Open Access

Genome-wide subcellular localization of putative outer membrane and extracellular proteins in Leptospira interrogans serovar Lai genome using bioinformatics approaches

  • Wasna Viratyosin1Email author,
  • Supawadee Ingsriswang1,
  • Eakasit Pacharawongsakda1 and
  • Prasit Palittapongarnpim1, 2
BMC Genomics20089:181

DOI: 10.1186/1471-2164-9-181

Received: 04 December 2007

Accepted: 21 April 2008

Published: 21 April 2008

Abstract

Background

In bacterial pathogens, both cell surface-exposed outer membrane proteins and proteins secreted into the extracellular environment play crucial roles in host-pathogen interaction and pathogenesis. Considerable efforts have been made to identify outer membrane (OM) and extracellular (EX) proteins produced by Leptospira interrogans, which may be used as novel targets for the development of infection markers and leptospirosis vaccines.

Result

In this study we used a novel computational framework based on combined prediction methods with deduction concept to identify putative OM and EX proteins encoded by the Leptospira interrogans genome. The framework consists of the following steps: (1) identifying proteins homologous to known proteins in subcellular localization databases derived from the "consensus vote" of computational predictions, (2) incorporating homology based search and structural information to enhance gene annotation and functional identification to infer the specific structural characters and localizations, and (3) developing a specific classifier for cytoplasmic proteins (CP) and cytoplasmic membrane proteins (CM) using Linear discriminant analysis (LDA). We have identified 114 putative EX and 63 putative OM proteins, of which 41% are conserved or hypothetical proteins containing sequence and/or protein folding structures similar to those of known EX and OM proteins.

Conclusion

Overall results derived from the combined computational analysis correlate with the available experimental evidence. This is the most extensive in silico protein subcellular localization identification to date for Leptospira interrogans serovar Lai genome that may be useful in protein annotation, discovery of novel genes and understanding the biology of Leptospira.

Background

Leptospirosis is a globally widespread zoonosis caused by the animal spirochete pathogen Leptospira interrogans [1]. The clinical feature of its severe disease form, known as Weil's syndrome, or acute renal failure, is associated with multiple system complications, including renal failure, meningitis, and pulmonary haemorrhage. Although early treatment for leptospirosis is important for ensuring a favorable clinical outcome, this is often difficult to achieve, as symptoms during the early stages of infection resemble those of several other systematic diseases.

One potential method for controlling the spread of leptospirosis is through the development of vaccines. Candidates for vaccine production include outer membrane (OM) and extracellular (EX) proteins, several of which have been implicated in chemotaxis, adherence and other pathogenic steps. Attempts to identify such proteins have been performed previously by experimental [214] and computational methods [1520]. Complete genome sequences of two serovars, Lai and Copenhageni of L. interrogans have been reported [1517]. Hundreds of putative membrane proteins and lipoproteins were predicted, although in many cases, gene annotation may be incomplete or inaccurate to reliably identify putative vaccine candidates.

Previous studies have tried to identify potential vaccine candidates using experimental methods and in silico predictions. Proteomic analysis of purified outer membrane vesicles (OMVs) of L. interrogans serovar Copenhageni was performed by Nally et al. and revealed 33 intact OM proteins [13]. The study by Gamberini et al. [18] showed 16 predicted surface exposed lipoproteins of L. interrogans serovar Copenhageni via whole genome analysis, only four of which are conserved among 8 pathogenic serovars. Since leptospiral lipoproteins are usually (but not exclusively) surface exposed proteins, and many are vaccine candidates, Setubal et al. [19] focused on lipoprotein prediction using spirochaetal lipoprotein (SpLip) program and identified 146 predicted lipoproteins (but not their localizations) for L. interrogans serovar Lai. The search for new potential vaccine candidates was continued by Yang et al. [20], who used a filtering approach combining in silico analysis, comparative genome hybridization, and microarray methods to identify 226 leptospiral surface exposed proteins. All of the previous studies summarized above focus on identification of vaccine candidates.

However, both computational and experimental have their own drawbacks [21, 22] Computational methods, for instance, depend on the presence of type I signal peptides [23, 24], transmembrane helices [2426], or other particular features specifically found in previously identified membrane proteins, which may not be highly specific or sensitive. Experimental methods, on the other hand, yield results that may be complicated by cross-compartment contamination occurring during the preparation of samples, which can also result in the inclusion of false positive results in data sets [21, 22]. Hence, results obtained from both methods can occasionally lead to conflicting conclusions. We believe that such a focused approach without attempt to accurately identify periplasmic proteins (PP) and cytoplasmic membrane (CM) proteins can lead to erroneous identification of PP and CM as OM or EX by both in silico and experimental approaches. A holistic prediction of all membrane protein localizations will lead to better accuracy in genome annotation of membrane proteins, including vaccine candidates.

In this study we utilized a combination of three computational prediction tools PSORTb [27, 28], Proteome Analyst (PA) [29], and ProtCompB [30] to perform whole genome analysis of protein subcellular localization, and to identify novel putative L. interrogans serovar Lai OM and EX vaccine candidates. We combined the results derived from these three prediction algorithms into a consensus vote, resulting in a more accurate protein subcellular localization prediction. Furthermore, we incorporated homology searching against the DBSubloc database [31] and structural information from the GTD prediction [32] to enhance genome annotation, and to infer OM, EX and PP localized proteins. We also developed a specific classifier based on Linear Discriminant Analysis (LDA) for identification of leptospiral cytoplasmic proteins (CP) and cytoplasmic membrane proteins (CM), using a training set obtained from the consensus vote. We were able to assign subcellular localizations to several previously uncharacterized hypothetical proteins, thus improving L interrogans genome annotation.

