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BMC Research Notes

Open Access

Experimental validation of predicted subcellular localizations of human proteins

  • Nagendra K Chaturvedi1,
  • Riyaz A Mir1,
  • Vimla Band1, 2,
  • Shantaram S Joshi1 and
  • Chittibabu Guda1, 2, 3, 4Email author
BMC Research Notes20147:912

https://doi.org/10.1186/1756-0500-7-912

Received: 1 October 2014

Accepted: 10 December 2014

Published: 15 December 2014

Abstract

Background

Computational methods have been widely used for the prediction of protein subcellular localization. However, these predictions are rarely validated experimentally and as a result remain questionable. Therefore, experimental validation of the predicted localizations is needed to assess the accuracy of predictions so that such methods can be confidently used to annotate the proteins of unknown localization. Previously, we published a method called ngLOC that predicts the localization of proteins targeted to ten different subcellular organelles. In this short report, we describe the accuracy of these predictions using experimental validations.

Findings

We have experimentally validated the predicted subcellular localizations of 114 human proteins corresponding to nine different organelles in normal breast and breast cancer cell lines using live cell imaging/confocal microscopy. Target genes were cloned into expression vectors as GFP fusions and cotransfected with RFP-tagged organelle-specific gene marker into normal breast epithelial and breast cancer cell lines. Subcellular localization of each target protein is confirmed by colocalization with a co-expressed organelle-specific protein marker. Our results showed that about 82.5% of the predicted subcellular localizations coincided with the experimentally validated localizations. The highest agreement was found in the endoplasmic reticulum proteins, while the cytoplasmic location showed the least concordance. With the exclusion of cytoplasmic location, the average prediction accuracy increased to 90.4%. In addition, there was no difference observed in the protein subcellular localization between normal and cancer breast cell lines.

Conclusions

The experimentally validated accuracy of ngLOC method with (82.5%) or without cytoplasmic location (90.4%) nears the prediction accuracy of 89%. These results demonstrate that the ngLOC method can be very useful for large-scale annotation of the unknown subcellular localization of proteins.

Keywords

Protein subcellular localizationngLOC predictionGene cloningExperimental validationGFP fusionLive cell imaging/confocal microscopy

Findings

Background

Subcellular localization of proteins to specific compartments is fundamental to the structural organization and functioning of all living cells. Proteins that are localized to unintended organelles have been implicated in the development of many human diseases; therefore, knowledge of the protein subcellular localization can benefit target identification in the drug discovery process [1].

Protein subcellular localization is an important attribute of protein function; thus, prediction of the same aids in genome annotation of high-throughput studies. Numerous computational methods have been used for the prediction of proteins subcellular localization [2]. Among these, some are limited by predicting only a small number of organelles in the cell [3, 4] while some others exhibit lack of a balance between sensitivity and specificity [5, 6]. Previously, we have developed a method called ngLOC, an n-gram based Bayesian method that can predict a wide range of subcellular locations including multiple localizations of proteins [7, 8]. This method makes its predictions solely based on the protein sequence information without the need for any extraneous information; therefore ngLOC is highly favorable for proteome-wide prediction of subcellular localizations.

The ngLOC method predicts subcellular locations at a high overall accuracy of 89%, while the accuracy is much higher (93-96%) in organelles with smaller proteomes such as lysosomes, peroxisomes, Golgi, etc., [7] that are typically difficult to predict due to lack of sufficient size datasets. Although computational predictions provide wealth of information for the subcellular localization of proteins, these predictions remain questionable unless they are validated by experimental methods. In the present study, we report experimental validations for ngLOC predicted subcellular localizations of human proteins. Our results corroborated the predicted results; thus ngLOC method can be used for proteome-wide annotation of protein localizations.

