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MCSS-based Predictions of Binding Mode and Selectivity of Nucleotide Ligands

Abstract : Computational fragment-based approaches are widely used in drug design and discovery. One of their limitations is the lack of performance of docking methods, mainly the scoring functions. With the emergence of fragment-based approaches for single-stranded RNA ligands, we analyze the performance in docking and screening powers of an MCSS-based approach. The performance is evaluated on a benchmark of protein-nucleotide complexes where the four RNA residues are used as fragments. The screening power can be considered the major limiting factor for the fragment-based modeling or design of sequence-selective oligonucleotides. We show that the MCSS sampling is efficient even for such large and flexible fragments. Hybrid solvent models based on some partial explicit representation improve both the docking and screening powers. Clustering of the {\it n} best-ranked poses can also contribute to a lesser extent to better performance. A detailed analysis of molecular features suggests various ways to optimize the performance further.
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Contributor : Fabrice Leclerc <>
Submitted on : Thursday, March 18, 2021 - 11:39:55 PM
Last modification on : Thursday, May 6, 2021 - 10:52:03 AM



Roy González-Alemán, Nicolas Chevrollier, Manuel Simoes, Luis Montero-Cabrera, Fabrice Leclerc. MCSS-based Predictions of Binding Mode and Selectivity of Nucleotide Ligands. Journal of Chemical Theory and Computation, American Chemical Society, 2021, ⟨10.1021/acs.jctc.0c01339⟩. ⟨hal-03174156⟩



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