Clean a disposable glass culture tube by washing three times with 100% methanol. Allow to air-dry.

Pipette 0.5µL of Diacylglycerol (stock concentration is 10mg/mL) and 5µL of Phosphatidylserine (stock concentration is 10mg/mL) into the cleaned and dried glass tube.

Vacuum dry lipids using a SpeedVac system for 0h 10m 0s. This should leave a visible, translucent lipid pellet.

Resuspend lipids from step 3 in 50µL of 25millimolar (mM) HEPES 7.4 , 50millimolar (mM) KCl. Vortex gently until pellet is no longer visible.

Prepare a primary “2X master mix” containing 50millimolar (mM) HEPES 7.5, 100millimolar (mM) KCl, 0.2% (v/v) 2‐Mercaptoethanol, 20millimolar (mM) MgCl₂, 2millimolar (mM) ATP, 2millimolar (mM) CaCl₂, 200μg/ml Phosphatidylserine and 20μg/ml Diacylglycerol.

For each reaction, add 15µL of the primary “2X master mix” to a clean Eppendorf tube.

Add 7.5µL of 200nanomolar (nM) LRRK1 wild type protein (final concentration is 50nanomolar (nM)) to each reaction and allow equilibration On ice for 0h 5m 0s.

Start the kinase reaction by adding 7.5µL of 400nanomolar (nM) PKC Alpha protein (final concentration is 100nanomolar (nM)).

Transfer the Eppendorf tubes to the thermo mixer set at 30°C, 1000rpm,0h 0m 0s. Incubate for 0h 45m 0s.

Stop the kinase reaction by adding 10µL of 4X LDS loading buffer to the reaction mix to a final concentration of 1X.

Incubate the samples for 0h 5m 0s at 70°C on a heat block before proceeding to SDS-polyacrylamide gel electrophoresis (SDS-PAGE) section.

Load samples onto a NuPAGE 4–12% Bis–Tris Midi Gel (ThermoFisherScientific, Cat#WG1402BOX or Cat#WG1403BOX), alongside pre-stained molecular weight markers (ranging from 10 kDa to 250 kDa). Rinse wells carefully with running buffer before loading samples.

Electrophorese samples at 130V with MOPS SDS running buffer for 2h 0m 0s or until the blue dye runs off the gel.

Place gel in a clean glass 15 cm dish and cover with 15mL-20mL of InstantBlue® Coomassie Protein stain. Incubate on see-saw rocker for 1h 0m 0s at Room temperature.

Replace the InstantBlue® Protein stain with double distilled water and allow to de-stain at Room temperature 0h 2m 0s before proceeding with peptide digestion as described in Total Protein Digestion section.

Using a clean scalpel, excise stained-bands corresponding to LRRK1 from gel and cut into approximately 1mm² gel pieces.

Transfer the gel pieces into a low-bind tube.

De-stain gel pieces by repeated 0h 10m 0s washes in 40% (v/v) ACN in 40millimolar (mM) NH₄HCO_3.

Reduce peptides by addition of 100µL of 5millimolar (mM) DTT in 40millimolar (mM) NH₄HCO₃. Incubate on thermomixer at 56°C for 0h 30m 0s, 1200rpm,0h 0m 0s.

Remove the DTT solution and incubate gel pieces in 40% (v/v) ACN in 40millimolar (mM) NH₄HCO₃for 0h 10m 0s at Room temperature?

Alkylate peptides by addition of 20millimolar (mM) iodoacetamide in 40millimolar (mM) NH₄HCO₃ and incubate at Room temperature for 0h 30m 0s, 1200rpm,0h 0m 0s.

Dehydrate gel pieces by washing in 100% (v/v) ACN for 0h 10m 0s.

Remove supernatant using a pipette and vacuum dry gel pieces to remove any residual CAN.

Add 100ng of protease in 100µL of appropriate buffer (See Table 1) to the gel pieces from step 23 and incubate 0h 10m 0s on thermomixer at 37°C, 1200rpm,0h 0m 0s.

