Physical Binding Pocket Induction for Affinity Prediction
Physical Binding Pocket Induction for Affinity Prediction
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DOI：
10.1021/jm901096y
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发表时间：
2009-10-08
影响因子：
7.3
通讯作者：
Jain, Ajay N.
中科院分区：
文献类型：
--
作者：
Langham, James J.;Cleves, Ann E.;Jain, Ajay N.
在未知蛋白质结构的情况下，用于预测配基亲和力的计算方法通常采用基于分子特征的回归分析的形式，该分子特征仅与蛋白质/配体结合事件具有切线关系。当结构变化超出同类系列时，这种方法的实用性有限。我们提出了一种基于Compass的多示例学习方法的新方法，其中结合位点的物理模型是从配体及其对应的活性数据中归纳出来的。该模型由分子片段组成，可以解释文字蛋白残基的多个位置。我们通过在一系列有限支架变化的训练和大量具有不同支架的配体上的测试来演示该方法。预测误差在0.5-1.0个对数单位(0.7-1.4千卡/摩尔)之间，具有统计上显著的等级相关性。使用验证方法证明了新配体的准确活性预测，其中使用在固定时间点已知的少量有限结构变化的配体对变化很大的分子的盲测试集进行预测，其中一些是在更晚的时间点发现的。
Computational methods for predicting ligand affinity where no protein structure is known generally take the form of regression analysis based on molecular features that have only a tangential relationship to a protein/ligand binding event. Such methods have limited utility when structural variation moves beyond congeneric series. We present a novel approach based on the multiple-instance learning method of Compass, where a physical model of a binding site is induced from ligands and their corresponding activity data. The model consists of molecular fragments that can account for multiple positions of literal protein residues. We demonstrate the method on 5HT1a ligands by training on a series with limited scaffold variation and testing on numerous ligands with variant scaffolds. Predictive error was between 0.5 and 1.0 log units (0.7-1.4 kcal/mol), with statistically significant rank correlations. Accurate activity predictions of novel ligands were demonstrated using a validation approach where a small number of ligands of limited structural variation known at a fixed time point were used to make predictions on a blind test set of widely varying molecules, some discovered at a much later time point.