Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing, China.
J Mol Recognit. 2021 Jun;34(6):e2887. doi: 10.1002/jmr.2887. Epub 2021 Jan 14.
Protein-RNA interactions play essential roles in a wide variety of biological processes. Recognition of RNA-binding residues on proteins has been a challenging problem. Most of methods utilize the position-specific scoring matrix (PSSM). It has been found that considering the evolutionary information of sequence neighboring residues can improve the prediction. In this work, we introduce a novel method SNB-PSSM (spatial neighbor-based PSSM) combined with the structure window scheme where the evolutionary information of spatially neighboring residues is considered. The results show our method consistently outperforms the standard and smoothed PSSM methods. Tested on multiple datasets, this approach shows an encouraging performance compared with RNABindRPlus, BindN+, PPRInt, xypan, Predict_RBP, SpaPF, PRNA, and KYG, although is inferior to RNAProSite, RBscore, and aaRNA. In addition, since our method is not sensitive to protein structure changes, it can be applied well on binding site predictions of modeled structures. Thus, the result also suggests the evolution of binding sites is spatially cooperative. The proposed method as an effective tool of considering evolutionary information can be widely used for the nucleic acid-/protein-binding site prediction and functional motif finding.
蛋白质与 RNA 的相互作用在各种生物过程中起着至关重要的作用。识别蛋白质上的 RNA 结合残基一直是一个具有挑战性的问题。大多数方法都利用位置特异性评分矩阵（PSSM）。已经发现，考虑序列相邻残基的进化信息可以提高预测的准确性。在这项工作中，我们引入了一种新的方法 SNB-PSSM（基于空间邻居的 PSSM），结合了结构窗口方案，其中考虑了空间相邻残基的进化信息。结果表明，我们的方法始终优于标准 PSSM 和平滑 PSSM 方法。在多个数据集上进行测试，与 RNABindRPlus、BindN+、PPRInt、xypan、Predict_RBP、SpaPF、PRNA 和 KYG 相比，该方法表现出令人鼓舞的性能，尽管不如 RNAProSite、RBscore 和 aaRNA。此外，由于我们的方法对蛋白质结构变化不敏感，因此可以很好地应用于建模结构的结合位点预测。因此，结果还表明结合位点的进化具有空间协同性。该方法作为一种考虑进化信息的有效工具，可以广泛应用于核酸/蛋白质结合位点预测和功能模体发现。
