A novel methodology on distributed representations of proteins using their interacting ligands

TL;DR

This paper introduces SMILESVec, a ligand-based protein representation method using SMILES strings, demonstrating comparable performance to traditional sequence-based methods in protein clustering, with potential applications in bioinformatics tasks.

Contribution

The study presents a novel ligand-based approach for protein representation using SMILES strings, offering an alternative to sequence or structure-based methods.

Findings

Ligand-based protein representation performs as well as sequence-based methods in clustering.

SMILESVec effectively captures protein functional properties from ligand information.

Ligand-based methods can be applied to various bioinformatics problems.

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.