Liu Tianyun, Ish-Shalom Shirbi, Torng Wen, Lafita Aleix, Bock Christian, Mort Matthew, Cooper David N, Bliven Spencer, Capitani Guido, Mooney Sean D, Altman Russ B
Department of Bioengineering, Stanford University, Stanford, California.
Biomedical Informatics Training Program, Stanford University, Stanford, California.
Proteins. 2018 Mar;86 Suppl 1(Suppl Suppl 1):374-386. doi: 10.1002/prot.25396. Epub 2017 Oct 17.
Our goal is to answer the question: compared with experimental structures, how useful are predicted models for functional annotation? We assessed the functional utility of predicted models by comparing the performances of a suite of methods for functional characterization on the predictions and the experimental structures. We identified 28 sites in 25 protein targets to perform functional assessment. These 28 sites included nine sites with known ligand binding (holo-sites), nine sites that are expected or suggested by experimental authors for small molecule binding (apo-sites), and Ten sites containing important motifs, loops, or key residues with important disease-associated mutations. We evaluated the utility of the predictions by comparing their microenvironments to the experimental structures. Overall structural quality correlates with functional utility. However, the best-ranked predictions (global) may not have the best functional quality (local). Our assessment provides an ability to discriminate between predictions with high structural quality. When assessing ligand-binding sites, most prediction methods have higher performance on apo-sites than holo-sites. Some servers show consistently high performance for certain types of functional sites. Finally, many functional sites are associated with protein-protein interaction. We also analyzed biologically relevant features from the protein assemblies of two targets where the active site spanned the protein-protein interface. For the assembly targets, we find that the features in the models are mainly determined by the choice of template.
与实验结构相比,预测模型对功能注释有多大用处?我们通过比较一系列功能表征方法在预测结构和实验结构上的性能,评估了预测模型的功能实用性。我们在25个蛋白质靶点中确定了28个位点来进行功能评估。这28个位点包括9个已知配体结合的位点(全位点)、9个实验作者预期或建议的小分子结合位点(空位点),以及10个包含重要基序、环或具有重要疾病相关突变的关键残基的位点。我们通过将预测结构的微环境与实验结构进行比较,评估了预测的实用性。整体结构质量与功能实用性相关。然而,排名最佳的预测(全局)可能不具有最佳的功能质量(局部)。我们的评估提供了区分具有高结构质量的预测的能力。在评估配体结合位点时,大多数预测方法在空位点上的性能高于全位点。一些服务器在某些类型的功能位点上表现出持续的高性能。最后,许多功能位点与蛋白质-蛋白质相互作用相关。我们还分析了两个靶点的蛋白质组装体中与生物学相关的特征,其中活性位点跨越了蛋白质-蛋白质界面。对于组装靶点,我们发现模型中的特征主要由模板的选择决定。