A knowledge-based method for configurational entropy prediction of proteins is presented; this methodology is extremely fast, compared to previous approaches, because it does not involve any type of configurational sampling. Instead, the configurational entropy of a query fold is estimated by evaluating an artificial neural network, which was trained on molecular-dynamics simulations of ∼1000 proteins. The predicted entropy can be incorporated into a large class of protein software based on cost-function minimization/evaluation, in which configurational entropy is currently neglected for performance reasons. Software of this type is used for all major protein tasks such as structure predictions, proteins design, NMR and X-ray refinement, docking, and mutation effect predictions. Integrating the predicted entropy can yield a significant accuracy increase as we show exemplarily for native-state identification with the prominent protein software FoldX. The method has been termed Popcoen for Prediction of Protein Configurational Entropy. An implementation is freely available at http://fmc.ub.edu/popcoen/.

中文翻译：

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蛋白质构型熵（Popcoen）的预测

提出了一种基于知识的蛋白质构型熵预测方法。与以前的方法相比，该方法非常快，因为它不涉及任何类型的配置采样。取而代之的是，通过评估人工神经网络来估计查询折叠的构型熵，该人工神经网络是在约1000种蛋白质的分子动力学模拟中训练的。基于成本函数最小化/评估，可以将预测的熵合并到一大类蛋白质软件中，其中出于性能原因，目前忽略了配置熵。这种类型的软件可用于所有主要的蛋白质任务，例如结构预测，蛋白质设计，NMR和X射线优化，对接和突变效应预测。集成预测的熵可以产生显着的准确性提高，正如我们使用著名的蛋白质软件FoldX进行自然状态鉴定所显示的那样。该方法已被称为PopcoenP rediction Ø ˚F P rotein有限公司nfigurational恩tropy。可以从http://fmc.ub.edu/popcoen/免费获得实现。