Results

We performed the subcellular localization prediction of L. interrogans serovar Lai using the pipeline described in the Material and methods section (shown in Figure 1), following the steps of training set verification, consensus vote, homology and structural prediction, and finally LDA-based classification.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-181/MediaObjects/12864_2007_Article_1374_Fig1_HTML.jpg
Figure 1

Flow chart of the method used for subcellular localizations of Leptospira interrogans serovar Lai genome. Protein sequences of Leptospira interrogans serovar Lai genome (4,727 ORFs) were analyzed for subcellular localization using PSORTb, ProtCompB, and Proteome analyst (PA) prediction. (a) The consensus vote was obtained from the majority vote type procedure to obtain the result with high prediction accuracy. If all 3 methods agree for localization it was assigned as a consensus vote. The remaining (1 or 2 out of 3 predicted result) was assigned as non-consensus vote. The consensus vote of CP and CM was used as a training set for the development of an LDA-based classifier for CP and CM in the next step. (b) The non-consensus vote results of OM, PP, and EX were further analyzed for sequence and structure homology by DBsubloc and GTD prediction. The non-consensus vote of EX, OM, and PP with significant homology or/and structure information were identified by DBsubloc and GTD prediction. (c) Non-consensus votes of CP, CM and the non predicted data from DBsubloc and GTD predictions were further analyzed for subcellular localization using LDA-based classifier for CP and CM. Significantly predicted results were proteins classified with more than 0.90 probability for CP and CM proteins. The remaining queries that could not be identified in this step were classified as "unknown" results.

Training set verification: Localization predictions of a set of experimentally verified proteins with known localization

To evaluate the robustness and versatility of our protein localization procedure, we used a set of well- characterized Gram-negative bacterial proteins with experimentally verified localizations taken from the work by Gardy and Brinkman [22] as a test set. The data set comprising 299 proteins was first analyzed by using PSORTb, PA, and ProtCompB. We found that, individually, PSORTb, PA, and ProtCompB assigned 73%, 71% and 79% of the verified protein localizations respectively (recall rate in Table 1). The overall precision rates were 97%, 95 and 83%, respectively. As expected, the overall recall rate was highest for ProtCompB, while its precision rate was also the lowest. The recall rate based on "consensus vote" (see materials and methods) results derived from all three methods was 48% without any false positives. Relaxing the criteria by considering predicted results of any two methods or the "majority vote" resulted in an overall recall rate of 77% with a single false positive.
Table 1

Localization predictions of a set of 299 experimentally verified proteins with known localization

Actual localization

Total

TP

FP

FN

TN

Precision

Recall

PSORTb

       

CP

145

110

1

35

110

99.10%

75.86%

CM

69

55

2

14

166

96.49%

79.71%

PP

29

18

0

11

207

100.00%

62.07%

OM

38

30

0

8

195

100.00%

78.95%

EX

18

6

3

12

216

66.67%

33.33%

Total

299

219

6

80

894

97.33%

73.24%

Proteome Analyst

       

CP

145

94

0

51

119

100.00%

64.83%

CM

69

59

2

10

162

96.72%

85.51%

PP

29

19

3

10

201

86.36%

65.52%

OM

38

31

0

7

192

100.00%

81.58%

EX

18

9

6

9

207

60.00%

50.00%

Total

299

212

11

87

881

95.07%

70.90%

ProtCompB

       

CP

145

127

11

18

144

92.03%

87.59%

CM

69

55

7

14

227

88.71%

79.71%

PP

29

19

9

10

261

67.86%

65.52%

OM

38

23

18

15

243

56.10%

60.53%

EX

18

11

4

7

277

73.33%

61.11%

Total

299

235

49

64

1152

82.75%

78.60%

Consensus vote

       

CP

145

67

0

78

154

100.00%

46.21%

CM

69

43

0

26

230

100.00%

62.32%

PP

29

11

0

18

270

100.00%

37.93%

OM

38

19

0

19

261

100.00%

50.00%

EX

18

4

0

13

216

100.00%

23.53%

Total

299

144

0

154

1131

100.00%

48.32%

Majority vote (2 out of 3 predictions)

       

CP

145

121

0

24

154

100.00%

83.45%

CM

69

59

0

10

230

100.00%

85.51%

PP

29

17

0

12

270

100.00%

58.62%

OM

38

29

0

10

213

100.00%

74.36%

EX

18

6

1

12

215

85.71%

33.33%

Total

299

232

1

68

1082

99.57%

77.33%

Combination method

       

CP

N/A

N/A

N/A

N/A

N/A

N/A

N/A

CM

N/A

N/A

N/A

N/A

N/A

N/A

N/A

PP

29

25

0

4

56

100.00%

86.20%

OM

38

34

1

4

46

97.14%

89.47%

EX

18

12

2

6

65

85.71%

66.67%

Total

85

71

3

14

167

95.95%

87.53%

299 proteins obtained from the test set used in comparison study by Gardy and Brinkman [22] Majority vote is the result from 2 out of 3 predictions. Combination method: the result from non-consensus vote with significant DBsubloc [31] and/or GTD prediction [32] Precision is calculated as TP/(TP+FP), Recall is calculated as TP/(TP+FN) TP = true positive, TN = true negative, FP = false positive, FN = false negative, N/A= Not applicable

Since the number of outputs for EX and OM proteins agreed by all three predictions was low (low recall rate), we used structure-based homology information from GTD and/or homology search results from DBSubloc prediction as the additional information for inferring protein localization. Using this information, we assessed the likelihood of the "non-consensus vote" outputs (see material and methods) for being EX or OM proteins. When the information from DBSubloc and GTD predictions were also used, the overall recall rates for the EX, OM and PP increased to 67%, 89% and 86% respectively as shown in Table 1. The method resulted in 96% precision. This performance was much better than any of the three individual methods, or any of the above combinations. Therefore, we have shown that the combination of prediction tools, DBSubloc homology search and GTD structural-based prediction markedly improved the accuracy and recall for EX, OM and PP protein localization prediction. Therefore, our prediction pipeline is applicable for subcellular localization prediction of hypothetical, or unknown proteins.

Subcellular localization predictions of L. interrogans: Step 1 Consensus votes

After demonstration of the accuracy of our pipeline prediction with the training set, the whole predicted proteome of L. interrogans serovar Lai was analyzed using three computational predictions for protein subcellular localization: PSORTb, ProtCompB, and Proteome analyst (PA). The results obtained from each prediction program are shown in Table 2. ProtCompB assigned subcellular localizations to all protein queries whereas approximately 50% of protein queries were assigned as unknown localization by PSORTb and PA.
Table 2

Predicted protein subcellular localizations of L. interrogans by PSORb, PA, ProtCompB and consensus vote predictions.

