Structural Bioinformatics Inspection Of NeXtProt PE5 Proteins In The .
Articlepubs.acs.org/jprStructural Bioinformatics Inspection of neXtProt PE5 Proteins in theHuman ProteomeQiwen Dong,†,‡ Rajasree Menon,† Gilbert S. Omenn,*,†,§ and Yang Zhang*,†, †Department of Computational Medicine and Bioinformatics, §Departments of Internal Medicine and Human Genetics and School ofPublic Health, Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan 48109-2218, United States‡School of Computer Science, Fudan University, Shanghai, 204433, ChinaABSTRACT: One goal of the Human Proteome Project is to identify at least one protein product for each of the 20 000human protein-coding genes. As of October 2014, however, there are 3564 genes (18%) that have no or insufficient evidence ofprotein existence (PE), as curated by neXtProt; these comprise 2647 PE2 4 missing proteins and 616 PE5 dubious proteinentries. We conducted a systematic examination of the 616 PE5 protein entries using cutting-edge protein structure and functionmodeling methods. Compared to a random sample of high-confidence PE1 proteins, the putative PE5 proteins were found to beover-represented in the membrane and cell surface proteins and peptides fold families. Detailed functional analyses show thatmost PE5 proteins, if expressed, would belong to transporters and receptors localized in the plasma membrane compartment.The results suggest that experimental difficulty in identifying membrane-bound proteins and peptides could have precluded theirdetection in mass spectrometry and that special enrichment techniques with improved sensitivity for membrane proteins couldbe important for the characterization of the PE5 “dark matter” of the human proteome. Finally, we identify 66 high scoring PE5protein entries and find that six of them were reported in recent mass spectrometry databases; an illustrative annotation of thesesix is provided. This work illustrates a new approach to examine the potential folding and function of the dubious proteinscomprising PE5, which we will next apply to the far larger group of missing proteins comprising PE2 4.KEYWORDS: Human Proteome Project, missing proteins, neXtprot, PeptideAtlas, protein folding, I-TASSER, COFACTOR,structure-based function annotation INTRODUCTIONProteins are the workhorse molecules of life, participating inessentially every activity of various cellular processes. The nearcompletion of the Human Genome Sequence Project1 generateda valuable blueprint of all of the genes encoding the amino acidsequences of the entire set of human proteins, providing animportant first step toward interpreting their biological andcellular roles in the human body. However, due to the dynamicrange and complexity of proteins and their isoforms as well as thesensitivity limits of current proteomics techniques, manypredicted proteins have not yet been detected in proteomicsexperimental data.2In 2011, the Human Proteome Organization (HUPO)launched the Human Proteome Project (HPP),3 which includesthe Chromosome-Centric HPP (C-HPP)4 and Biology/DiseaseDriven HPP (B/DHPP).5 A major goal of the HPP is to identify 2015 American Chemical Societyat least one representative protein product and as many posttranslational modifications, splice variant isoforms, and nonsynonymous SNP variants as feasible for each human gene. Thisambitious goal is being pursued through 50 internationalconsortia for each of the 24 chromosomes, the mitochondria,and many organs, biofluids, and diseases.2 Five extensive dataresources contribute the baseline and annually updated metricsfor the HPP:2,6 the Ensembl database7 and neXtProt8 provide thenumber of predicted protein-coding genes (a total of 20 055 inneXtProt 2014-09-19); PeptideAtlas9 and GPMdb10 independently reanalyze, using standardized pipelines, a vast array of massSpecial Issue: The Chromosome-Centric Human Proteome Project2015Received: June 3, 2015Published: July 21, 20153750DOI: 10.1021/acs.jproteome.5b00516J. Proteome Res. 2015, 14, 3750 3761
