Tools and Resources

FLEXIQuant-LF to quantify protein modification extent in label-free proteomics data

F.M. Kirby Neurobiology Center, Boston Children’s Hospital, United States
Department of Neurology, Harvard Medical School, United States
Department of Pathology, Boston Children’s Hospital, United States
Bioinformatics Unit (MF1), Robert Koch Institute, Germany
Department of Medical Biotechnology, Institute of Biotechnology, Technische Universität Berlin, Germany
Department of Pathology, Harvard Medical School, United States
Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Germany
Precision Vaccines Program, Boston Children’s Hospital, United States

Dec 7, 2020

https://doi.org/10.7554/eLife.58783

Open access
Copyright information

Figures
Tables
Additional files

4 figures, 3 tables and 4 additional files

Figures

Figure 1

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Workflow and FLEXIQuant-LF concept.

(A) Workflow. (A1) HeLa cells were synchronized in S-phase using thymidine (dark orange). Upon release from thymidine block, cells were treated with nocodazole and samples were collected after 4 hr (medium dark orange), 8 hr (orange), and 10 hr (light orange). (A2) APC/C was co-immunoprecipitated using an anti-CDC27 antibody. (A3) All samples were trypsinized separately and analyzed by LC-MS/MS in DDA mode to generate a spectral library, which was subsequently analyzed by SWATH-MS (see Supplementary file 1 for raw peptide intensities of all quantified APC/C proteins). (A4) FLEXIQuant-LF-based differential modification analysis of APC/C proteins (see Supplementary file 2 for all resulting RM scores). (B) FLEXIQuant-LF overview. (B1) Firstly, intensities of unmodified peptides are used to indirectly identify and quantify the modification extent of a protein using the following steps. (B2) A RANSAC-based robust linear regression model is fitted to the intensities of unmodified peptide species using a reference sample as independent variable and the sample of interest as dependent variable and the vertical distance to the regression line of each peptide is determined. (B3) For each peptide, the distance is then normalized by dividing by the slope of the regression line multiplied by the intensity of the peptide in the reference sample and the result is subtracted from one to yield raw scores. (B4) Peptides with a raw score above three standard deviations of the median (MAD) of all raw scores are classified as outliers and excluded from the subsequent RM score calculation. The remaining raw scores are then scaled using the median of the three highest raw scores resulting in a metric, termed RM score which is equal to one minus the extent of modification. (B5) Lastly, peptides are classified in three categories based on their RM scores and the extent of modification is visualized: (i) RM score <0.5: peptide is likely differentially modified (magenta bars), (ii) 0.5 ≤ RM score<0.6: peptide is possibly differentially modified (blue bars), and (iii) RM score ≥0.6: peptide is likely not differentially modified (green bars).

Figure 2

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Benchmarking FLEXIQuant-LF with CDC27 and APC5.

Peptide classification and extent of modification quantification of CDC27 peptides (A) and APC5 peptides (B) with protein sequences and measured peptides highlighted underneath. Green bars indicate peptides that were classified as likely *not* differentially modified (RM score ≥0.6), blue bars indicate peptides classified as possibly differentially modified (0.5 ≤ RM score<0.6) and magenta bars indicate peptides that were classified as likely differentially modified (RM score ≤0.5). Shading indicates timepoints from 0 hr (S phase; darkest shade) to 10 hr (brightest shade). Positions of peptides within the protein are given from the N- to C-terminus. Orange stars indicate modifications identified in the DDA dataset while yellow stars indicate modifications described in the literature. (A) FLEXIQuant-LF analysis of 32 CDC27 peptides after filtering classified nine and four peptides as likely and possibly differentially modified over the course of the experiment. We found PTM evidence for all peptides classified as likely differentially modified (for five in our DDA data and for all peptides online [see also Table 1]) as well as for three out of four peptides classified as possibly differentially modified (for one in our DDA data and all except of peptide [23-30] described online). (B) Out of 35 quantified peptides of APC5, four and five peptides were classified as likely and possibly differentially modified. We found evidence for the likely differentially modified peptides in our DDA data or as previously reported. Additionally, we found evidence for four out of five possibly differentially modified peptides online. Only for [99-106], we could not find any evidence for modification.

