dataset, It was an increase of about 28 % over RPISeq-RF (0.739), and stacked ensembling increased the accuracy from 0.813 of best individual predictor to 0.945 at large margin. Meanwhile it also had a very huge improvement compared to the previous reported accuracy of 78 % using RF classifiers with different sources of features . The aboTo verify IPMiner’s ability of predicting RNA-protein interaction, we further investigated the performance of our trained model from RPI488 on NPInter2.0, RPI367 and RPIntDB dataset. There is no overlapped interaction pairs between RPI488 and the 3 datasets. For NPInter2.0, IPMiner yielded the promising predictions of interactions, it correctly predicted 96.7 % of total interactions, which is better than 90 % of RPI-Pred . As showFurthermore, IPMiner correctly predicted 86.4 % of all interactions on the largest dataset RPIntDB with 44,586 interactions , 40, whiIn addition, we ran CD-HIT tool to reduce sequence identity between the testing datasets and RPI488. Take NPInter2.0 for example, we removed similar sequences using CD-HIT against RPI488, so that there were no sequences with sequence similarity greater than 80 % for protein and RNA sequences between NPInter2.0 and RPI488. Then we removed those interaction pairs whose protein or RNA has sequence identity greater than 80 % with RPI488. Finally, the number of interaction pairs for NPInter2.0 are reduced from 10,412 to 10,350. We tested the non-redundant 10,350 pairs using our trained model on RPI488, it yielded an accuracy of 95.7 %, which was a little lower than 96.7 % on original NPInter2.0. The results shown in Additional file We further applied our trained model from RPI488 on NPInter2.0 dataset to construct network for ncRNAs and proteins, which can be used to infer the functions of ncRNAs. For constructing network, we represented ncRNA-protein pairs as a weighted network, where the edge weight between ncRNA and protein was predicted probability from IPMiner, then we used Markov cluster (MCL) algorithm  to do clIn this study, we presented a computational method based on deep learning and stacked ensembing to predict ncRNA-protein interactions. It achieved an accuracy of 0.891, sensitivity of 0.939, specificity of 0.831, precision of 0.945 and MCC of 0.784 on our constructed lncRNA-protein dataset, respectively. Comprehensive experimental results on other previous published datasets also were indicative of the effectiveness of IPMiner. On some datasets, it improved the model performance with an increase of roughly 20 % over other existing sequence-based methods. The results also indicated that stacked autoencoder can extract the discriminant high-level features, which is very crucial for building machine learning models. Where high-level features are the features automatically learned from multiple layers of neural network.The k-mer frequency itself indicated sequence-binding preference of RBPs bound by ARE-binding proteins , where oDeep learning is able to model complicate statistical characteristics in data , 30. k-mDifferent predictors have different performances on different datasets, no single methods can surpass others in all datasets. Different from manually designed average voting or majority voting, stacked ensembing can better integrate the strengths of individual predictors, which is implemented in the form of adding another classifier layer to automatically figure out how to fuse the outputs from individual predictors.IPMiner demonstrated good performance both on ncRNA-protein and RNA-protein prediction, which is better than the state-of-the-art methods. It is mainly due to the following reasons: RNA-protein interaction network can offer deep insights into RNA cellular mechanisms , 49. ForCurrently our method is still trained on small dataset because of the difficulties to collect large-scale experimentally verified lncRNA-protein pairs from complex structures in PDB. On the other hand, deep learning is expected more powerful on big dataset, then it will automatically learn more representative features . So we nAlthough IPMiner can achieve much better performance, there are still some limits similar to other machine learning-based approaches. It is a black box learning algorithm, and does not provide biological insights into ncRNA-protein interactions. IPMiner tries to automatically capture high-level features using deep neural network, and those learned high-level features have strong discriminate power but are still not well explained from biological perspectives. In future work, we expect to design better network architectures to learn high-level features with biological insights, such as introducing convolutional neural network to capture regulatory motifs .In this study, we presented a computational method IPMiner to mine the hidden interaction patterns between ncRNAs and proteins, which is based on stacked autoencoder, and further improves the performance by stacked ensembling. From simple sequence composition features, IPMiner can automatically learn high-level abstraction features, which has strong discriminant ability for RNA-protein detection. IPMiner achieved high performance on our constructed benchmark lncRNA-protein dataset. Meanwhile, comprehensive experimental results on other RNA-protein datasets also indicated that it can yield better performance than other state-of-the-art methods.RNA can be catalogued into mRNA and non-coding RNA (ncRNA), where ncRNA includes small ncRNA, such as miRNA, snoRNA, and long ncRNA (lncRNA), whose size is longer than 200nt. Different RNAs have different biological functions, but their binding motifs may be similar. To verify the robustness of IPMiner, we validate it on