ArticleJournal of Proteome ResearchFigure 1. Flowchart of structure and function prediction for PE5 missing proteins.spectrometry studies; the Human Protein Atlas11,12 uses a hugeantibody library to map the expression of proteins by tissue, cell,and subcellular location; and, finally, neXtProt8 curates proteinexistence (PE) evidence and assigns one of five levels ofconfidence (PE1 5). Proteins at the PE1 level (16 491) havehighly credible evidence of protein existence identified by massspectrometry, immunohistochemistry, 3D structure, and/oramino acid sequencing. At the PE2 level (2647), there isevidence of transcript expression but not of protein expression.PE3 protein sequences (214) lack protein or transcript evidencein humans, but they have homologous proteins reported in otherspecies. Proteins at the PE4 level (87) are hypothesized fromgene models. Together, protein entries designated PE2 4represent missing proteins in the HPP.6 Finally, the predictedprotein sequences at PE5 (616) have dubious or uncertainevidence; a small number of these seemed to have some proteinlevel evidence in the past, but curation has since deemed suchidentifications doubtful, primarily because of genomic information, such as lack of promoters or multiple mutations. Each year,a small number are nominated for re-evaluation in light ofadditional experimental data.Since 2011, the proteomics community and the HPP haveachieved steady progress in human proteome annotations. Now,85% of putative human protein-coding genes have highconfidence PE1 protein existence, as curated by neXtProt.6The remaining 2948 genes at levels PE2 4 have no orinsufficient evidence of identification by any experimentalmethods and are thus termed missing proteins.6 Many of thesemissing proteins are presumed to be hard to detect because oflow abundance, poor solubility, or indistinguishable peptidesequences within protein families, even in tissues in whichtranscript expression is detected. The HPP has begun acomplementary process of closely examining the missingproteins to recognize those genes that are very unlikely togenerate proteins at all or proteins detectable by currentmethods. PE5 protein entries are considered to be dubiousproteins due to their lack of essential features for transcriptionand/or mutations of the sequence in the numerous cases ofpseudogenes. At the HUPO2013 World Congress in Yokohama,it was decided to remove the PE5 entries from the denominatorof protein-coding genes, but the community was invited topropose PE5 proteins that might have substantial new evidenceor newly predicted features that might make them candidates foractive protein expression.To help address that challenge, we conducted a systematicbioinformatics inspection of the 616 PE5 predicted proteins byevaluating their potential for folding and generating biologicalfunctions using the cutting-edge structure folding and structurebased function prediction tools, I-TASSER13,14 and COFACTOR.15,16 One reason that we focused on PE5 proteins is that thePE5 sequences represent the most dubious set of missingproteins. Therefore, evidence of protein-coding genes from PE5proteins will help to highlight their importance as the othercategories, PE2 4 proteins (to which the next step of ouranalysis will be applied), are revisited. In addition, a critical studyof these proteins from multiple approaches, including bothproteomics and bioinformatics, is becoming increasingly urgentbefore these genes are removed from the coding-genedenominator. This study will help to demonstrate the analysisof PE5 proteins and lay the foundation for similar analysis of themuch larger set of PE2 4 protein entries.Since the default I-TASSER folding simulation uses fragmentsfrom the Protein Data Bank (PDB), the results of which can beeasily contaminated by the existence of homologous proteins, wehave exploited a stringent filter (sequence identity 25% or PSIBLAST E-value 0.5) to exclude all homologous proteins fromthe template structure library. In fact, PE5 genes have homologywith few entries in current structure and function databases fromour threading search results (this holds even for many PE5pseudogenes since we found that most pseudogenes do not havehomology in the PDB library); therefore, the exclusion ofhomologous templates did not result in observable differences inthe I-TASSER folding results. In this context, the results offolding simulations are more sensitive to the physicalcomponents of the I-TASSER force field that is used to justifythe foldability of the sequences than they are to the existence ofhomologous templates.It is important to recognize that there are many pseudogenesin DNA that have lost their protein-coding ability due to theaccumulation of multiple mutations. However, these genes often3751DOI: 10.1021/acs.jproteome.5b00516J. Proteome Res. 2015, 14, 3750 3761
