Title: A novel methodology on distributed representations of proteins using their interacting ligands,
Abstract: The effective representation of proteins is a crucial task that directly
affects the performance of many bioinformatics problems. Related proteins
usually bind to similar ligands. Chemical characteristics of ligands are known
to capture the functional and mechanistic properties of proteins suggesting
that a ligand based approach can be utilized in protein representation. In this
study, we propose SMILESVec, a SMILES-based method to represent ligands and a
novel method to compute similarity of proteins by describing them based on
their ligands. The proteins are defined utilizing the word-embeddings of the
SMILES strings of their ligands. The performance of the proposed protein
description method is evaluated in protein clustering task using TransClust and
MCL algorithms. Two other protein representation methods that utilize protein
sequence, BLAST and ProtVec, and two compound fingerprint based protein
representation methods are compared. We showed that ligand-based protein
representation, which uses only SMILES strings of the ligands that proteins
bind to, performs as well as protein-sequence based representation methods in
protein clustering. The results suggest that ligand-based protein description
can be an alternative to the traditional sequence or structure based
representation of proteins and this novel approach can be applied to different
bioinformatics problems such as prediction of new protein-ligand interactions
and protein function annotation.