different RNA-protein interactions datasets, including mRNA-protein and lncRNA-protein datasets. Currently there are relatively fewer study about lncRNA than mRNA, and the mechanism and functions of lncRNAs remain largely unknown, but lncRNAs are increasingly being studied.least atom distance as the criterion [Firstly we downloaded 18 ncRNA-protein complexes according to  from theriterion : if therriterion , the pairiterion ) for botriterion . After rBesides, to see the impact of sequence similarity on IPMiner, we also constructed another two datasets with lower sequence similarity. For the first dataset called RPI325, the sequence similarity for protein is smaller than 30 % and for RNA is smaller than 80 % , and we obtained 325 lncRNA-protein pairs consisting of 153 interactive pairs and 172 non-interactive pairs. In the second dataset called RPI419, the sequence similarity for protein is smaller than 50 % and for RNA is smaller than 80 % like RPI325, we obtained 419 lncRNA-protein pairs consisting of 203 interactive pairs and 216 non-interactive pairs.To test the robustness of IPMiner, we also collected other RNA-protein datasets from the previous studies, such as RPI1807 , RPI369 To obtain raw features for stacked autoencoder, we extracted simple sequence component composition features both for RNAs and proteins. Conjoint triad (3-mer) of protein is composed by 3 amino acids . FirstlyIn this study, we proposed IPMiner to predict ncRNA-protein interactions, it proceeds in the following phases: 1) Extract conjoint triad (3-mer) from protein sequences and 4-mer frequency from RNA sequences; 2) Apply stacked autoencoder to extract high-level features, called SDA, from the extracted sequence features of RNAs and proteins, respectively. So two sub-networks for protein and RNA are generated; 3) Add another softmax layer to merge the two sub-networks of RNA and protein, and then use label information of training data for fine tuning the above stacked autoencoder, update the weights of networks and extracted features from updated stacked autoencoder, the new feature is called SDA-FT; 4) Feed the extracted raw features, SDA and SDA-FT features to random forest classifier, respectively, and the 3 classifiers are named as RPISeq-RF , SDA-RF The flowchart of proposed IPMiner is shown in Fig. Deep learning , 28 is wx with d-dimension, autoencoder network first map the x into y. Assume we have an input data f is a non-linear function. After this mapping is done, the embedding y is mapped back to reconstruction z of the same shape as x, which is performed as follows: where g is another non-linear function, and the weights of two mappings have the constraint WT=Wwhere x and z, which can be optimized using stochastic gradient descent (SGD) [The reconstruction error can be measured using squared error between nt (SGD) .Stacked autoencoder is a deep network formed from stacking multiple autoencoders . It can When learning the parameters of stacked autoencoder, it optimizes objective function using greedy layer-wise learning, which learns each layer individually while freezing parameters of other layers. To produce better performance, after this unsupervised learning, fine-tuning based on back-propagation is used to tune the parameters of all layers. It is supervised learning phase, which can improve stacked autoencoder a lot.The layer types used in our model are fully connected layer and dropout layer . For drohttps://github.com/fchollet/keras. The value of nb_epoch and batch_size are both 100.In this study, we implement stacked autoencoder using keras library In general, different classifiers have different performance, ensemble learning makes use of multiple classifiers to approximately obtain the optimum target function. How to integrate the individual outputs when implementing the ensembling mechanism is very crucial. Previous studies include majority voting  and averIn stacked ensembling, following the deep learning intuition using multiple layers of neural networks, the combining strategy is that the outputs of the level 0 classifiers will be served as training data for another level 1 classifier . Where lx is vector of output probability for SDA-FT-RF, RPISeq-RF and SDA-RF, and w is the weight vector for the three classifiers. In this study, implementation of logistic regression is from Scikit-learn [where it-learn .In this study, we classify protein and ncRNA pairs to be interacting or not. We follow the widely used evaluation measure by means of the classification accuracy, precision, sensitivity, specificity and the Matthews correlation coefficient (MCC) as defined respectively by: where TP, TN, FP, and FN represents true positive, true negative, false positive, and false negative, respectively. We also exploit Receiver Operating Characteristic (ROC) curve and calculate the area under the ROC curve (AUC). 5-fold cross-validation is used to evaluate the performance of IPMiner. To guarantee the unbiased comparison, the testing and training datasets do not overlap with each other.RBPs, RNA binding proteins; ncRNAs, non-coding RNAs; lncRNAs, long non-coding RNAs; GO, gene ontology; IPMiner, interaction pattern miner; RF, random forest; SVM, support vector machine; SDA-RF, stacked denoising autoencoder random forest; SDA-RF, stacked denoising autoencoder fine tunning random forest; ROC, receiver operating characteristic; AUC, the area under the ROC curve; MCC, Matthews correlation coefficient; MCL, Markov clustering; SGD, stochastic gradient descent