ArticleJournal of Proteome Researchcorrelation coefficient 0.91 was found between the C-score andthe actual accuracy of the I-TASSER models. In a recentcomputational protein design folding experiment, it was foundthat the I-TASSER C-score is also highly correlated with thelikelihood of the computationally designed sequences folding inthe physiological environment.24Starting from the I-TASSER models, the enzyme commission(EC), gene ontology (GO), and ligand-binding site functionalannotations are generated using COFACTOR. 15,16 TheCOFACTOR algorithm has been designed to derive functionalinsights by global and local (binding-pockets and active sites)structure comparisons of the target with known proteins in theBioLip function library.25 The functional insights are thentranslated from known proteins to the target sequencesaccording to a scoring function that combines the structuraland evolutionary matches between the target and templateproteins. For ligand-binding and enzyme commission assignments, the scoring function of the COFACTOR annotations alsocombines a chemical feature match and physical fit of the ligandand cofactors with the putative binding/active sites on the ITASSER structure models. COFACTOR was ranked as the mostsensitive algorithm for ligand-binding recognition in the recentCASP experiment.26Finally, the subcellular localizations of the query proteins arepredicted by the widely used Hum-PLoc2.0 software,27 whichderives protein locations through the clustering of gene ontologyannotations. Hum-PLoc2.0 can generate predictions for 14subcellular locations (centriole, cytoplasm, cytoskeleton, endoplasmic reticulum, endosome, extracellular, Golgi apparatus,lysosome, microsome, mitochondrion, nucleus, peroxisome,plasma membrane, and synapse) and has a success rate of 70%in large-scale jackknife cross-validation tests.27have a very similar sequence to that of their original functionalprotein ancestors, which makes it difficult to use sequencehomology-based bioinformatics approaches (like BLAST) todistinguish the pseudogenes. An advantage of the combined ITASSER and COFACTOR procedure over traditional sequencebased homologous approaches is that the I-TASSER foldingresults are less dependent on homologous proteins afterhomologous templates are excluded. Moreover, the follow-upCOFACTOR algorithm conducts functional annotations basedon a function library derived from canonical protein products,assisted with composite examinations from biochemical featurematching and physics-based fitting calculations, including sterictesting and ligand-docking scores. This functional analysisensures further discrimination of distantly related pseudogenes,which face no functional selection during the accumulation ofrandom mutations. These pseudogenes usually do not satisfy thestringent requirements for biological functions, such as subtlebinding pockets and functional sites with appropriate physicochemical characteristics.All of the I-TASSER modeling and COFACTOR annotationresults for the PE5 proteins are made publicly available at http://zhanglab.ccmb.med.umich.edu/HPSF/. We expect that theavailability of these high-resolution and structure-basedannotations from bioinformatics approaches will provide usefulinsights complementary to other proteome investigations andwill help to guide further experimental designs for thecharacterization of dubious and missing proteins. EXPERIMENTAL SECTIONComputational modeling of protein sequences in this studyconsists of three general steps: threading and domain parsing,structure folding simulation, and structure-based functionannotations (Figure 1).First, the query sequence is threaded through a nonredundantset of PDB structures by LOMETS, which is designed to detectpossible structural template and super secondary structurefragments using nine state-of-the-art threading algorithms.17 Toavoid homologous contaminants, all