Datasets and analyses of molecular dynamics simulations of covalent binary and ternary complexes of MHC class I-related molecule/T-cell receptor (MR1/TCR) agonists to understand complex formation and conditions of fluorescent labelling.

Ligand induced-fit Molecular dynamics of MAIT cell ligands Photoinduced electron transfer (PET) of Alexa Fluor™ 488 fluorophore Ternary MR1/TCR ligand complexes

Journal

Data in brief
ISSN: 2352-3409
Titre abrégé: Data Brief
Pays: Netherlands
ID NLM: 101654995

Informations de publication

Date de publication:
Feb 2021
Historique:
received: 02 12 2020
revised: 23 12 2020
accepted: 24 12 2020
entrez: 28 1 2021
pubmed: 29 1 2021
medline: 29 1 2021
Statut: epublish

Résumé

Data of molecular dynamics (MD) simulations were obtained for mucosal-associated invariant T (MAIT) cell ligands complexed with MR1 or MR1/TCR. Ligands included in the simulations were natural ligands 5-(2-oxoethylideneamino)-6-D-ribitylaminouracil (5-OE-RU), 5-(2-oxopropylideneamino)-6-(D-ribitylamino)uracil (5-OP-RU), their C5' ethinylated analogs in S or R configuration, as well as the corresponding fluorophore-reacted products. All-atom models of the binary and ternary complexes were constructed using PDB entry 4NQE and docked poses [1]. Missing loops, N- and C-termini were completed by homology modelling, the loop conformations optimized, and the models energy minimized prior to setup for MD simulations. A standard pre-equilibration protocol was applied before the production phase of 120 ns simulation as NPT ensemble at 300 K and 1 atm applying an explicit solvent model with OPLS3 force field parameters. Atomic coordinates and energies were recorded every 60 ps and 12 ps, respectively. The corresponding raw data files of the MD simulations are part of this dataset. All simulations were analysed with respect to root mean square deviations (rmsd) and root mean square fluctuations (rmsf) of the coordinates of protein and ligand atoms, stability of protein secondary structure, protein-ligand contacts, ligand torsion profiles, and ligand properties. More detailed statistics of non-covalent interaction counts were also collected. Radial distribution functions (rdf) were calculated when relevant. Visualization of the trajectories permits appreciation of the molecular dynamics of both, ligands and proteins and their interactions, thereby supporting drug design of MAIT cell ligands; furthermore, additional analysis of e.g. conformational changes or interactions not reported in the primary publication [1] can be performed on the data. The raw data may also be used as starting point for extension of the simulations or more sophisticated MD techniques.

Identifiants

pubmed: 33506079
doi: 10.1016/j.dib.2020.106704
pii: S2352-3409(20)31583-3
pmc: PMC7815464
doi:

Types de publication

Journal Article

Langues

eng

Pagination

106704

Informations de copyright

© 2021 The Authors.

Déclaration de conflit d'intérêts

The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article. Else, Anke Steinmetz is an employee of Sanofi-Aventis R&D.

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Auteurs

Anke Steinmetz (A)

Centre de Recherche et Développement Vitry-Alfortville, IDD/ISDD, Sanofi-Aventis R&D, Vitry-surSeine, 94400, France.

Thomas Yvorra (T)

Institut Curie, PSL University, CNRS UMR3666, INSERM U1143, Paris, 75005, France.

Pascal Retailleau (P)

Institut de Chimie des Substances Naturelles, CNRS UPR 2301, Université Paris-Saclay, 1 avenue de la Terrasse, Gif-sur-Yvette, 91190, France.

Olivier Lantz (O)

Institut Curie, PSL University, INSERM U932, Paris, 75005, France.
Institut Curie; Laboratoire d'Immunologie Clinique, Paris, 75005, France.
Centre d'Investigation Clinique en Biothérapie, Institut Curie (CIC-BT1428), Paris, 75005, France.

Frédéric Schmidt (F)

Institut Curie, PSL University, CNRS UMR3666, INSERM U1143, Paris, 75005, France.

Classifications MeSH