Localization

Subcellular localization prediction

 

PSORTb

PA

ProtCompB

Consensus vote

Cytoplasm (CP)

1125

921

2013

418

Cytoplasmic membrane (CM)

606

715

1726*

332

Outer membrane (OM)

112

28

 

15

Periplasmic (PP)

30

86

478

17

Extracellular (EX)

29

326

510

15

Unknown

2825

2652

-

3930

* Note that ProtCompB prediction in this version, CM and OM were predicted as membrane proteins.

After inspection of the prediction results derived from the three prediction algorithms, it was found that 797 out of 4,727 ORFs of L. interrogans serovar Lai genome had the following consensus vote predicted localizations: 418 cytoplasmic proteins (CP), 332 cytoplasmic membrane proteins (CM), 17 periplasmic proteins (PP), 15 outer membrane proteins (OM), and 15 extracellular/secreted proteins (EX) (Table 2, 3, 4 Additional file 1, 2, 3). The biological functions of most of the localized proteins are already annotated. Only about 9% (68 of 797 ORFs) were proteins annotated as conserved hypothetical or unknown proteins. This shows that the consensus vote approach has a high accuracy of subcellular localization prediction for L. interrogans. However, this recall of these methods is unacceptably low, since the localization of the majority of proteins remains unknown (3930 out of 4727 proteins).
Table 3

Putative extracellular proteins (EX) predicted by the consensus vote

Lai Locus

Copen Locus

Protein annotation

LA3731

LIC10497

Fmh-like protein/hypothetical protein

LA0587

LIC12988

Lactonizing lipase/lipase

LA0872

LIC12760

Microbial collagenase

LA1450

LIC12302

Probable O-sialoglycoprotein endopeptidase

LA2448

LIC10830

Putative outermembrane protein/putative lipoprotein

LA1765

LIC12047

Rhs family protein/cytoplasmic membrane protein

LA4161

LIC13320

Thermolysin/thermolysin precursor

LA4164

LIC13321

Thermolysin/thermolysin homolog precursor

LA2303

LIC11634

3-oxoacyl- [acyl-carrier protein] reductase/CsgA

LA0873

LIC12759

LRR containing protein/cytoplasmic membrane protein

LA2964

LIC11098

LRR containing protein/conserved hypothetical protein

LA3028

LIC11051

LRR containing protein/conserved hypothetical protein

LA3320

LIC10831

LRR containing protein/conserved hypothetical protein

LA3323

LIC10829

LRR containing protein/conserved hypothetical protein

LA0709

LIC12896

Unknown protein/conserved hypothetical protein

Note LRR: Leucine-rich repeat

Table 4

Putative outer membrane proteins (OM) predicted by the consensus vote

Lai Locus

Copen Locus

Protein annotation

LA2375

LIC11570

General secretory pathway protein D

LA3149

LIC10964

Hemin receptor/TonB-dependent outer membrane hemin receptor

LB328

LIC20250

Outer membrane protein OmpA/PG-associated CM protein

LA3615

LIC10592

Outer membrane protein OmpA family/PG-associated CM protein

LA1963

LIC11941

Outer membrane protein precursor CzcC/heavy metal efflux pump

LA3927

LIC13135

Outer membrane protein tolC precursor/outer membrane protein

LA1356

LIC12374

Probable TonB-dependent receptor

LA2641

LIC11345

Probable TonB-dependent receptor/ferrichrome-iron receptor

LA3468

LIC10714

Probable TonB-dependent receptor/outer membrane receptor protein

LB191

LIC20151

Putative TonB-dependent outer membrane receptor protein (Hbp A)

LA2510

LIC11458

Conserved hypothetical protein/outer membrane protein, porin superfamily

LA4337

LIC13479

Conserved hypothetical protein/PG-associated CM protein

LA0572

LIC12998

Conserved hypothetical protein/TonB-dependent outer membrane receptor

LA3258

LIC10881

Hypothetical protein/outer membrane protein, TonB dependent

LA2186

LIC11739

Conserved hypothetical protein

Lai locus: L. interrogans serovar Lai locus

Copen locus: L. interrogans serovar Copenhageni locus

When comparing the concordance or prediction agreement rates between the three prediction methods (excluding proteins with unknown localization by one or two programs), the rates for PSORTb and PA, PSORTb and ProtCompB, and PA and ProtCompB were 70.3%, 80%, and 59.5%, respectively. PSORTb was found to have a strong propensity to assign protein queries to CP and OM proteins, while PA was found to assign preferentially to CM, PP and EX proteins (p < 0.001, chi-square tests).

Step 2: Homology-based and protein folding recognition predictions for non-consensus vote localizations

The non-consensus vote OM, EX, and PP proteins were further analyzed for localizations using DBsubloc, and GTD. As presented in Table 5, 6, 99 more proteins (43 out of 83 proteins predicted by two previous methods and 56 out of 617 proteins predicted by one previous method) were additionally identified as putative EX, while 48 proteins (23 out of 59 proteins predicted by two methods, and 25 from 980 proteins predicted by one method) were additionally identified as putative OM proteins as shown in Table 7, 8. Moreover, 58 proteins (20 out of 20 proteins predicted by two methods and 38 out of 504 proteins predicted by one method) were additionally predicted as PP proteins (Additional file 1). It is of interest that several protein loci currently annotated as hypothetical proteins without localization information were predicted in EX, OM and PP compartments by the combination method (Tables 3, 4, 5, 6, 7, 8, 9 and Additional file 1). The homology search and structural information from DBSubloc and GTD thus allowed further identification of EX, OM, and PP from the non-consensus vote set, however, 3725 protein localizations remain unknown.
Table 5

43 Putative extracellular proteins (EX) derived from the 2 out of 3 predictions with significant DBSubloc or/and GTD prediction

Lai Locus

Copen Locus

Protein annotation

SWISS-PROTa

PDB Codeb

LA1027

LIC12632

Sphingomyelinase C precursor (Sph1)/hemolysin

-

1bix

LA1029

LIC12631

Sphingomyelinase C precursor (Sph2)/hemolysin

-

1bix

LA4004

LIC13198

Sphingomyelinase C precursor hemolysin (Sph3)/sph- like

-

1bix

LA3540

LIC10657

Sphingomyelinase C precursor; hemolysin

-

1bix

LA3050

LIC11040

Hemolytic protein-like protein/hemolysin (sph4)