A	B
Protease	Buffer
Trypsin + LysC	50 mM TEABC
Asp-N	50 mM Tris-HCl
Chymotrypsin	100 mM Tris-HCl + 10 mM CaCl2

Table 1: Protease combinations used for total protein digestion and appropriate buffers for each protease.

Supplement samples from step 24 with 50µL of extraction buffer (80% ACN in 0.2% Formic Acid) and incubate on thermomixer at Room temperature for 0h 10m 0s at 1200rpm,0h 0m 0s.

Centrifuge samples for 0h 1m 0s at 2000x g,0h 0m 0s to pellet the gel pieces and using a pipette carefully transfer the supernatant to a new low-binding? tube.

Repeat step 25 until the gel pieces appear completely dried. Each time, transfer the supernatant into the same tube (from step 26).

Vacuum dry the combined supernatants (containing the digested peptides) and proceed with C18 clean-up protocol (as described in C18 stage-tip protocol section).

Prepare single layer of C18 stage-tip using 16-gauge syringe [FT(1].

Resuspend the vacuum dried peptides from step 28 in 80µL of Solvent A1 (0.1% (by vol) TFA in MQ-H2O).

Add 80µL of 100% (by vol) ACN to the C18 stage-tip from Step 29 and centrifuge at 2000x g,0h 0m 0s for 0h 2m 0s at Room temperature. Discard flow through.

Add 80µL Solvent A1 (0.1% (by vol) TFA (by vol) in MQ-H2O)) and centrifuge at 2000x g,0h 0m 0s for 0h 2m 0s at Room temperature. Discard flow through. Repeat this step.

Load the acidified peptide digest from Step 30 to the C18 stage-tip from step 32 and centrifuge at 1500x g,0h 0m 0s for 0h 5m 0s at Room temperature.

Reapply the flow through to the C18 stage-tip column and centrifuge at 1500x g,0h 0m 0s for 0h 5m 0s at Room temperature.

Add 80µL of Solvent A1 (0.1% (by vol) TFA v/v) in MQ-H2O)?) to the C18 stage-tip column and centrifuge at 2000x g,0h 0m 0s for 0h 2m 0s at Room temperature. Discard flow through. Repeat again.

Place the C18 stage-tip from step 35 into a new 1.5 ml low binding tube.

Elute peptides from the C18 stage-tip by adding 40µL of Elution buffer (Solvent B1: 40% (by vol) acetonitrile in 0.1% (by vol) TFA) in MQ-H2O and centrifuge at 1500x g,0h 0m 0s for 0h 2m 0s.

Repeat step 37.

Elute peptides from the C18 stage-tip by adding 40µL of Elution buffer (Solvent B1: 40% (by vol) acetonitrile in 0.1% (by vol) TFA) in MQ-H2O and centrifuge at 1500x g,0h 0m 0s for 0h 2m 0s.

Immediately snap freeze the eluted peptides from step 38 on dry ice and vacuum dry.

Perform mass spectrometry analysis of the peptides as described in LC-MS/MS analysis section.

Dissolve the peptides in LC-Buffer (3% ACN (v/v) in 0.1% Formic acid (v/v)).

Take 200ng of the peptide digest of LRRK2 in 5µL or 10µL in LC-buffer and prepare it for the Evotips loading. The Evo tips are a versatile disposable trap columns that enables <0.1% carry-over between samples.

Prepare the Evotips as described in the Protocol in PMID: 33367571.

Place the Evotips on EvoSep autosampler and used the 30 sample per day (30SPD) method to execute the LC method through Xcalibur interface that is inline with Orbitrap Exploris 240 mass spectrometer.

EvoSep LC system injects and executes a partial elution of the sample from Evotip and loads onto the long storage loop in which the pre-formed gradient generated at the initial step. Following the loading the High-pressure pump pushes the sample into the analytical column (ReproSil-Pur C18, 1.9 µm beads by Dr Maisch. #EV1113).

