Structural disorder of plasmid-encoded proteins in Bacteria and Archaea

18 Pages·2017·2.89 MB·English

by  Nenad S. Mitić

Mitićetal.BMCBioinformatics (2018) 19:158
https://doi.org/10.1186/s12859-018-2158-6
RESEARCH ARTICLE Open Access
Structural disorder of plasmid-encoded
proteins in Bacteria and Archaea
Nenad S. Mitić1*, Saša N. Malkov1, Jovana J. Kovačević1, Gordana M. Pavlović-Lažetić1 and Miloš V. Beljanski2
Abstract
Background: Inthe last decade and a half it has been firmly establishedthat a large numberof proteins do not
adopt a well-defined (ordered) structure under physiological conditions. Such intrinsically disordered proteins (IDPs)
and intrinsicallydisordered(protein) regions (IDRs) are involved inessential cell processes through two basic
mechanisms: the entropic chain mechanism which is responsible for rapid fluctuations among many alternative
conformations, and molecular recognitionvia short recognition elements that bindto othermolecules.IDPs
possess a high adaptive potential and thereis special interest in investigating theirinvolvement in organism
evolution.
Results: We analyzed 2554 Bacterial and 139 Archaeal proteomes, with a total of 8,455,194 proteins for disorder
content and itsimplicationsfor adaptation oforganisms,using three disorder predictors and three measures. Along
withotherfindings,we revealed that for all three predictors and all three measures (1) Bacteria exhibit significantly
more disorder than Archaea; (2) plasmid-encodedproteinscontain considerably more IDRs thanproteins encoded
onchromosomes(or wholegenomes) in both prokaryotesuperkingdoms; (3)plasmid proteins are significantly
more disordered than chromosomal proteins only inthegroup ofproteins with noCOG category assigned; (4)
antitoxin proteins in comparisonto other proteins,are themost disordered (almost double) inboth Bacterial and
Archaeal proteomes; (5) plasmidal proteins are more disorderedthan chromosomal proteins inBacterial antitoxins
and toxin-unclassified proteins, but have almostthe same disordercontent intoxin proteins.
Conclusion: Our results suggestthat whiledisordercontent depends on genome and proteome characteristics, it
is more influencedby functional engagements than by gene location (onchromosomeor plasmid).
Keywords: Intrinsically disordered proteins, Plasmid-encoded proteins,Toxin/antitoxin, Bacteria and Archaea
Background proteins belong to the so-called singleton ORFan category,
Prokaryoticplasmidsareextrachromosomalnon-obligatory consisting of proteins with no sequence homologies in
DNA molecules that replicate independently. They are other genomes, which are characterized by relatively short
transmitted between organisms by horizontal gene transfer lengths,rapidevolutionandareencodedbygenelowerGC
and may be considered as mobile genetic elements, like contents(itwasshownthatgeneswithalowerGCcontent
transposonsorprophages[1]. tend to evolveat a faster rateascompared to genes with a
Plasmid backbone genes encode for proteins that are higher GC content, although many other factors may also
mostly involved in replication, copy number, partitioning, contributetotheevolutionaryrateofproteins[2,4]).These
stability, etc. [2]. However, most plasmid genes encode for proteins have novel functions and are mostly annotated as
proteinswithanunknownfunction.AccordingtotheClus- hypotheticalproteinsofunknownfunction[5].
ters of Orthologous Groups (COGs) classification, more Aside from backbone genes, plasmids also contain
than 25% of plasmid proteins have not been assigned to genes that are involved in adaptive traits, such as the
COGs [3]. Also, it was estimated that 13% of plasmid ability to exploit new environments or compounds,
pathogenesisandantibiotic resistance.Ofspecialinterest
*Correspondence:[email protected] are toxin/antitoxin genes and their products, because
1DepartmentofComputerScience,FacultyofMathematics,Universityof they often contribute to the maintenance of plasmids or
Belgrade,P.O.B.550Studentskitrg16,Belgrade11001,Serbia
genomic islands [6]. Toxin/antitoxin systems are found
Fulllistofauthorinformationisavailableattheendofthearticle
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InternationalLicense(http://creativecommons.org/licenses/by/4.0/),whichpermitsunrestricteduse,distribution,and
reproductioninanymedium,providedyougiveappropriatecredittotheoriginalauthor(s)andthesource,providealinkto
theCreativeCommonslicense,andindicateifchangesweremade.TheCreativeCommonsPublicDomainDedicationwaiver
(http://creativecommons.org/publicdomain/zero/1.0/)appliestothedatamadeavailableinthisarticle,unlessotherwisestated.
