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

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 representations improve both the docking and screening powers. Clustering of the 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.

Automated summary

Computational fragment-based approaches are widely used in drug design and discovery, with limitations in the performance of docking methods that need further optimization. The emergence of fragment-based approaches for single-stranded RNA ligands shows potential in docking and screening capabilities for modeling or designing sequence-selective oligonucleotides. Efficient MCSS sampling and hybrid solvent models improve docking and screening powers, while clustering of the n best-ranked poses contributes to a lesser extent to better performance.