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An optimal distance cutoff for contact-based Protein Structure Networks using side-chain centers of mass.


ABSTRACT: Proteins are highly dynamic entities attaining a myriad of different conformations. Protein side chains change their states during dynamics, causing clashes that are propagated at distal sites. A convenient formalism to analyze protein dynamics is based on network theory using Protein Structure Networks (PSNs). Despite their broad applicability, few efforts have been devoted to benchmarking PSN methods and to provide the community with best practices. In many applications, it is convenient to use the centers of mass of the side chains as nodes. It becomes thus critical to evaluate the minimal distance cutoff between the centers of mass which will provide stable network properties. Moreover, when the PSN is derived from a structural ensemble collected by molecular dynamics (MD), the impact of the MD force field has to be evaluated. We selected a dataset of proteins with different fold and size and assessed the two fundamental properties of the PSN, i.e. hubs and connected components. We identified an optimal cutoff of 5?Å that is robust to changes in the force field and the proteins. Our study builds solid foundations for the harmonization and standardization of the PSN approach.

SUBMITTER: Salamanca Viloria J 

PROVIDER: S-EPMC5460117 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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An optimal distance cutoff for contact-based Protein Structure Networks using side-chain centers of mass.

Salamanca Viloria Juan J   Allega Maria Francesca MF   Lambrughi Matteo M   Papaleo Elena E  

Scientific reports 20170606 1


Proteins are highly dynamic entities attaining a myriad of different conformations. Protein side chains change their states during dynamics, causing clashes that are propagated at distal sites. A convenient formalism to analyze protein dynamics is based on network theory using Protein Structure Networks (PSNs). Despite their broad applicability, few efforts have been devoted to benchmarking PSN methods and to provide the community with best practices. In many applications, it is convenient to us  ...[more]

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