Little is known why proteins and RNAs exhibit half-lives varying over several magnitudes. Despite many efforts, a conclusive link between half-lives and gene function could not be established suggesting that other determinants may influence these molecular attributes.Here, I find that with increasing gene age there is a gradual and significant increase of protein and RNA half-lives, protein structure, and other molecular attributes that tend to affect protein abundance. These observations are accommodated in a hypothesis which posits that new genes at ‘birth’ are not optimized and thus their products exhibit low half-lives and less structure but continuous mutagenesis eventually improves these attributes. Thus, the protein and RNA products of the oldest genes obtained their high degrees of stability and structure only after billions of years while the products of younger genes had less time to be optimized and are therefore less stable and structured. Because more stable proteins with lower turnover require less transcription to maintain the same level of abundance, reduced transcription-associated mutagenesis (TAM) would fixate the changes by increasing gene conservation.Consequently, the currently observed diversity of molecular attributes is a snapshot of gene products being at different stages along their temporal path of optimization. Typical protein attributes such as abundance, stability, number of protein-protein interactions, or folded vs. unfolded regions are increasingly characterized on a global level. Large-scale analyses offer the opportunity for an unbiased characterization of such innate molecular attributes. For example, protein half-life and turnover is an attribute which has been thoroughly investigated –9. HowevDuring evolution, mutations to genes lead to functional divergence of their protein products but they also affect innate molecular attributes such as turnover or stability. However, if and how continuous mutagenesis changed innate molecular attributes over time is rarely discussed , 13. TheTriplicate, high-throughput mass spectrometry (MS) was previously used to analyze and compare protein turnover in non-dividing arrested human cervical HeLa and differentiated mouse muscle C2C12 cells . MS baser = − 0.20, P < 0.0001) as were most differences between taxonomically grouped proteins  . These correlations are supported by a previous publication showing that human protein stability in terms of free energy folding ΔG was higher for old genes vs. young ones [For human proteins, the correlation of decreasing protein turnover with increasing protein age was significant   although there is a minor decrease rather than increase from chordates to mammals   levels negatively correlated with protein half-lives , proteinr = 0.12, P < 0.0001)    score [P < 0.0001, Mann-Whitney test) compared to younger genes without orthologs.RNA half-lives were larger for old genes compared young genes  score , for oldr = 0.08, P < 0.0001).The translation initiation efficiency describes how well a particular mRNA assembles with 80S ribosomes which is an indication of the fraction of mRNA molecules that can be translated. Genes with higher values are thus able to produce more proteins within a certain time window. Using published data on the translation initiation efficiency in human HeLa cells  older ger = 0.67, P < 0.0001)  or unicellular eukaryotes . Thus, old genes are more conserved than essential genes and consequently protein function appears to have lesser role in determining gene conservation than generally assumed. In addition, essential mouse genes [It has long been assumed that core cellular proteins (‘housekeeping genes’) are ubiquitously expressed and intuitively should be more conserved . Howeverse genes  also shor = 0.67, P < 0.0001). However, because of extensive cross-annotations and functional overlap between categories, it was not possible to establish a common temporal ranking based on both species. Instead, a manually curated selection of human categories is shown in Table GO (gene ontology) biological process analyses of human and mouse turnover data allowed ranking of GO categories according to their median turnover values   Fig. a. Most (st) Fig. c. This iTo assess if high turnover proteins affect cellular fitness, the protein turnover values of all genes in the ENSEMBL database known to produce lethal phenotypes were correlated. Such an unbiased approach did not reveal any obvious association between known lethal diseases and human proteins of high turnover. However, there was a clear association between lethal phenotypes and the ratio of human/mouse turnover values from orthologs detected in both species. As expected from the high correlation of turnover values between both species , most orEscherichia coli where the abundance of phosphorylation < acetylation [Leptospira interrogans where phosphorylation < acetylation < methylation [P < 0.0001). For example, for very old proteins that originated during the stage of ‘unicellular organisms’, the percentage of acetylated proteins was twice as high compared to the percentage of phosphorylated proteins  which suggests that reduced transcription accounts to some extent for the increased fixation of old genes. From a molecular perspective, any random mutation that reduces the necessity for transcription, i.e. by ultimately increasing mRNA and protein abundance, should principally be fixated because of TAM. Such fixation would occur ‘passively’ and not by selection based on function. To this end, it is known that old, conserved housekeeping genes generally exhibit lower evolutionary rates [How are the different optimized attributes fixated and selected for during evolution? For example, reduced turnover requires less energy for cellular homeostasis, but these changes are incremental as the energy needs for the production of one protein compared to all proteins in a cell is negligible. With more than 10,000 different protein species detected in eukaryotic cells , changinry rates . Since IMolecular attributes such as protein or RNA stability often vary over several magnitudes but a conclusive explanation for this variability has been missing. A new hypothesis was introduced that views individual attributes of every molecule as being at different stages along a path of optimization based on continued mutagenesis over time. The hypothesis does not attempt to link the various attributes of the same molecule with function but rather allows attributes to be affected independently by a mutation. Thus, the biological challenge would be to maintain molecular function in the face of ever changing molecular attributes. Based on these assumptions, this hypothesis can be tested by in vitro evolution.http://lighthouse.ucsf.edu/ProteinHistorian/) [All analyses were based on previously published data. Statistical data analyses were performed using Prism 5.0 . Each gene obtained from the different molecular attributes data sets was given its published age as described in the database for human gene/protein age (‘ProteinHistorian’/ torian/) . Accordihttp://genie.weizmann.ac.il/pubs/PARS10/pars10_catalogs.html), gene conservation scores (https://dsgweb.wustl.edu/hutz/index.html), and bacterial orthologues in yeast (http://makana.berkeley.edu/phylofacts/) [2 for all proteins in the respective bin (93 proteins/bin). Peptides were required to be quantified with at least three peptides during the MS experiments.Protein and peptide turnover values as well as protein abundance were from a previous publication . RNA folofacts/)  were obtofacts/) , human tofacts/) , human hofacts/) , human [ofacts/)  and mousofacts/)  essentiaofacts/)  and phosofacts/)  proteinsofacts/) , human tofacts/) , human mofacts/)  were eacwww.ensemble.org). The MaxQuant software [Ranking of biological process GO categories was based on the median protein turnover of each particular category. In parallel, 1000 sets of random protein turnover values were iteratively created (“bootstrapping”) where each set contained as many values as the number in the respective category and the mean of all 1000 random sets produced the bootstrap distribution. The difference between the bootstrap distribution and the mean of the category was computed in standard deviations and yielded a probability value for a null correlation . An unbisoftware  was usedsoftware  of mousesoftware .Additional file 1: Figure S1. Age-dependent changes of protein turnover. (a) Box plot of human protein turnover  Median level of protein disorder for taxonomic groups in yeast. Number of proteins: u org=1336, u euk=1693, OBD=562, chor=1192, mamm=1070. (b) Distribution of protein abundance of HeLa MS data set. (c) Median human mRNA abundance for taxonomic groups. u org=190, u euk=337, OBD=208, chor=163, mamm=51. (d) Median mouse mRNA half-lives for taxonomic groups. Number of different mRNA species: u org=1072, u euk=2553, OBD=1636, chor=1122, mamm=313. (e) Mean gene conservation score for taxonomic groups. This graph is based on the same data as Fig. Additional file 3: Figure S3. Age-dependent changes of human housekeeping genes. (a) Mean gene conservation score for taxonomic groups. Number of genes: u org=394, u euk=852, OBD=369, chor=237, mamm=39. (b) Median level of protein disorder for taxonomic groups. Number of proteins: u org=385, u euk=823, OBD=366, chor=225, mamm=38. (c) Median protein turnover for taxonomic groups. Number of proteins: u org=303, u euk=588, OBD=167, chor=85, mamm=10. (d) Median mRNA half-lives for taxonomic groups. Number of different mRNA species: u org=360, u euk=793, OBD=341, chor=220, mamm=40. .Additional file 4: Figure S4. Age-dependent changes of human transcription factors and mouse essential genes. (a) Median protein turnover for taxonomic groups of transcription factors. Number of proteins: u org+ u euk=34, OBD=34, chor+ mamm=23. Groups with less than 10 genes were added to the neighboring group  (b) Median mRNA half-lives for taxonomic groups of transcription factors. Number of different mRNA species: u org+ u euk=51, OBD=172, chor=175, mamm=89.(c) Mean gene conservation score for taxonomic groups of transcription factors. Number of genes: u org+u euk=79, OBD=471, chor=509, mamm=241. (d) Median level of protein disorder for taxonomic groups of transcription factors. Number of proteins: prok+ u euk=82, OBD=531, chor=512, mamm=322. (e) Median protein turnover for taxonomic groups of mouse essential genes. Number of proteins: u org=92, u euk=168, OBD=129, chor+ mamm=73. (f) Median mRNA half-lives for taxonomic groups of mouse essential genes. Number of different mRNA species: u org=133, u euk=296, OBD=327, chor=222, mamm=13. .Additional file 5: Table S3 and Figure S5. Table S3: Median protein turnover values for GO categories based on mass spectrometry proteomics data from arrested human HeLa and differentiated mouse muscle C2C12 cells. Probability indicates difference to average turnover of all proteins. Figure S5: Evolutionary changes of protein turnover between human and mouse. (a) Graphical representation of Supplementary Table 3. (b) Median peptide turnover values of the ‘histone deacetylase and nucleosome remodeling activities complex’ proteins. (c) Grouping of proteins into equal size bins of 210 and quantification of homologous proteins with a turnover increase from human to mouse.Additional file 6: Tables S1 and S2. Median protein turnover values for GO categories based on mass spectrometry proteomics data from arrested human HeLa and differentiated mouse muscle C2C12 cells. Probability indicates difference to average turnover of all proteins.