Mitićetal.BMCBioinformatics (2018) 19:158 Page2of18
in plasmids and phages, as well as in chromosomes. Taxonomically, IDPs are present in the proteomes of
They invade Bacterial genomes through horizontal gene all of the three superkingdoms (Archaea, Bacteria and
transfer and participate in a wide range of cellular events, Eukarya), as well as in their viruses. The analysis of dis-
such as plasmid maintenance (via the mechanism of post- order content revealed that Bacteria have a slightly
segregation killing), dormancy and persistence, phage higher level of protein disorder than Archaea. Depend-
defense,generalstressresponse,etc.Atpresent,toxin/anti- ing on the predictor and measure used, the disorder
toxin systems are classified according to their genetic content varies in the range of 12 to 32% for Archaea,
structure and regulation into six types (I-VI) [7, 8]. They and 18 to 35% for Bacteria [17, 19, 20]. Eukarya gener-
are composed of closely linked genes encoding a stable ally contain higher disorder content, ranging from 35 to
toxin, typically a low molecular weight protein, which 50%, while in viruses the disorder content varies to a
causesgrowtharrestbyinhibitionofessentialcellularpro- largeextent from 2.9to23.1%[21].
cesses(includingDNAreplication,translation,celldivision, The aim of this work was to examine protein disorder
etc.),anditscognatelabileantitoxin,whichcaneitherbea contents: (1) in Bacterial and Archaeal plasmids and to
non-codingRNA(typesIandIII)orasmallprotein(types compare them with those in chromosomes; (2) in Bac-
II, IV,V, and VI), which protects the host from the toxin’s terial and Archaeal plasmids and chromosomes as a
deleterious effect. During normal growth conditions, the function of genome size, proteome size, average protein
antitoxins must be constantly synthesized in order to in- length and GC percentage; (3) in plasmid-encoded pro-
hibit their cognate toxins. The function of chromosomally teins classified according to COGs, and (4) in toxin and
encoded toxin/antitoxin systems is less clear [9]. In terms antitoxin plasmid- and chromosome-encoded proteins,
of their structure-function relationship, it is of special as a specific group of proteins with known functions.
interest that antitoxins often lack a well-defined 3D struc- Our results suggest that while disorder content depends
ture,i.e.theyareintrinsicallydisordered[7]. on genome and proteome characteristics, it is more in-
Intrinsically disordered proteins (IDPs) and intrin- fluenced by functional engagements than by gene loca-
sically disordered (protein) regions (IDRs) within tion (onchromosome orplasmid).
structured proteins are defined by the absence of a
stable tertiary structure and a corresponding high de-
gree of flexibility under physiological conditions [10]. Dataset
IDPs usually lack rigid three-dimensional structures The dataset was collected in May 2015 from the
“due to diminished hydrophobic interactions deter- NCBI database (currently available at ftp://ftp.ncbi.
mined by the specific amino acid (AA) compositions nlm.nih.gov/genomes/archive/old_refseq/Bacteria/)
which are typically depleted in hydrophobic, order- and the toxin/antitoxin database (http://202.120.12.
promoting residues, but are enriched in polar and 135/TADB2/). Material downloaded from NCBI site
charged disorder-promoting residues” [11]. IDPs were includes COG functional classification of proteins.
recently reviewed in a special edition of Chemical Re- Only proteins that were already included in the
views [12] and described in detail in the monograph downloaded material were selected from toxin/anti-
[13]. Since IDPs are a challenge to study experimen- toxin database. In addition, we calculated a number
tally, a number of prediction tools (currently, over 60) of genome and proteome characteristics from the
have been developed [14, 15]. downloaded sequences; these included genome size,
IDPsperformtheirfunctionviatwobasicmechanisms: number of chromosomes, number of plasmids, the
(1) the entropic chain mechanism which is responsible percentage of GC nucleotides, proteome size and
forrapidfluctuationsamongmanyalternative conforma- average protein length.
tions, providing different biological functions to IDPs The dataset included 2554 Bacterial and 139 Archaeal
(such as linkers, spacers, bristles or springs), and (2) by organisms with 2842 chromosomes (2703 in Bacteria,
molecular recognition via short recognition elements, 139 in Archaea) and 2063 plasmids (2040 in Bacteria, 23
that bind to other molecules such as: performed struc- in Archaea). The maximum number of plasmids in a
tural elements, molecular recognition features, or short Bacterial organism is 39, in an Archaeal organism is 2.
linear motifs [16]. Functional classification of proteins The distribution of organisms related to the number of
according to COGs shows that proteins belonging to the plasmids and chromosomes is shown in Table 1. There
Metabolism group (Me) have a lower disorder content are 8,455,194 different proteins – 8,158,660 Bacterial
than proteins in Cellular processes and signaling (Cp) (7,919,866 chromosomal and 238,794 plasmidal) and
and Information storage and processing (Isp) groups 296,534 Archaeal (295,083 chromosomal and 1451 plas-
[17], i.e. the structural disorder is enriched in proteins midal). The Additional file 1: Figure S1 presents the dis-
involved in signaling and regulatory functions and de- tribution of protein number and average length over
pleted inenzymes[18]. subsetsofthematerialinthe dataset.