The following MS instrument method can be constructed for the High-resolution HCD fragmentation analysis:

A	B	C
Instrument	Thermo Scientific Orbitrap Exploris 240
LC system	EvoSep Liquid Chromatography system	30 SPD method
Method duration	45 min
MS Global settings:
	Infusion mode:	Liquid Chromatography
	Expected LC peak width (s):	15
	Advanced Peak determination:	TRUE
	Default charge state:	2
	Internal mass calibration:	off
Full scan settings:
	Orbitrap resolution:	120000
	Scan range (m/z):	375-1500
	RF lens(%):	70
	AGC target:	Custom
	Normalized AGC target (%):	300
	Maximum injection Time mode:	Custom
	Maximum injection Time (ms):	25
	Micorscans:	1
	Data type:	Profile
	Polarity:	Positive
Filters:
MIPS	Monoisotopic peak determination:	Peptide
	Relax restrictions when too few precursors are found:	TRUE
Intensity	Filter Type:	Intensity Threshold
	Intensity Threshold:	5.00E+03
Charge State	Include charge state(s):	2 to 6
	Include undetermined charge states:	False
Dynamic Exclusion	Dynamic Exclusion Mode:	Custom
	Exclude after n times:	1
	Exclusion duration (s):	5
	Mass Tolerance:	ppm
	Low:	10
	High	10
	Exclude isotopes:	TRUE
	Perform dependent scan on single charge state per precursor only:	FALSE
Data Dependent	Data Dependent Mode:	Number of Scans
	Number of Dependent Scans	10
ddMS2 settings	Isolation Window (m/z):	1.2
	Isolation Offset:	Off
	Collision Energy Mode:	Fixed
	Collision Energy Type:	Normalized
	HCD Collision Energy (%):	28
	Orbitrap resolution:	15000
	First Mass (m/z):	110
	Scan range mode:	Auto
	AGC target:	Standard
	Maximum injection Time mode:	Custom
	Maximum injection Time (ms):	100
	Micorscans:	1
	Data type:	Profile
	Polarity:	Positive

Transfer the raw data to search with Thermo Scientific Proteome Discoverer 2.4 Software suite that is integrated with Sequest-HT search algorithm.

We recommend creating a custom protein sequence FASTA file rather than using the entire Uniprot Human or Mouse proteome FASTA file. For example: Copy the Human LRRK1 FASTA sequence and past it into a Notepad++ and save with LRRK1.FASTA .

Import the LRRK1.FASTA sequence into the PD 2.4 software.

