A novel empirical free energy function that explains and predicts protein-protein binding affinities
A novel empirical free energy function that explains and predicts protein-protein binding affinities
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DOI：
10.1016/j.bpc.2007.05.021
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发表时间：
2007-09-01
影响因子：
3.8
通讯作者：
Scarlata, Suzanne
中科院分区：
文献类型：
--
作者：
Audie, Joseph;Scarlata, Suzanne
自由能函数可以定义为将宏观自由能变化与微观或分子性质相关联的数学表达式。自由能函数可用于解释和预测配体对蛋白质的亲和力，并对天然和非天然结合模式进行评分和区分。然而，在开发一个足够快的函数来解决评分问题，但又足够严格地解释和预测结合亲和力之间存在着天然的紧张关系。在这里，我们提出了一种新的，基于物理的自由能函数，计算成本低，但解释性和预测性。该函数的结果从推导，假设极性去溶剂化的成本可以忽略不计，其中包括一个独特的和隐含的处理界面水桥相互作用。该函数在内部一致的高质量训练集上进行参数化，得到R-2 = 0.97和Q（2）= 0.91。我们使用该函数对31种野生型蛋白质-蛋白质和蛋白质-肽复合物的不同测试集进行盲测并成功预测结合亲和力（R-2 = 0.79，rmsd= 1.2 kcal mol（-1））。与最近描述的基于知识的潜力直接比较，该功能表现得非常好，并且该功能似乎是可转移的。我们的研究结果表明，我们的功能是非常适合解决广泛的蛋白质/肽的设计和发现问题。(c)2007 Elsevier B. V.保留所有权利。
A free energy function can be defined as a mathematical expression that relates macroscopic free energy changes to microscopic or molecular properties. Free energy functions can be used to explain and predict the affinity of a ligand for a protein and to score and discriminate between native and non-native binding modes. However, there is a natural tension between developing a function fast enough to solve the scoring problem but rigorous enough to explain and predict binding affinities. Here, we present a novel, physics-based free energy function that is computationally inexpensive, yet explanatory and predictive. The function results from a derivation that assumes the cost of polar desolvation can be ignored and that includes a unique and implicit treatment of interfacial water-bridged interactions. The function was parameterized on an intemally consistent, high quality training set giving R-2 = 0.97 and Q(2) = 0.91. We used the function to blindly and successfully predict binding affinities for a diverse test set of 31 wild-type protein-protein and protein-peptide complexes (R-2 = 0.79, rmsd= 1.2 kcal mol(-1)). The function performed very well in direct comparison with a recently described knowledge-based potential and the function appears to be transferable. Our results indicate that our function is well suited for solving a wide range of protein/peptide design and discovery problems. (c) 2007 Elsevier B.V. All rights reserved.