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.

基于计算片段的方法在药物设计与发现领域应用广泛。此类方法的局限性之一在于对接方法的性能欠佳，尤以打分函数为甚。随着单链RNA配体片段类方法的问世，我们对一种基于MCSS的方法的对接与筛选性能展开了分析。该方法的性能通过一套蛋白质-核苷酸复合物基准数据集进行评估，测试时将4个RNA残基作为片段使用。对于基于片段的序列选择性寡核苷酸建模与设计而言，筛选能力可被视为主要的限制因素。研究表明，即便针对此类大尺寸且柔性较强的片段，MCSS采样依然具备较高效率。基于部分显式溶剂表示的混合溶剂模型，可同时提升对接性能与筛选能力。对排名前n的构象进行聚类，也能在一定程度上改善模型性能。对分子特征的详细分析，为进一步优化模型性能提供了多种可行路径。