Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France.
Unité de Mathématiques et Informatique Appliquées de Toulouse, INRA, Castanet Tolosan cedex, France.
Bioinformatics. 2018 Aug 1;34(15):2581-2589. doi: 10.1093/bioinformatics/bty092.
Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces.
Using guaranteed Cost Function Network algorithms, Rosetta energy functions and Dunbrack's rotamer library, we developed and assessed EasyE and JayZ, two methods for binding affinity estimation that ignore or include conformational entropic contributions on a large benchmark of binding affinity experimental measures. If both approaches outperform most established tools, we observe that side-chain conformational entropy brings little or no improvement on most systems but becomes crucial in some rare cases.
as open-source Python/C++ code at sourcesup.renater.fr/projects/easy-jayz.
Supplementary data are available at Bioinformatics online.
准确且经济的方法来预测突变对蛋白质结合自由能的变化对于加速蛋白质的设计具有广泛的应用至关重要。自由能由焓和熵贡献定义。随着基于人工智能的算法在保证 NP 难的能量优化和配分函数计算方面的最新进展，快速计算最小能量构象并可靠估计大蛋白质界面自由能变化中侧链的熵贡献成为可能。
使用有保证的 Cost Function Network 算法、Rosetta 能量函数和 Dunbrack 的构象库，我们开发并评估了 EasyE 和 JayZ 两种方法，用于结合亲和力估计，这两种方法忽略或包含了大量结合亲和力实验测量的构象熵贡献。如果这两种方法都优于大多数已建立的工具，我们观察到侧链构象熵对大多数系统几乎没有或没有改进，但在某些罕见情况下变得至关重要。
作为开源 Python/C++代码，可在 sourcesup.renater.fr/projects/easy-jayz 获得。
补充数据可在 Bioinformatics 在线获得。