Construct the Processing and Consensus workflows

A	B	C
------------------------------------------------------------------
The Processing workflow tree
------------------------------------------------------------------
(0) Spectrum Files
(1) Spectrum Selector
(2) Sequest HT
(3) Fixed Value PSM Validator
(4) IMP-ptmRS
(5) Minora Feature Detector
------------------------------------------------------------------
Processing node 0	Spectrum Files
------------------------------------------------------------------
Input Data		Note
File Name(s)		Specify the sample condtion and the Enyzme associated with the digestion
	RN-AM_211216_LRRK1_+PKC_Tryp-LysC_01.raw
	RN-AM_211216_LRRK1_+PKC_Tryp-LysC_01.raw
	RN-AM_211216_LRRK1_-PKC_Tryp-LysC_01.raw
	RN-AM_211216_LRRK1_-PKC_Tryp-LysC_01.raw
------------------------------------------------------------------
Processing node 1	Spectrum Selector
------------------------------------------------------------------
1. General Settings
Precursor Selection	Use MS1 Precursor
Use Isotope Pattern in Precursor Reevaluation	True
Provide Profile Spectra	Automatic
2. Spectrum Properties Filter
Lower RT Limit	0
Upper RT Limit	0
First Scan	0
Last Scan	0
Lowest Charge State	0
Highest Charge State	0
Min. Precursor Mass	350 Da
Max. Precursor Mass	5000 Da
Total Intensity Threshold	0
Minimum Peak Count	1
3. Scan Event Filters
Mass Analyzer	Is FTMS
MS Order	Is MS2; MS1
Activation Type	Is HCD
Min. Collision Energy	0
Max. Collision Energy	1000
Scan Type	Is Full
Polarity Mode	Is +
4. Peak Filters
- S/N Threshold (FT-only)	1.5
5. Replacements for Unrecognized Properties
Unrecognized Charge Replacements	Automatic
Unrecognized Mass Analyzer Replacements	FTMS
Unrecognized MS Order Replacements	MS2
Unrecognized Activation Type Replacements	HCD
Unrecognized Polarity Replacements	+
Unrecognized MS Resolution@200 Replacements	120000
Unrecognized MSn Resolution@200 Replacements	30000
6. Precursor Pattern Extraction
Precursor Clipping Range Before	2.5 Da
	5.5 Da
------------------------------------------------------------------
Processing node 2	Sequest HT
------------------------------------------------------------------
1. Input Data
Protein Database	LRRK1.FASTA
Enzyme Name	Trypsin (Full)	Here, specify AspN and Chymotrypsin separately fof the searches associated with those conditions
Max. Missed Cleavage Sites	2
Min. Peptide Length	7
Max. Peptide Length	144
Max. Number of Peptides Reported	10
2. Tolerances
Precursor Mass Tolerance	10 ppm
Fragment Mass Tolerance	0.05 Da
Use Average Precursor Mass	False
Use Average Fragment Mass	False
3. Spectrum Matching
Use Neutral Loss a Ions	True
Use Neutral Loss b Ions	True
Use Neutral Loss y Ions	True
Use Flanking Ions	True
Weight of a Ions	0
Weight of b Ions	1
- Weight of c Ions	0
Weight of x Ions	0
Weight of y Ions	1
Weight of z Ions	0
4. Dynamic Modifications
Max. Equal Modifications Per Peptide	3
Max. Dynamic Modifications Per Peptide	4
- 1. Dynamic Modification	Oxidation / +15.995 Da (M)
- 2. Dynamic Modification	Phospho / +79.966 Da (S, T, Y)
7. Static Modifications
- 1. Static Modification	Carbamidomethyl / +57.021 Da (C)
------------------------------------------------------------------
Processing node 3	Fixed Value PSM Validator
------------------------------------------------------------------
1. Input Data
Maximum Delta Cn	0.05
Maximum Rank	0
------------------------------------------------------------------
Processing node 4	IMP-ptmRS
------------------------------------------------------------------
1. Scoring
PhosphoRS Mode	True
Report only PTMs	True
Use Diagnostic Ions	True
Use Fragment Mass Tolerance of Search Node	True
Fragment Mass Tolerance	0.5 Da
Consider Neutral Loss peaks for CID, HCD and EThcD	Automatic
Maximum Peak Depth	8
Use a Mass accuracy correction	False
2. Performance
Maximum Number of Position Isoforms	500
Maximum PTMs Per Peptide	10
------------------------------------------------------------------
Processing node 5	Minora Feature Detector
------------------------------------------------------------------
1. Peak & Feature Detection
Min. Trace Length	5
- Max. ΔRT of Isotope Pattern Multiplets [min]	0.2
2. Feature to ID Linking
PSM Confidence At Least	High