-

1aq0

LA3466

LIC10715

Thermolysin

P43133

1hyt

LA3454

LIC10723

Flagellar hook-associated protein(fliD)

Q9KWW7

1osp

LA3097

LIC11003

Treponemal membrane protein B precursor-like protein/LipL71

P19649

1l8w

LA1530

LIC12234

LRR containing protein

Q9RBS2

1d0b

LA1324

LIC12401

LRR containing protein/cytoplasmic membrane protein

-

1ogq

LA1354

LIC12375

LRR containing protein/cytoplasmic membrane protein

Q9RBS2

1ogq

LA2452

LIC11504

LRR containing protein/cytoplasmic membrane protein

Q9RBS2

1ogq

LA2862

LIC11180

LRR containing protein/cytoplasmic membrane protein

Q9RBS2

1ogq

LA2966

LIC11097

LRR containing protein/cytoplasmic membrane protein

Q9RBS2

1ogq

LA3324

LIC10831

LRR containing protein/conserved hypothetical protein

Q9RBS2

1ogq

LA3321

LIC10830

LRR containing protein/putative lipoprotein

Q9RBS2

1ogq

LA3322

LIC10830

LRR containing protein/putative lipoprotein

Q9RBS2

1ogq

LA0701

LIC12901

LRR containing protein/molybdate metabolism regulator

Q9RBS2

1ogq

LA2377

LIC11568

Peptidase, M23/M37/membrane associated peptidase

P24204

1acc

LA0505

LIC13050

Probable glycosyl hydrolase/conserved hypothetical protein

-

1f00

LA3725

LIC10502

Probable phenazine biosynthesis family protein/CM protein

-

1air

LA3730

LIC10498

Putative lipoprotein

P15921

1rmg

LA1368

LIC12364

Putative outer membrane protein/CagA

P47460

-

LA1759

LIC12050

Putative outer membrane protein/conserved hypothetical protein

Q52657

1czf

LA2443

LIC11507

Putative outer membrane protein/conserved hypothetical protein

Q9RBS2

1ogq

LA2447

LIC11505

Putative outer membrane protein/conserved hypothetical protein

Q9RBS2

1jl5

LA2450

LIC11505

Putative outer membrane protein/conserved hypothetical protein

Q9RBS2

1ogq

LA1915

LIC11990

TPR-repeat-containing proteins/cytoplasmic membrane protein

P80544

1qqe

LA0043

LIC10038

TPR-repeat-containing proteins/conserved hypothetical protein

Q9KQ40

1qqe

LA2773

LIC11246

Conserved hypothetical protein

Q06852

1l8w

LA3233

LIC10902

Conserved hypothetical protein

O83497

1qcx

LB001

LIC20001

Conserved hypothetical protein

-

1eut

LA1499

LIC12259

Conserved hypothetical protein/cytoplasmic membrane protein

P35825

1dab

LA1766

LIC12047

Conserved hypothetical protein/cytoplasmic membrane protein

Q07833

1czf

LA3333

LIC10825

Conserved hypothetical protein/cytoplasmic membrane protein

Q07833

1acc

LA2208

LIC11720

Conserved hypothetical protein/hypothetical protein

-

1e15

LA3276c

LIC10868

Conserved hypothetical protein/hypothetical protein

P15345

1dab

LA0022

LIC10021

Conserved hypothetical protein/putative lipoprotein

-

1dab

LA3210

LIC10920

Conserved hypothetical protein/putative lipoprotein

-

1rmg

LA3726

LIC10501

Conserved hypothetical protein/putative lipoprotein

Q9PJY2

1acc

LB216

LIC20172

Conserved hypothetical protein/putative lipoprotein

-

1wxr

LB225

LIC20176

Conserved hypothetical protein/putative lipoprotein

-

1wxr

LA4135d

LIC13296

hypothetical protein/putative lipoprotein

-

1koe

Note LRR: Leucine rich repeat

a: Swiss-Prot ID derived from DBsubloc database

b: PDB code derived from GTD prediction

c: Pfam: PF06739: SBBP (Seven Beta Blade Propeller domain)

d: pfam07588: DUF1554

Table 6

56 Putative extracellular proteins (EX) derived from the 1 out of 3 predictions with significant DBSubloc or/and GTD prediction

Lai Locus

Copen locus

Protein annotation

SWISS-PROTa

PDB codeb

LB258

LIC20197

Cysteine protease

-

1deu

LA0975

LIC12680

Fimh-like protein

-

1a6c

LA0858

LIC12930

Fimh-like protein/hypothetical protein

-

1dab

LA0492

LIC13060

LipL36 protein

-

1acc

LA3469

LIC10713

Iron-reglulated protein A/LruB/putative lipoprotein

-

1rmg

LA3075

LIC10464

Surface protein Lk90-like protein/Ig-like repeat domain

P35828

1dab

LA3778

LIC10464

Surface protein Lk90-like protein/Ig-like repeat domain

Q52657

1dbg

LA0378

LIC10325

TPR-repeat-containing proteins/hemolysin

Q98KC1

1a17

LA3138

LIC10973

Transmembrane outer membrane protein L1

-

1acc

LA1353

LIC12375

LRR containing protein

Q9RBS2

1jl5

LB196

LIC20154

LRR containing protein/lipoprotein

-

1d0b

LA0416e

LIC10365

Putative lipoprotein (LpL effector)

-

1gq8

LA0962d

LIC12690

Putative lipoprotein

-

1eut/1koe

LA1569c

LIC12208

Putative lipoprotein

P15345

1acc

LA2823e

LIC11207

Putative lipoprotein

-

1gq8

LA3064e

LIC11030

Putative lipoprotein

-

1czf

LA3848c

LIC13075

Putative lipoprotein

-

1qjv

LA3867

LIC13086

Putative lipoprotein

-

1cwv

LA1159

LIC12525

Putative outer membrane protein/putative lipoprotein

-

1cs6

LA1905

LIC11996

Putative outer rmembrane protein/hypothetical protein

-

1kit

LA1939

LIC11966

Putative outer membrane protein/hypothetical protein

-

1fio

LA2273

LIC11665

Putative outer membrane protein/hypothetical protein

 

1air

LA0563d

LIC13006

Hypothetical protein/putative lipoprotein (LenC)

