About: Protein–ligand binding with the coarse-grained Martini model

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Protein–ligand binding with the coarse-grained Martini model
Creator	Alessandri, Riccardo Conflitti, Paolo Limongelli, Vittorio Raniolo, Stefano Ramírez-Palacios, Carlos Souza, Paulo Thallmair, Sebastian Marrink, J Siewert, ✉
Source	Medline; PMC
abstract	The detailed understanding of the binding of small molecules to proteins is the key for the development of novel drugs or to increase the acceptance of substrates by enzymes. Nowadays, computer-aided design of protein–ligand binding is an important tool to accomplish this task. Current approaches typically rely on high-throughput docking essays or computationally expensive atomistic molecular dynamics simulations. Here, we present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein–ligand interactions of small drug-like molecules. Remarkably, we achieve high accuracy without the need of any a priori knowledge of binding pockets or pathways. Our approach is applied to a range of systems from the well-characterized T4 lysozyme over members of the GPCR family and nuclear receptors to a variety of enzymes. The presented results open the way to high-throughput screening of ligand libraries or protein mutations using the coarse-grained Martini model.
has issue date	2020-07-24(xsd:dateTime)
bibo:doi	10.1038/s41467-020-17437-5
bibo:pmid	32709852
has license	cc-by
sha1sum (hex)	f48ce822722a8b655a8c8c03f8cf1c64047fd628
schema:url	https://doi.org/10.1038/s41467-020-17437-5
resource representing a document's title	Protein–ligand binding with the coarse-grained Martini model
has PubMed Central identifier	PMC7382508
has PubMed identifier	32709852
schema:publication	Nat Commun
resource representing a document's body	covid:f48ce822722a8b655a8c8c03f8cf1c64047fd628#body_text
is schema:about of	named entity 'enzymes' named entity 'typically' named entity 'Martini' named entity 'proteins' named entity 'The' named entity 'tool' named entity 'coarse-grained' named entity 'OUR APPROACH' named entity 'MARTINI' named entity 'APPROACHES' named entity 'binding' named entity 'binding pockets' named entity 'model' named entity 'Our' named entity 'high' named entity 'substrate' named entity 'bile acid synthesis' named entity 'tyrosine kinases' named entity 'ligand' named entity 'free energy' named entity 'T4 lysozyme' named entity 'indole' named entity 'ethylbenzene' named entity 'metabolic disorders' named entity 'electrostatic interactions' named entity 'acceptance' named entity 'variety' named entity 'GPCR' named entity 'millisecond' named entity 'essays' named entity 'protein' named entity 'binding pockets' named entity 'nuclear receptors' named entity 'computer-aided design' named entity 'protein' named entity 'high-throughput' named entity 'small molecules' named entity 'ligand binding' named entity 'coarse-grained' named entity 'range' named entity 'GPCR' named entity 'ligand binding' named entity 'coarse-graining' named entity 'competitive binding' named entity 'ligand' named entity 'one-dimensional' named entity 'receptor' named entity 'pyridine' named entity 'toluene' named entity 'ligand' named entity 'receptor ligands' named entity 'propranolol' named entity 'kinase' named entity '2.2' named entity 'benzene' named entity 'TM7' named entity 'nuclear receptors' named entity 'chlorobenzene' named entity 'dasatinib' named entity 'hydrophobic' named entity 'solvation' named entity 'ligand' named entity 'n-propylbenzene' named entity 'n-propylbenzene' named entity 'RMSD' named entity 'AAK1' named entity 'dasatinib' named entity 'cofactors' named entity 'extracellular' named entity 'benzene' named entity 'benzene' named entity 'binding pocket' named entity 'isosurfaces'

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