Design and Development of PCN-Miner: A tool for the Analysis of Protein Contact Networks

TL;DR

This paper introduces PCN-Miner, a comprehensive Python tool for analyzing protein contact networks, facilitating clustering, embedding, and functional analysis to aid researchers in structural biology.

Contribution

The paper presents PCN-Miner, a unified software platform that integrates multiple analysis algorithms for protein contact networks, streamlining research workflows.

Findings

Enables import of proteins in PDB format to generate contact networks.

Provides algorithms for clustering and embedding of PCNs.

Supports diverse applications in protein structure and function analysis.

Abstract

Protein Contact Network (PCN) is a powerful tool for analysing the structure and function of proteins. In particular, PCN has been used for disclosing the molecular features of allosteric regulation through PCN clustering. Such analysis is relevant in many applications, such as the recent study of SARS-CoV-2 Spike Protein. Despite its relevance, methods for the analysis of PCN are spread into a set of different libraries and tools. Therefore, the introduction of a tool that incorporates all the function may help researchers. We present PCN-Miner a software tool implemented in the Python programming language able to import protein in the Protein Data Bank format and generate the corresponding protein contact network. Then it offers a set of algorithms for the analysis of PCS that cover a large set of applications: from clustering to embedding and subsequent analysis. Software is available at \url{https://github.com/hguzzi/ProteinContactNetworks}