-

1koe

LA0695d

LIC12906

Hypothetical protein/putative lipoprotein (LenA/LfhA/Lsa24)

-

1koe

LA1433d

LIC12315

Hypothetical protein/putative lipoprotein (LenD)

-

1koe

LA3103d

LIC10997

Hypothetical protein (LenB)

-

1koe

LA4073d

LIC13248

Hypothetical protein/putative lipoprotein (LenF)

-

1koe

LA4324d

LIC13467

Hypothetical protein/conserved hypothetical protein (LenE)

-

1koe

LA3370

LIC10793

Conserved hypothetical protein/surface antigen (Lp24)

-

1l0q

LA0965

LIC12676

Conserved hypothetical protein

P25156

1d0b

LA1066

LIC12601

Conserved hypothetical protein

-

1dbg

LA1498

LIC12260

Conserved hypothetical protein

-

1ogq

LA2811

LIC11217

Conserved hypothetical protein

P25146

1ogq

LA3734

LIC10495

Conserved hypothetical protein/CM protein

-

1dab

LA3834c

LIC13066

Conserved hypothetical protein

P15345

1acc

LA4227

LIC13381

Conserved hypothetical protein

-

1sli

LA0663

LIC12930

Conserved hypothetical protein/hypothetical protein

-

1acc

LA0423c

LIC10371

Conserved hypothetical protein/putative lipoprotein

P15345

1qjv

LA1567c

LIC12209

Conserved hypothetical protein/putative lipoprotein

P15345

1czf

LA1568c

LIC12209

Conserved hypothetical protein/putative lipoprotein

P15345

1czf/1dbg

LA1691c

LIC12099

Conserved hypothetical protein/putative lipoprotein

-

1acc

LA3340e

LIC10821

Conserved hypothetical protein/putative lipoprotein

-

1ee6

LA3394e

LIC10774

Conserved hypothetical protein/putative lipoprotein

-

1gq8

LA3501

LIC10686

Conserved hypothetical protein/putative lipoprotein

-

1air

LA0283c

LIC10239

Hypothetical protein

-

1air

LA0426c

LIC10373

Hypothetical protein

P56964

1acc

LA0996d

LIC12668

Hypothetical protein

-

1koe

LA1764

LIC12048

Hypothetical protein

-

1qlg

LA1869

LIC12023

Hypothetical protein

-

1k14

LA2272

LIC11664

Hypothetical protein

 

1dab

LA3240

LIC10898

Hypothetical protein

 

1rmg

LA0074

LIC10067

Hypothetical protein/conserved hypothetical protein

-

1dbg

LA1065

LIC12602

Hypothetical protein/conserved hypothetical protein

-

1dab

LA1762

LIC12048

Hypothetical protein/conserved hypothetical protein

-

1qcx

LA3649

LIC10561

Hypothetical protein/conserved hypothetical protein

-

1qcx

LA3881

LIC13101

Hypothetical protein/OM with integrin like repeat domains

P35825

1dab

Note LRR: Leucine-rich repeat, a: Swiss-Prot ID derived from DBsubloc database, b: PDB code derived from GTD prediction, c: pfam06739: SBBP (seven bladed beta propeller) repeat d: pfam07588: DUF1554, e: pfam07602: DUF1565

Table 7

23 Putative outer membrane proteins (OM) derived the 2 out of 3 predictions with significant DBSubloc or/and GTD prediction

Lai locus

LIC locus

Protein annotation

SWISS-PROTa

PDB codeb

LA3471

LIC10711

Iron-reglulated protein A/cytoplasmic membrane protein

P12608

1i5p

LA1161

LIC12524

Long-chain fatty acid transport protein/fatty acid transport protein

-

1kmo

LA1100

LIC12575

Outer membrane efflux protein/cytoplasmic membrane protein

-

1ek9

LA1445

LIC12307

Outer membrane efflux protein/OM- TolC superfamily

P50468

1ek9

LA3685

LIC10537

Outer membrane protein/PG- associated periplasmic protein

P38369

1r1m

LA0056

LIC10050

Outer membrane protein OmpA family/PG-associated CM protein

Q05146

1r1m

LA2318

LIC11623

Predicted outer membrane protein/outer membrane protein

-

1a0t

LA1968

LIC11935

Putative outer membrane protein/conserved hypothetical protein

-

1a0t

LA2444

LIC11506

Putative outer membrane protein/outer membrane protein

-

1fep

LB110

LIC20087

Putative outer membrane protein/outer membrane protein

-

1uyn

LA2242

LIC11694

TonB-dependent outer membrane receptor

P46359

1fep

LA3242

LIC10896

TonB-dependent outer membrane receptor

P37409

1kmo

LA0465

LIC10405

TPR-repeat-containing proteins/conserved hypothetical

P58937

-

LA3675

LIC10544

Hypothetical protein/outer membrane protein

-

1a0t

LA2063

LIC11851

Conserved hypothetical protein/cytoplasmic membrane protein

-

1by5

LA3102

LIC10998

Conserved hypothetical protein

P76115

1nqe

LA3675

LIC10544

Hypothetical protein/outer membrane protein

-

1a0t

LA2168

-

Hypothetical protein

P43153

1a0t

LA3809

LIC10439

Hypothetical protein

-

1a0t

LA1501

LIC12258

Hypothetical protein

-

2mpr

LA3552

LIC10647

Hypothetical protein/conserved hypothetical protein

-

1kmo

LA2818

LIC11211

Hypothetical protein/conserved hypothetical protein

-

2mpr

LA4059

LIC13238

Hypothetical protein/conserved hypothetical protein

-

1by5

LB279

LIC20214

Hypothetical protein/conserved hypothetical protein

-

1kmo

Note a: Swiss-Prot ID derived from DBsubloc database, b: PDB code derived from GTD prediction

Table 8

25 Putative outer membrane proteins (OM) derived from the 1 out of 3 predictions with significant DBSubloc and/or GTD prediction