Protein–protein interaction (PPI) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Especially, the PPIs associated with human malignant neoplasms can unveil the biology behind these neoplasms. However, such PPI database is not currently available.In this work, a database of protein–protein interactions associated with 171 kinds of human malignant neoplasms named HMNPPID is constructed. In addition, a visualization program, named VisualPPI, is provided to facilitate the analysis of the PPI network for a specific neoplasm.HMNPPID can hopefully become an important resource for the research on PPIs of human malignant neoplasms since it provides readily available data for healthcare professionals. Thus, they do not need to dig into a large amount of biomedical literatures any more, which may accelerate the researches on the PPIs of malignant neoplasms. The research on protein–protein interactions (PPIs) is critical to understand how proteins function within the cell. Therefore, hundreds of thousands of PPIs generated by high-throughput methods such as yeast two-hybrid screening and affinity purification coupled to mass spectrometry have been collected together in specialized biological databases such as Database of Interacting Proteins (DIP)S. cerevisiae (yeast), C. elegans (worm), D. melanogaster (fly), R. norvegicus (rat), M. musculus (mouse), and H. sapiens (human)) and up to 30 tissues per species [Furthermore, PPI data is used globally for the prediction of protein properties, systematic network analysis, and evaluation of novel datasets of PPIs produced in a high-throughput fashion . To this species . The STR species . Apart fIn addition, there are also some protein-pathway association databases. For example, PathDIPSince the dysfunction of some PPIs leads to many diseases , the analysis of PPI networks has become one of the powerful approaches to elucidate the molecular mechanisms underlying the complex diseases on the system level , 19. SomCurrently, the PPIs in these cancer-related PPI databases are manually extracted and curated by human experts from literatures. However, since the number of biomedical literatures regarding PPIs is growing at an explosive speed, automatically extracting PPIs from the literature is adopted to improve the efficiency of PPI information extraction.To this end, in this work, a Human Malignant Neoplasm Protein–Protein Interaction Database (HMNPPID) was constructed, whose data was extracted by an automatic PPI extraction tool, named PPIExtractor , from a The number of biomedical literatures involving PPIs is increasing at an explosive speed and, for PPI database curators, it is extremely difficult to curate them efficiently. Therefore, we have developed PPIExtractor in our previous work to automatically extract the PPIs from biomedical literature . Given ahttps://browse10/browse10/2016/en) classifies the diseases, making them an orderly combination and representing them with the coding method. According to the classification in ICD-10, we chose 171 kinds of malignant neoplasms  uniform method established by World Health Organization (WHO) and according to the disease etiology, pathology, clinical presentation, anatomical location, and other characteristics, ICD-10 version 2016  OR cancer) AND lung AND protein.” The second step is to retrieve the relevant abstracts from PubMed using the query string. In addition. the filters “Humans” and “English” are activated to obtain only English abstracts associated with human species, and the query time is set as December 1, 2015. In the last step, the downloaded abstracts are input into the PPIExtractor to extract the PPIs. Each PPI is assigned a confidence score by PPIExtractor to reflect its reliability. Usually with a confidence score equal to or greater than zero, one PPI can be regarded as reliable. However, in HMNPPID, the PPIs with the confidence scores higher than − 0.6 are retained since, due to the complexity of natural language expression, PPIs with the confidence scores less than 0 may be true ones. The reason why the threshold is − 0.6 is that, in our previous study of protein complex detection in PPI networks [To obtain the relevant abstracts of all these malignant neoplasms, constructing the accurate query string for PubMed search is the first step. For example, the query string for the disease networks , the intIn HMNPPID, two PPI file types  are provided for each malignant neoplasm. As shown in Table According to the classification in ICD-10 (version 2016), we extracted the PPIs of 171 kinds of human malignant neoplasms and obtained a total of 266,107 PPIs (with threshold − 0.6). By contrast, the number of PPIs with a confidence score greater than or equal to zero is 72,866. The number of specific neoplasm related abstracts downloaded from PubMed and the number of the PPIs extracted from those abstracts can be found on the web site.malignant neoplasms of digestive organs (C15-C26), breast (C50), and stated or presumed to be primary, of lymphoid, hematopoietic and related tissue (C81-C96) have much more PPIs than malignant neoplasm of bone and articular cartilage (C40–C41).Figures p53 has been described as “the guardian of the genome” because of its role in conserving stability by preventing genome mutation [p53 and MIB-1 demonstrates prognostic significance in male germ cell tumors [p53 binds DNA and activates expression of several genes including WAF1/CIP1 encoding for p21 and hundreds of other downstream genes [p53 and Ki-67 could be used to discriminate low-risk luminal A subtype in breast cancer [p53, cathepsin D, and B cell lymphoma 2 (Bcl-2) are joint prognostic indicators of breast cancer metastatic spreading [ribosomal S6 kinase 1 (S6K1) is a downstream component of the mammalian target of rapamycin (mTOR) signaling pathway and plays a regulatory role in translation initiation, protein synthesis, and muscle hypertrophy [In addition, the occurrence frequencies of unique PPIs in 13 categories of malignant neoplasms are presented in Fig. mutation . The coml tumors  and humal tumors   and the comparison with it is meaningful. HPRD includes 39,240 PPIs obtained from a set of published articles. We used PPIExtractor to extract 54,808 unique PPIs with the threshold 0 from the abstracts of the same article set  and 12,870 of HPRD PPIs  were matched.We further analyzed some of the results to find the recall error types. The PPIs in HPRD were curated by expert biologists from both abstracts and full text. Since PPIExtractor was applied only on the abstracts, the PPIs present in the full text were missed out. This accounts for about 68% of total recall errors. In addition, some PPIs in HPRD were extracted by PPIExtractor but with a threshold less than zero . The reason is that due to the complexity of the protein interaction expression, PPIExtractor may fail to extract some true PPIs. In fact, if the threshold is relaxed to − 0.6, almost half (48.08%) of HPRD PPIs could be extracted.INSR 00975 NP_000199.2 FABP4 02698 NP_001433.1 in vitro; in vivo 1648089), it can be extracted from the sentence “Kinetic analysis indicated that stimulation of ALBP phosphorylation by insulin was attributable to a 5-fold increase in the Vmax…” in the abstract with PubMed ID 164808. ALBP is an alias of FABP4 (fatty acid-binding protein 4) and insulin refers to insulin receptor, an alias of INSR. However, the failure of matching insulin with INSR by the matching program leads to the recall error of this HPRD PPI. Such errors account for about 11% of total recall errors.Finally, the names of the proteins of HPRD PPIs are the formal ones assigned by expert biologists which usually are not the same with those used in texts. For example, for a HPRD PPI (http://nesg.org:9090/HCPIN/ShowPathway.jsp) and only a total of 5,815 PPIs were obtained. As a result, 1636 PPIs of HCPIN  were found in HMNPPID . Similar to the case of HPRD, the mismatching between the protein names in texts with the ones in HCPIN results in many recall errors.Furthermore, to assess the quality of the data in HMNPPID, we compared it with PPIs in HCPIN. There are 9,784 PPIs among HCPIN proteins. However, since these PPIs are not available, we reconstructed them from the PPIs of seven pathways  provided on HCPIN website  of HMNPPID data with HPRD and HCPIN are still acceptable.What is more, the 39,240 PPIs in HPRD were curated by expert biologists from 20,074 articles, which means less than two PPIs were curated from one article on average. In fact, only one PPI was curated from one article in most cases. This shows that expert biologists usually only curate the few novel PPIs while ignoring many other PPIs in the article. In contrast, PPIExtractor will extract all the PPIs in the abstracts into HMNPPID, which is especially useful for the researchers who need to explore the relations between the multiple PPIs from one single article or a set of related articles . This is also the reason why PPIExtractor can extract more PPIs than HPRD from the same article set . However, the quality of the PPI data in HMNPPID but not in HPRD or HCPIN is difficult to evaluate due to the lack of gold standard.http://202.118.75.18:8082/HMNPPID.asp. As shown in Fig. As has been mentioned in the previous section, the PPIs of 171 types of malignant neoplasms were extracted with PPIExtractor, and then used to construct the PPI database of human malignant neoplasms, HMNPPID. HMNPPID can be accessed through In addition, the website also supports the query function . In fact, it achieved a precision of 79.23% on a DIP subset .https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/BioC-PMC/). As discussed in section Evaluation of HMNPPID data, this will improve the recall performance of PPI extraction. This paper is a revised and expanded version of a paper [To keep the data up to date, we plan to update HMNPPID every half year . In addition, our future research will focus on two areas in order to improve the quality and utility of the PPI database. First, we will improve the performance of PPIExtractor with the introduction of the popular deep learning method . Second, a paper  presente