A	B
The Consensus workflow tree
------------------------------------------------------------------
(0) MSF Files
(1) PSM Grouper
(2) Peptide Validator
(3) Peptide and Protein Filter
(4) Protein Scorer
(5) Protein Grouping
(6) Peptide in Protein Annotation
(15) Modification Sites
(7) Protein FDR Validator
(16) Peptide Isoform Grouper
(10) Feature Mapper
(11) Precursor Ions Quantifier
Post-processing nodes
--------------------------------
(12) Result Statistics
(13) Display Settings
(14) Data Distributions
------------------------------------------------------------------
Processing node 0	MSF Files
------------------------------------------------------------------
1. Storage Settings
Spectra to Store	Identified or Quantified
Feature Traces to Store	All
2. Merging of Identified Peptide and Proteins
Merge Mode	Globally by Search Engine Type
3. FASTA Title Line Display
Reported FASTA Title Lines	Best match
Title Line Rule	standard
4. PSM Filters
Maximum Delta Cn	0.05
Maximum Rank	0
Maximum Delta Mass	0 ppm
Hidden Parameters
MSF File(s)	RN-AM_211216_LRRK1_Sequest-Trypsin-(1).msf
------------------------------------------------------------------
Processing node 1	PSM Grouper
------------------------------------------------------------------
1. Peptide Group Modifications
Site Probability Threshold	75
------------------------------------------------------------------
Processing node 2	Peptide Validator
------------------------------------------------------------------
1. General Validation Settings
Validation Mode	Automatic (Control peptide level error rate if possible)
Target FDR (Strict) for PSMs	0.01
Target FDR (Relaxed) for PSMs	0.05
Target FDR (Strict) for Peptides	0.01
Target FDR (Relaxed) for Peptides	0.05
2. Specific Validation Settings
Validation Based on	q-Value
Target/Decoy Selection for PSM Level FDR Calculation Based on Score	Automatic
Reset Confidences for Nodes without Decoy Search (Fixed Score thresholds)	False
------------------------------------------------------------------
Processing node 3	Peptide and Protein Filter
------------------------------------------------------------------
1. Peptide Filters
Peptide Confidence At Least	High
Keep Lower Confident PSMs	False
Minimum Peptide Length	7
Remove Peptides without Protein Reference	False
2. Protein Filters
Minimum Number of Peptide Sequences	1
Count Only Rank 1 Peptides	False
Count Peptides only for Top Scored Protein	False
------------------------------------------------------------------
Processing node 4	Protein Scorer
------------------------------------------------------------------
No parameters
------------------------------------------------------------------
Processing node 5	Protein Grouping
------------------------------------------------------------------
1. Protein Grouping
Apply Strict parsimony principle	True
------------------------------------------------------------------
Processing node 6	Peptide in Protein Annotation
------------------------------------------------------------------
1. Flanking Residues
Annotate Flanking Residues of the Peptide	True
Number Flanking Residues in Connection Tables	1
2. Modifications in Peptide
Protein Modifications Reported	Only for Master Proteins
3. Modifications in Protein
Modification Sites Reported	All And Specific
Minimum PSM Confidence	High
Report only PTMs	True
4. Positions in Protein
Protein Positions for Peptides	Only for Master Proteins
------------------------------------------------------------------
Processing node 15	Modification Sites
------------------------------------------------------------------
1. General
Report only PTMs	True
only Master Proteins	True
Motif Radius	10
------------------------------------------------------------------
Processing node 7	Protein FDR Validator
------------------------------------------------------------------
1. Confidence Thresholds
Target FDR (Strict)	0.01
Target FDR (Relaxed)	0.05
------------------------------------------------------------------
Processing node 16	Peptide Isoform Grouper
------------------------------------------------------------------
No parameters
------------------------------------------------------------------
Processing node 10	Feature Mapper
------------------------------------------------------------------
1. Chromatographic Alignment
Perform RT Alignment	True
- Maximum RT Shift [min]	10
Mass Tolerance	10 ppm
Parameter Tuning	Coarse
2. Feature Linking and Mapping
RT Tolerance [min]	0
Mass Tolerance	0 ppm
Min. s/N Threshold	5
------------------------------------------------------------------
Processing node 11	Precursor Ions Quantifier
------------------------------------------------------------------
1. General Quantification Settings
Peptides to Use	Unique + Razor
Consider Protein Groups for Peptide Uniqueness	True
Use Shared Quan Results	True
Reject Quan Results with Missing Channels	False
2. Precursor Quantification
Precursor Abundance Based on	Intensity
Min. # Replicate Features [%]	0
3. Normalization and Scaling
Normalization Mode	Total Peptide Amount
Scaling Mode	On All Average
4. Exclude Peptides from Protein Quantification
for Normalization	Use All Peptides
for Protein Roll-Up	Use All Peptides
for Pairwise Ratios	Exclude Modified
5. Quan Rollup and Hypothesis Testing
Protein Abundance Calculation	Summed Abundances
N for Top N	3
Protein Ratio Calculation	Pairwise Ratio Based
Maximum Allowed Fold Change	100
Imputation Mode	None
Hypothesis Test	t-test (Background Based)
6. Quan Ratio Distributions
- 1st Fold Change Threshold	2
- 2nd Fold Change Threshold	4
- 3rd Fold Change Threshold	6
- 4th Fold Change Threshold	8
- 5th Fold Change Threshold	10