Lai locus

Copen Locus

Protein annotation

SWISS-PROTa

PDB Codeb

LA0616

LIC12966

LipL41/Outer membrane lipoprotein lipL41

-

1a17

LA2295

LIC11643

LipL45 protein

P02977

1l8w

LA0957

LIC12693

Outer membrane efflux protein/conserved hypothetical protein

P24145

1ek9

LA0581

LIC12990

Outer membrane efflux protein/conserved hypothetical protein

Q9ZHD2

1ek9

LA3733

LIC10496

Outer membrane efflux protein/conserved hypothetical protein

-

1ek9

LA0301

LIC10258

Outer membrane protein OmpA family/hypothetical protein

Q926C3

1r1m

LA0222

LIC10191

Outer membrane protein OmpA family/PG-associated CM protein

P22263

1r1m

LA1192

LIC12499

Putative outer membrane protein

-

1fep

LA1404

LIC12337

Putative outer membrane protein

-

2mpr

LA1931

LIC11975

Putative outer membrane protein/outer membrane protein

-

2mpr

LA1987

LIC11918

Putative outer membrane protein/conserved hypothetical protein

-

1osp

LB199

LIC20157

Putative outer membrane protein/conserved hypothetical protein

-

1fep

LA1030

LIC12630

TPR-repeat-containing proteins/hypothetical protein

P58937

1a17

LA0568

LIC13002

Conserved hypothetical protein

-

1kmo

LA1510

LIC12252

Conserved hypothetical protein

-

2mpr

LA0835

LIC12791

Hypothetical protein/conserved hypothetical protein

-

1fnf

LA2746

LIC11268

Hypothetical protein/conserved hypothetical protein

-

2mpr

LA2940

LIC11121

Hypothetical protein/conserved hypothetical protein

-

2mpr

LA2976

LIC11086

Hypothetical protein/conserved hypothetical protein

-

2mpr

LA3870

LIC13089

Hypothetical protein/conserved hypothetical protein

-

2mpr

LA4272

LIC13418

Hypothetical protein/conserved hypothetical protein

-

2mpr

LA4335

LIC13477

Hypothetical protein/conserved hypothetical protein

-

1kmo

LA0706

LIC12898

Unknown protein

P38370

1fep

LA1507

LIC12254

Unknown protein/outer membrane protein

-

1a0t

LA3853

LIC13078

Unknown protein/conserved hypothetical protein

-

1bxw

Note a: Swiss-Prot ID derived from DBsubloc database, b: PDB code derived from GTD prediction

Table 9

Protein subcellular localizations of L. interrogans predicted by PSORTb, PA, ProtCompB and the combination prediction

Localization

Subcellular localization prediction

 

PSORTb

PA

ProtCompB

Combination prediction

Cytoplasm (CP)

1125

921

2013

2690

Cytoplasmic membrane (CM)

606

715

1726*

813

Outer membrane (OM)

112

28

 

63

Periplasmic (PP)

30

86

478

75

Extracellular (EX)

29

326

510

114

Unknown

2825

2652

-

972

* Note that in ProtCompB prediction in this version, CM and OM were predicted as membrane proteins.

Step 3: Cytoplasmic (CP) and cytoplasmic membrane proteins (CM) identified by Linear Discriminant Analysis (LDA)

The remaining 3725 proteins with unknown localization after step 2 were further analyzed using an LDA-based classifier we developed to identify CP and CM proteins using the set of CP and CM consensus outputs (418 CP proteins and 332 CM proteins) predicted by all of the three prediction programs (Additional file 2, 3) as a training set (see Materials and Methods). 2272 CP and 481 CM proteins were additionally identified from the 3725 "unknown set" by this approach (Additional file 4, 5). We also found that 66% (1501 out of 2272) of the LDA based predicted CP and 54% (260 out of 481) of the LDA based predicted CM are hypothetical or unknown proteins. In other words, overall 56.3 % (1516 out of 2690) of hypothetical and/or unknown proteins in the whole genome were assigned as CP and 38 % as CM or helix transmembrane proteins.

After the final step in the prediction method, we are able to confidently predict the localization of 3755 (79.4%) Leptospiral proteins. Our combination method thus has a considerably improved recall over the PSORTB and PA methods, approaching that of ProtCompB (Table 1). To test the final prediction accuracy with estimated % agreement and % coverage of our combination method, we then performed the localization prediction of 28 experimentally verified proteins from several studies of Leptospiral outer membrane and extracellular, or cell surface proteins.

Protein subcellular localization prediction on the experimentally verified leptospiral outer membrane and extracellular proteins

As shown in the Additional file 6, the three prediction programs PSORTb, PA and ProtCompB gave markedly different predictions from one another for 28 experimentally OM and EX. Each of the three prediction programs had weaknesses, either poor agreement (ProtCompB) or low coverage (PSORTb and PA). Our combination approach was much better in the respect and showed good agreement and coverage.

Discussion

Computational prediction for protein subcellular localization is a key step for genome annotation and development of drug and vaccine target. In this study, we used a combination method to putatively assign CP, CM, PP, OM, and EX proteins. We combined the results from three different algorithms namely PSORTb, PA and ProtCompB into a consensus vote to obtain higher prediction accuracy. The combination approach has previously been used to significantly reduce, or exclude false positive predictions for membrane topology prediction [33], and outer membrane prediction [34]. In our case, the accuracy of consensus vote is very high, since well characterized OM and EX proteins were predicted including lactonizing lipase [35], microbial collagenase [36], O-sialoglycoprotein endopeptidase [37], Rhs family protein [38], CsgA or C factor [39], thermolysin [40], leucine rich repeat proteins (LRR) [4143], Ton-B dependent outer membrane receptor proteins, OmpA, porin, heavy metal efflux pump, TolC, and general secretory pathway protein D (Table 4).

On the other hand, the recall, or sensitivity of consensus vote prediction is low, especially for EX and OM. The recall for consensus vote is low, because PSORTb and PA programs are known to have limitations for some proteins. PSORTb requires a training set from a limited number of experimentally-determined proteins, while PA has a disadvantage in that query proteins have to share similarity to known proteins in the Swiss-Prot database [44]. Among high-throughput computational predictions for protein subcellular localization, PSORTb has been reported as the prediction tool that achieves the highest overall accuracy, followed closely by PA [22].

To overcome the limitations in PSORTb, PA and ProtCompB, the predictions for proteins predicted by only one or two out of the three prediction methods (the non consensus vote) were refined by homology-based search using the DBSubloc database and structural annotation in GTD. This allowed us to identify protein localizations with greater confidence. The advantage of GTD is that protein folding recognition or threading methods can determine pairs of proteins that have no obvious similarities in sequence, but have similar folds. It was previously suggested this approach should be carried out to increase prediction sensitivity for specific protein localization [22, 45, 46]. To our knowledge, this study is the first to employ GTD information to infer leptospiral protein localizations.

