RING-PyMOL: residue interaction networks of structural ensembles and molecular dynamics

Abstract

• RING-PyMOL is a plugin for PyMOL providing a set of analysis tools for structural ensembles and molecular dynamic (MD) simulations. RING-PyMOL combines residue interaction networks, as provided by the RING software, with structural clustering to enhance the analysis and visualization of the conformational complexity. It combines precise calculation of non-covalent interactions with the power of PyMOL to manipulate and visualize protein structures. The plugin identifies and highlights correlating contacts and interaction patterns that can explain structural allostery, active sites and structural heterogeneity connected with molecular function. It is easy to use and extremely fast, processing and rendering hundreds of models and long trajectories in seconds. RING-PyMOL generates a number of interactive plots and output files for use with external tools. The underlying RING software has been improved extensively. It is ten times faster, can process mmCIF files and it identifies typed interactions also for nucleic acids. Availability and implementation https://github.com/BioComputingUP/ring-pymol

Probing conformational landscapes of binding and allostery in the SARS-CoV-2 omicron variant complexes using microsecond atomistic simulations and perturbation-based profiling approaches: hidden role of omicron mutations as modulators of allosteric signaling and epistatic relationships

“…The weights of the network edges in the residue interaction networks are determined by dynamic residue cross-correlations obtained from MD simulations 109 and coevolutionary couplings between residues measured by the mutual information scores. 110 Residue Interaction Network Generator (RING) program 111–113 was employed for generation of the initial residue interaction networks. The edge lengths in the network are then adjusted using the generalized correlation coefficients associated with the dynamic correlation and mutual information shared by each pair of residues.…”

Key interaction networks: Identifying evolutionarily conserved non‐covalent interaction networks across protein families

“…Protein interaction networks are essential to facilitating protein function, and understanding their evolutionary conservation gives insight into how novel protein functions evolve (Jack et al, 2016 ). While there exist are a number of tools that can calculate protein interaction networks, including from dynamical trajectories (del Conte et al, 2023 ; Huggins et al, 2018 ; Sladek et al, 2021 ), these typically do not additionally focus on evolutionary conservation, and tools that study evolutionary conservation typically focus on protein–protein interaction networks (Alhindi et al, 2017 ; Ali & Deane, 2020 ; Fraser et al, 2002 ; Levy & Pereira‐Leal, 2008 ; Pawlowski et al, 2013 ; Schoenrock et al, 2017 ; Schüler & Bornberg‐Bauer, 2011 ; Stumpf et al, 2007 ; Sun & Kim, 2011 ; Wagner, 2001 ; Zitnik et al, 2019 ) rather than interactions within an individual protein subunit. We note here, however, a recent related study that exploited MD‐based correlation‐based networks in combination with sequence conservation analysis for protein engineering, in order to design new variants of the tryptophan synthase complex using activity enhancing distal mutations (Maria‐Solano et al, 2021 ).…”

“…In this study, we introduce a tool named Key Interaction Networks (KIN), a Python package that can construct a conservation‐based RIN for a set of evolutionary related proteins. While there already exist a number of tools for analyzing protein interactions in multi‐state structures (del Conte et al, 2023 ; Huggins et al, 2018 ; Sladek et al, 2021 ), KIN provides the additional capability to analyze any group of related proteins by finding a common network of interactions that are conserved within the family. Specifically, using KIN, the RIN across the protein family can be projected onto a structure of interest and used to identify both conserved and variable interaction subnetworks throughout the family.…”

EsigPBP3 Was the Important Pheromone-Binding Protein to Recognize Male Pheromones and Key Eucalyptus Volatiles

“…The optimal binding site and conformation were selected based on the minimum binding energy, and their interactions were further analyzed using the Discovery Studio software 2019 (DS, Accelrys Inc., San Diego, CA, USA) and PLIP 2021 (https://plip-tool.biotec.tu-dresden.de/ (accessed on 15 July 2023)) [54]. Finally, the docking results of EsigPBP3 with sex pheromones and plant volatiles were visualized in PyMol (ver 2.5.1) [55].…”