Morera Diego Barquero, Mattiotti Giovanni, Kocev Alexandar, Rousselot Amshuman, Meuret Louis, Rouaud Lucas, Santuz Hubert, Baaden Marc, Taly Antoine, Pasquali Samuela
Laboratoire Biologie Functionnelle et Adaptative, Université Paris Cité, Inserm ERL U1133, 35 Rue Hélène Brion, Paris 75013, France.
Laboratoire de Biochimie Théorique, Université Paris Cité, CNRS, Institut Biologie Physico-Chimique, 13 Rue Pierre et Marie Curie, Paris 75005, France.
J Chem Theory Comput. 2025 Sep 23;21(18):9120-9135. doi: 10.1021/acs.jctc.5c00688. Epub 2025 Sep 1.
Developing a physical understanding of the interactions between a macromolecular target and its ligands is a crucial step in structure-based drug design. Although many tools exist to characterize protein-binding pockets in silico, this is not yet the case for RNA, which has been recognized only recently as a suitable target for small ligands. Molecular Interaction Fields (MIFs) are useful tools to characterize the interactions of a given binding pocket. However, classical MIFs heavily rely on the use of probes, which makes their calculations accurate but very specific to the binding partners in question. We develop here a simple version of MIF, that we call Statistical Molecular Interaction Fields (SMIFs), based on functional forms inspired by coarse-grained models and parametrized based on PDB structures and previous statistical analysis of the main form of interactions typical of macromolecules, namely, hydrogen bonding, stacking, and hydrophobic interactions. We show that these fields, despite their simplicity, are very informative and, overall, in agreement with pharmacophoric models. Thanks to a carefully optimized code, our calculations are fast and can be performed in bulk on a large set of binding pockets or even on a full macromolecule. As shown in a few representative examples, the latter possibility opens the way to the analysis of systems as large as 20000 to 80000 atoms in relation to the surrounding environment, i.e., a lipidic membrane, a small ligand, or another macromolecular partner, allowing for a detailed visualization of the possible interactions. The complete software and its documentation are available here: https://smiffer.mol3d.tech/.
在基于结构的药物设计中，深入理解大分子靶点与其配体之间的相互作用是至关重要的一步。尽管目前有许多工具可用于在计算机上表征蛋白质结合口袋，但对于RNA而言却并非如此，RNA直到最近才被确认为小分子配体的合适靶点。分子相互作用场（MIFs）是表征特定结合口袋相互作用的有用工具。然而，传统的MIFs严重依赖探针的使用，这使得它们的计算准确，但对所讨论的结合伙伴非常特定。我们在此开发了一种简单版的MIF，我们称之为统计分子相互作用场（SMIFs），其基于受粗粒度模型启发的函数形式，并根据PDB结构以及对大分子典型相互作用主要形式（即氢键、堆积和疏水相互作用）的先前统计分析进行参数化。我们表明，尽管这些场很简单，但信息量很大，总体上与药效团模型一致。由于精心优化的代码，我们的计算速度很快，可以对大量的结合口袋甚至整个大分子进行批量计算。如几个代表性例子所示，后一种可能性为分析与周围环境（即脂质膜、小分子配体或另一个大分子伙伴）相关的多达20000至80000个原子的系统开辟了道路，从而可以详细可视化可能的相互作用。完整的软件及其文档可在此处获取：https://smiffer.mol3d.tech/ 。
