# SMAD Quick Start

Qingzhou (Johnson) Zhang*

*zqzneptune@hotmail.com

#### 2021-05-19

#### Package

SMAD 1.8.0

# 1 Introduction

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).

# 2 Prepare Input Data

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.

# 3 Methods

## 3.1 CompPASS

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.

## 3.2 HGScore

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.