Proteotypes, like genotypes, have been found to vary between individuals in several studies, but consistent molecular functional traits across studies remain to be quantified. In a meta-analysis of 11 proteomics datasets from humans and mice, we use co-variation of proteins in known functional modules across datasets and individuals to obtain a consensus landscape of proteotype variation. We find that individuals differ considerably in both protein complex abundances and stoichiometry. We disentangle genetic and environmental factors impacting these metrics, with genetic sex and specific diets together explaining 13.5% and 11.6% of the observed variation of complex abundance and stoichiometry, respectively. Sex-specific differences, for example, include various proteins and complexes, where the respective genes are not located on sex-specific chromosomes. Diet-specific differences, added to the individual genetic backgrounds, might become a starting point for personalized proteotype modulation toward desired features.  •Benchmarking of datasets on human and mouse proteotypes•Consistent co-variation landscape of functional modules across individuals•Protein complexes vary in their stoichiometry across individuals•Quantifying effects of genetic sex and specific diets on complexes Protein functional module variation between individuals might serve as molecular fingerprints for a wide range of environmental and genetic factors that are tunable by intervention toward desired proteotypes. Recent advances in the experimental throughput of mass spectrometry (MS)-based proteomics have enabled large-scale studies of proteotypes, defined as the proteome complement of a genotype , which cEstablishing such connections from genetic or environmental factors to the individual proteotype remains challenging, however. This is due to technical limitations, in particular the variable experimental noise across studies, but also biological buffering mechanisms . HoweverSeveral seminal studies have shown the variability of protein abundances across individuals in human and mice . AlthougA case in point is the lack of stratification of male and female organisms at the proteotype level. Various studies have reported protein abundance variation due to the genetic sex of an organism, but focus only on chromosome X/Y-specific protein expression rather than on the systemic differences in the overall proteotypic patterns . ExploriHere, we analyze 11 public datasets to investigate proteotypes of healthy and diseased individuals from human and mice. The proteotype of an individual describes, more generally, the state of a proteome  in conjunction with the presence and state of posttranslational modifications. It differs from cell type to cell type  and chanProteins are not functioning alone, but are organized into functional modules and networks, spanning from complexes to pathways and entire organelles. In order to understand which features or levels of organization define the proteome state of individuals, we tested to what extent known functional modules or protein associations can be recovered in different proteomics datasets. Implicitly, we thereby tested the power of each dataset and ensured consistent results. We examined datasets resulting from profiling proteins across cancer patients , in line with previous work suggesting an important role of post-translational mechanisms in shaping protein complex abundance and stoichiometry across cell types  . We ranked protein complexes according to their level of co-abundance across individuals and identified a subset that is rigorously maintained in stoichiometry (r per complex > 0.46 [75th percentile]). They included the mini-chromosome maintenance (MCM) complex, complexes associated with the translational apparatus  and mitochondrial complexes within the electron transport chain, such as the F0/F1 ATP synthase, cytochrome bc1 complex, and the cytochrome c-oxidase. Variable complexes (median Pearson’s r < 0.17 [25th percentile]), on the other hand, were enriched in chromatin-associated processes  such as the RNA polymerase, the mediator complex, the BAF complex, etc. The range of complexes in between the two extremes represented instances where both co-regulated parts of complex, as well as more variable members are present, such as in the COPI/COPII, the nuclear pore complex, and the 26S proteasome. We observed a high consistency between datasets after the dissection of modules into stable and variable sub-parts . Variable components, if identified at p value <0.1, made up 2%–20% of the overall structure of the complexes . ATPIF1 is known to be the master regulator of the F0/F1 ATP-synthase   B. Using  U test) A and S2B U test)  that is hiometry . Of 96 womplexes . We founomplexes . For exasynthase . Its binsynthase . Variabl t test) . Further t test) . We also−4, one-sided t test). This sub-complex is known to be replaced by PSMB5, PSMB6, and PSMB7 depending on the cellular context, and these components also vary strongly across individuals  mice strains, namely their sex and their diet, with half of the animals fed with rodent chow and the other with high-fat diet . We captFor complex abundances, differences between male and female mice were evaluated using a standard t test and Cohen distances to yield effect size estimates for each complex A, top. FThe effect of diet on protein complex abundance, on the other hand, was less pronounced; out of 96 considered complexes, 7 complexes had a higher abundance in high-fat diet fed mice and 16 were more abundant in mice exposed to chow . These complexes were mainly enriched in mitochondrial functions and RNA-processing, primarily spliceosome-related sub-complexes.r = −0.03). For example, the retromer complex only yielded a minimal signal with regard to complex abundance . The functional consequences of the observed changes in complex stoichiometry and their propagation to other cellular processes within an individual remain to be explored. Similar effects were also observed within pathways where the stoichiometry of their members is functionally relevant, such as kinase signaling   .−8, permutation test) and PAPSS2 , which are crucial for sulfation of the androgen precursor  .To see whether the effect of the animal’s sex on protein abundances is reflective of the underlying genetic factors, we compared the obtained effect sizes directly with the results from  t test) A. The ge−7, permutation test). Other pathways, including androgen and glucocorticoid biosynthesis, had an expectedly large fraction of their abundance variation explained by sex differences   and cholesterol biosynthesis . For protein complexes, we could recover a similar span of effect sizes: on average, 10.50% of the abundance variation of complexes could be ascribed to the cumulative effect of the sex and diet of the animals. Some complexes, however, were affected by neither (<1%), whereas other complexes had more than 30% of their abundance variation explained by these co-variates  and the nucleosome protein complex , as deduced from co-variate analysis controlling for diet. On the other hand, the effect sizes of diet on protein complex median abundances reached up to only 15% and were generally restricted to another set of complexes, such as the Dsl1p complex , the HOPS complex  and mitochondrial complexes, e.g., the cytochrome bc1 complex (11.94%)  . The effvariates C. For ex(11.94%) C.−3, permutation test). Specifically, the subunits DLD and BCKDHA were found to be on average higher in abundance under chow diet conditions as compared to high-fat diet conditions  . Howevernditions B.r = 0.016), underscores the functional complementarity of genetic and environmental factors .Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact, Peer Bork  A549, GAMG, HEK293, HeLa, HepG2, K562, MCF7, RKO, U2OS, LnCap and Jurkat cells , (ii) kihttp://www.bork.embl.de/Docu/proteotype_genetic_environment_impact/.The underlying data and code for each processing step can be found in the following web resource: version 10.5) (https://reactome.org/download-data/). Cellular locations were extracted from the Human Protein Atlas (downloaded February 2017) (mygene (https://pypi.org/pypi/mygene) using the hg19 GenBank assembly for human and the mm10 genome assembly for mice, respectively. Finally, essentiality of genes was defined based on the genetic screen performed in the human cell lines KBM7, K562, Jiyoye, and Raji by Protein-protein interactions were obtained from the STRING database (on 10.5) ; interacon 10.5) , and higry 2017)  considerFor the delineation of protein abundances across individuals, we primarily considered large-scale shotgun proteomics studies on human individuals, cancer patients and mouse strains. For control purposes, we also included the proteomic profiles of 11 human cell lines generated by For each dataset we checked whether batch effects or possible normalization issues could have arisen in the published processing [Step2 in web resource]. To this end, each sample was tested for normality using the Shapiro-Wilk test, as this is a standard framework to test for any possible deviations from normality; only in datasets from pathways we excluded interactions within protein complexes (such as the ribosome complex). When considering chromosome location, we defined true positive “interactions” to exist between genes encoded on the same chromosome. For the categories essentiality and housekeeping role true positive interactions were to occur between essential genes and housekeeping genes, respectively. The full set of condition negatives consists of all other pairs of proteins. For computational reasons, we randomly sampled from the full set of condition negatives the same number of respective condition positives to compute ROC curves. The area under the curve (AUC) value was calculated using the trapezoidal rule. We applied Mann-Whitney U-statistics, which is directly connected to the AUC metric, to test whether correlation values derived from proteins that are in the same modules, are significantly different from correlation values derived from random proteins that are not part of any module. To make a conservative estimate of the effect size , we applied the Mann-Whitney U-test 1000 times to a randomly sampled selection of 1000 items from the two distributions, respectively, and calculated the mean p-value. [Step6 in web resource]For the Receiver Operating Curve (ROC) analysis across different types of modules in different datasets, condition positives were defined based on the databases as outlined above. The lowest number of condition positives occurring is 1.540 (interactions). For U-test was applied to assess significance. [Step4 in web resource]As mentioned above, the database of complexes was manually compiled and curated from COMPLEAT and CORUM by https://david-d.ncifcrf.gov). The GO domains ‘Biological Process’, ‘Molecular Function’ and ‘Cellular Compartment’ were considered; the background for the GO-analysis was represented by all quantified proteins in a given dataset. Results were filtered according to FDR (Benjamini-Hochberg) of less than 0.01; the fold-changes associated with those significantly enriched GO-terms are shown  analyses in this study, respective genes were analyzed using DAVID , whereas the lowest quantile were considered highly variable (Pearson’s r < 0.2). To assess the consistency of the complex variability landscape, we calculated the Spearman correlation of the ranked median co-abundance across datasets  of these complexes were considered to be highly stable . A one-sided t test accounts for the unidirectionality of our hypothesis and gives conservative results. This procedure was done for all proteins in each complex, and resulting p-values were adjusted using the Benjamini-Hochberg method. [Step8 in web resource]To assess compositional rearrangements of protein complexes as opposed to their overall abundance changes, a module-wise normalization was performed, as previously described . Protein complex . Similarz-scores . To see P-values were further adjusted using the Benjamini-Hochberg procedure (significance α = 0.05), and complex structures were considered significantly different in case of q-value < 0.01. [Step10 in web resource]To assess the differences in abundances of entire complex structures between male/female mice, and mice exposed to high-fat and chow diet, the median abundances of each complex was calculated in each individual/sample (protein subunits were required to be quantified in at least 50% of samples). For each complex it was then assessed via a t test whether median complex abundances in male mice were significantly different from the ones in female mice; the effect size was monitored as the Cohen distance. The applied t test is two-sided as no directionality is implied. R-package LIMMA   were collected. P-values were adjusted using the Benjamini-Hochberg procedure across all complexes and proteins. In case of q-value < 0.01 the corresponding protein was considered to be stoichiometrically changing in a given complex. The underlying statistical test is denoted as ‘LIMMA-based t test’ throughout the main text. The corresponding fold-changes are highlighted in volcano plots in q-values of their individual components were combined using Fisher’s method. Lastly, the resulting combined p-values from all complexes were adjusted using the Benjamini-Hochberg method. [Step11 in web resource]For internal rearrangements of the complex (stoichiometry), we performed a separate analysis applying the nalysis)  using thscikit Python package (https://scikit-learn.org). We compared models that predict complex abundance or complex stoichiometries, using as predictors: (i) genetic sex, (ii) diet, and (iii) the combination of genetic sex and diet together. We assessed the quality of each model by the coefficient of determination (R2). This was done for every module considered (complexes and pathways), for abundance, as well as module-normalized data. For pathways we only considered those that were showing a high co-abundance  as compared to co-abundances derived from a reshuffled dataset.To understand to what extent both protein complex abundance and stoichiometry are affected by either sex or diet, a L2-regularized Multiple Linear Regression (Ridge regression with a regularization parameter of 1) was used, as implemented in the R2 is reported. While the R2-score represents a measure of effect size, an additional target-decoy strategy was applied to estimate the significance of those scores. An identical analysis was conducted with a reshuffled dataset per complex, and the corresponding performance metrics were used in a permutation test approach to assign significance to the true ridge regression coefficients. Specifically, we use the latter distribution to calculate an empirical FDR. Throughout the main text, the global R2 performance metric derived for the module or protein is reported as the effect size with its respective FDR-corrected p-value. [Step12 in web resource]To estimate prediction performance we used a 10-fold cross-validation scheme. Briefly, we randomly separated the dataset (per complex) into ten groups of equal size, in order to iteratively train a model with nine of them, and to assess the testing performance in the held-out group. For each module the median global Additionally to the proteomic datasets derived from mammalian organisms, we also analyzed published MS-datasets of yeast proteomes and their corresponding RNA-seq datasets if available. A total of eight independent publications were considered: (i) Effect size estimations of sex- and diet on proteins and modules”). Samples for dataset (i) and (ii) were respectively grouped into the environmental conditions, whereas samples for dataset (iii) were grouped into related sets of yeast strains (according to source and collection). For each dataset these categorizations were then tested as predictors for complex abundance and stoichiometry. For each complex, the quality of each model was assessed by the coefficient of determination (R2) in a 10-fold cross-validation scheme, as described above. [Step20 in web resource]This analysis was performed with three yeast proteomic datasets that showed a reliable recovery of known protein-protein interactions (AUC > 0.7), namely (i) http://www.bork.embl.de/Docu/proteotype_genetic_environment_impact/. The web resource allows interrogating every step of the computational analysis, with corresponding in- and output data.All scripts for analyzing data and generating figures are available at


Identification of protein complex is very important for revealing the underlying mechanism of biological processes. Many computational methods have been developed to identify protein complexes from static protein-protein interaction (PPI) networks. Recently, researchers are considering the dynamics of protein-protein interactions. Dynamic PPI networks are closer to reality in the cell system. It is expected that more protein complexes can be accurately identified from dynamic PPI networks. In this paper, we use the undulating degree above the base level of gene expression instead of the gene expression level to construct dynamic temporal PPI networks. Further we convert dynamic temporal PPI networks into dynamic Temporal Interval Protein Interaction Networks (TI-PINs) and propose a novel method to accurately identify more protein complexes from the constructed TI-PINs. Owing to preserving continuous interactions within temporal interval, the constructed TI-PINs contain more dynamical information for accurately identifying more protein complexes. Our proposed identification method uses multisource biological data to judge whether the joint colocalization condition, the joint coexpression condition, and the expanding cluster condition are satisfied; this is to ensure that the identified protein complexes have the features of colocalization, coexpression, and functional homogeneity. The experimental results on yeast data sets demonstrated that using the constructed TI-PINs can obtain better identification of protein complexes than five existing dynamic PPI networks, and our proposed identification method can find more protein complexes accurately than four other methods. The majority of proteins interact with each other to perform a specific biological process . The fasOver the past decades, the