Piggy-backing protein domains with Formal Concept Analysis

TL;DR

This paper applies Formal Concept Analysis to improve the identification of reliable domain-domain interactions by leveraging the relationship between formal concepts to address domain promiscuity issues.

Contribution

It introduces a novel FCA-based method that enables rare domains to support promiscuous domains, enhancing the ranking of reliable DDIs.

Findings

FCA effectively elevates the rank of promiscuous domains.

The method enriches highly ranked domain-pairs with reliable DDIs.

Addresses domain promiscuity challenge in DDI prediction.

Abstract

Identifying reliable domain-domain interactions (DDIs) will increase our ability to predict novel protein-protein interactions (PPIs), to unravel interactions in protein complexes, and thus gain more information about the function and behavior of genes. One of the challenges of identifying reliable DDIs is domain promiscuity. Promiscuous domains are domains that can occur in many domain architectures and are therefore found in many proteins. This becomes a problem for a method where the score of a domain-pair is the ratio between observed and expected frequencies because the PPI network is sparse. As such, many protein-pairs will be non-interacting and domain-pairs with promiscuous domains will be penalized. This domain promiscuity challenge to the problem of inferring reliable DDIs from PPIs has been recognized, and a number of work-arounds have been proposed. In this paper, we report an application of Formal Concept Analysis (FCA) to this problem. We find that the relationship between formal concepts provide a natural way for rare domains to elevate the rank of promiscuous domains, and enrich highly ranked domain-pairs with reliable DDIs. This piggy-backing of promiscuous domains onto rare domains is possible due to the domain architecture of proteins which mixes promiscuous with rare domains.