Template-Based Modeling of Protein-RNA Interactions

Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes.RNA与RNA结合蛋白通过特异性识别所形成的蛋白-RNA复合物，在诸多生物过程中发挥关键作用。人类中已被注释整理的此类蛋白已逾千种，且仍有大量新型RNA结合蛋白有待发掘。受限于实验技术的固有局限性，亟需借助计算手段对蛋白-RNA相互作用进行系统表征。尽管该领域已取得诸多进展，但目前仍缺乏能够可靠提供原子级分辨率结构细节的成熟方法。尽管蛋白-RNA自由对接方法已被证实具备一定实用价值，但总体而言，基于模板的预测方法能够生成质量更优的结果。模板是构建高质量复合物模型的核心要素。本研究依托蛋白质数据库（Protein Data Bank, PDB）中的代表性二元蛋白-RNA复合物数据集，对序列与结构的关联规律展开了系统分析。我们针对多套靶标-模板两两比对方法进行了测试验证。分析结果表明，在随机结合模式与正确结合模式之间存在一处临界过渡点。研究结果显示，在筛选优质模板方面，结构比对方法的性能优于序列比对；其可生成更接近天然构象的蛋白-RNA复合物模型，且整体性能优于自由对接方法，能够成功预测自由对接无法处理的复合物体系，包括结合过程中发生显著构象变化的案例。本研究开发了一款基于模板的蛋白-RNA相互作用建模流程PRIME，并通过代表性复合物数据集对其进行了基准性能测试。