studies on identifying protein complexes from static protein-protein interaction network (SPIN) have yielded many effective methods. The clustering-based methods such as MCODE , ClusterThe aforementioned methods mainly focus on identifying complexes from static protein-protein interaction network (SPIN). However, the real PPI network in cell keeps changing over different stages of the cell cycle . In fact k-sigma method to determine whether a protein at a time point is active and constructed a noise-filtered active protein interaction network (NF-APIN) to detect protein complexes. The fore-mentioned methods mainly consider how to construct the dynamic PPI networks and then apply existing identification method to find protein complexes from the constructed dynamic PPI networks.Fortunately, by monitoring simultaneous and quantitative changes in RNA concentration of thousands of genes under various experimental conditions, DNA microarray technology produced a large number of gene expression data , 23. TheFurthermore, some researchers have not only investigated how to construct the dynamic PPI networks but also designed identifying methods to find protein complexes from the constructed dynamic PPI networks. By combining the active probability of proteins and Pearson correlation coefficient of PPIs with static PPI networks, Zhang et al.  construcWe observed that all the existing methods determine active time point of proteins by a conservative and relative high threshold. This leads to losing dynamic information of gene with expression value that is lower than the specified threshold. In this paper, we first exploited the undulating degree above the base level of gene expression instead of the gene expression level to determine the active time point of a protein and constructed temporal PPI networks (TPNs) by protein interaction data and gene expression data. We then proposed a method which not only converts TPNs into temporal interval PPI networks (TI-PINs) but also identifies more colocalized, coexpressed, and functionally significant protein complexes from the constructed TI-PINs by multisource biological data. Finally, we evaluated our constructed TI-PINs and other dynamic PPI networks and compared our proposed identification method with four other competing methods.In this section, we describe how to construct temporal interval PPI networks (TI-PINs) and identify protein complexes from TI-PINs. G= represent a static protein-protein interaction network (SPIN), where V is a set of nodes (proteins) and N=|V|, E is a set of edges (protein-protein interactions), and e denotes the edge between nodes i and j, where i, j=1, 2,…, N. Let Sppi= denote a set of protein-protein interactions with reliability score, where PP is a set of interacting protein pairs and s denote the reliability score of the interacting protein pair  in PP. Thus, we use GW= to denote the graph G weighted by Sppi, where the edge weight w is defined as follows:Let a graphAN×N is used to represent the reliability score matrix of GW, where the element ai,j of AN×N is defined as follows:Furthermore, matrix ai,j ≥ r, we define the edge e as a r-reliable link between nodes i and j, where r is a given reliability threshold and r ∈ {1,2,…, 999} [If ,…, 999} .GEN×T denote the matrix of gene expression of N proteins across T time points. For a gene i, let evi,t represent the expression value of gene i at time t and gepi = {gepi(t)∣t = 1,…, T} denote the gene expression pattern of gene i, where gepi(t) = /(ev_maxi − ev_mini), ev_mini = mint=1Tevi,t, and ev_maxi=maxt=1Tevi,t, i=1,2,…,N, t=1,…,T. In fact, gepi is composed of T normalized gene expression values. The normalized gene expression data can be used to measure the undulating degree above the base level of gene expression during a whole cell cycle.Let  i exceeds a specified threshold at a certain time point, we call that the product of gene i is activated at this time point. Let api(t) denote the active state of protein i at time point t, if protein i is active, api(t)=1, otherwise, api(t)=0, i=1,..., N, and t=1,..., T. For a given active threshold φ, api(t) is defined as follows:When a protein is involved in a specific biological process, the expression data of the protein-coding gene exhibits the undulation above the base level of the gene expression during the biological process. If the normalized expression value of gene φ preserves more gene dynamical information. The best active threshold φ will be discussed in the section “The effect of active threshold”.Obviously, a lower active thresholdTPNt = denote a temporal PPI network at time point t, where Vt and Et is the set of proteins and the set of interactions between active proteins at time point t, respectively, t=1, 2,…, T. We use DAt to represent the reliability score matrix of TPNt, where element dai,jt in DAt is computed as follows:In order to model the dynamics of active proteins mentioned above, the dynamical PPI network is introduced. In the following, we discuss how to construct temporal PPI networks (TPNs) by incorporating time-course gene expression data into static PPI network SPIN. Let TPNt =, t=1,...,T, let TI-PINtlow,ttop and Btlow,ttop denote the temporal interval PPI network and its reliability score matrix from tlow to ttop, respectively, where tlow and ttop are two time points and 1 ≤tlow≤ttop≤ T. TI-PINtlow,ttop and element bi,jtlow,ttop in Btlow,ttop are defined as follows:A protein complex is corresponding to a subgraph in PPI network. To represent the subgraph of a protein complex which appears in several successive temporal PPI networks, we introduce temporal interval PPI networks (TI-PINs). A temporal interval PPI network (TI-PIN) can be generated by merging several successive temporal PPI networks (TPNs). Given tlow = t = ttop≤ T, then bi,jtlow,ttop=bi,jt,t=dai,jt, namely, TI-PINt,tis the same as TPNt. If 1 ≤tlow < ttop≤ T, thenTI-PINtlow,ttop is newly constructed by TPNt, tlow ≤ t ≤ ttop. Here, let l=ttop-tlow+1 denote the temporal interval length. TI-PINtlow,ttop by merging the successive temporal PPI networks (TPNs) from tlow to ttop. From  T TPNs can generate T·(T-1)/2 TI-PINs.Obviously, if 1 ≤tlow and ttop, and tlow<ttop, if the PPI subgraph Gpc of a protein complex appears in all TPNtlow,TPNtlow+1,…, and TPNttop, then Gpc also appears in all TI-PINt1,t2, t1 ≤ t2 and t1, t2=tlow, tlow+1,..., and ttop. Because the number of TI-PINt1,t2 is larger than the number of TPNt, the chance of exactly identifying the protein complex from TI-PINt1,t2 is higher than that from TPNt, where t1 ≤ t2 and t, t1, t2=tlow, tlow+1,..., ttop.For given time points In this section, we introduce the concepts of the joint colocalization condition, the joint coexpression condition, the GO-based functional similarity between proteins, and the expanding cluster condition and then present our identification method.To accomplish a specific biological process, some proteins physically interact with each other to form a protein complex at the same subcellular localization. Huh et al.  investig p, let LV(p) denote the localization vector of the protein p and LVi(p) denote the i-th element of LV(p), i=1,…, 22. If the protein p is once localized at the i-th subcellular localization category in a whole cell cycle, LVi(p)=1, otherwise, LVi(p)=0, i=1,…, 22.Given a protein PS of k proteins {p1, p2,…, pk} and LV(pj), j=1,…, k, let JLV(PS)= {JLV1(PS), JLV2(PS),…, JLV22(PS)} denote the joint localization vector of PS, where JLVi(PS)= ∧j=1kLVi(pj), i=1,…,22, and “∧” is the logical AND operation on the elements among the localization vectors of proteins in PS. If all proteins in PS perform a specific function at the i-th subcellular localization category, then JLVi(PS)=1, otherwise, JLVi(PS)=0, i=1,…, 22. Obviously, JLV(PS) is also a 22-dimension 0-1 vector.Given a set PS of proteins and its JLV(PS), let JC(PS)=∑i=122JLVi(PS) denote the joint colocalization count of PS. Clearly, JC(PS) is the sum of all elements in JLV(PS). If JC(PS)>0, there exists at least one subcellular localization category where all proteins in PS are jointly colocalized in a whole cell cycle. If JC(PS)=0, all proteins in PS are not jointly colocalized at any subcellular localization category in a whole cell cycle. We define “JC(PS)>0” as the joint colocalization condition.Given a setThere exists a correlation between gene expression level and protein activity . The sub GS of k genes {g1, g2,…, gk} and the normalized gene expression value gepi(t) of gene i at time point t, t=1,…,T, i=1,…,k, we use JGEGS={JGEGS(t)∣t = 1,…, T} to denote the joint gene expression profile of GS, where JGEGS(t) = ∏i=1kgepi(t) and “Π” is the multiplication operation on the expression pattern values among k genes. In essence, we can generate JGEGS(t) by calculating the product of the normalized expression values of k genes in GS at time point t, t=1,..., T.Given a set GS, we use JQ(GS)=(1/T)∑t=1TJGEGS(t) to denote the joint coexpression quantity of GS. If JQ(GS)≥γ, all genes in GS are jointly coexpressed, where γ is the given threshold. We define “JQ(GS)≥γ” as the joint coexpression condition.To measure the joint coexpression level of l, we use l+4 successive expression data to analyze the joint coexpression condition. We set a time window, which covers l+4 successive time points, on the normalized expression data. If the current temporal interval is , the time window covers l+4 time points including tlow-2, tlow-1, tlow,..., ttop, ttop+1, and ttop+2. If tlow < 3, the time window consists of the following time points: 1, 2,..., ttop, ttop+1, and ttop+2. If ttop > T-2, the time window consists of the following time points: tlow-2, tlow-1, tlow,...,T-1, and T.When the temporal interval length isOntology provides well-defined, structured, and computable semantics of domain knowledge . Becausesimgo between two proteins P1 and P2 by formula  and Sim are computed by formula  is computed by formula Simgo formula .