Mitićetal.BMCBioinformatics (2018) 19:158 Page3of18
Table1Organismsinthedataset
Withoutplasmids Withplasmids
#Organisms #Phyla #Classes Total 1chr >1chr Total 1chr(1pls/>1pls) >1chr(1pls/>1pls)
Total 2693 41 80 1796 1717 79 897 844(434/410) 53(28/25)
Archaea 139 6 17 119 119 0 20 20(17/3) 0
Bacteria 2554 35 63 1677 1598 79 877 824(417/407) 53(28/25)
Thereare12organismswith10ormoreplasmids,withonechromosomeeach,8ofwhichfromthephylumSpirochaetes,onefromthephylumProteobacteria,
andthreefromthephylumFirmicutes.Thereare115Bacterialorganismswith2chromosomesand17Bacterialorganismswith3chromosomes.AllArchaeal
organismshaveexactly1chromosome
Proteins are assigned to COG categories (20 in total), expected that the former will predict a higher disorder
which are further grouped in the COG groups as pro- content than the latter (as is the case in the D2P2 data-
teins participating in Cellular Processes (Cp), Informa- base (http://d2p2.pro/)). The disorder content predicted
tion Storage and Processing (Isp), Metabolism (Me), as by IsUnstruct is between these two. Predictions were
Poorly characterized (Pc) proteins and as proteins Not performed for all 8,455,194 proteins using IUPred-L and
in COGs (N.C.) [3]. Proteins labeled as “unknown” IsUnstruct predictors, whereasVSL2b performed predic-
(COG determined but not cited in the downloaded ma- tions for 8,448,127 proteins (since other protein se-
terial) were added to the N.C. group (7161 Bacterial and quences contain some amino acid tags that VSL2b does
50 Archaeal). The protein distribution according to notrecognize).Haloarchaeanproteomes,duetoadaptive
COG groups and categories is presented in Additional pressure, have specific AA contents, which lead to IDP
file 1: Figures S2 and S3, respectively. The total number prediction errors as revealed by Xue et al. [19, 20], and
of proteins in COG groups are slightly higher than the Syutkinandall[27],andwereaccordinglyexcludedfrom
number of different proteins because there are proteins theanalysis.
that have been assigned to more than one COG group We calculated three measures of protein disorder con-
orcategory. tent in Bacteria and Archaea proteomes in three data
There are 11,564 toxin/antitoxin proteins included in collections: complete genomes, chromosomes and plas-
the dataset. The distribution of toxin/antitoxin proteins mids. The first measure is the averaged fraction of disor-
over COG groups in the subsets (chromosomes and dered AAs by proteins in a proteome (percentage of all
plasmids) is shown in the Table 2 and Additional file 1: predicted disorderedAAsinaproteinandthen averaged
TableS1. by all the proteins in the proteome). The second meas-
ure is the percentage of AAs in long (>30 AA) disor-
Methods dered regions; this was averaged over all of the proteins
Intrinsicallydisorderedproteins in a proteome. The last measure is the percentage of
We could not use data from databases containing pre- proteins (in a proteome) with at least one long disor-
calculated disorder level(such as[22,23])because ofthe dered region. Having calculated the disorder of a prote-
small intersection of protein sets in our material and in ome, disorder of a collection of proteomes (set of
these databases. For example, MobiDB includes only 5% organisms,setofchromosomes,setofplasmids)wascal-
of proteins from our dataset (comparison was done by culated as the average disorder over all the proteomes in
using corresponding UniProt ids). The disorder level for the collection[28,29].