If the database search is to be done using MaxQuant then refer below settings

A	B
Parameter	Value
Version	2.0.3.0
User name	RNirujogi
Machine name	MRC-MS-R640-4
Date of writing	05/23/2022 15:15:41
Include contaminants	TRUE
PSM FDR	0.01
SM FDR Crosslink	0.01
Protein FDR	0.01
Site FDR	0.01
Use Normalized Ratios For Occupancy	TRUE
Min. peptide Length	7
Min. score for unmodified peptides	0
Min. score for modified peptides	40
Min. delta score for unmodified peptides	0
Min. delta score for modified peptides	6
Min. unique peptides	0
Min. razor peptides	1
Min. peptides	1
Use only unmodified peptides and	TRUE
Modifications included in protein quantification	Oxidation (M);Acetyl (Protein N-term);Deamidation (NQ)
Peptides used for protein quantification	Razor
Discard unmodified counterpart peptides	TRUE
Label min. ratio count	2
Use delta score	FALSE
iBAQ	FALSE
iBAQ log fit	FALSE
Match between runs	FALSE
Find dependent peptides	FALSE
Fasta file	C:\Raja\Database\LRRK1.FASTA
Decoy mode	revert
Include contaminants	TRUE
Advanced ratios	TRUE
Fixed andromeda index folder
Combined folder
Second peptides	TRUE
Stabilize large LFQ ratios	TRUE
Separate LFQ in parameter groups	FALSE
Require MS/MS for LFQ comparisons	TRUE
Calculate peak properties	FALSE
Main search max. combinations	200
Advanced site intensities	TRUE
Write msScans table	FALSE
Write msmsScans table	TRUE
Write ms3Scans table	TRUE
Write allPeptides table	TRUE
Write mzRange table	TRUE
Write DIA fragments table	FALSE
Write DIA fragments quant table	FALSE
Write pasefMsmsScans table	TRUE
Write accumulatedMsmsScans table	TRUE
Max. peptide mass [Da]	4600
Min. peptide length for unspecific search	8
Max. peptide length for unspecific search	25
Razor protein FDR	TRUE
Disable MD5	FALSE
Max mods in site table	3
Match unidentified features	FALSE
Epsilon score for mutations
Evaluate variant peptides separately	TRUE
Variation mode	None
MS/MS tol. (FTMS)	20 ppm
Top MS/MS peaks per Da interval. (FTMS)	12
Da interval. (FTMS)	100
MS/MS deisotoping (FTMS)	TRUE
MS/MS deisotoping tolerance (FTMS)	7
MS/MS deisotoping tolerance unit (FTMS)	ppm
MS/MS higher charges (FTMS)	TRUE
MS/MS water loss (FTMS)	TRUE
MS/MS ammonia loss (FTMS)	TRUE
MS/MS dependent losses (FTMS)	TRUE
MS/MS recalibration (FTMS)	FALSE
MS/MS tol. (ITMS)	0.5 Da
Top MS/MS peaks per Da interval. (ITMS)	8
Da interval. (ITMS)	100
MS/MS deisotoping (ITMS)	FALSE
MS/MS deisotoping tolerance (ITMS)	0.15
MS/MS deisotoping tolerance unit (ITMS)	Da
MS/MS higher charges (ITMS)	TRUE
MS/MS water loss (ITMS)	TRUE
MS/MS ammonia loss (ITMS)	TRUE
MS/MS dependent losses (ITMS)	TRUE
MS/MS recalibration (ITMS)	FALSE
MS/MS tol. (TOF)	40 ppm
Top MS/MS peaks per Da interval. (TOF)	10
Da interval. (TOF)	100
MS/MS deisotoping (TOF)	TRUE
MS/MS deisotoping tolerance (TOF)	0.01
MS/MS deisotoping tolerance unit (TOF)	Da
MS/MS higher charges (TOF)	TRUE
MS/MS water loss (TOF)	TRUE
MS/MS ammonia loss (TOF)	TRUE
MS/MS dependent losses (TOF)	TRUE
MS/MS recalibration (TOF)	FALSE
MS/MS tol. (Unknown)	20 ppm
Top MS/MS peaks per Da interval. (Unknown)	12
Da interval. (Unknown)	100
MS/MS deisotoping (Unknown)	TRUE
MS/MS deisotoping tolerance (Unknown)	7
MS/MS deisotoping tolerance unit (Unknown)	ppm
MS/MS higher charges (Unknown)	TRUE
MS/MS water loss (Unknown)	TRUE
MS/MS ammonia loss (Unknown)	TRUE
MS/MS dependent losses (Unknown)	TRUE
MS/MS recalibration (Unknown)	FALSE
Site tables	Deamidation (NQ)Sites.txt;Oxidation (M)Sites.txt;Phospho (ST)Sites.txt

