Exploring the RNA-Recognition Mechanism Using Supervised Molecular Dynamics (SuMD) Simulations: Toward a Rational Design for Ribonucleic-Targeting Molecules?

Abstract

Although proteins have represented the molecular target of choice in the development of new drug candidates, the pharmaceutical importance of ribonucleic acids has gradually been growing. The increasing availability of structural information has brought to light the existence of peculiar three-dimensional RNA arrangements, which can, contrary to initial expectations, be recognized and selectively modulated through small chemical entities or peptides. The application of classical computational methodologies, such as molecular docking, for the rational development of RNA-binding candidates is, however, complicated by the peculiarities characterizing these macromolecules, such as the marked conformational flexibility, the singular charges distribution, and the relevant role of solvent molecules. In this work, we have thus validated and extended the applicability domain of SuMD, an all-atoms molecular dynamics protocol that allows to accelerate the sampling of molecular recognition events on a nanosecond timescale, to ribonucleotide targets of pharmaceutical interest. In particular, we have proven the methodological ability by reproducing the binding mode of viral or prokaryotic ribonucleic complexes, as well as that of artificially engineered aptamers, with an impressive degree of accuracy.

Exploring Ligand Binding to Calcitonin Gene-Related Peptide Receptors

“…The supervised molecular dynamics (SuMD) is an adaptive sampling method ( Deganutti and Moro, 2017a ) for speeding up simulation of the binding ( Cuzzolin et al, 2016 ; Deganutti et al, 2015 ; Deganutti and Moro, 2017b ; Salmaso et al, 2017 ; Sabbadin and Moro, 2014 ; Bower et al, 2018 ; Bissaro et al, 2019 ; Bissaro et al, 2020 ) and unbinding processes ( Deganutti et al, 2020 ). In the first SuMD implementation ( Sabbadin and Moro, 2014 ; Cuzzolin et al, 2016 ), sampling is gained without the introduction of any energetic bias, by applying a tabu–like algorithm to monitor the distance between the centers of mass (or the geometrical centers) of the ligand and the predicted binding site or the receptor.…”

RNA–ligand molecular docking: Advances and challenges

“…However, further applications of MORDOR are limited by the high computational cost, which can take up to hours for a docking run. To accelerate simulation, Supervised Molecular Dynamics (SuMD) has been proposed to sample the conformations of RNA‐drug complexes 170 . SuMD accelerates the simulation by applying a tabu‐like algorithm to guide the docking when the ligand is far away from the binding site and a conventional MD simulation when the ligand is close to the binding site.…”

“…Although the simulated trajectory does not necessarily represent the physical binding process, SuMD may capture possible conformational changes. The reliability of SuMD for RNA–ligand docking is supported by success in predicting binding modes for several pharmaceutically important RNAs, 170 where SuMD predicts RNA–ligand docking mode with a minimum RMSD of 0.34 Å for the best case.…”

“…To accelerate simulation, Supervised Molecular Dynamics (SuMD) has been proposed to sample the conformations of RNA-drug complexes. 170 SuMD accelerates the simulation by applying a tabu-like algorithm to guide the docking F I G U R E 5 Different approaches to modeling RNA flexibility in RNA-ligand interactions illustrated using HIV-TAR RNA (PDB: 1ANR 145 ) as an example. The orange and blue regions correspond to rigid and flexible portions of RNA, respectively.…”

Shedding Light on the Molecular Recognition of Sub-Kilodalton Macrocyclic Peptides on Thrombin by Supervised Molecular Dynamics

“…Despite the exploration of the recognition event, SuMD has been previously proved to be able to reproduce the experimental bound state of various kinds of complexes with great geometric accuracy. 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 ).…”