Mahmoudi Ikram, Quignot Chloé, Martins Carla, Andreani Jessica
Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France.
PLoS Comput Biol. 2024 Dec 3;20(12):e1012650. doi: 10.1371/journal.pcbi.1012650. eCollection 2024 Dec.
Protein-RNA interactions play a critical role in many cellular processes and pathologies. However, experimental determination of protein-RNA structures is still challenging, therefore computational tools are needed for the prediction of protein-RNA interfaces. Although evolutionary pressures can be exploited for structural prediction of protein-protein interfaces, and recent deep learning methods using protein multiple sequence alignments have radically improved the performance of protein-protein interface structural prediction, protein-RNA structural prediction is lagging behind, due to the scarcity of structural data and the flexibility involved in these complexes. To study the evolution of protein-RNA interface structures, we first identified a large and diverse dataset of 2,022 pairs of structurally homologous interfaces (termed structural interologs). We leveraged this unique dataset to analyze the conservation of interface contacts among structural interologs based on the properties of involved amino acids and nucleotides. We uncovered that 73% of distance-based contacts and 68% of apolar contacts are conserved on average, and the strong conservation of these contacts occurs even in distant homologs with sequence identity below 20%. Distance-based contacts are also much more conserved compared to what we had found in a previous study of homologous protein-protein interfaces. In contrast, hydrogen bonds, salt bridges, and π-stacking interactions are very versatile in pairs of protein-RNA interologs, even for close homologs with high interface sequence identity. We found that almost half of the non-conserved distance-based contacts are linked to a small proportion of interface residues that no longer make interface contacts in the interolog, a phenomenon we term "interface switching out". We also examined possible recovery mechanisms for non-conserved hydrogen bonds and salt bridges, uncovering diverse scenarios of switching out, change in amino acid chemical nature, intermolecular and intramolecular compensations. Our findings provide insights for integrating evolutionary signals into predictive protein-RNA structural modeling methods.
蛋白质 - RNA相互作用在许多细胞过程和病理过程中起着关键作用。然而，蛋白质 - RNA结构的实验测定仍然具有挑战性，因此需要计算工具来预测蛋白质 - RNA界面。尽管进化压力可用于蛋白质 - 蛋白质界面的结构预测，并且最近使用蛋白质多序列比对的深度学习方法已从根本上提高了蛋白质 - 蛋白质界面结构预测的性能，但由于结构数据的稀缺性以及这些复合物所涉及的灵活性，蛋白质 - RNA结构预测仍滞后。为了研究蛋白质 - RNA界面结构的进化，我们首先确定了一个包含2022对结构同源界面（称为结构互作同源物）的大型多样数据集。我们利用这个独特的数据集，基于所涉及的氨基酸和核苷酸的特性，分析结构互作同源物之间界面接触的保守性。我们发现，平均而言，73%的基于距离的接触和68%的非极性接触是保守的，即使在序列同一性低于20%的远缘同源物中，这些接触也具有很强的保守性。与我们之前对同源蛋白质 - 蛋白质界面的研究相比，基于距离的接触也更加保守。相比之下，氢键、盐桥和π - 堆积相互作用在蛋白质 - RNA互作同源物对中非常多样，即使对于具有高界面序列同一性的近缘同源物也是如此。我们发现，几乎一半的非保守基于距离的接触与一小部分界面残基有关，这些残基在互作同源物中不再形成界面接触，我们将这种现象称为“界面切换出”。我们还研究了非保守氢键和盐桥的可能恢复机制，发现了切换出、氨基酸化学性质变化、分子间和分子内补偿等多种情况。我们的研究结果为将进化信号整合到预测性蛋白质 - RNA结构建模方法中提供了见解。