(9)simgosimmf, the CC term based similarity simcc, and the BP term based similarity simbp between proteins P1 and P2, respectively. The values of simmf, simcc, and simbp range from 0.0 to 1.0. The larger these values are, the more similar proteins P1 and P2 are. If simmf≥ω, proteins P1 and P2 are judged to be similar based on the MF term, where ω is a given threshold. Similarly, if simcc≥σ and simbp≥θ, proteins P1 and P2 are judged to be similar based on the CC term and the BP term, respectively, where σ and θ are given thresholds.Correspondingly, we use formulas -9) to c to c9) tPC). Our method uses the expanding cluster condition to iteratively add the functionally similar proteins into the protein cluster PC to generate candidate protein complexes with functional homogeneity.It is well known that members of a protein complex are similar to each other in functionality. In order to use the seed expanding strategy to mine a functional aggregation cluster from a PPI network, we devise an expanding cluster condition to judge whether a protein is functionally similar to a protein cluster , the MF term based minimal similarity MF, and the BP term based minimal similarity BP between PC and u are defined by formulas , a, a PC anformulas , respect CC, MF, and BP exceed their specified thresholds σ, ω, and θ, respectively, we define three Boolean variables bcc, bmf, and bbp as follows:To judge whether bcc, bmf, and bbp are “true” at the same time, the value of EC in formula =true” as the expanding cluster condition.If at least two out of three Boolean variables formula  will bec t1 to time point t2, TI-PINt1,t2 and identify protein complexes from TI-PINt1,t2, 1 ≤ t1 ≤ t2 ≤ T. To construct different temporal interval TI-PINs, our algorithm dynamically constructs TI-PINs in a bottom-up approach as shown in  l=1 are constructed. Next, the TI-PINs of temporal interval length l=2 are constructed, and so on. In PCs) are identified one by one from the constructed TI-PINs. We call our algorithm as ICJointLE-DPN . The main idea of our algorithm is to iteratively construct temporal interval PPI network (TI-PIN) from time pointBy converting temporal PPI networks TPNs into temporal interval PPI networks TI-PINs, the constructed TI-PINs preserve only interactions lasting over the temporal interval. Besides, the amount of the constructed TI-PINs is more than that of TPNs. So, our constructed TI-PINs can offer more opportunities to accurately identify more protein complexes. T time points, ICJointLE-DPN can construct T∙(T+1)/2 TI-PINs. For each constructed TI-PIN, there are at most N protein nodes, where N is the total number of protein nodes in the constructed TI-PIN. For each protein node not being a member of any identified protein cluster, ICJointLE-DPN selects this protein node as an initial protein cluster and expands the protein cluster by checking N-1 other protein nodes. The time complexity of identifying protein complexes from each constructed TI-PIN is O(N∙(N-1)), namely, O(N2). Therefore, the time complexity of ICJointLE-DPN is O(N2∙T∙(T+1)/2)=O((N∙T)2).Now we analyze the time complexity of ICJointLE-DPN. Consider In the following section, we evaluate our constructed TI-PINs and other dynamic PPI networks and compare our proposed identification method with other competing methods.In this section, we first introduce the testing data sets and the benchmark data. Subsequently, we describe metrics evaluating the quality of identified protein complexes. Finally, we present the experimental results and comparative analysis.To construct temporal interval PPI networks (TI-PINs), we selected three yeast PPI data sets. The first one, downloaded from the STRING database V10 version , consisthttp://www.ncbi.nlm.nih.gov/sites/GDSbrowser. GSE3431 is an expression profile of yeast by Affymetrix Yeast Genome S98 Array over three successive metabolic cycles. GSE3431 contains 36 raw gene expression data gathered at 25-minute interval. Let T1, T2,..., and T36 denote the 36 successive time points, thus we can calculate the average value ave_gei of three raw gene expression data at three time points Ti, Ti+12, and Ti+24 for each gene in GSE3431. The average value ave_gei is used to represent the i-th gene expression value, i=1, 2,..., 12. We used the 12 gene expression values for each gene to analyze joint coexpression condition and construct TI-PINs for GSE3431. Another data set GSE4987 [Furthermore, we selected two yeast gene expression data sets to conduct the comparative experiment. One data set, GSE3431 , is extrhttp://yeastgfp.yeastgenome.org, to analyze joint colocalization condition. The GO term annotations of the yeast-related proteins were downloaded from http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3431. We used the GO term annotations to calculate the GO term based functional similarity between proteins. The known complexes set CYC2008 containing 408 manually curated heterometric protein complexes was downloaded from http://wodaklab.org/cyc2008/ [In addition, we used the yeast-related protein localization data , downloacyc2008/ .Comparing identified complexes with known ones is a commonly used evaluation. There are several statistical matching-based metrics, which evaluate the quality of identified complexes and the performance of identification methods. The biological relevance-based metrics, which are independent of the known complexes, are used to evaluate the biological significance of identified complexes. ic and a known complex kc, OS, is calculated as follows [Vic and Vkc are the protein set of ic and the protein set of kc respectively. If OS≥λ, ic is matched with kc, where the threshold λ is usually set to 0.2 [ OS=1 indicates that ic is completely matched with kc.If an identified complex and a known complex overlap each other, there exist common proteins between them. The overlapping score between an identified complex follows :(17)OSict to 0.2 , 11. Par IC be the set of complexes identified by computational method and KC be the set of the known complexes. Then let Mic represent the number of identified complexes which matches at least one known complex in KC, and Mkc denote the number of known complexes which matches at least one identified complex in IC. Mic and Mkc are defined as follows [Let follows :(18)Mic=prec), recall(rec), and f-measure (fm) [We evaluate the quality of identified complexes by precision (ure (fm) .(20)precFrac is defined as the fraction of matched complexes, which measures the percentage of known complexes matched with identified complexes [ Frac is equivalent to rec.omplexes . In factMMR) [ MMR is calculated as follows [kci is the i-th known complex, i=1,...,n, and n= |KC|, and icj is the j-th identified complex, j=1,...,m and m= |IC|.The maximum matching ratio (MMR)  is based follows :(23)MMR=ni be the number of proteins in the i-th known complex and tij be the number of common proteins between the i-th known complex and the j-th identified complex, i=1,…,n, and j=1,…,m. Sensitivity (Sn), positive predictive value (PPV), and geometric mean (Acc) of Sn and PPV [ Sn, PPV, and Acc are computed by formulas (Let  and PPV  are usedformulas -26)..(27) FAM FAM is a metric measuring statistical match and is mainly used to statistically evaluate the identification accuracy.Obviously,PM be the number of identified complexes that match with known complexes exactly. In fact, #PM is a metric for evaluating the degree of exact match between the identified complexes and known complexes.Let #PM and FAM to comprehensively compare the quality of two sets of identified complexes via analyzing the relative performance of these two sets of identified complexes.In the following, we will illustrate how to use both #PM and FAM, S1 with #PM1 and FAM1 and S2 with #PM2 and FAM2, let G1,2 denote the geometric mean of the relative performances of S1 and S2, and G1,2 is calculated as follows:For two sets of identified complexes with metrics # G1,2> 1, then the quality of S1 will be superior to that of S2 in terms of the product of #PM and FAM; otherwise, the quality of S2 will be superior to that of S1 in terms of the product of #PM and FAM. Hence, whether the quality of a set of identified complexes is superior to that of another set of identified complexes can be judged in terms of the product of #PM and FAM. In essence, we treat the product of #PM and FAM as a comprehensive score of exact match and statistical match. So, in our experiments, we chose the product of #PM and FAM, #PM×FAM, as the major metric to comprehensively evaluate the quality of identified complexes.IfWe noticed that the known complexes are generally incomplete . Even th N proteins, we use K to denote the total number of the term X-annotated proteins in the PPI network. For a given complex containing ns proteins, the p-value of this complex is defined as the probability that the number of term X-annotated proteins in a protein set of size ns is not less than ks, where ks is the number of the term X-annotated proteins in this complex [ p-value is computed as follows [The GO term based over-expression