homologous proteins thathave a sequence identity 25% or are detectable by PSI-BLASTwith an E-value 0.5 were excluded from the LOMETS templatelibrary. Starting from the multiple threading alignments, thequery sequence is parsed into individual domains byThreaDom,18 which decides the domain boundary and linkerregions of the query sequence based on the conservation and gapand insertion scores in the multiple template alignments.For each domain, I-TASSER is used to conduct the foldingsimulations by reassembling the continuous structure fragmentsexcised from the continuous threading alignments throughreplica-exchange Monte Carlo simulations, under the guidance ofa highly optimized knowledge-based force field.13,14 For proteinswith multiple domains, the quaternary structure is constructed bydocking the models of the individual domains based on the fulllength I-TASSER models, followed by fragment-guidedmolecular dynamics (FG-MD) refinement.19 I-TASSER hasbeen recognized as being one of the most robust methods fornonhomologous protein structure prediction in the communitywide CASP experiments.20 22 The confidence of the foldingsimulations is evaluated by the C-score,23 which is calculated bycombining the significance score of the threading alignment andthe extent of the convergence of the Monte Carlo simulations. Cscore is normally in the range of [ 5,2], with a C-score 1.5indicating confident models with correct fold according theformer large-scale benchmark test experiment,23 where a Pearson RESULTS AND DISCUSSIONData SetsThe dubious or uncertain missing proteins comprisingconfidence code PE5 were extracted from the neXtProtdatabase8 of 19 September 2014. There are 616 predictedproteins in this category, with lengths ranging from 21 to 2252residues. As a control study, we collected all of the highconfidence PE1 proteins from neXtProt for which a structure issolved in the PDB library. A random list of 616 proteins was thenchosen that has a distribution of lengths that is similar to that forthe PE5 proteins.Benchmark Test of Structure and Function Predictions onControl Proteins in PE1As part of the effort to test the I-TASSER and COFACTORscoring function, as well as to establish a control set for the PE5proteins, we first conducted structure and function modelingsimulations on the 616 highly confident PE1 proteins selectedfrom neXtProt. The structural accuracy of the I-TASSER modelscan be measured by their TM-score28 in comparison to that ofthe known experimental structures. The TM-score has a range of[0,1]; a TM-score 0.5 generally corresponds to structuralsimilarity in the same SCOP/COTH fold family.29 Although nohomologous templates from the PDB library were employed, 515of the 616 PE1 proteins have been correctly folded by I-TASSER,with an average TM-score 0.78. The I-TASSER simulationsgenerally refined the threading templates closer to the nativestructure. If we account for the best templates from threadingfrom which the I-TASSER simulations start, then there are only285 targets that have a TM-score 0.5 and the average TM-score3752DOI: 10.1021/acs.jproteome.5b00516J. Proteome Res. 2015, 14, 3750 3761
ArticleJournal of Proteome Research 0.69. Such a significant increase in the folding rate and TMscore of the I-TASSER models from the threading templates ismainly attributed to the highly optimized I-TASSER force field,which has the capacity to reassemble unrelated fragments into acorrect global fold.30Here, we have employed the TM-score to assess the accuracyof the modeling using PE1 proteins for which an experimentalstructure has been solved. For PE5 proteins, however, none ofthe sequences has an experimental structure available, so we willuse the confidence score (C-score) of the I-TASSER simulationsto estimate the accuracy of the modeling and foldability. InFigure 2, we present a histogram of the I-TASSER C-score of theFigure 3. Histogram distribution of COFACTOR F-scores for PE1 andPE5 proteins.proteins that have a F-score above 0.6, then the success rates ofthe functional assignments are 93, 95, 93, 94, and 91% for GOmolecular function, GO biological process, GO cellularcomponent, enzyme commission, and ligand-binding sites,respectively, which are significantly higher than those with