MFSPSSMpred: identifying short disorder-to-order binding regions in disordered proteins based on contextual local evolutionary conservation.
MFSPSSMpred: identifying short disorder-to-order binding regions in disordered proteins based on contextual local evolutionary conservation.
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DOI：
10.1186/1471-2105-14-300
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发表时间：
2013-10-04
影响因子：
3
通讯作者：
Yamana H
中科院分区：
文献类型：
--
作者：
Fang C;Noguchi T;Tominaga D;Yamana H
分子识别特征(Morf)是位于较长的固有无序蛋白质区域的短结合区域。虽然这些短区域在自然状态下缺乏稳定的结构，但它们在与伴侣分子结合时很容易经历无序到有序的转变。Morf在细胞的分子相互作用网络中起着关键作用，并与许多人类遗传病有关。因此，MORF的鉴定对于了解这些蛋白质的功能和在药物设计中的应用是重要的一步。在这里，我们提出了一种新的识别Morf的方法，称为MFSPSSMpred(掩蔽、过滤和平滑位置特定评分矩阵预测)。首先，使用掩蔽法计算特定位置评分矩阵(PSSM)中掩蔽窗口长度内残基的平均局部守恒分数。然后，低于平均分的分数被过滤掉。最后，使用平滑方法合并每个残基的侧翼区域的特征，以准备用于预测的特征集。我们的方法不使用其他分类器的预测结果作为输入，即该方法中使用的所有特征都只从序列的PSSM中提取。实验结果表明，与在相同数据集上测试的其他方法相比，我们的方法取得了最好的性能：在TEST419上测试时，AUC值比其他方法高0.004~0.079；在TEST2012上测试时，AUC值比其他方法高0.045~0.212。此外，当在一个独立的膜蛋白相关数据集上进行测试时，MFSPSSMpred的性能显著优于现有的预测指标MoRFpred。这项研究表明：1)MORF侧翼区域的氨基酸组成和理化性质与一般的非MORF区域有很大的不同；2)MORF既含有高度保守的残基，又含有高度可变的残基，总体上是局部高度保守的；3)结合上下文信息和残基的局部保守信息有助于MORF的预测。
Molecular recognition features (MoRFs) are short binding regions located in longer intrinsically disordered protein regions. Although these short regions lack a stable structure in the natural state, they readily undergo disorder-to-order transitions upon binding to their partner molecules. MoRFs play critical roles in the molecular interaction network of a cell, and are associated with many human genetic diseases. Therefore, identification of MoRFs is an important step in understanding functional aspects of these proteins and in finding applications in drug design. Here, we propose a novel method for identifying MoRFs, named as MFSPSSMpred (Masked, Filtered and Smoothed Position-Specific Scoring Matrix-based Predictor). Firstly, a masking method is used to calculate the average local conservation scores of residues within a masking-window length in the position-specific scoring matrix (PSSM). Then, the scores below the average are filtered out. Finally, a smoothing method is used to incorporate the features of flanking regions for each residue to prepare the feature sets for prediction. Our method employs no predicted results from other classifiers as input, i.e., all features used in this method are extracted from the PSSM of sequence only. Experimental results show that, comparing with other methods tested on the same datasets, our method achieves the best performance: achieving 0.004~0.079 higher AUC than other methods when tested on TEST419, and achieving 0.045~0.212 higher AUC than other methods when tested on TEST2012. In addition, when tested on an independent membrane proteins-related dataset, MFSPSSMpred significantly outperformed the existing predictor MoRFpred. This study suggests that: 1) amino acid composition and physicochemical properties in the flanking regions of MoRFs are very different from those in the general non-MoRF regions; 2) MoRFs contain both highly conserved residues and highly variable residues and, on the whole, are highly locally conserved; and 3) combining contextual information with local conservation information of residues facilitates the prediction of MoRFs.
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