Materials and methods

Reagents and materials

Restriction enzymes and DH5α-competent cells were purchased from New England Biolabs (MA, USA). Trizol™, transfection reagent Lipofectamine2000TM, and red fluorescent tagged-subcellular markers including Mitotracker™ Red FM, Lysotracker™ Red, ER-Tracker™ Red, BODIPY® TR ceramide, Hoechst 33342 and Alexa Fluor® 594 WGA were obtained from Invitrogen (CA, USA). A cDNA synthesis and ligation kit was purchased from Promega (WI, USA). Primers of all cloned genes for the PCR amplification were obtained from Integrated DNA Technologies Inc. (Coralville, IA). A 2X PCR amplification kit was purchased from Applied Biological Materials Inc. (Richmond, Canada). Plasmid and DNA gel extraction kits were obtained from Qiagen Inc. (Valencia, CA). Fluorodish 35 mm petriplates for live cell imaging were purchased from World Precision Instruments (Sarasota, FL). All plastic wares for mammalian cell culture were purchased from Corning Costar Corp. (NY, USA).

Plasmids and constructs

pEGFP-N1 vector was kindly provided by Dr. Hamid Band (UNMC). Ten GFP-tagged full-length human gene constructs (ngLOC predicted), which include: SACM1L, ST13, TUBAL3, USMG5, DECR2, AMY2B, UXS1, LGMN, NR2F1 and NAPB were obtained from Origene Technologies Inc. (Rockville, MD). Six RFP-tagged subcellular specific human gene constructs (positive markers) which include: endoplasmic reticulum specific ETS, Golgi specific TGOLN, peroxisome specific PXPM2, mitochondria specific PDHA1, plasma membrane specific LCK and cytoskeleton specific β-ACTIN were also purchased from Origene Technologies Inc. (Rockville, MD).

Isolation of RNA and cDNA preparation

Total RNA was extracted from HEK-293 T cells using the TRIzol™ method according to the manufacturer’s instructions. RNA quantity and purity were determined by UV spectrophotometry and by electrophoresis on a 2% agarose gel. Two micrograms of RNA was then reverse transcribed using random hexamer primers and the superscript RT enzyme according to the manufacturer’s instructions (Invitrogen, CA).

PCR amplification and gene cloning

PCR Amplification was achieved with the 2X PCR master mix kit containing Taq DNA polymerase using 30–35 cycles according to the manufacturer’s protocols. For amplification, the two sets of primers with appropriate restriction enzymes were used against full-length ORF of each human gene. The primers used for the genes cloning of this study have been tabulated in Additional file 1. Each PCR amplified gene product was separated on 1% agarose gel in 1X TAE buffer (pH 8.0) and visualized by ethidium bromide staining. The gel extraction of PCR amplified gene products were purified using a gel extraction kit; then these purified genes products were double digested with restriction enzymes using the combination of either NheI/XhoI, NheI/HindIII or BglII/BamHI. Following restriction digestions, the full-length genes were cloned into a pEGFP-N1 vector using a LigaFast ligation kit and were transformed into E. coli (DH5α) bacterial strain. The positive clones of the genes were screened and confirmed, following appropriate restriction digestion.

Cell lines and culture conditions

The normal breast epithelial cell lines MCF-10A and MCF-12 F were obtained from the American Type of Culture Collection (Rockville, MD). These cell lines were maintained in D-media described previously [9]. The breast cancer epithelial cell lines MCF-7 and MDA-MB-231 were kindly provided by Dr. Vimla band (UNMC). These cells were cultured in α-MEM media supplemented with 10% FBS (Invitrogen, CA), 2 mM glutamine (Invitrogen, CA), 50 μg/ml gentamicin (Invitrogen CA), 1x sodium pyruvate (Invitrogen CA), 1x MEM non-essential amino acid (Invitrogen, CA), 1x HEPES (Invitrogen, CA) and 1 μg/ml insulin (Sigma). The cultures were maintained in a humidified incubator adjusted at 5% CO2 and 95% air atmosphere at 37°C. All cultures were passaged twice a week and maintained at a concentration no greater than 1 × 106/ml.

