An iterative knowledge-based scoring function to predict protein-ligand interactions: I. Derivation of interaction potentials
An iterative knowledge-based scoring function to predict protein-ligand interactions: I. Derivation of interaction potentials
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DOI：
10.1002/jcc.20504
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发表时间：
2006-11-30
影响因子：
3
通讯作者：
Zou, Xiaoqin
中科院分区：
文献类型：
--
作者：
Huang, Sheng-You;Zou, Xiaoqin
使用一种新的迭代方法，我们已经开发了一个基于知识的评分函数（ITScore）来预测蛋白质-配体相互作用。ITScore的对势源自蛋白质数据库中786个蛋白质-配体复合物结构的训练集。基于SYBYL软件的原子类型类别，使用26种原子类型。迭代方法避免了在基于知识的评分函数的推导中长期存在的参考状态问题。其基本思想是通过迭代来提高对电位，直到它们正确地区分实验确定的结合模式与训练集中配体-蛋白质复合物的诱饵配体姿势。该迭代方法是有效的，通常在20个迭代步骤内收敛。基于衍生电位的评分函数在140种蛋白质-配体复合物的不同集合上进行了测试，用于亲和力预测，得到0.74的高相关系数。由于ITScore使用SYBYL定义的原子类型，因此此评分功能易于用于SYBYL准备的分子文件或由BABEL等软件转换的分子文件。(C)2006 Wiley Periodicals，Inc.
Using a novel iterative method, we have developed a knowledge-based scoring function (ITScore) to predict protein-ligand interactions. The pair potentials for ITScore were derived from a training set of 786 protein-ligand complex structures in the Protein Data Bank. Twenty-six atom types were used based on the atom type category of the SYBYL software. The iterative method circumvents the long-standing reference state problem in the derivation of knowledge-based scoring functions. The basic idea is to improve pair potentials by iteration until they correctly discriminate experimentally determined binding modes from decoy ligand poses for the ligand-protein complexes in the training set. The iterative method is efficient and normally converges within 20 iterative steps. The scoring function based on the derived potentials was tested on a diverse set of 140 protein-ligand complexes for affinity prediction, yielding a high correlation coefficient of 0.74. Because ITScore uses SYBYL-defined atom types, this scoring function is easy to use for molecular files prepared by SYBYL or converted by software such as BABEL. (C) 2006 Wiley Periodicals, Inc.