a Fscore below 0.6 (i.e., 79, 81, 78, 65, and 53%). These data showthe efficiency of COFACTOR for structure-based functionalassignments and the ability of the F-score to distinguish correctfrom incorrect functional assignments.Figure 2. Histogram distribution of I-TASSER C-scores for PE1 andPE5 proteins.Summary of the Predicted Structure and Function of thePutative Proteins in PE5616 PE1 proteins, where 519 proteins have a C-score above 1.5,which largely corresponds to the number of proteins with a TMscore 0.5. The average TM-scores for the proteins with C-score 1.5 and 1.5 are 0.86 and 0.32, respectively, which confirmsthe strong correlation of the C-score and the quality of the ITASSER models, as observed in previous benchmark tests.23Starting from I-TASSER models, COFACTOR can generatethree aspects of functional annotations: enzyme commission,gene ontology, and ligand-binding site predictions. Among the616 PE1 proteins, 582, 585, 556, and 224 proteins have GOmolecular function, GO biological process, GO cellularcomponent, and enzyme commission annotations in neXtProtdatabase, respectively; 276 proteins have ligand-binding sitesannotated in the BioLip database.25 Although there are nohomologous templates used, the COFACTOR models have 508,515, 432, and 161 proteins for which the GO molecular function,GO biological process, GO cellular component, and enzymecommission are correctly assigned, which corresponds to anaccuracy of 87, 88, 77, and 72%, respectively. Here, a correct ECassignment is defined as having the first three digits correctlypredicted, and a correct GO assignment is defined as having theGO item at the first level correctly identified. Among the 276proteins of the ligand-binding data, 172 (62%) have more than70% of their binding sites correctly predicted. The majority oftargets with a correct functional assignment are also correctlyfolded with the I-TASSER model, i.e., TM-score 0.5, showingthe dependence of the functional annotations on the correctnessof the structure’s folding.Figure 3 presents a histogram of the confidence score (Fscore) of the COFACTOR predictions. If we account for the 85Because PE5 proteins have not been validated by any proteomicsexperimental method, the native structure and function of theseproteins are unknown. In Figure 2, we show the C-scorehistograms of PE5 proteins in comparison with those of PE1proteins. As expected, the population of proteins with a highconfidence folding score is much lower in the PE5 group thanthat in the PE1 group. For example, there are 519 PE1 proteinsthat have a C-score 1.5, whereas the number for the PE5proteins is only 188. This is understandable because most PE1proteins are well-characterized proteins with regular structuralfolds, whereas, by definition, PE5 proteins are dubious oruncertain and their gene sequences may not code for expressableproteins. Here, we note that the best C-score of all domains formultidomain proteins is reported in Figure 2 for PE5 proteinssince the existence of one domain from a protein sequence can besufficient to confirm that the corresponding protein is a genecoding protein.Nevertheless, the data seems to suggest that not all PE5proteins are from noncoding genes. If we consider a stringent Cscore cutoff 0.0, in which all proteins have I-TASSER modelswith a correct fold in our benchmark test on the PE1 proteins aswell as in the former benchmark experiment,23 then there are 66PE5 proteins that meet this criterion; these are the most likely tocorrespond to gene-coding proteins from the viewpoint of nonhomology-based structure folding. A summary of these proteinsis listed in Table 1; the data are also available at http://zhanglab.ccmb.med.umich.edu/HPSF/66.html. We acknowledge thatproteins with a lower C-score may also be correctly folded in ITASSER, but the likelihood of success is lower than for thosewith a higher C-score.3753DOI: 10.1021/acs.jproteome.5b00516J. Proteome Res. 2015, 14, 3750 3761