The unraveling and control of protein stability at different temperatures is a fundamental problem in biophysics that is substantially far from being quantitatively and accurately solved, as it requires a precise knowledge of the temperature dependence of amino acid interactions. In this paper we attempt to gain insight into the thermal stability of proteins by designing a tool to predict the full stability curve as a function of the temperature for a set of 45 proteins belonging to 11 homologous families, given their sequence and structure, as well as the melting temperature ( de novo design. In this paper we develop a method for predicting the stability curve of proteins. This curve encodes the temperature dependence of the folding free energy (The prediction of protein stability remains one of the key goals of protein science. Despite the significant efforts of the last decades, faster and more accurate stability predictors on the proteomic-wide scale are currently demanded. The determination and control of protein stability are indeed fundamental steps on the path towards  The understanding of the mechanisms used by nature to stabilize proteins against thermal inactivation is still an open issue of primary importance. From a theoretical perspective, such comprehension is fundamental in the study of the adaptive strategies used by the organisms to inhabit extreme environments. Due to evolution, such organisms are not only able to tolerate extreme temperature conditions, that range from less than ten degree Celsius to more than 120 In silico engineering approaches based on sequence conservation or free energy calculation methods have also been developed but with only partial success In the last decades, different attempts and methods have been developed to obtain proteins of increased thermal stability. Protein engineering methods that include directed evolution methods vice versa) Recently, we developed a thermal stability prediction tool based on (melting)-temperature dependent statistical potentials that are derived from datasets in which only proteins with given thermostability properties are included Proteins use different ways to promote their thermoresistance, which can – in a first approximation – be classified in three main strategies according to the Nojima analysis The first strategy that a protein can use to increase its thermostability m Eq.(3) . In the gion see .Thermococcus kodakaraensis (Tk-MGMT) with Escherichia coli (Ec-AdaC) with It is, in general, not obvious to determine which type of strategy is adopted by a given protein; often several strategies are used in combination The strategies for improving the thermal resistance of a protein sometimes also improve the thermodynamic stability, defined by the folding free energy and  see .It is unfortunately quite difficult to get accurate predictions of thermal stability. The results described in the literature are in general family-dependent and sometimes even contradictory In this paper we go a step further than previous analyses aiming at evaluating either In this section we describe the main tools used in this analysis, namely the statistical potentials, and how they have been optimized for the current investigation. The main steps of our approach are schematically illustrated in The statistical potentials are well known since some seminal papers From these different datasets, statistical potentials were derived using the standard formalism of the inverse Boltzmann law While the torsion potentials describe local interactions along the chain and are a measure of the propensity of a given amino acid to adopt certain backbone torsion angles, the distance potentials describe the tertiary interactions and measure the propensity of amino acids to be separated by a certain spatial distance Note that we have made the Due to the smallness of the dataset, some techniques are required to smooth the potentials and improve their performances. A first modification that has been performed is a correction for sparse data consisting in rewriting the frequencies as Predicting the stability curve of proteins from their sequence and structure alone is quite a difficult task. To slightly simplify the problem, we focused on families of homologous proteins, and make predictions that take into account some informations from the other family members. We therefore searched the full protein set Some quantities  from the three values of the folding free energies given by Eqs(7–9), for the set of 45 proteins from the 11 protein families. Let us assume for the moment that the However, this simple approach does not give accurate predictions, both because the The first issue concerns the sign of the second derivative of the curve. In a few cases (less than 10%), this sign is wrong, which implies that the curve is upside-down and the protein seems unfolded in the physiological temperature range. This error is related to the fact that the three points given in Eq.(12) are too close along the To overcome this problem, we imposed a fourth point in the fitting procedure, in addition to those given in Eq.