analysis for biological process and molecular function can be used to reveal functional homogeneity of proteins in a complex to some extent . For a P complex . The p-v follows :(29)p-va p-value of an identified complex.We used the open source software GO::TermFinder  to calcu p-value<ψ, we call that the term X-annotated proteins enrich the complex at ψ-level [ ψ is a given threshold. If the term X-annotated proteins enrich a complex at the level of ψ=0.01 [If ψ-level , where ψf ψ=0.01 , this cof ψ=0.01 . The ove φ on the quality of protein complexes identified from temporal PPI networks (TPNs). Secondly, we assessed the protein complexes identified from temporal interval PPI networks (TI-PINs). Finally, we compared our method ICJointLE-DPN with Zhang's method [Firstly, we evaluated the effect of active thresholds method , DPC-NADs method , TS-OCD s method , and DCAs method .PM×FAM of complexes identified from different constructed TPNs with the changing φ.Here we first constructed different temporal PPI networks (TPNs) by combining three yeast PPI data sets  with two yeast gene expression data sets (GSE3431 and GSE4987). And then we evaluated the quality of the complexes identified from these different TPNs. PM×FAM of the complexes identified from the constructed TPNs is the largest when φ=0.01 for DIP and φ=0.1 for STRING and BioGrid respectively. At the meantime, from PM×FAM is the largest when φ=0.05 for DIP, and the value of #PM×FAM is the largest when φ=0.2 for STRING and BioGrid. Hence, in the following experiments, these values of φ, shown in From  σ, ω, and θ to enable ICJointLE-DPN to perform well. We adjusted the value of parameters σ, ω, and θ from 0.1 to 0.9 by increment 0.1 through several experiments respectively, and set these parameters to the appropriate values.In our experiments, we empirically adjusted the value of parametersPM×FAM, which is shown in To evaluate the quality of complexes identified by our method ICJointLE-DPN, we first constructed TPNs and TI-PINs. And then we executed algorithm ICJointLE-DPN to identify complexes from SPIN, TPNs, and TI-PINs respectively. Finally, we compared the quality of the complexes identified from SPIN, TPNs, and TI-PINs respectively in terms of value of #PM×FAM resulting from both TPNs and TI-PINs are apparently larger than that resulting from SPIN. This indicates that identifying protein complexes from dynamic PPI networks can improve the quality of identified complexes. From PM×FAM resulting from TI-PINs is larger than that from TPNs. It means that identifying protein complexes from TI-PINs can further enhance the quality of identified complexes. As mentioned in the section “temporal interval PPI networks”, the use of TI-PINs constructed by several successive TPNs can provide more opportunities to accurately identify more protein complexes.As seen in To further illustrate the effect of our constructed TI-PINs, we ran our algorithm ICJointLE-DPN to identify complexes from TI-PINs and other existing dynamic PPI networks respectively. The experimental results are shown in PM×FAM of complexes identified by ICJointLE-DPN from the constructed TI-PINs is apparently larger than that from other dynamic PPI networks. Such results may partly be attribute to using the relatively low active threshold φ. In addition, by preserving continuous interactions, our constructed TI-PINs can indeed offer more opportunities to identify more protein complexes accurately.We can see from As a result, our constructed TI-PINs have more contributions to identification of protein complexes than other dynamic PPI networks such as TEPIN, DPIN, NF-APIN, DPPN, and TC-PINs. r are used to identify protein complexes. For fair comparison, we removed those PPIs with reliability score lower than reliability threshold r in three yeast PPI data sets before executing four other competing methods. For DPC-NADPIN method, no parameters need to be set. Zhang's method uses two parameters Pre_thresh and Complex_thresh whose default values are 0.5 and 0.1. For DCA, we set parameters to the recommended values α=0.6, β=0.55, and γ=1.4. The setting of nine parameters used in TS-OCD method is shown in In order to evaluate the performance of the identification methods, we compared our method ICJointLE-DPN to three other competing methods Zhang's method , DPC-NADBy analyzing known complexes in CYC2008, we found that the number of the complexes of size two to six exceeds 84% of the total number of known complexes. To evaluate the ability of identifying complexes of size two to six, From To evaluate the overall performance of five competing methods, we reported the statistical matching-based metrics of the identified complexes in  #PM, Frac, MMR, FAM, and #PM×FAM. We also see that, concerning fm, ICJointLE-DPN obtains almost all the largest values except for one among five competing methods, and with regard to Acc, ICJointLE-DPN is ranked top two. Overall, our method ICJointLE-DPN can not only identify complexes accurately but also identify more complexes exactly matched with known complexes from TI-PINs.From Now we give two examples related to the complexes identified from dynamical PPI networks which are constructed via incorporating GSE3431 into DIP. As can be seen from Similarly, From IC is the total number of identified complexes, #SC denotes the number of identified complexes with significant enrichment.To evaluate the functional enrichment of identified complexes, we compared our method ICJointLE-DPN to other four competing methods with respect to biological process (BP) enrichment analysis. For complexes identified by ICJointLE-DPN from different TI-PINs, their raw data of BP enrichment analyses and their results of significant statistics are presented in Supplementary Materials .  Table As seen from  #PM, the fraction of known complexes matched with identified complexes FRAC, maximum matching ratio MMR, comprehensive score FAM, and the product of #PM and FAM. Concerning the significant enrichment, five competing methods overall perform well when they identify complexes of size larger than 6; when identifying complexes of size not larger than 6, our proposed method performs slightly weaker than DPC-NADPIN, TS-OCD, and DCA but stronger than Zhang's method.In summary, our proposed identification method overall outperforms other four competing methods in terms of the number of identified complexes exactly matched with known complexesGene expression data contains temporal information of protein activity. By integrating gene expression data into PPI data to determine active time point of interacting proteins, we exploited temporal dynamics of proteins to construct temporal PPI networks TPNs. In order to accurately identify more protein complexes, we further converted TPNs into temporal interval PPI networks TI-PINs. The experimental results confirmed that our constructed TI-PINs have more contributions to identification of protein complex than TEPIN (Time-Evolving PIN), DPIN (dynamic protein interaction networks), NF-APIN (noise-filtered active protein interaction networks), DPPN (dynamic probabilistic protein interaction networks), and TC-PIN (time-course protein interaction networks).Based on our constructed TI-PINs, we devised a novel method ICJointLE-DPN which uses multisource biological data to identify protein complexes. First, our proposed method employs protein localization data to analyze the joint colocalization condition to judge whether a group of proteins is of joint colocalization. Secondly, our proposed method uses gene expression data to analyze the joint coexpression condition to judge whether a group of proteins is of joint coexpression. Thirdly, our method exploits three types of similarity to analyze the expanding cluster condition to judge whether a group of proteins is of functional homogeneity. As a result, by combining these three conditions, our proposed method can accurately identify more protein complexes from TI-PINs than other four competing methods Zhang's method, DPC-NADPIN, TS-OCD, and DCA.Identifying protein complexes from dynamic PPI networks remains to be a challenging work in postgenomic era. In cell system, protein activity and protein-protein interaction have dynamical characteristics. Hence, it is important for identifying protein complexes to construct dynamic PPI networks close to reality. Due to the limited gene expression samples and failure to capture some transient interactions, it is difficult to construct dynamic PPI networks completely expressing protein interactions in cell system. Although many works have made to construct effective dynamic PPI networks to identify protein complexes, the efforts on constructing nearly real PPI networks will still be encouraged. In addition, it is also important to design an effective method to identify protein complexes from dynamic PPI networks. To find protein complexes with biological relevance by computational approach, multisource biological data should be used to identify protein complexes from dynamic PPI networks. As seen from


