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

Abstract

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.

Molecular Dynamics Investigations of Human DNA-Topoisomerase I Interacting with Novel Dewar Valence Photo-Adducts: Insights into Inhibitory Activity

“…Ligand–receptor binding interactions were assessed using the MD-ligand-receptor pipeline [ 22 ], a specialised software designed to investigate ligand–receptor interactions using molecular dynamics trajectories. The program interfaces with GROMACS to extract conformational data from the trajectories and employs PLIP [ 41 ] to analyse ligand–receptor binding interactions.…”

“…All the main analyses of this article were performed with software tools optimized for high-performance computing (HPC) to handle large volumes of data in bioinformatics [ 20 ]. HPC bioinformatics infrastructures enable the examination of complex biological macromolecules [ 21 , 22 ], as well as the analysis of vast quantities of novel biological data [ 23 , 24 ].…”

“…These data were used for subsequent phases of analysis, focused on studying the interactions between the compounds and TOP1 over time. In addition, the MD-ligand-receptor HPC pipeline [ 22 ] was utilised to profile the protein–compound interactions. This allowed us to quantify the duration of each interaction over time, followed by their classification (e.g., hydrogen bonds, hydrophobic interactions), and identification on the basis of atom tuples.…”

Discovery of Novel Aldose Reductase Inhibitors via the Integration of Ligand-Based and Structure-Based Virtual Screening with Experimental Validation

“…Docking predicts the binding affinity and orientation of small molecules within the target protein, simulating their pose and estimating binding affinity through computational algorithms. , In tandem, MD simulations offer a dynamic perspective, showcasing a drug’s behavior within the binding site over time. These simulations track atomic and molecular movements, allowing researchers to observe complex stability, identify potential binding events, and understand conformational changes. , Collectively, molecular docking and MD simulations offer a comprehensive understanding of drug–target interactions, guiding optimization for increased efficacy and reduced toxicity, , thus enhancing the efficiency of drug discovery.…”

“…These simulations track atomic and molecular movements, allowing researchers to observe complex stability, identify potential binding events, and understand conformational changes. 26 , 27 Collectively, molecular docking and MD simulations offer a comprehensive understanding of drug–target interactions, guiding optimization for increased efficacy and reduced toxicity, 26 , 28 thus enhancing the efficiency of drug discovery.…”