each residue of each protein in our dataset was calcu-
lated using three different disorder predictors: PONDR DisordercontentofdifferentCOGgroups
VSL2b®[24],IsUnstruct[25]andIUpred-L[26]. FunctionalclassificationbyCOGsisthe result of protein
These predictors are widely used and are based on dif- sequence homology, implying their structural and thus
ferent approaches. VSL2b is a combination of neural functionalsimilarity.We chosethe COGfunctionalclas-
network predictors for both short and long disordered sification (among different existing ones) because most
regions. IsUnstruct is based on an approximation of the genomes are COGged and COG annotations are easily
Ising model, a mathematical modelof ferromagnetism in accessible [3]. We extended our previous research on
statistical mechanics, using penalty for changing be- COG-related disorder to three separate data subsets -
tween ordered/disordered states among neighboring complete genomes, chromosomes and plasmids from
amino acids; IUPred-L (long) assigns a disorder score to the superkingdoms Bacteria and Archaea, and COG
an amino acid based on the pairwise interaction energy functionalgroups andcategories (A-Z).Themainreason
score.SincetheVSL2bpredictorpredictswellbothshort for this type of analysis was to determine the sources of
and long disordered regions while the IUPred-L predicts (possible)differentlevels ofdisorder inproteomesofdif-
long disordered regions better than short ones, it is ferent DNA molecules (chromosomes, plasmids) and
Mitićetal.BMCBioinformatics (2018) 19:158 Page4of18
2
Num.ofproteins 126 218 137,096 0 1 1042 1021 1549 4,584,66 86 130 165,363
ofAAs
%d. 4 1 4 1 6 2 9 8 5 9
g.or 34 09 51 23 62 74 68 83 32 96 25
Gs Avdis 15. 37. 12. – 8.8 21. 19. 37. 16. 17. 41. 20.
CO ot.
n pr 6 5 5 5 4 6
Noti Avg.Len. 116.0 88.37 246.8 – 68 234.3 120.5 107.7 289.7 139.3 111.2 256.9
Num.ofproteins 328 243 43,957 1 0 98 3127 1336 738,635 243 106 15,407
d ofAAs
cterize Avg.%disord. 11.476 17.169 10.437 4.575 – 18.528 15.596 32.195 15.268 17.357 39.203 16.074
a
plasmids Poorlychar Avg.prot.Len. 130.14 113.67 290.67 153 – 401.25 144.9 106.33 303.64 136.46 111.05 313.49
d 0
mesan Num.ofproteins 1 1 65,161 0 0 55 114 12 1,435,90 13 0 29,551
o
mos ofAAs
o %d. 4 6 7 1 8 7
hr g.or 98 76 93 90 18 81 16 99 41
verc m Avdis 6.1 0.4 8.7 – – 20. 13. 11. 11. 13. – 11.
psando Metabolis Avg.prot.Len. 242 629 346.28 – – 347.38 210.61 378.66 377.01 256.61 – 386.8
u
o
COGgr ocessing Num.ofproteins 46 47 35,181 0 0 198 217 1493 695,033 19 99 19,305
er pr
sov and ofAAs
protein storage Avg.%disord. 22.849 36.007 13.823 – – 21.676 19.733 36.092 17.496 23.417 43.268 18.968
sified mation prot. 9 6 6 7 7 3
unclas Infor Avg.Len. 99.32 88.57 292.7 – – 449.5 134.4 115.7 329.4 110.5 99.33 326.2
n/toxin- Num.ofproteins 47 10 23,786 0 0 100 566 481 815,045 41 48 17,210
xi
ntito ofAAs
oxin/a cessing Avg.%disord. 26.543 27.634 11.602 – – 17.609 21.259 42.618 17.384 23.104 48.819 17.976
t o
of pr ot.
n ar pr 8 2 9 6
butio Cellul Avg.Len. 86.48 77.8 353.4 – – 482.5 109.2 87.32 388.7 109.3 86.45 399.3
Table2Distri Archaea chromosome toxin antitoxin unclassified plasmid toxin antitoxin unclassified Bacteria chromosome toxin antitoxin unclassified plasmid toxin antitoxin unclassified
Mitićetal.BMCBioinformatics (2018) 19:158 Page5of18
complete genomes, i.e. whether there is an increased (or and different predictors; similar results were obtained
decreased) number of proteins in disorder-abundant for other measures, see Fig. 1). The Student’s t-test for
COGs, or disorder-abundant (or depleted) content of equality of means resulted in a p-value <0.01. The abso-
proteomesingeneral. lute values differed among the predictors and among the
Since a large number of proteins belong to the “Not in measures, but the relationship between the disorder con-
COG” (N.C.) group, we repeated the complete analyses tentinBacteriaandArchaeagenerallyremainedthesame.
for a reduced dataset that consisted of “mostly COGged” Thisrelationshipwasconfirmed bythe highvalues ob-
organisms so as to be able to compare and verify the re- tained for Pearson’s correlation coefficients for different
sults obtained for the whole dataset. We analyzed only measures of disorder and different disorder predictors
those organisms where the total length ofproteinsin the (correlation coefficients ranging from 0.88 to 0.98 for
N.C. group was at most 20% of their total proteome different measures on the same predictors and from 0.74
length. The selected subset includes 4,332,156 proteins. to 0.81 for different predictors and the same measure).
