CATH functional families predict protein functional sites
CATH functional families predict protein functional sites
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DOI：
10.1101/2020.03.23.003012
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Sayoni Das;Harry M. Scholes;C. Orengo
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文献类型：
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作者：
Sayoni Das;Harry M. Scholes;C. Orengo
蛋白质功能位点的动机识别对于功能表征、变体解释和药物设计是必不可少的。有几种方法可用于预测通用功能部位或特定类型的功能部位。在这里，我们提出了FunSite，这是一个机器学习预测器，使用来自蛋白质序列和结构的特征以及Cath功能家族(FunFams)的进化数据来识别催化、配体结合和蛋白质-蛋白质相互作用功能位点。结果FunSite的预测性能使用交叉验证和坚持数据集进行了严格的基准测试。FunSite的表现优于所有公开可用的功能位点预测方法。我们发现FunFams中的保守残基在功能位点上富含。我们发现，FunSite的性能在很大程度上取决于功能站点注释的质量和训练数据中FunFam的信息含量。最后，我们分析了哪些结构和进化特征对功能位点最具预测性。数据集和预测模型可根据要求提供。联系c.orengo@ucl.ac.uk补充信息补充数据可在BioInformation Online上获得。
Motivation Identification of functional sites in proteins is essential for functional characterisation, variant interpretation and drug design. Several methods are available for predicting either a generic functional site, or specific types of functional site. Here, we present FunSite, a machine learning predictor that identifies catalytic, ligand-binding and protein-protein interaction functional sites using features derived from protein sequence and structure, and evolutionary data from CATH functional families (FunFams). Results FunSite’s prediction performance was rigorously benchmarked using cross-validation and a holdout dataset. FunSite outperformed all publicly-available functional site prediction methods. We show that conserved residues in FunFams are enriched in functional sites. We found FunSite’s performance depends greatly on the quality of functional site annotations and the information content of FunFams in the training data. Finally, we analyse which structural and evolutionary features are most predictive for functional sites. Availability The datasets and prediction models are available on request. Contact c.orengo@ucl.ac.uk Supplementary information Supplementary data are available at Bioinformatics online.