ArticleJournal of Proteome ResearchTable 1. List of 66 PE5 Proteins That Have C-Score 0 in I-TASSER Folding SimulationsIDaChrbNamecCdFeDmfLocgClassh1NX A6NI0311TRIM64B1.610.23YCytoplasmAll beta proteins2NX A6NLI511TRIM64C1.510.23YCytoplasmAll beta proteins34NX Q6ZN08NX ellSmall proteinsAlpha and beta proteins (a b)5NX A6NK024TRIM75P1.120.37YCytoplasmAll beta proteins67NX A6NMB9NX sAlpha and beta proteins (a b)Alpha and beta proteins (a b)8NX A6NGE713URAD1.070.67NExtracellAll alpha proteins9NX A6NHM97MOXD2P1.030.06YExtracellAll beta proteins10NX Q96TA05PCDHB180.920.48YLow resolution protein structures11NX A4D2B87PMS2P10.910.57YPlasmamembraneNucleus12NX Q9H5609ANKRD19P0.90.96NAlpha and beta proteins (a b)13NX Q8N7Z55ANKRD310.90.12YPlasmamembraneCytoplasm14NX O9539712PLEKHA8P10.890.44YCytoplasmAll alpha proteins15NX Q6ZTB919ZNF833P0.890.91NNucleusDesigned proteins16NX B5MCN322SEC14L60.880.49YCytoplasmAlpha and beta proteins (a b)17NX A0PJZ018ANKRD20A5P0.870.97NAlpha and beta proteins (a b)18NX C9J7987RASA4B0.860.34Y19NX neNucleus20NX A4QPH222PI4KAP20.790.69YNucleusAlpha and beta proteins (a b)21NX Q6ZT7719ZNF826P0.760.81NNucleusDesigned proteins22NX A6NIE916PRSS29P0.730.97YExtracellAll beta proteins23NX Q8NGA419GPR32P10.730.59N24NX e and cell surfaceproteins and peptidesAll alpha proteins25NX P0CB337ZNF735P0.710.12YNucleusDesigned proteins26NX E5RG023PRSS460.710.81YExtracellAll beta proteins27NX A6NEY82PRORSD1P0.690.49NCytoplasmAlpha and beta proteins (a b)28NX Q9HAU68TPT1P80.650.76NCytoplasmAll beta proteins29NX A8MUV87ZNF727P0.650.43YNucleusDesigned proteins30NX P12525XMYCLP10.630.31YNucleusAll alpha proteins31NX P0C7 4XFTH1P190.620.9YAll alpha proteins32NX Q63ZY67NSUN5P20.60.82NPlasmamembraneNucleus33NX A8MV571MPTX10.60.68NExtracellLow resolution protein structures34NX Q6NSI116ANKRD26P10.60.94NNucleusAlpha and beta proteins (a b)35NX Q8IWF7XUBE2DNL0.590.56NNucleusAlpha and beta proteins (a b)36NX A8MUU17FABP5P30.580.46NCytoplasmAll beta proteins3754Alpha and beta proteins (a b)Alpha and beta proteins (a b)All alpha proteinsAll beta proteinsAlpha and beta proteins (a b)HGNCiKjPkgene with proteinproductgene with proteinproductunknownpseudogene(ID 0.45)pseudogene(ID 0.47)unknownpseudogene(ID 0.93)gene with proteinproductpseudogene(ID 0.40)pseudogene(ID 0.78)pseudogene(ID 0.38)pseudogene(ID 0.88)gene with proteinproductpseudogene(ID 0.97)pseudogene(ID 0.69)gene with proteinproductpseudogene(ID 0.92)gene with proteinproductpseudogene(ID 0.95)pseudogene(ID 0.93)pseudogene(ID 0.50)pseudogene(ID 0.46)pseudogene(ID 0.83)pseudogene(ID 0.85)pseudogene(ID 0.81)gene with proteinproductpseudogene(ID 0.27)pseudogene(ID 0.69)pseudogene(ID 0.71)pseudogene(ID 0.73)pseudogene(ID 0.54)pseudogene(ID 0.76)pseudogene(ID 0.50)pseudogene(ID 0.54)pseudogene(ID 0.71)pseudogene(ID NNNYYNYNNNNYYNNNNNNNNNNNNNNDOI: 10.1021/acs.jproteome.5b00516J. Proteome Res. 2015, 14, 3750 3761
ArticleJournal of Proteome ResearchTable 1. continuedIDaChrbNamecCdFeDmfLocgClassh37NX Q9NSJ121ZNF355P0.540.36YNucleusSmall proteins38NX Q96P881GNRHR20.540.51N3940NX Q6ZUV0NX raneCytoplasmCytoplasmMembrane and cell surfaceproteins and peptidesAlpha and beta proteins (a b)Alpha and beta proteins (a b)41NX Q6P47416PDXDC2P0.480.82NCytoplasmAlpha and beta proteins (a b)42NX Q3KNT77NSUN5P10.410.6NNucleusAlpha and beta proteins (a b)43NX A6NGU522GGT3P0.40.47YExtracellAlpha and beta proteins (a b)44NX Q6ZSU119CYP2G1P0.40.68NAll alpha proteins45NX m464748NX Q7RTY9NX B5MD39NX 0.4YNNExtracellExtracellCytoplasmAll beta proteinsAlpha and beta proteins (a b)All beta proteins49NX D6RBM54USP17L230.310.7NNucleusAll alpha proteins50NX Q8NHW52RPLP0P60.290.78YNucleusLow resolution protein structures51NX Q58FF615HSP90AB4P0.280.57YCentrosomeAlpha and beta proteins (a b)52NX Q994635NPY6R0.270.68N53NX O607741FMO6P0.260.47YMembrane and cell surfaceproteins and peptidesAlpha and beta proteins (a b)54NX eticulumNucleus55NX A8MVU17NCF1C0.20.12YCytoplasmAll beta proteins56NX P018936HLA-H0.20.97YAlpha and beta proteins (a b)57NX Q1594019ZNF726P10.190.34NPlasmamembraneNucleus58NX Q9BYX72POTEKP0.190.99NCytoskeletonLow resolution protein structures59NX A4D1Z87GRIFIN0.130.98NExtracellAll beta proteins60NX O957447PMS2P20.130.91YNucleusAlpha and beta proteins (a b)61NX Q5VTE09EEF1A1P50.080.95NCytoplasmLow resolution protein structures62NX P0CG0019ZSCAN5DP0.060.12YNucleusSmall proteins63NX P0CF974FAM200B0.060.46YNucleusAlpha and beta proteins (a b)64NX Q8WTZ4XCA5BP10.030.9NCytoplasmAll beta proteins65NX Q9NRI717PPY20.020.14NExtracellPeptides66NX Q6ZRF719ZNF818P00.85NNucleusDesigned