Extended connectivity interaction features: improving binding affinity prediction through chemical description
Extended connectivity interaction features: improving binding affinity prediction through chemical description
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DOI：
10.1093/bioinformatics/btaa982
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发表时间：
2021-05-15
期刊：
影响因子：
5.8
通讯作者：
Barril, Xavier
中科院分区：
文献类型：
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作者：
Sanchez-Cruz, Norberto;Medina-Franco, Jose L.;Barril, Xavier
动机：已经发现机器学习评分函数（SF）在蛋白质-配体复合物的结合亲和力预测方面优于标准SF。大量的报告集中在越来越复杂的算法的实施，而系统的化学描述还没有得到充分explored.Results：在这里，我们引入扩展连接性相互作用特征（ECIF）来描述蛋白质配体复合物和建立机器学习的SFs与改进的预测结合亲和力。ECIF是一组蛋白质-配体原子类型对计数，其考虑每个原子的连接性来描述它，从而定义对类型。ECIF用于构建不同的机器学习模型来预测蛋白质-配体亲和力（pK（d）/pK（i））。这些模型在2016年评分函数比较评估中根据“评分能力”进行了评估。建立在ECIF上的最佳模型在单独使用时达到了0.857的Pearson相关系数，在与配体描述符组合使用时达到了0.866，证明了ECIF的描述能力。
Motivation: Machine-learning scoring functions (SFs) have been found to outperform standard SFs for binding affinity prediction of protein-ligand complexes. A plethora of reports focus on the implementation of increasingly complex algorithms, while the chemical description of the system has not been fully exploited.Results: Herein, we introduce Extended Connectivity Interaction Features (ECIF) to describe protein-ligand complexes and build machine-learning SFs with improved predictions of binding affinity. ECIF are a set of protein-ligand atom-type pair counts that take into account each atom's connectivity to describe it and thus define the pair types. ECIF were used to build different machine-learning models to predict protein-ligand affinities (pK(d)/pK(i)). The models were evaluated in terms of 'scoring power' on the Comparative Assessment of Scoring Functions 2016. The best models built on ECIF achieved Pearson correlation coefficients of 0.857 when used on its own, and 0.866 when used in combination with ligand descriptors, demonstrating ECIF descriptive power.