Number of organisms, chromosomes and plasmids in The difference in disorder content in Bacteria and Ar-
thesubsetisshown inAdditionalfile1:TableS2. chaea is not a consequence of different proteome sizes –
we compared Archaea with subset of Bacterial pro-
Statisticalanalysis teomeswithsimilarproteomesizes(upto4000proteins)
All the calculations (average protein length, GC percent, and observed the same difference in disorder content in
etc.) wereperformed on a per- organism bases.The same favorofBacteria (seeFig.2).
alsoholdsfor plasmids and chromosomes.Inorder toin- In further analysis we applied all three predictors and
vestigatethelinear(oratleastmonotonic)relationshipbe- used all three (highly correlated) disorder measures;
tweendifferentphenomena,wecalculatedPearson’slinear however, for clarity, we have presented in the main text
correlation coefficients. The difference in the distribution each result by just one predictor and one measure (we
of the disorder content among different data collections usedthepercentageofAAsinlong(>30AA)disordered
was tested using the Mann-Whitney-Wilcoxon U test of regions, unless otherwise specified), while some results
equality of medians and Student’s t-test of equality of for other predictors and measures are presented in Add-
means. The impact that that different attributes have on itionalfile.
proteindisorderisestimatedbydevelopingadisorderpre-
diction model using IBM InfoSphere Warehouse Intelli- Disordercontentofchromosomesandplasmids
gent Miner. Intelligent Miner is IBM’s commercial data A comparative analysis of the disorder content in proteins
mining software included in InfoSphere® Warehouse encodedbyplasmidsandchromosomeswasperformedfor
whichisa suiteofproductsthatcombinesthestrengthof thefirsttime.ItrevealedthatinbothBacteriaandArchaea
DB2 with a data warehousing infrastructure from IBM® plasmid-encodedproteinscontainconsiderablymoreIDRs
(https://www.ibm.com/). It includes variety of algo- than proteins encoded on chromosomes (Fig. 3 represents
rithms for mining association rules, clustering, classi- these findings for long disorder measure and the IsUn-
fication (prediction), sequential patterns, regression, struct predictor; similar findings for all the threemeasures
and time series. IBM Intelligent Miner can perform and all the three predictors, for different data subsets -
mining functions against traditional relational data- plasmids, chromosomes, genomes with and without plas-
bases or flat files, and is able to work with large mids, are presented in Additional file 1: Figure S4). These
quantity of data that cannot fit into memory. Predic- findingsarestatisticallysignificantaccordingtotheMann-
tion algorithm generates, as a component of predic- Whitney nonparametric test and Student’s t-test (for the
tion model, an estimation of the impact of the input IsUnstruct predictor and the percentage of disordered
components on model, which is in this research used AAs, the p-value <0.00001). Also, the range of IDR con-
to estimate impact of protein characteristics on pro- tent is much larger for plasmid encoded proteins in com-
tein disorder. parison to chromosome encoded ones (0 to 40 and 2 to
17%forplasmidsandchromosomes,respectively).
Results and Discussion Relatively wide range of IDP content was also observed
DisordercontentofBacteriaandArchaea forviralandbacteriophagalproteomes[20].Manyofthem
The results of disorder content analysis in Bacteria and have high IDP content, especially those with increased
Archaea were generally in accordance with our previous proteome size, which is similar to plasmids [20, 30]. In
findings [17] and the results of others (e.g. [22]). For all order to enable replication, viral proteomes have been
three predictors and all three measures, Bacteria exhibit shaped by interactions with the host proteome, i.e. they
significantly more disorder than Archaea (ranging on haveevolvedtomimichostcellularprocessesandtointer-
average from 6.88 to 23.53% for Bacteria and 3.35 to 20. ferewiththem. This is possible due to the highercontent
77% for Archaea, for the percentage of disordered AAs of IDPs [20] because of their special functional attributes,
Mitićetal.BMCBioinformatics (2018) 19:158 Page6of18
Fig.1DisordercontentinArchaeaandBacteria.Disordercontentispredictedusingthreepredictors(IUPred-L,IsUnstructandVSL2b)and
threemeasures
as observed in viral proteins which display a high occur- both Archaea and Bacteria, are presented in Additional file
rence of disorderedsegments, afeaturethatmightendow 1: Figure S5). Similar holds for Archaeal chromosomes and
viral proteins with increased structural flexibility and ef- plasmids, although this trend is less expressed, due to
fective ways to interact with host components [31]. The smaller number of Archaeal genomes, as well as smaller
increaseddisordercontentinplasmidsisthusnotsurpris- range of the corresponding characteristics (proteome size,
ing since both plasmids and phages need to be incorpo- G+Ccontentandespeciallygenomesize).