Figure 3 with 2 supplements

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Application of FLEXIQuant-LF to remaining APC/C core components.

Peptide classification and extent of modification of APC1. Seven out of 50 quantified peptides were classified as likely differentially modified and four peptides as possibly differentially modified. We found evidence of modification for six out of seven peptides classified as likely differentially modified (five in our DDA data and six described in the literature) as well as for three out of four peptides classified as possibly differentially modified in our DDA data (see also Table 1). Additionally, two peptides were classified as likely differentially modified and possibly differentially modified (indicated by arrow heads) but no evidence of modification was found in literature. Interestingly, these two peptides are consecutive, and the second peptide starts with threonine (AA 1077). An as of now undiscovered threonine modification such as a phosphorylation would likely lead to a highly increased missed cleavage rate and could explain the reduction of the signal intensities of both peptides. Classification and extent of modification of the remaining APC/C core components is shown in Figure 3—figure supplements 1 and 2.

Figure 3—figure supplement 1

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FLEXIQuant-LF analysis of remaining APC/C components analyzed as superprotein.

Peptide classification and extent of modification of (A) APC4, (B) APC7, (C) APC10, (D) CDC20, (E) CDC16, (F) CDC23 analyzed with FLEXIQuant-LF. APC15 and APC16 were analyzed using the ‘superprotein’ approach, the remaining proteins were analyzed individually. Green bars indicate peptides that were classified as likely not differentially modified at 10 hr (RM score ≥0.6), blue bars indicate peptides classified as possibly differentially modified (0.5 ≤ RM score<0.6) and magenta bars indicate peptides that classified as likely differentially modified (RM score ≤0.5). For each class, the four time points are shown in different color shades, from time point 0 hr (S phase; darkest shade) to time point 10 hr (brightest shade). Positions of peptides within the protein are given from the N- to C-terminus. Peptides labeled with an orange star indicate that a modification was identified on this peptide in the DDA dataset and peptides labeled with a yellow star indicate a modification that was described in the literature.

Figure 3—figure supplement 2

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FLEXIQuant-LF analysis of APC/C components as superprotein.

Peptide classification and extent of modification of (A) APC15 and (B) APC16 analyzed with FLEXIQuant-LF using the ‘superprotein’ approach as number of peptides per protein was below threshold of five peptides. Green bars indicate peptides that were classified as likely not differentially modified at 10 hr (RM score ≥0.6). Positions of peptides within the protein are given from the N- to C-terminus.

Figure 4

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FLEXIQuant-LF reproducibility and error estimation.

(A) Number of ambiguous samples with different number of RANSAC initiations. The number of samples with ambiguous results over 1000 runs decreases with increasing number of RANSAC initiations but starts to oscillate between one and two ambiguous samples from 50 initiations upwards. (B) Fraction of optimal and suboptimal outcomes of PMF with sample 30. The frequency of a suboptimal outcome decreases with increasing number of RANSAC initiations. (C) Correlation of the expected/in silico created change with the RM score difference before and after the in silico change. Expected and measured changes highly correlate (Pearson r = 0.98). (D) Classification error estimation using definitions of likely not, possibly, and likely differentially modified as described in Figure 1B. With a sensitivity and precision of 72.2% and 88.6%, respectively, the transition area of possibly differentially modified evaluates expectedly worse than the likely not and likely differentially modified classifications (both with sensitivity and precision >96%). (E) Cumulative quantification error frequencies show 94.7% of cases below 0.1. (F) Quantification error associated with number of peptides used per protein (boxes indicated 2nd and 3rd quartiles while whiskers indicate data 1.5 times the inter quartile range above and below these quartiles). Overall quantification errors are very low and further improve with the number of peptides used for FLEXIQuant-LF analysis.

