Skip to content
Open access

Protein contact network explorer: topological analysis of protein structures

Jul 2026 · Frontiers in Bioinformatics · Vol 6 · 0 citations · 32 references
Medicine

TL;DR

This study introduces Protein Contact Network Explorer (PCNE), a tool for simple construction, visualisation, and analysis of protein contact networks derived from structure data, and provides flexible residue contact definitions, the exploration of interactive networks, and the extraction of graph-theoretic measures relevant to understanding protein stability, allosteric communication, and functional organisation.

Abstract

Introduction The functions of proteins are primarily governed by coordinated interactions among amino acid residues throughout their three-dimensional structures. Large-scale determination of protein structures has long been made possible by experimental and computational methods; however, studying complex, dynamic, or multimeric systems remains challenging. Protein contact networks (PCNs) offer a graph-based representation of residue-level interactions and enable the application of network analysis techniques to structural data. Nevertheless, many existing tools mainly focus on creating static networks, which limits analytical flexibility. Methods In this study, we introduce Protein Contact Network Explorer (PCNE), a tool for simple construction, visualisation, and analysis of protein contact networks derived from structure data. Results The tool provides flexible residue contact definitions, the exploration of interactive networks, and the extraction of graph-theoretic measures relevant to understanding protein stability, allosteric communication, and functional organisation. Discussion PCNE supports the analysis of key interaction patterns, facilitating both exploratory and hypothesis-driven research in structural biology. The PCNE can be accessed via https://lactdr5rfibhg9m5tmamwg.streamlit.app/.

Read PDF

Similar papers

#protein folding Review Open access Sep 2026

Graph-based representations in modern protein science

Abstract For more than 50 years, the linear sequence and the multiple sequence alignment have been the foundational data structures of protein science, and they remain central to homology search, phylogenetic inference, covariance-based contact prediction, and modern protein language models. However, relational and gra...

Dana S. Matthews, S. B. Pulsford, Anthony Barancewicz et al. · 0 citations

A Graph-based Approach to Predicting Protein-Protein Interactions

Accurate identification of protein-protein interactions (PPIs) is fundamental for understanding cellular mechanisms and facilitating drug discovery. Although high-throughput experimental methods have expanded the known interactome, they remain resourceintensive and prone to noise. Consequently, computational approaches...

Pantelis Makrygiannis, Nikitas-Rigas Kalogeropoulos, Agorakis Bompotas et al. · 0 citations
#graph neural networks Open access Sep 2026

Fast and Interpretable Estimation of Amino Acid Residue Surface Accessibility Based on Protein Contact Graph

The solvent-accessible surface area (SASA) of amino acid residues is a crucial parameter for protein structure analysis; however, precise computational methods such as FreeSASA are computationally expensive. As an alternative, empirical approximations based on residue interaction network (RIN) graphs can offer high spe...

A. Timofeev, Alexander Bratchikov, Alexander Anufriev · 0 citations
Open access Sep 2026

PDBe PISA: Enhanced Analysis of Macromolecular Interactions

Abstract Protein interactions play a pivotal role in determining the biological functions of living cells. Gaining structural insights into protein interfaces can illuminate their role in function and diseases and highlight their potential as therapeutic targets. In response, there has been a marked increase in efforts...

G. D. Leines, Paulyna Magana, S. Nair et al. · 0 citations
Aug 2026

NSSGRN: Network Structure Selection Method for Gene Regulatory Network Construction.

Gene regulatory networks (GRNs) play essential roles in cellular control and various biological processes. Analyzing gene expression data and inferring GRNs provides crucial insights into organismal growth, development, and disease mechanisms. However, prevailing inference approaches often concentrate on a limited gene...

Wei Liu, Xue-Xuan Ma, Xingen Sun et al. · 0 citations
Open access Sep 2026

PCIPG: A comprehensive framework for protein complex identification based on a probabilistic graphical model

PCIPG is presented, a multi-scale probabilistic graph framework that jointly models residues, proteins, interactions and complexes and bridges residue-scale structural cues with interactome-scale organization to enable interpretable and scalable protein complex identification.

Yi-Xiang Huang, Lei Yang, Jiu-Dong Wang et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.