(12). This point is taken at a temperature of 0°K, where we impose This procedure has been applied when the predicted curve is upside-down, but also when the value of The second issue is the determination of the overall scaling factor vice versa in the low temperature region . The results are the 45 stability curves given explicitly in To make the analysis quantitative, we extracted from these predicted stability curves three independent thermodynamic parameters that define the transition, namely vely see . We repoLet us start with the analysis of the melting temperature whose values are simply extracted from the protein stability curves The value of the standard deviation between the experimental and the so computed excluded . This vai.e. experimental We also computed the The standard deviation between the experimental and the predicted values of We chose as last independent quantity that can be extracted from the predicted curves the folding free energy at 25 A further outcome that can be derived from the predicted stability curves is a better understanding of the strategies used within each protein family to reach a higher thermal stability. In particular, we can evaluate quantitatively the correlation between the thermodynamic and thermal stabilities: the linear anticorrelation between The other two mechanisms for enhancing the thermostability, discussed in the previous sections, turn out to be important too even though they show a lower correlation with the melting temperature. In particular, the shift of the maximum stability temperature These predicted values can be compared with experimental data for the few proteins for which the full stability curve has been determined and thus similar correlation coefficients between The full understanding of protein thermal stability remains a challenge in protein science despite the large amount of research on this topic the last decades. As a matter of fact, it is globally more intricate to understand than the thermodynamic stability. Indeed, besides the problem due to the marginal stabilization achieved by a delicate balance of opposite forces, it poses the additional – and not the least – issue of the temperature dependence of the amino acid interactions, which is barely known.We have designed a method based on (melting)temperature-dependent statistical potentials to deepen the thermal stability investigation. The basic idea behind this approach is simple and consists in constructing different datasets in which only proteins with given thermal properties were considered. Mean force potentials were extracted from sequence-structure frequencies computed from these datasets, following the standard statistical potential formalism, and hence reflect their thermal characteristics. They actually represent the amino acid interactions at some temperature that is related to the average Before concluding with future perspectives, let us summarize briefly the performance of the method and the main errors that affect it. The standard deviations between the experimental and computed quantities are equal, in cross-validation, to 13 A noteworthy result that can be deduced from our predictive approach is that the preferred mechanism for enhancing the thermostability is an increase of the thermodynamic stability, in agreement with previous results based on experimental data We would like to underline the main strength of our approach that is the possibility to predict at once all the thermodynamic parameters that characterize the protein folding transition. We can indeed predict with our method both the thermodynamic and thermal stabilities in a large temperature range. As far as we know this is the only method that is able to do that, and moreover it does so in a fast and relatively accurate way. Neither the standard statistical potential formalism nor the molecular dynamics simulations or the coarse-grained computational approaches to protein folding are able to consider explicitly the temperature dependence of the amino acid interactions and give predictions for both kinds of stabilities.However, some points of the present analysis can still be improved, and we plan to do so in a future investigation. In particular, we will try to supply to the lack of data by enlarging the dataset of proteins whose thermal properties have been measured experimentally and subdivide it in more than three subsets so as to be able to get more reliable fits of the stability curves.Two different ways can be explored to enlarge the datasets. The first consists in adding proteins with known structure but unknown melting temperature. To decide to which of the thermal ensembles these additional proteins belong, one could estimate their We expect the enlargement of the datasets to play an important role in the reduction of the prediction errors, since it will allow us to define more than three datasets and thus to compute the folding free energies of a target protein at more than three different temperatures. This should definitely reduce the consequence of the errors on the predicted points in the Note finally that the current version of our prediction method is family-dependent, as the datasets vary slightly from one family to another and the optimization of some parameters is performed inside the families (see Methods section). We would like to stress that this procedure does in no way bias the predictions. All our tests are indeed performed in pure cross validation. Rather, this procedure improves the predictions by exploiting relevant information that characterizes the homologous families. Another promising improvement of our prediction method, which would make it applicable to any target protein of known structure, consists in extending the current version without too much accuracy loss to the more general case that ignores any reference to homologous proteins.In conclusion, although there is still room for improvements and generalizations, our approach has opened a novel and original way for designing fast and accurate predictors of protein stability at different temperatures.Table S1List of 45 proteins with known melting temperature analyzed in this study.(PDF)Click here for additional data file.Table S2Predicted and experimental values of the thermodynamic and thermal parameters for the set of 45 proteins.(PDF)Click here for additional data file.Table S3Analytic expression of the predicted stability curves  for the set of 45 proteins.(PDF)Click here for additional data file.