SILACtor relies on 3rd party software, but don't worry, you probably already have it in your lab. If not, it can be found online for free. Here is the list of software you will need:

  • Database searching software (such as Mascot)
  • Hardklör

Step #1: Process your data
Using your database searching software of choice and Hardklör, analyze your MS/MS and MS spectra to obtain peptide identifications and a list of precursor ions, respectively. It is important to optimize Hardklör for your mass spectrometer and instrument settings to achieve the best results with SILACtor. To help you do this, follow the Hardklör Tutorial.

Step #2: Set up SILACtor
SILACtor has many parameters and must coordinate the analysis of numerous data file. To make this as easy as possible, a configuration file is used. A sample configuration file is here for you to modify and use for your own purposes. Just right-click the link, and choose "save as".

Configuration files are simple text with tab-delimited tags and values. Any line can be ignored by placing a '#' symbol at the start of the line. There are a total of seven different tags in the configuration file. They are listed immediately below and will be explained in more detail further down.

  • S - Specify a mass difference for a pair of SILAC peptides.
  • DB - Indicates to build a peptide database from Mascot results.
  • DBA - Indicates to build a peptide database from an Accurate Mass and retention Time (AMT) list.
  • P - Specify a user defined parameter.
  • F - Identifies a precursor ion data file to be used in the analysis.
  • R - Indicates to combine technical replicates into a single data set.
  • T - Performs a timepoint analysis on a set of data files.

SILACtor can be configured for any SILAC-labeled amino acids in your experiment. Use the S tags to indicate the expected mass shifts. You can specify more than one mass shift to accomodate multiple labeled amino acids, or multiple labels per peptide. Below is an example for a typical Lys-6, Arg-8 data set, and includes combinations of KK, RR, and KR resulting from missed cleavage:

S	6.0201324 
S	8.0142036 
S	12.0402648
S	14.034336 
S	16.0284072

A list of peptide sequences are needed to provide identification of observed SILAC features from the precursor data. There are two ways to import the data, with the recommended way being to provide an AMT database appropriate for your data acquisition. To do so, use the DBA tag:

DBA	080410_HeLa_KT_AMT.txt

The AMT database must be tab-delimited text with the following columns:

"protein" can be any character string designation for your proteins up to 32 characters. "peptide" is a peptide sequence up to 64 characters. "charge" is the integer charge state of the peptide identification. "mass" is the monoisotopic, zero charge mass value in Daltons for the identified peptide. "firstRT" is the lower limit of retention time (in minutes) over which the peptide is observed. "lastRT" is the upper limit of retention time (in minutes) over which the peptide is observed.

Alternatively, you can have SILACtor build an AMT database for you (currently Mascot users only). Export your Mascot data to a comma separated value file (.CSV) and provide that file, plus the original raw file with the DB tag:

DB	..\080410_HeLa_KT_0hrs_01.csv	..\080410_HeLa_KT_0hrs_01.RAW
DB	..\080410_HeLa_KT_0hrs_02.csv	..\080410_HeLa_KT_0hrs_02.RAW
DB	..\080410_HeLa_KT_1hrs_01.csv	..\080410_HeLa_KT_1hrs_01.RAW
DB	..\080410_HeLa_KT_1hrs_02.csv	..\080410_HeLa_KT_1hrs_02.RAW

Parameters that alter the behavior of SILACtor are specified with the P tag. There are five parameters to specify, each with a specific name. Here are examples of each:

P	RTime	10.0	120.0
P	DBMinFileCount	6    
P	DBMinEventCount	1    
P	DBRTTolerance	10.0 
P	CorrThreshold	0.90 

RTime limits the analysis of SILACtor to just the chromatographic regions specified (in minutes). In the example above, only data obtained between 10 and 120 minutes is used to exclude the loading and column equilibration phases of the data acquisition.

DBMinFileCount is only used if building your own AMT library with the DB tags. Here, the parameter requires the peptides to be observed in 6 of the files that go into building the database.

DBMinEventCount is only used if building your own AMT library with the DB tags. Here, the parameter requires the peptides to be observed just once in each the database search result file to be considered valid.

DBRTTolerance is only used if building your own AMT library with the DB tags. This parameter removes bad peptide IDs, defined as those whose lower and upper retention time boundaries exceed a logical limit. Here, the limit is 10 minutes.

CorrThreshold is the minimum correlation required between any two peptide extracted ion chromatograms to be considered a light-heavy pair if they differ by the expected mass. The use of a correlation reduces the liklihood of spurrious pairings between different peptide signals by requiring both extracted ion chromatograms to have a similar elution profile as judged using Pearson's correlation coefficient. (Setting this to 0, effectively turns off the validation).

Precursor information is included in the analysis using the F tags. Valid files are the Hardklör results files. Each file must be assigned a number. No two files can be assigned the same number. You must also specify an output file for the SILAC results and a time label for timepoint analysis (arbitrary units).

F	1	..\	KT-0hrs-01.txt	0.0
F	2	..\	KT-0hrs-02.txt	0.0
F	3	..\	KT-1hrs-01.txt	1.0
F	4	..\	KT-1hrs-02.txt	1.0

In the sample above, four files are analyzed, two replicates from timepoint 0, and two replicates from time point 1.

Replicate timepoints should be combined into a single set for more robust data analysis. This is done using the R tag. Each replicate analysis must be assigned a number, and these numbers must be different than the F line numbers. Also, indicate an output file for the combined results, an output file for a targeted mass list, and the file numbers from the F tags that you wish to combine (tab-delimited):

R	5	KT-0hrs.txt	KTlist-0hr.txt	1	2
R	6	KT-1hrs.txt	KTlist-1hr.txt	3	4

In the example above, we combined the two replicates at time point 0 (indicated with the F tag numbers 1 and 2) and output the results to KT-0hrs.txt. Also a mass and time targeted inclusion list was generated for follow up analysis of the most interesting peptides that did not receive a sequence ID. This replicate analysis was assigned the number 5. Likewise, a replicate analysis for time point 1 was performed and assinged the number 6.

To generate a timepoint analysis, the T tag is used. It is also assinged a number that was not used previously in the F or R lines. An output file must be specified and indicate the file numbers or replicate numbers to be included (tab-delimited). Note that you can use file numbers from the F lines or replicate numbers from the R lines if you have replicates:
T	7	KT-timepoints.txt	5	6

In the above example, the replicates at time point 0 (number 5) and time point 1 (number 6) are used to make a time point analysis that is output to KT-timepoints.txt.

Step #3: Running SILACtor
SILACtor is operated from the command line. If you are not comfortable setting a path to SILACtor on your system, simply copy and paste SILACtor.exe into the directory containing your data. Create or modify a SILACtor parameters file in the same directory. Then open up a command console, navigate to the directory, and type:

SILACtor.exe silactorConf.txt

where silactorConf.txt can be whatever you name your configuration file.

SILACtor will run, outputting messages to your console indicating its progress. Depending on the size of your experiment, typical run times are anywhere from 5 minutes to an hour.