SMAD 1.20.0
This R package implements statistical modelling of affinity purification–mass spectrometry (AP-MS) data to compute confidence scores to identify bona fide protein-protein interactions (PPI).
Prepare input data into the dataframe datInput with the following format:
idRun | idBait | idPrey | countPrey | lenPrey |
---|---|---|---|---|
AP-MS run ID | Bait ID | Prey ID | Prey peptide count | Prey protein length |
library(SMAD)
#> Loading required package: RcppAlgos
data("TestDatInput")
head(TestDatInput)
#> idRun idBait idPrey countPrey lenPrey
#> 7452 68982 TIMP2 ACTC1 15 377
#> 8016 66491 CASP1 CDK4 9 303
#> 7162 68486 BTG3 RPL24 3 157
#> 8086 66491 CASP1 IMPDH2 9 514
#> 23653 72934 LUM THOP1 7 689
#> 9196 67747 FAS RFC5 9 340
The test data is subset from the unfiltered BioPlex 2.0 data, which consists of apoptosis proteins as baits.
Comparative Proteomic Analysis Software Suite (CompPASS) is based on spoke model. This algorithm was developed by Dr. Mathew Sowa for defining the human deubiquitinating enzyme interaction landscape (Sowa, Mathew E., et al., 2009). The implementation of this algorithm was inspired by Dr. Sowa’s online tutorial. The output includes Z-score, S-score, D-score and WD-score. In its implementation in BioPlex 1.0 (Huttlin, Edward L., et al., 2015) and BioPlex 2.0 (Huttlin, Edward L., et al., 2017), a naive Bayes classifier that learns to distinguish true interacting proteins from non-specific background and false positive identifications was included in the compPASS pipline. This function was optimized from the source code.
scoreCompPASS <- CompPASS(TestDatInput)
head(scoreCompPASS)
#> idBait idPrey AvePSM scoreZ scoreS scoreD Entropy scoreWD
#> 1 AIFM3 AIFM1 20 7.9382230 36.055513 36.055513 0 1.6903085
#> 2 AIFM3 ALDOA 14 2.6586313 9.095453 9.095453 0 0.2308028
#> 3 AIFM3 ATP5A1 5 0.5826082 5.700877 5.700877 0 0.1529845
#> 4 AIFM3 CALR 4 0.8703043 4.654747 4.654747 0 0.1161689
#> 5 AIFM3 CCT2 24 3.1558989 12.489996 12.489996 0 0.3398374
#> 6 AIFM3 CCT4 20 2.8371693 9.013878 9.013878 0 0.2135599
Based on the scores, bait-prey interactions could be ranked and ready for downstream analyses.
HGScore Scoring algorithm based on a hypergeometric distribution error model (Hart et al., 2007) with incorporation of NSAF (Zybailov, Boris, et al., 2006). This algorithm was first introduced to predict the protein complex network of Drosophila melanogaster (Guruharsha, K. G., et al., 2011). This scoring algorithm was based on matrix model. Unlike CompPASS, we need protein length for each prey in the additional column.
scoreHG <- HG(TestDatInput)
head(scoreHG)
#> InteractorA InteractorB ppiTN tnA tnB PPI NMinTn HG
#> 1 A2M ACLY 1 122 1197 A2M~ACLY 477317 3.264772
#> 2 A2M AGK 1 122 940 A2M~AGK 477317 3.707123
#> 3 A2M AGO1 1 122 1501 A2M~AGO1 477317 2.860551
#> 4 A2M AHCY 1 122 2349 A2M~AHCY 477317 2.098700
#> 5 A2M AHSA1 1 122 386 A2M~AHSA1 477317 5.399179
#> 6 A2M AKAP8 1 122 317 A2M~AKAP8 477317 5.782404
Noted that HG scoring implements matrix models which leads to significant increase of inferred protein-protein interactions.