Quantifying sequence and structural features of protein-RNA interactions.
Quantifying sequence and structural features of protein-RNA interactions.
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DOI：
10.1093/nar/gku681
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发表时间：
2014-09
影响因子：
14.9
通讯作者：
Standley DM
中科院分区：
文献类型：
--
作者：
Li S;Yamashita K;Amada KM;Standley DM
人们对蛋白质-RNA相互作用重要性的认识日益加深，促使许多方法基于序列或结构特征来预测蛋白质中残基水平的RNA结合位点。基于序列的预测者通常灵敏度高，但特异度低；相反，基于结构的预测者往往特异度高，但敏感度较低。在这里，我们使用机器学习方法量化了基于序列和基于结构的特征作为RNA结合倾向指标的贡献。为了捕捉未知结构蛋白质的结构信息，我们使用同源建模来提取相关的结构特征。一些新的和修改的特征增强了残基水平RNA结合倾向的准确性，超过了以前报道的，包括元预测服务器。这些特征包括：基于隐马尔可夫模型的进化守恒，基于拉普拉斯范数形式的表面变形，以及划分为主链和侧链贡献的相对溶剂可及性。我们构建了一个名为Aarna的网络服务器，它实现了所提出的方法，并演示了它在识别假定的RNA结合位点方面的使用。
Increasing awareness of the importance of protein–RNA interactions has motivated many approaches to predict residue-level RNA binding sites in proteins based on sequence or structural characteristics. Sequence-based predictors are usually high in sensitivity but low in specificity; conversely structure-based predictors tend to have high specificity, but lower sensitivity. Here we quantified the contribution of both sequence- and structure-based features as indicators of RNA-binding propensity using a machine-learning approach. In order to capture structural information for proteins without a known structure, we used homology modeling to extract the relevant structural features. Several novel and modified features enhanced the accuracy of residue-level RNA-binding propensity beyond what has been reported previously, including by meta-prediction servers. These features include: hidden Markov model-based evolutionary conservation, surface deformations based on the Laplacian norm formalism, and relative solvent accessibility partitioned into backbone and side chain contributions. We constructed a web server called aaRNA that implements the proposed method and demonstrate its use in identifying putative RNA binding sites.
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