Arpeggio: A Web Server for Calculating and Visualising Interatomic Interactions in Protein Structures

By Harry C Jubb, Alicia P. Higueruelo, Bernardo Ochoa-Montaño, W. Pitt, David B. Ascher, Tom L. Blundell. Journal of Molecular Biology, 2017

448 citations recorded by the source corpus.

Interactions between proteins and their ligands, such as small molecules, other proteins, and DNA, depend on specific interatomic interactions that can be classified on the basis of atom type and distance and angle constraints. Visualisation of these interactions provides insights into the nature of molecular recognition events and has practical uses in guiding drug design and understanding the structural and functional impacts of mutations. We present Arpeggio, a web server for calculating interactions within and between proteins and protein, DNA, or small-molecule ligands, including van der Waals', ionic, carbonyl, metal, hydrophobic, and halogen bond contacts, and hydrogen bonds and specific atom–aromatic ring (cation–π, donor–π, halogen–π, and carbon–π) and aromatic ring–aromatic ring (π–π) interactions, within user-submitted macromolecule structures. PyMOL session files can be downloaded, allowing high-quality publication images of the interactions to be generated. Arpeggio is implemented in Python and available as a user-friendly web interface at http://structure.bioc.cam.ac.uk/arpeggio/ and as a downloadable package at https://bitbucket.org/harryjubb/arpeggio.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a4a4b07ba0737a5252601953703826b4af0c972f

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Citations in the corpus

mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019, methodology): “We performed analysis of the non-covalent interactions for the wild-type residue and for the closest interface using the contacts calculated by Arpeggio (47).”
DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations (2020, methodology): “Changes in Gibbs Free energy of folding can occur due to a myriad of factors related and in order to incorporate these properties, we used Arpeggio 40 to calculate the number of hydrophobic contacts involving the wild-type residue and contact potential scores from AAINDEX database.”
How RNA binding proteins interact with RNA: molecules and mechanisms (2020, background): “ATP hydrolysis subsequently promotes conformational changes that cause the helicase to translocate 1 nt and/or unwind its substrate (Iost et al., 1999; Jankowsky, 2011; Jiang et al., 2011; Kainov et al., 2003).”
PLIP 2021: expanding the scope of the protein–ligand interaction profiler to DNA and RNA (2021, background): “There are a number of tools (29–37) analysing ligand–target interactions, but apart from one, they have a different focus and therefore suffer from some limitations: (29–33) detect only a limited set of interaction types and do not offer 3D visualisation, (34) requires extensive preparation of…”
DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability (2018, background): “DynaMut enables visualization of the non-covalent molecular interactions calculated by Arpeggio (45) (Supplementary Table S2, Supplementary Figure S8) and deformation energies and atomic fluctuations of wild-type and mutant residues (Supplementary Table S3, Supplementary Figure S9) in their respecti”