Transient transfections and confocal microscopy

Breast normal (MCF-10A, MCF-12 F) and cancer (MCF-7, MDA-MB-231) epithelial cells were seeded on 35-mm fluorodish petriplates to reach approximately 50-70% confluence in their respective medium. The next day, cells were transiently co-transfected with 1 μg of GFP-tagged predicted target gene and subcellular specific RFP-tagged marker gene (endoplasmic reticulum specific ETS, Golgi specific TGOLN, peroxisome specific PXPM2, mitochondria specific PDHA1, plasma membrane specific LCK and cytoskeleton specific β-ACTIN) for each of the localizations, using Lipofectamine in serum free MEM medium. After 6–8 hours of transfection incubation, cells were supplemented with a complete respective media and given another 12 hours of incubation for the protein expression. Following protein expression, subcellular distribution and co-localization of proteins were assessed under the confocal microscope. Alternatively, other red fluorescent subcellular specific markers (dye) were also used with live cells to validate the each localization. Each predicted localization was confirmed and validated when the co-localization produces a yellow color upon merging the images of specific subcellular markers. Nuclear stain Hoechst-33342 (1 μg/ml) was added to live cells for the visualization of nucleus. Fluorescence images of live cells were recorded through Zeiss LSM 710 confocal microscope (Jena, Germany) with 40X objective lens. Images were captured and analyzed with LSM software (Jena, Germany) and processed using standard software programs.

Results and discussion

The research strategy used for experimental validation of ngLOC predicted protein subcellular localizations is described in Figure 1. cDNA was synthesized from HEK-293 T cells; with the use of cDNA, the genes of 105 target proteins of human origin were PCR amplified and then cloned into a GFP expression vector (pEGFP-N1) with GFP at the N-terminus as a fusion gene. Using the ngLOC method, 114 target proteins with predicted subcellular localization (includes 105 locally cloned and nine commercially obtained) were selected for this validation study (Additional file 2). GFP expressing fusion genes along with corresponding location-specific RFP-tagged protein markers, were transiently co-expressed following gene transfection into two normal breast (MCF-10A, MCF-12 F) and two breast cancer (MCF-7, MDA-231) cell lines; then their subcellular localization was determined using live cell imaging/confocal microscopy. In the present study, nine different subcellular compartments were selected for validating the predicted subcellular localization of proteins. The images in Figure 2 show a representation of validated localizations for predicted proteins in each compartment. The localizations for each compartment (except for nucleus and cytoplasm) were determined by observing the colocalization of GFP- and RFP-tagged proteins, which produced a yellow color upon merging the images. For the nucleus and cytoplasm, we used a nuclear (Hoechst) stain to validate the protein subcellular localization in either location (Figure 2).
Figure 1

The cartoon shows the research strategy used to experimentally validate the predicted subcellular localization of proteins. Genes of interest were cloned into pEGP-N1 vector as GFP fusions. These GFP-tagged genes along with corresponding location-specific RFP-tagged gene markers, were transiently co-transfected using liposome-mediated method into two normal breast and breast cancer cell lines. Following 24 hours of transfection and protein expression, subcellular localization was determined using live cell imaging/confocal microscopy. Protein localizations to each compartment were confirmed by observing the colocalization of GFP- and RFP-tagged proteins that gives yellow color up on merging green and red images.

Figure 2

Experimental validation of predicted localization of human proteins. GFP-tagged full-length genes of target proteins and RFP-tagged compartment specific genes were transiently co-expressed in two normal and two breast cancer cell lines. Subcellular localization of the transiently expressed proteins was determined under the confocal microscope (40X). To facilitate the visualization of predicted subcellular localization, the specific RFP-tagged protein marker/dye for each localization was used in colocalization studies. Hoechst (nuclear dye) was used in all experiments. This figure shows a representative observation of colocalization in MCF-7 cells for each of the nine subcellular compartments used for validation in this study.

