Flexibility is an intrinsic essential feature of protein structures, directly linked to their functions. To this day, most of the prediction methods use the crystallographic data (namely B-factors) as the only indicator of protein's inner flexibility and predicts them as rigid or flexible. PredyFlexy stands differently from other approaches as it relies on the definition of protein flexibility (i) not only taken from crystallographic data, but also (ii) from Root Mean Square Fluctuation (RMSFs) observed in Molecular Dynamics simulations. It also uses a specific representation of protein structures, named Long Structural Prototypes (LSPs). From Position-Specific Scoring Matrix, the 120 LSPs are predicted with a good accuracy and directly used to predict (i) the protein flexibility in three categories (flexible, intermediate and rigid), (ii) the normalized B-factors, (iii) the normalized RMSFs, and (iv) a confidence index. Prediction accuracy among these three classes is equivalent to the best two class prediction methods, while the normalized B-factors and normalized RMSFs have a good correlation with experimental and in silico values. Thus, PredyFlexy is a unique approach, which is of major utility for the scientific community. It support parallelization features and can be run on a local cluster using multiple cores.

中文翻译：

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在计算机上使用局部结构方法预测蛋白质的柔韧性。

柔韧性是蛋白质结构的固有本质特征，直接与其功能相关。时至今日，大多数预测方法都使用晶体学数据（即B因子）作为蛋白质内部柔韧性的唯一指标，并预测它们是刚性的还是柔韧性的。PredyFlexy与其他方法截然不同，因为它依赖于蛋白质柔韧性的定义（i）不仅来自晶体学数据，而且（ii）来自在分子动力学模拟中观察到的均方根波动（RMSF）。它还使用蛋白质结构的特定表示形式，称为长结构原型（LSP）。根据特定位置评分矩阵，可以准确预测120个LSP，并将其直接用于预测（i）三类（柔性，中间和刚性）的蛋白质柔性，（ii）归一化的B因子，（iii）归一化的RMSF，以及（iv）置信指数。这三个类别之间的预测准确性等同于最佳的两个类别预测方法，而归一化的B因子和归一化的RMSF与实验值和计算机数值具有良好的相关性。因此，PredyFlexy是一种独特的方法，对于科学界来说，它具有重要的用途。它支持并行化功能，并且可以使用多个内核在本地群集上运行。这对科学界具有重大意义。它支持并行化功能，并且可以使用多个内核在本地群集上运行。对于科学界来说，这是非常有用的。它支持并行化功能，并且可以使用多个内核在本地群集上运行。