Structure-based information from GTD prediction revealed that the majority of the 99 EX predictions were proteins that may be secreted by the type III or the type V (autotransport) system. These proteins are shown in Table 5, 6 with their corresponding PDB code. Many of the putative EX proteins that are annotated as leucine rich repeat (LRR) containing proteins share sequence similarity to PopC protein (Q9RBS2), which is secreted through the hrp-secretion apparatus or the type III secretion pathway of Ralstonia solanacearum [41]. Structurally related well-characterized extracellular LRR proteins in other species include YopM (PDB code 1jl5), a Yersinia pestis cytotoxin [43], internalin B [47], a virulence factor of Listeria monocytogenase (PDB code 1d0b) and polygalacturonase inhibiting protein (PDB code 1ogq), a secreted protein involved in plant defense [48].

It is of interest to note that several L. interrogans proteins are contained within the LRR and TPR (Tetratricopeptide repeat) protein families, but predicted sub-cellular localization is not necessarily conserved among all members within each family (Table 3, 5, 6, 7, 8, 9 and Table in additional file 4). The majority of LRR proteins were predicted to be EX localized, while TPR proteins were predicted in all compartments except PP. This finding is consistent with the multiple functions of TPR homologues from more distantly related species in different sub-cellular milieux, including signal transduction, chaperone activity, cell-cycle, transcription, and protein transport [49, 50].

Out of 48 non-consensus vote of predicted OM, 24 were proteins annotated as outer membrane or putative outer membrane proteins, while of the remainder were proteins annotated as conserved hypothetical proteins. The structural information derived from the GTD prediction of the conserved or hypothetical proteins that were predicted as putative OM were the same as that of the annotated outer membrane proteins. As shown in Table 7, 8, it can be observed that 24 hypothetical proteins can now be annotated as putative OM.

Although it is clear that the consensus vote combined with DB and GTD prediction can give robust prediction for EX, OM and PP, there are many proteins with either CP or CM localization remaining. Using our combination approach, we found that about 17% of genes encode putative CM proteins in L. interrogans serovar Lai genome, which is of similar proportion to the 20% – 30% CM proteins in other bacterial species [25, 51]. From our subcellular location prediction we identified 63 OM and 114 EX proteins as potential vaccine candidates. On the other hand, it is possible to exclude 813 CM and 75 PP predicted proteins as vaccine candidates, on the basis of their localization.

We compared our predictions with the previously published works. We found that 10 of 16 membrane proteins predicted by Gamberini et al. 2006, including four also demonstrated to be immunogenic among 8 pathogenic serovars in that study, were also predicted by our method as membrane proteins (2 EX, 1OM, 1PP and 6 CM) [18]. We examined the localizations of the 145 putative lipoproteins reported by Setubal et al. [19], and found 29 EX, 2 OM, 7 PP and 26 CM proteins among 125 probable lipoproteins, and 1 PP and 3 CM among 21 possible lipoproteins. The localizations of 63 putative lipoproteins could not be identified, which included proteins containing signal peptidase II recognition sites and proteins lacking sequence and/or structural homology to known membrane proteins (see Additional file 7). Spirochaetal lipoproteins are found in four subcellular compartments: the periplasmic leaflet of the cytoplasmic membrane, the periplasmic outer leaflet of the outer membrane, or beyond the outer membrane into the environment as extracellular proteins [52]. Therefore, 15 of the 145 putative lipoproteins identified as CP by our method are unlikely to be lipoproteins because of their localization. These false positive lipoproteins include UDP-glucose 6-dehydrogenase, cell-division protein, regulator of chromosome condensation RCC1 family, and 3-oxoacyl- [acyl-carrier protein] reductase. The frequency of falsely-identified lipoproteins just exceeds the reported 1% false positive rate for the SpLip program [52]. Our results can be considered as complementary to those reported by Setubal et al. [52], and increase the accuracy of lipoprotein prediction.

We also compared our predictions with the 226 leptospiral surface exposed protein predictions (extracellular, outer membrane, periplasmic, inner (cytoplasmic) membrane by their localization definition) reported by Yang et al. [20] and found a concordance of 38.5 % (87/226) (see Additional file 8). We think the discrepancies arise from false assignments generated by the prediction algorithms used, which can be identified by comparison with proteins for which there are reliable experimental data of localization (see Additional file 6) [214, 5357]. Our predictions have a higher coverage and agreement with the experimentally tested L. interrogans protein set than the study by Yang et al. [20], suggesting that our prediction method may be of greater overall utility for genome annotation of membrane proteins. After manual inspection of predicted localizations, we found further examples of possible false assignments. The greatest discrepancy was found for 42 proteins were identified as CM by our method, but OM by Yang et al. Some proteins among this group have homologues in other species for which there is experimental evidence of CM location, including methyl-accepting chemotaxis protein mcpB [58], aerotaxis sensor receptor [59], and penicillin-binding protein [60].

It was found that several loci without localization annotation were assigned by the combination prediction method. Therefore, we propose that the annotations with respect to subcellular localization for these loci can be tentatively revised. Among this group of proteins, we noted additional similarities to known protein families. One prominent group with the the SBBP domain (seven beta blade propeller proteins, Pfam PF06739) contain 9 hypothetical proteins: LA0283 (LIC10239), LA0423 (LIC10371), LA0426 (LIC10373), LA1567 (LIC12209), LA1568(12209), LA1569 (LIC12208), LA1691 (LIC12099), LA3276 (LIC10868), LA3834 (LIC13066). Three loci annotated as hypothetical proteins or lipoproteins, namely LA0996 (LIC12668), LA0962 (LIC12690), and LIC13296 (LA4135), were predicted as EX localized (shown in Table 5, 6), and may belong to the Len (leptospiral endostatin-like lipoproteins) family, based on conservation of DUF1554 domain (pfam PF07588) and structural similarity to mammalian endostatin-like protein (PDB 1koe). These proteins act as adhesion proteins and bind to host extracellular matrix (ECM) [53, 57] or human factor H [56]. (Table 5, 6 and Table in the Additional file 6). Furthermore, three loci LIC11207 (LA2823), LIC10821 (LA3340) and LIC10774 (LA3394) and LIC10365 (LA0416), previously described to have similarity with the leptospiral effector protein [54] were identified as putative EX proteins in agreement with their proposed immunomodulator function.