Table 1 lists the prediction for each gene tested, along with the outcome of the validation experiment. Similarly, Figure 3 shows the number of tested and succeeded proteins in the validation experiments. Our live cell imaging results showed that overall about 82.5% (94 out of 114) of proteins validated in this study agreed with the ngLOC predicted localizations; these results were consistent in all four cell lines tested. However, with the exclusion of the cytoplasm location that shows the lowest accuracy (45%), the average prediction rate increases to 90.4% (85 out of 94). ngLOC method outputs the predictions in a ranked order by using the associated confidence score (probability) for each location. The top two locations can be predicted within a close confidence range, suggesting that either or both of the predictions can be true. It is known that a number of proteins are localized to multiple organelles in eukaryotic cells (7). To test the accuracy of the second choice we also validated the second predictions of 30 proteins, which included 17 proteins (Set I) whose first choice predictions were proven wrong and 13 proteins (Set II) whose first choice predictions were accurate in the above experiments. From Set I, 10 proteins have shown homogenous distribution in cells, suggesting their localization both in the cytoplasm and nucleus (Table 2). For seven of these 10 proteins, the top two ngLOC predictions were cytoplasm or nucleus, which support our results that these proteins are localized in both nucleus and cytoplasm. From the other 7 proteins in Set I, the second choice predictions were validated as correct only for 2 proteins (Table 2). Validation results on Set II showed that about 46% (6 out of 13) of the proteins tested have also agreed with the second prediction (Table 2), indicating that these proteins are dual localized. With the inclusion of the second prediction validations, we have experimentally validated the subcellular localization of 144 ngLOC predictions.
Table 1