rated into a living cell and utilize hostmolecular machine Specifically, there is an apparent increase in disorder
inordertoproliferate[32]. contentforG+Ccontentlargerthan50%,thatcanbeex-
plainedbythefactthatahighpercentageofGCincodons
Disordercontentofchromosomesandplasmidsvs. results in an increased presence of disorder promoting
genomeandproteomecharacteristics aminoacids(suchasGly,Ala,Arg,andPro)[17,18].The
OurdetailedanalysisofproteinsencodedbyBacterialchro- relativelyuniformdisordercontent for genomesthathave
mosomes and plasmids revealed a general increase in dis- a G+C contentbetween 30and 50% can beexplained by
order content as a function of genome size, G+C content the selective alteration in the G+C content on third and
andproteomesize,whileaverageproteinlengthexhibitsless first positions in codons, and consequently only a change
obviousrelationshiptodisorderlevel(Fig.4representsthese incodonusageandnotinAAusage.Asitconcernsprote-
findings for G+C content, long disorder measure and the ome size, a larger proteome implies more complex inter-
IsUnstructpredictor;resultsforotherthreecharacteristics - action networks and thus higher disorder content, since
genome size, proteome size and average protein length, for one of the main functions of IDPs is in molecular inter-
thesamedisordermeasureandtheIsUnstructpredictor,for actionandrecognition.
Mitićetal.BMCBioinformatics (2018) 19:158 Page7of18
Fig.2Disordercontentinlong(>30AA)disorderedregionsinBacteriaandArchaeawithsmallproteomes.Thedisordercontentrepresentsthe
percentageofaminoacidsinlongdisorderedregions,predictedbytheIsUnstructpredictor.SinceArchaeaproteomesizeisinrangeof1000to
4000proteins,onlyBacteriainthesamerangeareselected,inordertoemphasizethedifferenceinpredicteddisordercontentbetweenBacteria
andArchaeawithsimilarproteomesizes.Theboxdiagramsinthepaperfollowtheusualrepresentation:1)thehorizontallineinsideabox
representsthemedianvalue(50%ofthesamplesislowerand50%ofthesamplesarehigherthanmedian);2)lowerboxboundrepresentsfirst
quartilevalue(25%ofdataarelowerand75%arehigherthanfirstquartile);3)upperboxboundrepresentsthirdquartilevalue(75%ofdataare
lowerand25%arehigherthanthirdquartile);4)theboxheightrepresentsinterquartilerange(IQR);inthecaseofnormaldistribution,IQR=1.35x
σ;5)thewhiskers(verticallinesaboveandunderthebox)rangesuptothehighestdatumwithin1.5xIQRoftheupperquartileanddownto
thelowestdatumwithin1.5×IQRofthelowerquartile;6)thedotsabovethetopwhiskerandunderthebottomwhiskerrepresentoutliers,i.e.
thesamplesthatareoutoftherange(insomeofthediagramseachsampleisrepresentedasadot,andoutliersarenotspecificallyhighlighted,
becauseitisobviouswhichsampleslayoutofthewhiskersrange);7)insomeofthediagramsthereddotrepresentsthemeanvalue
Correlation analysis shows a statistically significant model for prediction percentage of protein disorder
positive linear correlation between disorder content of based on the specified organism characteristics. Predic-
Bacterial chromosome and plasmid proteomes and each tion is obtained by using the IBM Intelligent Miner tool
of the genome/proteome characteristics - G+C content, which identifies the characteristics having the highest
proteome and genome size and average protein length, impact on the prediction model. Figure 6 graphically
except for average protein length of plasmids. Archaeal represents impact of specific characteristics used in the
chromosomal proteomes exhibit statistically significant model for predicting percentage of protein disorder. The
correlation between disorder content and G+C content, results show that the COG classification has the highest
genome and proteome size. Archaeal plasmids (the sam- impact on disorder content, even higher than G+C
plebeingrathersmall)donotexhibitanysignificantcor- content.
