Abstract
Macrocycles are attractive structures for drug development due to their favorable structural features, potential in binding to targets with flat featureless surfaces, and their ability to disrupt protein–protein interactions. Moreover, large novel highly diverse libraries of low-molecular-weight macrocycles with therapeutically favorable characteristics have been recently established. Considering the mentioned facts, having a validated, fast, and accurate computational protocol for studying the molecular recognition and binding mode of this interesting new class of macrocyclic peptides deemed to be helpful as well as insightful in the quest of accelerating drug discovery. To that end, the ability of the in-house supervised molecular dynamics protocol called SuMD in the reproduction of the X-ray crystallography final binding state of a macrocyclic non-canonical tetrapeptide—from a novel library of 8,988 sub-kilodalton macrocyclic peptides—in the thrombin active site was successfully validated. A comparable binding mode with the minimum root-mean-square deviation (RMSD) of 1.4 Å at simulation time point 71.6 ns was achieved. This method validation study extended the application domain of the SuMD sampling method for computationally cheap, fast but accurate, and insightful macrocycle–protein molecular recognition studies.
Highlights
The ever-increasing expeditious development of computer hardware, software, and algorithms has positively contributed to many domains of research such as drug design
Among 99 supervised molecular dynamics (SuMD) replicas, 84 trajectories finished with the ligand arriving in the proximity of the binding site and its sub-pockets with different binding orientations and conformations, while 15 trajectories ended with the ligand stopping over a varied site categorized as “failed” based on SuMD termination criteria
The mean calculated root-mean-square deviation (RMSD) during this trajectory was 3.57 ± 0.47 Å (Supplementary Figure S1). Those RMSD values highlight a discrepancy between the experimental bound conformation and the one assumed once the system is equilibrated in a fully explicit solvent suggesting that the cMD could represent a more adequate comparison for SuMD
Summary
The ever-increasing expeditious development of computer hardware, software, and algorithms has positively contributed to many domains of research such as drug design. Our in-house alternative MD approach, compared to the classical method, named supervised molecular dynamics (SuMD), improves the efficiency of sampling a binding event and decreases the simulation time from a microsecond (μs) to a nanosecond (ns) timescale (Sabbadin and Moro, 2014). To do that, it applies a tabu-like algorithm to monitor the distance between the ligand center of mass and the target binding site center of mass during a short classical MD simulation; only productive simulations in terms of reducing this distance are considered productive. Its already validated application domain covers the molecular recognition simulation of small molecules, natural linear peptides, most classic peptidomimetics, and nucleic acids (Bissaro et al, 2020)
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