Experimental validation for ngLOC predicted proteins subcellular localization

Protein

Prediction

Validation

Protein

Prediction

Validation

NR2F1

NUC

Yes

FKBP7

END

Yes

LMO2

NUC

Yes

ZFAN2B

END

Yes

LEF1

NUC

Yes

USMG5

MIT

Yes

U2AF1L4

NUC

No

UQCR10

MIT

Yes

KLF7

NUC

Yes

COX6B1

MIT

Yes

PHF5A

NUC

No

BRP44L

MIT

Yes

LMO1

NUC

Yes

UCP3

MIT

Yes

HMNG4

NUC

Yes

SFXN1

MIT

Yes

SCNM1

NUC

Yes

NDUFS8

MIT

Yes

SNRNP27

NUC

Yes

ATP5S

MIT

Yes

SSX3

NUC

Yes

COX7C

MIT

Yes

LMO1

NUC

Yes

MRPL30

MIT

Yes

PRKRIP1

NUC

Yes

MRPL15

MIT

Yes

HNRNPCL1

NUC

Yes

PHB

MIT

Yes

MAB21L1

NUC

Yes

MRPL53

MIT

No

VGLL2

NUC

Yes

MRPS24

MIT

Yes

AES

NUC

Yes

MRPL10

MIT

Yes

ST13

CYT

Yes

MRPL2

MIT

Yes

MLST8

CYT

Yes

COX7B

MIT

Yes

GNPDA2

CYT

Yes

MRPL51

MIT

No

CARD17

CYT

No

MRP63

MIT

Yes

RAC1

CYT

No

COQ9

MIT

Yes

FKBP1B

CYT

Yes

LGMN

LYS

Yes

SPRR2F

CYT

Yes

CTSL2

LYS

Yes

SPRR2G

CYT

Yes

MMD

LYS

Yes

NUD10

CYT

Yes

RAB7A

LYS

Yes

PCTP

CYT

No

ITM2C

LYS

Yes

PEBP1

CYT

No

DECR2

POX

Yes

RPL36AL

CYT

No

ZADH2

POX

Yes

GST5A

CYT

No

PXMP4

POX

Yes

OTUB1

CYT

Yes

TUBAL3

CSK

Yes

PCMT1

CYT

No

TMSB15A

CSK

No

PGPEP1

CYT

No

DYNLL2

CSK

No

PGEP1-2

CYT

No

ACTBL2

CSK

Yes

PMP2

CYT

No

CAPZB

CSK

Yes

PPIAL4A

CYT

No

TMSB4Y

CSK

No

UBE2K

CYT

Yes

CAPZA1

CSK

Yes

UXS1

GOL

Yes

ANKRA2

CSK

Yes

UGCG

GOL

Yes

PDLIM1

CSK

Yes

GKAP1

GOL

Yes

TRIM54

CSK

Yes

HS2ST1

GOL

Yes

PNP

CSK

Yes

GKAP1-2

GOL

Yes

CDC42EP5

CSK

Yes

GCNT2

GOL

Yes

SEP3

CSK

Yes

GABRAPL2

GOL

No

ACTRT3

CSK

Yes

ZADHHC3

GOL

Yes

NABP

PLA

Yes

ST6SIA1

GOL

Yes

GNAS

PLA

Yes

SACM1L

END

Yes

KCNIP2

PLA

Yes

SEC11A

END

Yes

MOG

PLA

Yes

SEC11C

END

Yes

CD8B

PLA

Yes

CNPY3

END

Yes

CACNG4

PLA

Yes

CNPY3-2

END

Yes

IFITM2

PLA

No

SEC61G

END

Yes

STOML3

PLA

Yes

DGAT2

END

Yes

RTP1

PLA

Yes

ASPH

END

Yes

ABHD6

PLA

Yes

MEST

END

Yes

RASD2

PLA

Yes

DOLPP1

END

Yes

TMEM68

PLA

Yes

POFUT1

END

Yes

RHOV

PLA

Yes

Figure 3

Graph showing the total number of tested and those that are in agreement with the predicted localizations in each subcellular location. This graph was generated based on the data provided in Table 1.

Table 2

Experimental validation of ngLOC top second predicted proteins subcellular localization

Protein

First Prediction

Second Prediction

Validation for First prediction

Validation for Second prediction

U2AF1L4

NUC

CYT

*

*

CARD17

CYT

NUC

*

*

RAC1

CYT

PLA

No

No

PCTP

CYT

NUC

*

*

PEBP1

CYT

PLA

No

No

RPL36AL

CYT

NUC

*

*

GST5A

CYT

NUC

*

*

PCMT

CYT

NUC

*

*

PGPEP1

CYT

NUC

*

*

PMP2

CYT

NUC

*

*

PPIAL4A

CYT

MIT

No

Yes

GABRAPL2

GOL

CSK

No

Yes

MRPL51

MIT

CYT

No

No

MRPL53

MIT

CYT

No

No

DYNLL2

CSK

NUC

*

*

TMSB4Y

CSK

CYT

*

*

TMSB15A

CSK

NUC

*

*

PXMP4

POX

PLA

Yes

No

MMD

LYS

PLA

Yes

No

RAB7A

LYS

PLA

Yes

No

SEC61G

END

MIT

Yes

Yes

DGAT2

END

PLA

Yes

No

DOLPP1

END

PLA

Yes

Yes

SEC11A

END

PLA

Yes

No

CNPY3

END

PLA

Yes

No

LMO1

NUC

MIT

Yes

Yes

LMO2

NUC

PLA

Yes

Yes

NUDT10

CYT

PLA

Yes

No

CDC42EP5

CSK

PLA

Yes

Yes

ZDHHC3

GOL

PLA

Yes

Yes

The asterisk (*) represents homogenous distribution where localization of protein was seen in both cytoplasm and nucleus.

