Uncovering patterns of atomic interactions in static and dynamic structures of proteins

The number of structures and molecular dynamics simulations of proteins is exploding owing to dramatic advances in cryo-electron microscopy, crystallography, and computing. One of the most powerful ways to analyze structural information involves comparisons of interatomic interactions across differe...

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Published inbioRxiv
Main Authors Venkatakrishnan, A J, Fonseca, Rasmus, Ma, Anthony K, Hollingsworth, Scott A, Chemparathy, Augustine, Hilger, Daniel, Kooistra, Albert J, Ahmari, Ramin, M Madan Babu, Kobilka, Brian K, Dror, Ron
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 13.11.2019
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Summary:The number of structures and molecular dynamics simulations of proteins is exploding owing to dramatic advances in cryo-electron microscopy, crystallography, and computing. One of the most powerful ways to analyze structural information involves comparisons of interatomic interactions across different structures or simulations of the same protein or related proteins from the same family (e.g. different GPCRs). Such comparative analyses are of interest to a wide range of researchers but currently prove challenging for all but a few. To facilitate comparative structural analyses, we have developed tools for (i) rapidly computing and comparing interatomic interactions and (ii) interactively visualizing interactions to enable structure-based interpretations. Using these tools, we have developed the Contact Comparison Atlas, a web-based resource for the comparative analysis of interactions in structures and simulations of proteins. Using the Contact Comparison Atlas and our tools, we have identified patterns of interactions with functional implications in structures of G-protein-coupled receptors, G proteins and kinases and in the dynamics of muscarinic receptors. The Contact Comparison Atlas can be used to enable structure modeling, drug discovery, protein engineering, and the prediction of disease-associated mutations.
DOI:10.1101/840694