Our combination prediction method has high agreement and coverage of experimentally verified OM and EX proteins (see Additional file 6). On the other hand, experimental localization studies are limited by insufficient sensitivity to detect low abundance proteins and cross contamination of cellular compartments during sample purification, as discussed previously by Rey et al. [21]. It is of note that several predicted PP proteins in this work e.g. FlaB1 periplasmic flagellin (LA2017/LIC11890) have previously been identified as possible PP contaminants in experimental studies of OMV proteins [13, 20]; hence our prediction method may help in correct interpretation of future experimental verification studies, thus leading to better predictions in uncharacterized genomes. However, it should be emphasized that no automatic prediction can be accurate without experimental verification.

Conclusion

In this study, we have demonstrated that the specificity and sensitivity of protein subcellular localization prediction can be improved by incorporation of multiple predictive methods and structural information. By this approach, localizations can be assigned to previously hypothetical L. interrogans proteins. We think this approach is applicable for subcellular localization predictions in other prokaryote proteomes, with the caveat that some predictions are robust than others, i.e. CP and CM better than OM, EX or PP.

Materials and Methods

Data sets

Amino acid sequence queries were 4,727 proteins of Leptospria interrograns serovar Lai genome (chromosome I: NC_004342, chromosome II: NC_004343) [15] and 3,728 protein ORFs of Leptospira interrogans serovar Copenhageni strain (Fiocuz L1-I30) [accession number AEO16823 (chromosome I) and AEO16824 (chromosome II) [17] obtained from GenBank. Two datasets of proteins with known subcellular localization were used. One was an experimentally confirmed data set containing 278 CP and 309 CM of Gram-negative bacteria described by Gardy et al. 2003 [28] and used for validation of the LDA based classifier's performance. Another one was a 299 protein-data set containing 145 CP, 69 CM proteins, 29 PP, 38 OM and 18 EX which was the testing data previously used to evaluate various protein localization predictions in Gardy and Brinkman [22].

Computational Data sets mputational prediction tools for in silico protein localization

Several publicly available programs were used in combination of predictions. Protein subcellular localization for Gram-negative bacteria was carried out using PSORTb [27, 28], Proteome analysis (PA) [29], and ProtCompB [30]. Feature based predictions for signal peptide sequence and α helix transmembrane proteins were identified using SignalP [23] and TMHMM [24, 25] respectively.

Homology based searching and structural annotation

Homology search for subcellular localization information was carried out using BLAST search against DBSubloc, a localization specific protein database [31]. A protein folding recognition method for structural information used to predict the fold of protein sequence with distant homology to known structure was performed using homology search against GTD (the Genomic Threading Database) [32].

Prediction strategy (as shown in Figure 1)

Step 1. Consensus votes prediction

We reasoned that more accurate protein subcellular localization predictions can be gained from the consensus of methods. All leptospiral protein queries were analyzed using three subcellular localization prediction tools for Gram-negative bacteria, namely PSORTb, Proteome analysis (PA), and ProtCompB for cytoplasm (CP), cytoplasmic membrane (CM), periplasmic (PP), outer membrane (OM) and extracellular proteins (EX). Note that in this version ProtCompB prediction, CM and OM are not distinguished so both proteins are predicted as membrane proteins. The consensus prediction for each sequence was calculated using a simple majority vote type procedure. If all 3 methods agree for localization, it is assigned as a "consensus vote". The remaining results (1 or 2 out of 3 predicted) were assigned as "non-consensus vote". The CP and CM proteins assigned in this step were used as a training set for the development of LDA based classifier for CP and CM in a the next step.

Step 2. Homology-based and protein folding recognition prediction

Homology based and structural information can also be used to infer the potential localization site of query proteins [22, 45, 46]. Therefore, the remaining query proteins assigned as non-consensus vote results of PP, OM and EX were further analyzed for sequence and structure homology. Since subcellular localization is an evolutionarily conserved trait, if a protein query is homologous to a known protein with the same localization, the localization was assigned. The protein query sequences were compared to proteins in DBSubloc database at E-value ≤ 10-3 using BLAST search. Structure annotation of these queries was also performed using GTD prediction. The query proteins sequences were assigned to structures (shown as PDB code) with the high level of probability prediction (certain and high) for these protein queries. In this study, the confidence range based on p-value of measuring the reliability of the structure annotation as certain (0 ≤ p < 0.01%) and high (0.01% ≤ p < 0.1%) were considered as a statistically significant structure annotation.

Step 3. Identification of putative CP and CM using the LDA based classifier

A number of putative CP and CM identified as non-consensus vote results was further analyzed by SignalP and TMHMM. The feature attributors derived from SignalP and TMHMM predictions were then integrated and analyzed using the LDA based classifier. Proteins classified with probabilities ≥ 0.9 to be CP or CM proteins were taken as significant. The remaining queries that could not be identified in this step were classified as "unknown" results.

LDA based Classifier for CP and CM

We developed a specific classifier using the training set driven from the consensus vote prediction of leptospiral CP and CM proteins to increase the accuracy of prediction. In the classification-based prediction, our classifier was built on an LDA algorithm analyzing the value of multiple character vectors of SignalP-NN, SignalP-HMM and TMHMM prediction results of the set of training sequences. The accuracy of the LDA based classifier was investigated using leave-one out cross validation. We used experimentally determined or known CP and CM proteins of Gram-negative bacteria previously performed in the evaluation of PSORTb as a test dataset for validation of the LDA based classifier's performance [27]. Overall, the accuracy of LDA based classifier achieved 94.96%.

Declarations

Acknowledgements

We greatly thank Philip Shaw, Sastra Chaotheing and Duangdoa Wichadakul for their helpful critical reading and commend of the manuscript. This work was supported by the grant from the National Center for Genetic Engineering and Biotechnology, Thailand.

Authors’ Affiliations

(1)
BIOTEC Central Research Unit, National Center for Genetic Engineering and Biotechnology
(2)
Department of Microbiology, Faculty of Science, Mahidol University

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