Manually verify the MS/MS spectrum and phosphorylation localization score within PD2.4.

Now export the filtered Phosphosites from modifications table for each of the sample/category

Use the below scripts for parsing and combining the data to generate a heatmap representation.

The script below would first read phosphosite mapping result, then map them on to the original protein amino acid sequence through combining PeptideGroups and ModificationSites result text file. The data would be filtered by probability greater or equal to 75 and grouped by the different tryptic digestion enzymes used. Only entries with the highest abundance values according to the unique motif, position and sample condition are kept. Then based on the sequence length, the data was divided into instances of 500 amino acid continuous span on the protein sequence. Each of these instances would be used to create a heatmap where the abundance of the peptide would be the heatmap color, the sample condition would be presented on the X-axis while the position of the phosphosites are represented in the Y-axis in ascending order.

import numpy as np
import pandas as pd
from glob import glob
import re
import seaborn as sns
import matplotlib.pylab as plt
if __name__ == "__main__":
proteases = ["AspN", "Chymotrypsin",
#"Trypsin"
]
files = ["PeptideGroups", "ModificationSites"]
phospho_re = re.compile(r"Phospho [S(\d+)\((\d+)\)]")
results = {}
for i in glob(r"\\mrc-smb.lifesci.dundee.ac.uk\mrc-group-folder\ALESSI\Toan\TS22D4_Phosphosite mapping_02\*.txt"):
for p in proteases:
if p in i:
for f in files:
if f in i:
if p not in results:
results[p] = {}
results[p][f] = pd.read_csv(i, sep="\t")
break
break
merged_df = []
columns = set()
for p in proteases:
pg = results[p][files[0]]
ms = results[p][files[1]]
for i, r in pg.iterrows():
pg.at[i, "Primary IDs"] = ";".join([r["Master Protein Accessions"], r["Annotated Sequence"][4:len(r["Annotated Sequence"])-4]])
phos = []
s = re.search("\[(\d+)-(\d+)\]", r["Positions in Master Proteins"])

pos = []
if s:
pg.at[i, "Start"] = s.group(1)
mod_count = r["Modifications"].count("]; ")
if mod_count > 0:
for m in r["Modifications"].split("]; "):
if "Phospho" in m:
s = re.search("\[(.+)", m)
if s:
for si in s.group(1).split("; "):
sire = re.search("(\w)(\d+)\(", si)
if sire:
phos.append("".join([sire.group(1), sire.group(2)]))
pos.append(str(int(sire.group(2)) + int(pg.at[i, "Start"]) - 1))
else:
if "Phospho" in r["Modifications"]:
s = re.search("\[(.+)", r["Modifications"])
if s:
for si in s.group(1).split("; "):
sire = re.search("(\w)(\d+)\(", si)
if sire:
phos.append("".join([sire.group(1), sire.group(2)]))
pos.append(str(int(sire.group(2)) + int(pg.at[i, "Start"]) - 1))
pg.at[i, "Position"] = pos
pg.at[i, "Phospho"] = phos

