Predicting protein-protein interactions based on rotation of proteins in 3D-space

TL;DR

This paper introduces a novel 3D-space rotation-based sequence encoding method for predicting protein-protein interactions, improving accuracy by considering protein orientation and amino acid composition.

Contribution

It proposes a new sequence encoding approach using N-Gram and RVKDE that accounts for protein rotation in 3D space, enhancing prediction performance.

Findings

Achieved 2.5% improvement in F-measure on HPRD dataset.

Reduced dimensionality by considering undirected amino acid compositions.

Demonstrated superiority over existing methods in PPI prediction.

Abstract

Protein-Protein Interactions (PPIs) perform essential roles in biological functions. Although some experimental techniques have been developed to detect PPIs, they suffer from high false positive and high false negative rates. Consequently, efforts have been devoted during recent years to develop computational approaches to predict the interactions utilizing various sources of information. Therefore, a unique category of prediction approaches has been devised which is based on the protein sequence information. However, finding an appropriate feature encoding to characterize the sequence of proteins is a major challenge in such methods. In presented work, a sequence based method is proposed to predict protein-protein interactions using N-Gram encoding approaches to describe amino acids and a Relaxed Variable Kernel Density Estimator (RVKDE) as a machine learning tool. Moreover, since proteins can rotate in 3D-space, amino acid compositions have been considered with "undirected" property which leads to reduce dimensions of the vector space. The results show that our proposed method achieves the superiority of prediction performance with improving an F-measure of 2.5% on Human Protein Reference Dataset (HPRD).