We also looked into the correlation between the confidence score (CS) and prediction accuracy for ngLOC predictions. CS is expressed as percentage and the value can range from zero to 100. ngLOC method uses a minimum CS of 20 to make predictions (7), however we chose only a small subset of predicted proteins for validation. The CS for validated proteins ranges from 20 to 73 in this study. We divided the total number of validated proteins into two groups, low CS group (CS <46) and high CS group (CS >46); where, CS of 46 is the midpoint of the CS range for the proteins validated. Our validation results showed that 88% (50 out of 57) of the low CS group proteins were predicted accurately, compared to that of the high CS group proteins, which was 77% (44 out of 57). While these results are counter-intuitive, the high CS group contains a number of proteins that are predicted to be localized to cytoplasm, which has the highest false positive rate. Without counting the cytoplasmic proteins, the accuracies would be 92% for low CS group and 89% for the high CS group. These results demonstrate that there is no significant correlation between the CS and prediction accuracy. We presume that the lack of correlation is due to the unbalanced selection of validated proteins from a narrow range of confidence scores (see Additional file 3: Table S1), which in turn is due to feasibility (project costs limiting the sample size) and technical (PCR amplification of longer genes) issues that limited our ability to select proteins from a wider CS range for validation.Despite the lower CS range predictions for proteins localized to ER (33-65%), lysosomal (38-50%) and peroxisomal (23-39%), the validation accuracy is 100% at these locations. Similarly, plasma membrane, cytoskeletal, mitochondrial, Golgi and nuclear proteins recorded about 85% accuracy (Figure 3). Conversely, cytoplasmic proteins scored the lowest with only 45% prediction accuracy. The high false positives in this location can be attributed to the fact that cytoplasm location, being the default location for protein synthesis, lacks specific targeting signals that makes it difficult to predict. Another reason could be the dual- or multi-localization of about one-third of cytoplasmic proteins to other locations (7); where, the machine learning methods face difficulty in discriminating the cytoplasmic proteins compared to those from other locations.Overall, the experimental validations in this study prove that the ngLOC method can predict the subcellular localization of proteins at an accuracy of 82.5%, contrary to the reported accuracy of 89% (7). However, with the exclusion of the low performing cytoplasmic location (45%), the average accuracy rate jumped to 90.4% (85 out of 94). As shown in Figure 3, the accuracy is especially notable for the locations with smaller proteomes (ER, Golgi, Lysosome and Peroxisome), which are typically difficult to predict by machine learning methods. These results demonstrate the robustness, accuracy, and application in annotating the unknown subcellular localization of proteomes of eukaryotic species using the ngLOC method.

Conclusion

This study experimentally validates and reports the accuracy of a computational method called ngLOC that predicts the subcellular localization of protein sequences in eukaryotic cells. We validated 114 human proteins that were predicted to be localized to nine distinct subcellular locations in eukaryotic cells. The overall validation accuracy rate of ngLOC method is at 82.5%, while the rate improved to 90.4% just by excluding the cytoplasmic location, compared to the overall prediction accuracy of 89%. Thus, this validation study demonstrates that ngLOC can be reliably used (with the exception of cytoplasmic location) to annotate the subcellular localization of proteins and affirms the utility of this method in large-scale annotation of newly sequenced proteomes.

Abbreviations

GFP: 

Green fluorescent protein

RFP: 

Red fluorescent protein

PLA: 

Plasma membrane

CSK: 

Cytoskeleton

CYT: 

Cytoplasm

END: 

Endoplasmic reticulum

GOL: 

Golgi complex

MIT: 

Mitochondria

LYS: 

Lysosome

POX: 

Peroxisome

NUC: 

Nucleus

UNMC: 

University of Nebraska Medical Center.

Declarations

Acknowledgements

This research is fully supported by National Institutes of Health [1R01GM086533-01A1 to CG]. The authors thank the confocal core and the bioinformatics and systems biology core at UNMC for their help in this study. The authors also thank Dr. Hamid Band (UNMC) for providing the pEGFP-N1 vector for our cloning experiments, and Mrs. Megan Brown for proofreading the manuscript.

Authors’ Affiliations

(1)
Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center
(2)
Fred and Pamela Buffet Cancer Center
(3)
Eppley Institute for Cancer Research
(4)
Bioinformatics and Systems Biology Core, University of Nebraska Medical Center

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Copyright

© Chaturvedi et al.; licensee BioMed Central. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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