Network approach integrates 3D structural and sequence data to improve protein structural comparison

TL;DR

This paper presents a novel network-based method that integrates 3D structural and sequence data to enhance protein structural comparison, outperforming existing approaches in accuracy and speed.

Contribution

It introduces normalized graphlet measures and ordered graphlets to effectively compare PSNs and integrate sequence data, addressing limitations of previous network methods.

Findings

More accurate protein comparisons on synthetic and real data

Faster than existing network and 3D contact methods

Identifies biochemically interesting PSN patterns

Abstract

Initial protein structural comparisons were sequence-based. Since amino acids that are distant in the sequence can be close in the 3-dimensional (3D) structure, 3D contact approaches can complement sequence approaches. Traditional 3D contact approaches study 3D structures directly. Instead, 3D structures can be modeled as protein structure networks (PSNs). Then, network approaches can compare proteins by comparing their PSNs. Network approaches may improve upon traditional 3D contact approaches. We cannot use existing PSN approaches to test this, because: 1) They rely on naive measures of network topology. 2) They are not robust to PSN size. They cannot integrate 3) multiple PSN measures or 4) PSN data with sequence data, although this could help because the different data types capture complementary biological knowledge. We address these limitations by: 1) exploiting well-established graphlet measures via a new network approach, 2) introducing normalized graphlet measures to remove the bias of PSN size, 3) allowing for integrating multiple PSN measures, and 4) using ordered graphlets to combine the complementary PSN data and sequence data. We compare both synthetic networks and real-world PSNs more accurately and faster than existing network, 3D contact, or sequence approaches. Our approach finds PSN patterns that may be biochemically interesting.