proteinsAll alpha proteinsAlpha and beta proteins (a b)Designed proteinsHGNCiKjPkpseudogene(ID 0.67)pseudogene(ID 0.36)NEpseudogene(ID 0.87)pseudogene(ID 0.97)pseudogene(ID 0.91)pseudogene(ID 0.98)pseudogene(ID 0.60)pseudogene(ID 0.97)unknownunknownpseudogene(ID 0.94)gene with proteinproductpseudogene(ID 0.98)pseudogene(ID 0.82)pseudogene(ID 0.51)pseudogene(ID 0.71)pseudogene(ID 0.99)pseudogene(ID 0.99)pseudogene(ID 0.87)pseudogene(ID 0.76)pseudogene(ID 0.95)gene with proteinproductpseudogene(ID 0.54)pseudogene(ID 1.00)pseudogene(ID 0.76)gene with proteinproductpseudogene(ID 0.35)pseudogene(ID 0.29)pseudogene(ID NNNNNNNYNNNNNNNaID, neXtProt ID. bChr, order number of chromosome. cName, gene name from HGNC symbol. dC, I-TASSER C-score. For multidomain proteins,the highest C-score of all domains is listed. eF, F-score of COFACTOR prediction on GO molecular function. fDm: Y, multidomain protein; N,single-domain protein. gLoc, subcellular localization predicted by Hum-mPloc. hClass, fold class. iHGNC, HGNC annotation retrieved on 2014/9/5; the number in parentheses is the sequence identity (ID) between the pseudogene and the closest PE1 4 protein. jK: Y, detected by Kim et al;38N, not detected by Kim et al. kP: Y, included in PeptideAtlas 2014-08; N, not included in PeptideAtlas 2014-08.In Figure 3, we show the F-score distribution of PE5 proteinsin comparison with that for PE1 proteins. Again, there is a muchlower population of high F-score proteins in PE5 than there is inPE1. There are 85 PE5 proteins that have a F-score above 0.6, ofwhich 32 are also in the list of the 66 high C-score proteins fromthe I-TASSER folding simulations (Table 1); this agreementpartly confirms the coincidence of the structure and functionannotation data.We also examined and compared the intrinsically disorderedregions of the PE1 and PE5 sequences using the DisEMBL3755DOI: 10.1021/acs.jproteome.5b00516J. Proteome Res. 2015, 14, 3750 3761
ArticleJournal of Proteome ResearchFigure 4. Relative frequency distributions of the top-ten fold families assigned for (A) PE1 and (B) PE5 proteins. The corresponding frequencies fromproteins in the opposite protein sets are listed as a control.program.31 There are only two out of 616 PE1 sequences forwhich more than 40% of their regions are predicted to bedisordered by DisEMBL, whereas the corresponding number ofPE5 sequences is 79. Since intrinsically disordered regions do nothave regular 3D structures, the average I-TASSER C-score forthe disordered proteins is 3.44, which is 35% lower than that ofother PE5 proteins. Such a high fraction of disordered sequencesin PE5 proteins should also contribute to the low folding ratecompared to that of PE1 proteins.shows the top 10 folds for proteins in PE1 and PE5, respectively.As shown in the figure, the P-loop (C.37) and EF hand-like foldare among the most popular folds for both PE1 and PE5 proteins.However, the three largest fold families for PE5 proteins, alpha alpha superhelix, ferredoxin-like, and immunoglobulin-like betasandwich, have quite a low population among PE1 proteins.Noticeably, PE5 proteins are overexpressed in four of the 10families, i.e., family A G-protein-coupled receptor-like, parallelcoiled-coil, four-helical up-and-down bundle, and T-fold, inwhich there are no PE1 proteins.Since protein folds in the SCOPe database are specific to thearchitecture of secondary structure arrangements, the limitedproteins tested may result in variations in the above comparisons.In Figure 5, we compare PE1 and PE5 proteins based on theirStructure Classification Analyses of the I-TASSER ModelsTo assign analogous structure families, we match the I-TASSERmodels of the target proteins with the structure domains in theSCOPe library,32 an extended structure fold-family libraryintegrated from the standard SCOP33
Structural Bioinformatics Inspection of neXtProt PE5 Proteins in the Human Proteome Qiwen Dong,†,‡ Rajasree Menon,† Gilbert S. Omenn,*,†,§ and Yang Zhang*,†, †Department of Computational Medicine and Bioinformatics, §Departments of Internal Medicine and Human Genetics and School of Public Health, Department of Biological Chemistry, University of Michigan, Ann Arbor .
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