Tables

Key resources table

Reagent type (species) or resource	Designation	Source or reference	Identifiers	Additional information
Cell line (Homo sapiens)	HeLa S3	ATCC	Cat# CCL-2.2, RRID:CVCL_0058
Antibody	Mouse monoclonal CDC27 antibody (AF3.1)	Santa Cruz	Cat# sc-9972	IP (1:3)
Antibody	Mouse polyclonal Normal mouse IgG	Santa Cruz	Cat# sc-2025	IP (1:6)
Peptide, recombinant protein	HRM calibration peptides (iRT peptides)	Biognosys	Cat# Ki-3002
Peptide, Recombinant protein	Trypsin (sequencing grade modified trypsin)	Promega	V517
Chemical compound, drug	Nacodazole	Sigma Aldrich	Cat# M1404	100 ng/ml
Chemical compound, drug	Penicillin and streptomycin mix	Invitrogen	Cat# 15140–122	100 μg/ml
Chemical compound, drug	Halt Protease and Phosphatase Inhibitor	Thermo Fisher	Cat# 78442
Software, algorithm	FLEXIQuant-LF	This paper		See Abstract, Materials and methods, Data and Software Availability
Software, algorithm	MaxQuant v1.5.2.8	Cox lab, Max Planck Institute of Biochemistry	https://www.maxquant.org
Software, algorithm	ProteinPilot v4.5.1	Sciex	https://sciex.com/
Software, algorithm	Spectronaut v7.0	Biognosys	https://biognosys.com
Other	DMEM media	Invitrogen	Cat# 11965
Other	FBS	Invitrogen	Cat# 26140–079
Other	L-glutamine	Invitrogen		ThermoFisher: Cat# 25030149
Other	4–12% SDS-PAGE gel	Invitrogen	Cat# NP0329BOX
Other	MES buffer	Invitrogen	Cat# NP0002-02
Other	Thymidine	Sigma Aldrich	Cat# T1895
Other	RIPA Lysis Buffer	Santa Cruz	Cat# sc-24948
Other	Affi-Prep Protein A beads	Biorad	Cat# 1560006
Other	Formic acid (FA)	Thermo Fisher	Cat# A117-50
Other	Acetonitrile (ACN)	Thermo fisher	Cat# A955-4
Other	Water	Thermo fisher	Cat# W6-4
Other	TripleTOF 5600	Sciex	https://sciex.com/
Other	Nano cHiPLC trap column (200 µm x 0.5 mm Reprosil C18 3 µm 120 Å)	Eksigent	Cat# 804–00016
Other	Nano cHiPLC column (75 µm x 15 cm Reprosil C18 3 µm 120 Å)	Eksigent	Cat# 804–00011
Other	DTT	Sigma Aldrich	Cat# D9779

Table 1

Overview of FLEXIQuant-LF analysis for the APC/C complex components during nocodazole arrest.

Column name explanations: Quant. peptides: quantified peptides; Seq. cov.: sequence coverage; Likely diff. modified: number of peptides classified as likely differentially modified; Possibly diff. modified: number of peptides classified as possibly differentially modified; Excluded: number of peptides classified as outliers and thus excluded from RM score calculation; (DDA: number of peptides classified as differentially modified for which we found evidence in our DDA data; Lit.: number of peptides classified as differentially modified for which we found evidence for modification in the literature).

			FLEXIQuant-LF analysis			Evidence
Protein	Quant. peptides	Seq. cov.	Likely diff. modified	Possibly diff. modified	Excluded	DDA	Lit.	No evidence
APC1	54	39%	7	4	4	8	9	2
APC4	28	43%	0	0	2	-	-	0
APC5	37	59%	4	5	2	2	8	1
APC7	27	52%	4	2	1	1	4	2
APC10	9	69%	0	0	3	-	-	0
APC15	1	30%	0	0	0	-	-	0
APC16	3	30%	0	0	0	-	-	0
CDC16	23	41%	3	2	3	1	3	2
CDC20	7	22%	1	0	1	0	1	0
CDC23	33	52%	3	3	4	3	5	1
CDC27	33	55%	9	4	1	6	12	1
*in total*	*248*	*AVG* 47%	30 12%	20 8%	20 8%	21 42%	41 82%	9 18%

Table 2

Differentially modified peptides.

Overview of peptides classified as likely or possibly differentially modified and resulting RM scores of the FLEXIQuant-LF analysis of APC/C.

