Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA.
Biomolecules. 2022 Jun 25;12(7):888. doi: 10.3390/biom12070888.
Intrinsically disordered regions (IDRs) carry out many cellular functions and vary in length and placement in protein sequences. This diversity leads to variations in the underlying compositional biases, which were demonstrated for the short vs. long IDRs. We analyze compositional biases across four classes of disorder: fully disordered proteins; short IDRs; long IDRs; and binding IDRs. We identify three distinct biases: for the fully disordered proteins, the short IDRs and the long and binding IDRs combined. We also investigate compositional bias for putative disorder produced by leading disorder predictors and find that it is similar to the bias of the native disorder. Interestingly, the accuracy of disorder predictions across different methods is correlated with the correctness of the compositional bias of their predictions highlighting the importance of the compositional bias. The predictive quality is relatively low for the disorder classes with compositional bias that is the most different from the "generic" disorder bias, while being much higher for the classes with the most similar bias. We discover that different predictors perform best across different classes of disorder. This suggests that no single predictor is universally best and motivates the development of new architectures that combine models that target specific disorder classes.
无规卷曲区域（IDRs）执行许多细胞功能，其长度和在蛋白质序列中的位置在不同蛋白质中存在差异。这种多样性导致了潜在组成性偏向的变化，这在短 IDR 和长 IDR 中得到了证明。我们分析了四类无序：完全无序的蛋白质；短 IDR；长 IDR；和结合 IDR。我们确定了三个不同的偏向：对于完全无序的蛋白质，短 IDR 和长 IDR 以及结合 IDR 组合。我们还研究了主要无序预测器产生的假定无序的组成性偏向，发现它与天然无序的偏向相似。有趣的是，不同方法的无序预测的准确性与它们预测的组成性偏向的正确性相关，突出了组成性偏向的重要性。对于组成性偏向与“通用”无序偏向差异最大的无序类别，预测质量相对较低，而对于偏向最相似的类别，预测质量则要高得多。我们发现，不同的预测器在不同的无序类别中表现最佳。这表明没有一种单一的预测器是普遍最好的，这促使我们开发了新的架构，将针对特定无序类别的模型结合在一起。