relations with genome/proteome characteristics except If we consider the chromosome- and plasmid-encoded
forG+Ccontent(seeTable3). proteins separately with respect to COG groups, then the
overall increased level of disorder in plasmid-encoded
DisordercontentindifferentCOGgroupsin proteinscouldhavetwodifferentcauses:
chromosomesandplasmids
Our analysis showed that in both Bacteria and Archaea (a) because plasmidsareabundantinproteins inCOG
complete proteomes the Metabolism (Me) COG group functional groupswithhigher disorder,or
of proteins has the lowest disorder content among all (b) because thedisorderlevelperprotein ishigher in
COG groups, while Not in COGs (N.C.) and Poorly plasmid proteins thaninchromosomeproteins in
characterized (Pc)are abundant in IDR content. Figure 5 thesame COGgroups.
presents the overall long-disorder level per COG groups
of proteins in Archaea and Bacteria, obtained by the Theobtainedresultsshow that:
IsUnstruct predictor. Additional file 1: Figure S6 repre-
sentsthe correspondingdata forallthethreemeasures. (a) Plasmidsare notabundantinproteinsclassifiedin
Impact of different protein characteristics (super king- COGgroupswithhigherdisorder,except forthe
dom, chromosome/plasmid, COG group, toxin type) on NotinCOGs (N.C.)group(69%inplasmidalvs
protein disorder is represented through a data mining 56%inchromosomal proteins), asshown inFig. 7.
Mitićetal.BMCBioinformatics (2018) 19:158 Page8of18
Fig.3Disordercontentinlong(>30AA)disorderedregionsinBacteriaandArchaeapergenelocation.Thedisordercontentrepresentsthe
percentageofaminoacidsinlongdisorderedregions,predictedbytheIsUnstructpredictor.Theproteomesaredividedinproteinsetsencoded
bychromosome/plasmidDNA.Theoverallorganismsdisordercontentisalmostthesameasinthechorosome-encodedproteomesubset
Fig.4Disordercontentinlong(>30AA)disorderedregionsinBacteriabygenelocation,asafunctionofG+Ccontent.Disorderispredictedby
theIsUnstructpredictor
Mitićetal.BMCBioinformatics (2018) 19:158 Page9of18
Table3Statisticalcorrelationbetweenpredicteddisordercontentandorganismcharacteristics
Bacteria Archaea
Complete Seg1 Seg2 Seg3 Seg4 Complete Seg1 Seg2 Seg3 Seg4
Chromosomes Avg. Correlationcoef. 0.1042 −0.1278 −0.0714 0.1220 0.2643 0.1480 −0.3819 0.3125 0.1829 –
protein
Samplesize 2554 40 921 1504 89 139 6 124 9 0
len.
SignificanceofCC <0.0001 0.4319 0.0303 <0.0001 0.0123 0.0821 0.4550 0.0004 0.6376 –
G+C Correlationcoef. 0.6060 0.3054 0.2793 0.2741 0.3052 0.2667 −1.000 0.0653 0.1818 0.7369
content
Samplesize 2554 151 1043 756 604 139 2 77 54 6
SignificanceofCC <0.0001 0.0001 <0.0001 <0.0001 <0.0001 0.0015 – 0.5726 0.1883 0.0947
Proteome Correlationcoef. 0.2950 0.1345 0.0689 0.3442 0.2377 0.2978 0.0817 0.5330 – –
size
Samplesize 2554 1128 1118 276 32 139 115 24 0 0
SignificanceofCC <0.0001 <0.0001 0.0212 <0.0001 0.1902 0.0004 0.3854 0.0073 – –
Genome Correlationcoef. 0.3019 0.1592 0.1562 0.1159 0.8357 0.3585 0.3341 −0.8534 – –
size
Samplesize 2554 1469 995 87 3 139 136 3 0 0
SignificanceofCC <0.0001 <0.0001 <0.0001 0.2851 – <0.0001 <0.0001 – – –
Plasmids Avg. Correlationcoef. −0.0570 0.7456 0.0207 −0.1596 0.2914 0.0408 / −0.0671 −1.0000 /
rotein
Samplesize 877 4 371 491 11 20 1 17 2 0
len.
