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PPI_SVM: Prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables

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Abstract

Protein-protein interactions (PPI) control most of the biological processes in a living cell. In order to fully understand protein functions, a knowledge of protein-protein interactions is necessary. Prediction of PPI is challenging, especially when the three-dimensional structure of interacting partners is not known. Recently, a novel prediction method was proposed by exploiting physical interactions of constituent domains. We propose here a novel knowledge-based prediction method, namely PPI_SVM, which predicts interactions between two protein sequences by exploiting their domain information. We trained a two-class support vector machine on the benchmarking set of pairs of interacting proteins extracted from the Database of Interacting Proteins (DIP). The method considers all possible combinations of constituent domains between two protein sequences, unlike most of the existing approaches. Moreover, it deals with both single-domain proteins and multi domain proteins; therefore it can be applied to the whole proteome in high-throughput studies. Our machine learning classifier, following a brainstorming approach, achieves accuracy of 86%, with specificity of 95%, and sensitivity of 75%, which are better results than most previous methods that sacrifice recall values in order to boost the overall precision. Our method has on average better sensitivity combined with good selectivity on the benchmarking dataset. The PPI_SVM source code, train/test datasets and supplementary files are available freely in the public domain at: http://code.google.com/p/cmater-bioinfo/.

Abbreviations

AP:

appearance probability

BiFC:

biomolecular fluorescence complementation

BIND:

Biomolecular Interaction Network Database

DIP:

Database of Interacting Proteins

DPI:

dual polarization interferometry

FN:

false negatives

FP:

false positives

FPR:

false positive rate

FRET:

fluorescence resonance energy transfer

HMMs:

hidden Markov models

IgG:

Immunoglobulin G

IntAct:

open source molecular interaction database

MINT:

Molecular Interactions Database

MIPS:

Mammalian Protein-Protein Interaction Database

PID:

interacting domain pairs

PPI:

protein-protein interactions

RBF:

radial basis function

ROC:

receiver operator curve

SVM:

support vector machine

TAP:

tandem affinity purification

TN:

true negatives

TP:

true positives

TPR:

true positive rate

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Correspondence to Dariusz Plewczynski.

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Key words

  • Protein-protein interaction
  • Domain-frequency values
  • Domaindomain interaction affinity value
  • Proteome
  • Interactome
  • Brainstorming
  • Machine learning
  • Consensus
  • DIP
  • Protein domains
  • Sequences
  • Structures
  • Protein-protein complexes