Binding affinity prediction with property-encoded shape distribution signatures.
Binding affinity prediction with property-encoded shape distribution signatures.
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DOI：
10.1021/ci9004139
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发表时间：
2010-02-22
影响因子：
5.6
通讯作者：
Breneman CM
中科院分区：
文献类型：
--
作者：
Das S;Krein MP;Breneman CM
我们报道了使用被称为属性编码形状分布(PESD)的分子签名与标准支持向量机(SVM)技术来产生能够预测大量蛋白质配体复合体的结合亲和力的验证模型。该方法使用编码蛋白质和配体表面分子形状和性质分布的PESD特征作为特征，建立不需要主观特征选择的支持向量机模型。在开发过程中，采用了一种简单的协议来调整支持向量机模型，并将结果与SFCcore进行了比较--SFCcore是一种基于回归的方法，之前被证明比其他14种评分函数执行得更好。虽然PESD-支持向量机方法仅基于两个表面属性图，但总体结果具有可比性。对于大多数具有主要结合焓贡献的络合物(ΔH/-TΔS>；3)，观察到真实亲和力与预测亲和力之间有很好的相关性。目前的方法没有考虑到熵和溶剂，要进一步提高准确度，就需要严格计算这些成分。
We report the use of the molecular signatures known as “Property-Encoded Shape Distributions” (PESD) together with standard Support Vector Machine (SVM) techniques to produce validated models that can predict the binding affinity of a large number of protein ligand complexes. This “PESD-SVM” method uses PESD signatures that encode molecular shapes and property distributions on protein and ligand surfaces as features to build SVM models that require no subjective feature selection. A simple protocol was employed for tuning the SVM models during their development, and the results were compared to SFCscore – a regression-based method that was previously shown to perform better than 14 other scoring functions. Although the PESD-SVM method is based on only two surface property maps, the overall results were comparable. For most complexes with a dominant enthalpic contribution to binding (ΔH/-TΔS > 3), a good correlation between true and predicted affinities was observed. Entropy and solvent were not considered in the present approach and further improvement in accuracy would require accounting for these components rigorously.
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影响因子：
15
作者：
Head, RD;Smythe, ML;Marshall, GR
通讯作者：
Marshall, GR
影响因子：
15
作者：
DANG, LX;MERZ, KM;KOLLMAN, PA
通讯作者：
KOLLMAN, PA
影响因子：
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作者：
GEHLHAAR, DK;VERKHIVKER, GM;FREER, ST
通讯作者：
FREER, ST
影响因子：
7.3
作者：
Ajay;Murcko, MA
通讯作者：
Murcko, MA
影响因子：
5.6
作者：
Das, Sourav;Kokardekar, Arshad;Breneman, Curt M.
通讯作者：
Breneman, Curt M.