SignificanceofCC 0.0916 – 0.6911 0.0004 0.3846 0.8644 – 0.7980 – –
G+C Correlationcoef. 0.3324 0.4513 0.0693 0.0844 0.3494 0.5399 0.5155 0.0494 −0.6586 /
content
Samplesize 877 123 319 230 205 20 6 8 5 1
SignificanceofCC <0.0001 <0.0001 0.2171 0.2022 <0.0001 0.0140 0.2952 0.9075 – –
Proteome Correlationcoef. 0.1976 0.4958 0.0008 −0.1792 0.4609 0.0863 0.0866 0.1977 / /
size
Samplesize 877 215 392 238 32 20 13 7 0 0
SignificanceofCC <0.0001 <0.0001 0.9874 0.0056 0.0079 0.7175 0.7785 0.6709 – –
Genome Correlationcoef. 0.2048 0.4079 0.0518 0.1335 0.5414 0.0645 −0.1670 −0.9999 / /
size
Samplesize 877 259 460 137 21 20 17 3 0 0
SignificanceofCC <0.0001 <0.0001 0.2676 0.1199 0.0113 0.7870 0.5218 – – –
Thetablerepresentsthestatisticalcorrelationbetweenpredicteddisordercontentanddifferentorganismcharacteristics.Thedisordercontentispredictedusing
IsUnstructpredictorandmeasuredasapercentageofaminoacidsinlongdisorderedregions(>=30AA)
Foreachsampleset(Archaeal/Bacteralchromosomes,plasmids)andeachoftheobservedcharacteristics,thesamplesareadditionallyclassifiedin4segments
(quarters)byrangeoftheobservedcharacteristics.Correlationsarecomputedforthewholesampleandadditionallyforeachofthesegments,tofindoutifthe
correlationisstrongerforsomesegment(quarter)ofthecharacteristics’range.Thesignificantcorrelationsareemphasizedinboldface
Additionalfile1:FigureS7presents thedistribution Disorder content of Bacterial and Archaeal COG groups
ofproteins perCOG groupsinmore detail. andcategoriesrevealssimilardistribution,however,dueto
(b) Plasmidproteinsaremoredisorderedthan significantly smaller protein sample of Archaea they will
chromosomalproteinsintheN.C.group,asalso notbediscussedfurther,exceptfortheN.C.groupofpro-
showninFig.7fortheIsUnstructpredictorand teins. According to ACLAME database [2] on plasmid
percentageofdisorderedAA(thecorresponding encoded proteins, main functional categories found on
resultsforotherpredictorsandmeasuresare plasmids belong to Isp and Cp COG groups, almost twice
presentedinAdditionalfile1:FigureS8).Theresultis as many proteins as in functional categories in Me COG
statisticallysignificant(Student’st-test,pvalue<0.05). group. This may suggest the functions of N.C. group pro-
teinsinourdataset.
Plasmids encode for a small number of proteins in all Further analysis of proteins not categorized according
theCOGgroupsandcategories,exceptinN.C.group.IDR to COGs (N.C. group) inchromosomes and plasmids re-
contentinplasmidencodedproteinsishigherorsimilaras vealed that:
in chromosome encoded proteins for all COG categories
(see Fig. 8 for percentage of disordered AA per COG cat- 1. InBacteriaand Archaea,proteins belongingtoN.C.
egoriesinBacteria;similardataforothermeasuresandfor grouparemost abundantamongbothchromosome
Archaea are presented in Additional file 1: Figure S9). andplasmidencodedproteins, aspresentedin
Mitićetal.BMCBioinformatics (2018) 19:158 Page10of18
Fig.5Disordercontentinlong(>30AA)disorderedregionsfordifferentclustersoforthologousgroupsofproteins(COGgroups)inArchaeaand
Bacteria.DisorderispredictedbytheIsUnstructpredictor.COGgroupsare:Cp–CellularProcesses,Isp–InformationStorageandProcessing,Me
–Metabolic,N.C.–NotinCOG,Pc–Poorlycharacterized.Theboxdiagramsinthepaperfollowtheusualrepresentation(seeFig.2caption
fordetails)
proteindistributionaccordingtoCOGgroupsand chromosomeencoded and plasmid encoded
categoriesforBacteriainFig.9(seeAdditionalfile1: proteins.ThemajorityofN.C. proteins from
FigureS3forArchaeaanddetaileddata). Bacterialplasmids andboth Archaeal plasmidsand
2. Theaveragelength ofproteins intheN.C. groupis chromosomes,are hypothetical.Thefraction of
lowerincomparisontoother COG groups,forboth hypotheticalproteins encoded byBacterial
Fig.6Impactoftheattributesondisordercontent,VariableCOGdenotesaCOGgroupofagene/protein(similarlyforGC,Superkingdom.Toxintype,
Chromosome/plasmid).Barsizesdenotelevelofimpactofeachcharacteristicsonproteindisorder.“Importance”onthediagramactuallymeans
impact.ThehighestimpactonthepercentageofproteindisorderhasCOGgroup(N.C.,Cp,Isp,Pc,Me)theproteinbelongsto(52.25%),thenthe
percentageofGCnucleotides(38.60%),whileimpactofothercharacteristicsisconsiderablylower(Superkingdom-5.78%,Chromosome/plasmid-
2.96%iToxintype-0.41%)