Protein–ligand binding with the coarse-grained Martini model

Paulo C. T. Souza (), Sebastian Thallmair, Paolo Conflitti, Carlos Ramírez-Palacios, Riccardo Alessandri, Stefano Raniolo, Vittorio Limongelli () and Siewert J. Marrink ()
Additional contact information
Paulo C. T. Souza: University of Groningen
Sebastian Thallmair: University of Groningen
Paolo Conflitti: Università della Svizzera italiana (USI)
Carlos Ramírez-Palacios: University of Groningen
Riccardo Alessandri: University of Groningen
Stefano Raniolo: Università della Svizzera italiana (USI)
Vittorio Limongelli: Università della Svizzera italiana (USI)
Siewert J. Marrink: University of Groningen

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: 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.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-020-17437-5 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17437-5

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-17437-5

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().