MD-Ligand-Receptor: A High-Performance Computing Tool for Characterizing Ligand-Receptor Binding Interactions in Molecular Dynamics Trajectories.

computational modeling of molecular systems molecular dynamics nucleic acid–ligand interactions protein–ligand interactions

Journal

International journal of molecular sciences
ISSN: 1422-0067
Titre abrégé: Int J Mol Sci
Pays: Switzerland
ID NLM: 101092791

Informations de publication

Date de publication:
19 Jul 2023
Historique:
received: 23 06 2023
revised: 15 07 2023
accepted: 18 07 2023
medline: 31 7 2023
pubmed: 29 7 2023
entrez: 29 7 2023
Statut: epublish

Résumé

Molecular dynamics simulation is a widely employed computational technique for studying the dynamic behavior of molecular systems over time. By simulating macromolecular biological systems consisting of a drug, a receptor and a solvated environment with thousands of water molecules, MD allows for realistic ligand-receptor binding interactions (lrbi) to be studied. In this study, we present MD-ligand-receptor (MDLR), a state-of-the-art software designed to explore the intricate interactions between ligands and receptors over time using molecular dynamics trajectories. Unlike traditional static analysis tools, MDLR goes beyond simply taking a snapshot of ligand-receptor binding interactions (lrbi), uncovering long-lasting molecular interactions and predicting the time-dependent inhibitory activity of specific drugs. With MDLR, researchers can gain insights into the dynamic behavior of complex ligand-receptor systems. Our pipeline is optimized for high-performance computing, capable of efficiently processing vast molecular dynamics trajectories on multicore Linux servers or even multinode HPC clusters. In the latter case, MDLR allows the user to analyze large trajectories in a very short time. To facilitate the exploration and visualization of lrbi, we provide an intuitive Python notebook (Jupyter), which allows users to examine and interpret the results through various graphical representations.

Identifiants

pubmed: 37511429
pii: ijms241411671
doi: 10.3390/ijms241411671
pmc: PMC10380688
pii:
doi:

Substances chimiques

Ligands 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

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Auteurs

Michele Pieroni (M)

Department of Computer Science, "Sapienza" University of Rome, V. le Regina Elena 295, 00161 Rome, Italy.

Francesco Madeddu (F)

Department of Computer Science, "Sapienza" University of Rome, V. le Regina Elena 295, 00161 Rome, Italy.

Jessica Di Martino (J)

Department of Ecological and Biological Sciences, Tuscia University, Viale dell'Università s.n.c., 01100 Viterbo, Italy.

Manuel Arcieri (M)

Department of Health Technology, Technical University of Denmark, Anker Engelunds Vej 101, 2800 Kongens Lyngby, Denmark.

Valerio Parisi (V)

Department of Physics, "Sapienza" University of Rome, P. le Aldo Moro, 5, 00185 Rome, Italy.

Paolo Bottoni (P)

Department of Computer Science, "Sapienza" University of Rome, V. le Regina Elena 295, 00161 Rome, Italy.

Tiziana Castrignanò (T)

Department of Ecological and Biological Sciences, Tuscia University, Viale dell'Università s.n.c., 01100 Viterbo, Italy.

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Classifications MeSH