pg = pg.explode(["Phospho", "Position"])
pg = pg[pd.notnull(pg["Phospho"])]
pg["Position"] = pg["Position"].astype(int)
for i, r in ms.iterrows():
ms.at[i, "Primary IDs"] = ";".join([r["Protein Accession"], r["Peptide Sequence"]])
rpg = pg[[i for i in pg.columns if i.startswith("Abundance")] + ["Primary IDs", "Phospho", "Position", "Modifications"]]
rename = {}
for i in rpg.columns:
if "Abundance" in i:
rename[i] = re.sub("Abundance: F\d+: Sample, ", "", i)
columns.add(rename[i])
print(rpg["Primary IDs"])
print(ms["Primary IDs"])
rpg = rpg.rename(columns=rename)
ms["Phospho"] = ms["Target Amino Acid"] + ms["Position in Peptide"].astype(str)
ms["Enzymes"] = p
df = ms.merge(rpg, left_on=["Primary IDs", "Phospho"], right_on=["Primary IDs", "Phospho"])
merged_df.append(df)

merged_df = pd.concat(merged_df, ignore_index=True)
merged_df = merged_df[merged_df["Site Probability"]>=75]
result = pd.melt(merged_df, id_vars=[
"Phospho", "Position_y", "Enzymes", "Motif"], value_vars=list(columns),
var_name="Samples", value_name="Abundance")

a = result.groupby([
#"Phospho",
"Position_y", "Samples", "Enzymes", "Motif"]).max()

a.reset_index(inplace=True)
print(a["Samples"])
a["Conditions"], a["Replicates"] = a["Samples"].str.split("Rep-", expand=True)
for i, g in a.groupby([
# "Phospho",
"Position_y", "Motif"]):
remove_motif = True
for i2, g2 in g.groupby(["Enzymes", "Conditions"]):
if len(g2[pd.notnull(g2["Abundance"])].index) > 1:
remove_motif = False
break
if remove_motif:
a["Motif"].loc[g.index] = ""

a.sort_values("Position_y", inplace=True)
e = 1
n = 500
samples = a["Samples"].unique()
samples_columns = []
for p in proteases:
for s in samples:
samples_columns.append((p, s))
multiindex = pd.MultiIndex.from_tuples(samples_columns, names=["Enzymes", "Samples"])
while n:

c = a[(a["Position_y"] <= n)&(a["Position_y"] > (n-500))]
fontsize_pt = plt.rcParams['ytick.labelsize']
dpi = 72.27
top_margin = 0.2
bottom_margin = 0.2
left_margin = 0.2
right_margin = 0.2
figure_height = (len(c.index)/10) / (1 - top_margin - bottom_margin)
figure_width = 10 / (1-left_margin-right_margin)
c = c.set_index([
#"Phospho",
"Position_y", "Samples", "Enzymes", "Motif"])
c = c.unstack("Enzymes")

b = pd.pivot_table(c, values="Abundance", columns="Samples", index=["Position_y",
#"Phospho",
"Motif"])
b.fillna(0, inplace=True)
b = b.T

for i in b.columns:
b0 = b[i][b[i]==0]
b[i] = (np.log2(b[i], where=b[i]>0) - np.log2(b[i], where=b[i]>0).mean()) / np.log2(b[i], where=b[i]>0).std(ddof=1)
for ind in b0.index:
b[i].loc[ind] = np.nan
b = b.T
new_df = pd.DataFrame(index=b.index, columns=multiindex)
for i in new_df.columns:
if i in b.columns:
new_df[i] = b[i]
else:
new_df[i].fillna(0, inplace=True)

new_df.to_csv(f"merged{n}.csv")
fig, ax = plt.subplots(
figsize=(figure_width, figure_height),
gridspec_kw=dict(top=1-top_margin, bottom=bottom_margin, left=left_margin, right=1-right_margin)
)
mask = np.isnan(b)
sns.heatmap(new_df, cmap="YlGnBu", mask=mask, square=True, ax=ax)
ax.set_facecolor("silver")
ax.xaxis.tick_top()
ax.xaxis.set_label_position('top')
for label in ax.get_yticklabels():
label.set_weight("bold")
for label in ax.get_xticklabels():
label.set_weight("bold")
plt.xticks(rotation=90)
plt.savefig(f"result{n}.pdf")
for i, r in b.iterrows():
if i[1] != "":
p = re.compile(r"[RK]\w[ts]\w\w[RK]")
s = re.search(p, i[1])
if s:
print(i)
n += 500
e += 1
if n >= a["Position_y"].max():
break