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Supplementary Material
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Methods
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Programs were written in Microsoft Visual Studio 2008 in Visual Basic .NET. For fast sorting
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of the selected peptides masses, List(Of Long) Visual Basic class was used. Masses with five-
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digit precision and indices containing sequence information (protein number, start position in the
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sequence, and length of the peptides) were concatenated and converted to a long integer value
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and fast sorted using sort method of the class. After sorting, the long integer values were
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converted back to peptide masses and corresponding peptide sequence information was
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associated with them. The algorithm was implemented in the DXMSMS Match Sorting
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program, which is available free for download at
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http://www.creativemolecules.com/CM_Software.htm.
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Experimental crosslinking data were generated as described previously [1, 2]. Briefly, proteins
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were crosslinked with isotopically-coded amine reactive crosslinker CBDPS-H8/D8 (Creative
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Molecules Inc.), digested with proteinase K, the crosslinked peptides were enriched by using
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monomeric avidin and were analyzed by ESI-LC-MS and MS/MS using an Orbitrap mass
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spectrometer and the Mass Tags acquisition method.
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Mass spectrometric analysis was carried out with a nano-HPLC system (Easy-nLC II,
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ThermoFisher) coupled to the ESI-source of an LTQ Orbitrap Velos mass spectrometer
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(ThermoFisher Scientific). Samples were injected onto a 100 μm ID, 360 μm OD IntegraFrit
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trap column (New Objective Inc.) packed with Magic C18AQ (5 µm particle size, 100 Å pore
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size, Bruker-Michrom) and desalted by washing for 15 min 300 nl/min with 0.1% (v/v) formic
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acid. Peptides were subsequently injected onto a 75 μm ID, 360 μm OD IntegraFrit analytical
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column packed with Magic C18AQ (5 µm particle size, 100 Å pore size), equilibrated with 95%
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solvent A (2% (v/v) acetonitrile, 98% (v/v) water, 0.1% (v/v) formic acid), 5% solvent B (90%
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(v/v) acetonitrile, 10% (v/v) water, 0.1% (v/v) formic acid). Peptides were separated at a flow
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rate of 300 nl/min using a 70 minutes gradient (0–60 min: 4–40% solvent B, 60–62 min: 40–80%
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solvent B, 62–70 min: 80% solvent B).
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MS data were acquired with Xcalibur (ver. 2.1.0.1140) with Mass Tags and Dynamic Exclusion
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precursor selection methods enabled in global data dependent settings. For CBDPS-H8/D8, a
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mass difference between the light and heavy isotopic forms of 8.05824 Da was used in Mass
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Tags setting. Mass tags and inclusion list runs used the Top 3 method. MS scans (m/z range
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from 400 to 2000) and MSMS scans were acquired in the Orbitrap mass analyzer at 60000 and
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30000 resolution, respectively. Fragment ions for MSMS acquisition were produced by collision-
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induced dissociation (CID) at normalized collision energy of 35% for 10 ms at activation q=0.25.
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Data analysis was performed with DXMSMS Match of ICC-CLASS [3].
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Computationally, in order to implement the algorithm, all of the peptide masses are first sorted
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into a one-dimensional array. The search iterations are performed as two nested loops, with
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indices starting from opposite ends of the array and with index increments going in opposite
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directions (Figure 1S). The outer loop starts from the top of the one-dimensional array and the
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inner loop starts from the bottom of the one-dimensional array. The inner loop is set to start
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from MP2 + and exit after reaching the element of the one-dimensional array equal to MP2 –.
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Compared to conventional search algorithms, this strategy reduces the number of iterations
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required to search n peptides from ~0.5 n2 to ~6.8 n (Figure 2S).
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Figure 1S. Illustration of the fast mass matching algorithm calculation. Calculated peptide
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masses are sorted ascending in one-dimensional array. The sums of possible peptide masses are
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tested pairwise (Mi+Mj) to see if they fit the experimentally determined mass (Mobs). The search
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is organized as two nested loops. For each Mi (upper box, outer loop), which corresponds to
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MP1, only masses in the vicinity of Mj (i.e., masses between Mj+k and Mj-l -- the start and end
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points of the inner loop), are searched (lower box). The inner loop index (j+k) starts from the
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position where the previous examination of the MP2 values ended, and ends with j-l (i.e., when
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the element of the array with mass equal to MP2 - is reached. As the algorithm proceeds, the
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peptide mass Mi increases, while the peptide mass Mj decreases (arrows). In this way, for each
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Mi, only elements from j+k to j-l are searched instead of the entire array (from 1 to n). This
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results in a significant reduction in the number of iterations needed, and a significant
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improvement in search speed.
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M1
.
Mi
.
.
.
.
.
.
Mj-l
.
.
Mj
.
.
.
Mj+k
.
.
.
Mn
|Mobs-(Mi+Mj+MCL)|< 
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Figure 2S. Dependence of the ratio of the number of iterations required for the conventional
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method to the number of iterations required for the fast sorting algorithm on the size of the
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protein database. The ratio was calculated as number of iterations necessary for the conventional
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algorithm ((n+1)n/2) divided by the number of iterations used in the fast sorting algorithm for the
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peptide selections shown in Table 1, where n is the number of the peptides selected from the
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protein database in each case. The calculated curve for the conventional method is shown in
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Figure 2Sa, and the number of iterations required is approximately equal to ~0.5 n2. By
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performing a linear regression on the data for the fast sorting method, the number of iterations
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required is ~6.8n, Figure 2Sb. Thus the calculated number of iterations needed is reduced by a
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factor of ~0.07n (i.e., 0.5 n2/0.07n = 6.8n), Figure 2Sc.
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a)
number of iterations w/o fast sort
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4E+11
3.5E+1
1
y = 0.500n2
R2 = 1
3E+11
2.5E+1
1
2E+11
1.5E+1
1
1E+11
5E+10
0
0
100000 200000 300000 400000 500000 600000
700000 800000
900000 1000000
n, number of peptides
b)
number if iterations with fast sort
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7000000
y = 6.78n + 51786
R2 = 0.9603
6000000
5000000
4000000
3000000
2000000
1000000
0
0
100000 200000 300000 400000 500000 600000 700000 800000 900000 1000000
n, number of peptides
c)
ratio of iterations w/ and w/o fast sort
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70000
y = 0.074n + 252.64
R2 = 0.9805
60000
50000
40000
30000
20000
10000
0
0
100000 200000 300000 400000 500000 600000 700000 800000 900000 1000000
n, number of peptides
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References
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Proteinase K Non-Specific Digestion for Selective and Comprehensive Identification of
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Interpeptide Crosslinks: Application to Prion Proteins., Mol. Cell. Proteomics 2012, 11,
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M111.013524.
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[2] Petrotchenko, E. V., Serpa, J. J., Borchers, C. H., An Isotopically-coded CID-cleavable
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biotinylated crosslinker for structural proteomics, Mol. Cell. Proteomics 2011,
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doi:10.1074/mcp.M110.001420
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[3] Petrotchenko, E. V., Borchers, C. H., Isotopically-Coded Cleavable CrossLinking Analysis
99
Software Suite (ICC-CLASS) for the automated analysis of MALDI- and ESI-LC-MS-MS/MS
100
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[1] Petrotchenko, E. V., Serpa, J. J., Berjanskii, M., Suriyamongkol, B. P., et al., Use of
crosslinking data., BMC Bioinformatics, submitted.