Supporting Information & Tutorials may be found in the webpage of the tool.Understanding the functional effect of Single Amino acid Substitutions (SAS), derived from the occurrence of single nucleotide variants (SNVs), and their relation to disease development is a major issue in clinical genomics. Despite the existence of several bioinformatic algorithms and servers that predict if a SAS is pathogenic or not, they give little or no information at all on the reasons for pathogenicity prediction and on the actual predicted effect of the SAS on the protein function. Moreover, few actual methods take into account structural information when available for automated analysis. Moreover, many of these algorithms are able to predict an effect that no necessarily translates directly into pathogenicity. VarQ is a bioinformatic pipeline that incorporates structural information for the detailed analysis and prediction of SAS effect on protein function. It is an online tool which uses UniProt id and automatically analyzes known and user provided SAS for their effect on protein activity, folding, aggregation and protein interactions, among others. We show that structural information, when available, can improve the SAS pathogenicity diagnosis and more important explain its causes. We show that VarQ is able to correctly reproduce previous analysis of RASopathies related mutations, saving extensive and time consuming manual curation. VarQ assessment was performed over a set of previously manually curated RASopathies  related variants, showing its ability to correctly predict the phenotypic outcome and its underlying cause. This resource is available online at  The potential for genomics to contribute to clinical care has long been recognized, and the clinical use of information about a patient’s genome is rising. In this sense, precision medicine initiatives for disease treatment and prevention that take into account individual variability in genes are being implemented worldwide. Single nucleotide variants (SNVs) that manifest as protein variants are the best known and most important form of variations in the genome, therefore a critical problem is to understand how single amino acid substitutions (SAS) affect protein function and protein interaction networks  leading There are a several SAS effect predictors, most of them perform a bioinformatic analysis and provide a pathogenicity outcome or score, i.e., the propensity of a variant to be involved on the development of a disease, something which is not straightforward to determine. They are usually based on sequence information, focusing on residue conservation and lacking a structural viewpoint which has proven to be critical to identify their effect . MoreoveHere, we present VarQ, a Web Server that automatically analyzes several effects of SAS on protein function, particularly protein folding, activity, protein-protein interactions, and drug or cofactor binding. Analyzed variants are either automatically extracted from clinical databases and/or can be submitted directly by the user. VarQ automatically selects a critical set of representative structural conformations of the desired protein and diagnoses variants effect based on their impact. For this sake several properties are computed for each variant, including involvement in ligand binding, presence in the catalytic site , presencThe VarQ tool is built around a collection of structural analysis tools tied together with the help of the workflow system Ruffus . Having 1 allows both to visualize the results and download them for further analysis. Some of the features available are the mapping of the found variants on the protein sequence  provides as an annotation for each crystallized ligand  A dataset of solvent molecules were built to consider as binding residues only those interacting with non-solvents as binding residues. The information is parsed directly by the pipeline and the corresponding residues are labeled as involved in binding.Also, we used the FPocket software for each protein structure considered in the analysis to calculate the protein pockets. We only considered the pockets with Druggability Score greater than 0.5, and/or if a ligand is present in the pocket. All the residues belonging to the pocket (even those not in contact with the ligand) are considered as binding residues.Catalytic Site Atlas (CSA) is a database documenting the active sites and catalytic residues of enzymes. CSA contains 2 types of entries: original manual-annotated entries, derived from the primary literature and homology-determined entries, based on sequence comparison methods to one of the original entries. All the residues belonging to the CSA database are labeled as catalytic residues in the VarQ pipeline.All mutations that were mapped to any protein structure were analyzed with the FoldX software. The FoldX software predicts the free energy change of a given mutation on the protein stability or on the stability of a protein-protein complex, and has been widely tested as an accurate predictor for point mutations without the need of more time-consuming techniques like Molecular Dynamics or Monte Carlo simulations . MutagenFor each residue in each structure, the Solvent Accessible Surface Area (SASA) was computed. Sidechain exposure percentages of each residue are informed, and those with more than 50% of its surface exposed are labeled as exposed, or on the contrary, as buried. For this computation, the PyMol software  is used In order to detect those mutations that can affect protein-protein interaction, we decided to label the amino acids present in protein-protein interaction surfaces. To label a residue belonging to a protein-protein interface we evaluated the structures that have more than one chain. When an atom of a residue of one chain is at a distance of less than 5Å from an atom of the other chain the residue is labeled as interface residue. We also added an extra criteria; if the residue in the 3did database has been labeled as interacting we labeled the residue as 3did but we did not add the interface label. Despite the fact that the 3did database is not accurate enough for our automatic analysis pipeline, we decided to add this information since we considered it valuable to evaluate possible effects as it expands the database of residues which are involved in protein-protein interfaces, but needs manual inspection.The B-factor of a residue usually relates to its mobility and we inform this value with respect to the median of the B-factor of all the residues in the protein. We also calculate the Switchability, which informs the tendency of residues to switch from alpha-helix to a beta sheet type of secondary structure; the Aggregability, which is the tendency of a residue to generate aggregation when is mutated and is computed using the Tango Software; the conservation, calculated as the value in bits of the corresponding letter of the original amino acid in the Pfam family only if it is assigned for the analyzed position. This value is computed by an in-house developed script.The Web Server is developed to run proteins based on its UniProt ID. To test our developed pipeline we first run a manually curated set of variants derived from our previous work , comprisIn Figure Figure VarQ pipeline, further classifies all the amino acids of each protein automatically, in key categories related to their function  and presents that information to help decision making Table . For exaThe active site and binding residues are those either labeled as ligand-binding residues in the PDB file, those belonging to the same pocket of these residues as determined by the Fpocket program, and those belonging to the CSA database. For example, there are three HRAS structures, one bound to GNP , other to GTP  and another one with no ligand , and reported mutations in the binding pocket have been proposed to modify signaling cascades, which are involved in different diseases . VarQ prFolding residues are those that correspond to neither inter-domain nor Active Site having an accessible surface area percentage lower than 50%. In the particular case of the RASopathies proteins, a high proportion of the residues are marked as interface-involved because these proteins participate in a very complex network with a large set of protein-protein interactions. Localization mutations  of the 427 pathogenic variants in the original data set, showing that structural information can enrich the knowledge leading to SAS pathogenicity prediction  the same mutation could lead to different labeling, leaving to the user the final assessment of the effect caused by the variation. VarQ displays its full potential on human proteins with known structure(s) but can also be used with any protein. Usually clinical researchers, biochemists and geneticists do not have the bioinformatic resources to massively analyze variants so they use web servers or easy to use softwares to classify the variants. On the other hand, bioinformatic softwares usually are aimed at predicting only pathogenicity but do not give information for the users to be able to gain insight and finally decide the possible/potential effect of the mutation. Structural information is often not available for reported mutations: for the RASopathies analyzed proteins, a highly represented set on the PDB, only 20.3% of the Clinvar mutations for this proteins could be mapped into structure, 2103 vs. 427. When available, this information contributes to our understanding of the molecular basis of disease .The datasets generated and analyzed for this study can be found in the web page of VarQ (CK, LS, MM, and AT conceived the work. LR designed and programmed the pipeline with the support of CM, JD, JB, and SV. LR programmed the website with the support of CM, JD, JB, and SV. LR, CM, and CK analyzed the data. LR, CM, MM, and AT wrote the manuscript with support of JD, CK, and LS. All authors approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.