Protein	Peptide	Start	End	RM score
Protein	Peptide	Start	End	4 hr	8 hr	10 hr
APC1	GDSPVTSPFQNYSSIHSQSR	311	330	0.71	0.37	0.27
	SPSISNMAALSR	341	352	0.71	0.32	0.29
	FSEQGGTPQNVATSSSLTAHLR	285	306	0.69	0.34	0.30
	AHSPALGVHSFSGVQR	353	368	0.69	0.35	0.30
	NFDFEGSLSPVIAPK	680	694	0.80	0.43	0.36
	LLQLCamQR	1070	1076	0.57	0.51	0.45
	ARPSETGSDDDWEYLLNSDYHQNVESHLLNR	696	726	0.78	0.61	0.48
	LHDSLYNEDCamTFQQLGTYIHSIR	566	588	0.75	0.42	0.50
	SLCamLSPSEASQMK	727	739	0.83	0.61	0.51
	TMALPVGR	1077	1084	0.65	0.59	0.56
	LHDSLYNEDCamTFQQLGTYIHSIRDPVHNR	566	594	0.69	0.63	0.57
APC5	EELDVSVR	190	197	0.64	0.42	0.35
	EEEVSCamSGPLSQK	198	210	0.59	0.39	0.40
	ALTPASLQK	230	238	0.57	0.49	0.43
	CamQVASAASYDQPK	670	682	0.52	0.47	0.46
	KTVEDADMELTSR	168	180	0.76	0.64	0.50
	LILTGAESK	281	289	0.67	0.53	0.52
	LMAEGELK	99	106	0.70	0.58	0.54
	DSDLLHWK	404	411	0.58	0.58	0.57
	TVEDADMELTSR	169	180	0.82	0.63	0.59
APC7	VRPSTGNSASTPQSQCamLPSEIEVK	116	139	0.85	0.46	0.41
	AYAFVHTGDNSR	242	253	0.60	0.47	0.47
	DMAAAGLHSNVR	43	54	0.61	0.52	0.49
	YTMALQQK	100	107	0.70	0.47	0.54
	ALTQRPDYIK	468	477	0.67	0.55	0.57
CDC16	QTAEETGLTPLETSR	573	587	0.86	0.34	0.34
	CamYDFDVHTMK	544	553	0.60	0.55	0.45
	SSICamLLR	130	136	0.65	0.60	0.49
	IYDALDNR	139	146	0.71	0.56	0.52
	DPFHASCamLPVHIGTLVELNK	261	280	0.44	1.00	0.60
CDC20	VLSLTMSPDGATVASAAADETLR	446	468	0.51	0.32	0.34
CDC23	NQGETPTTEVPAPFFLPASLSANNTPTR	558	585	0.82	0.27	0.13
	RVSPLNLSSVTP	586	597	0.71	0.30	0.20
	VSPLNLSSVTP	587	597	0.82	0.36	0.28
	AALYFQR	350	356	0.67	0.57	0.52
	LWDEASTCamAQK	525	535	0.74	0.62	0.56
	NTSAAIQAYR	380	389	0.70	0.55	0.56
CDC27	EVTPILAQTQSSGPQTSTTPQVLSPTITSPPNALPR	341	376	0.46	0.31	0.19
	LDSSIISEGK	432	441	0.66	0.33	0.27
	LFTSDSSTTK	381	390	0.58	0.29	0.30
	YSLNTDSSVSYIDSAVISPDTVPLGTGTSILSK	224	256	0.56	0.32	0.33
	LNLESSNSK	215	223	0.69	0.33	0.33
	GGITQPNINDSLEITK	416	431	0.73	0.47	0.41
	QPETVLTETPQDTIELNR	197	214	0.71	0.42	0.41
	SVFSQSGNSR	331	340	0.66	0.35	0.42
	FTSLQNFSNCamLPNSCamTTQVPNHSLSHR	170	196	0.73	0.46	0.45
	ISTITPQIQAFNLQK	442	456	0.77	0.54	0.52
	ASVLFANEK	710	718	0.53	0.48	0.56
	HYNAWYGLGMIYYK	634	647	0.53	0.76	0.59
	DAVFLAER	23	30	0.62	0.61	0.59