Towards region-specific propagation of protein functions
Towards region-specific propagation of protein functions
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DOI：
10.1101/275487
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发表时间：
2018-03
期刊：
影响因子：
5.8
通讯作者：
Da Chen Emily Koo;Richard Bonneau
中科院分区：
文献类型：
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作者：
Da Chen Emily Koo;Richard Bonneau
由于实验注释的性质，大多数蛋白质功能预测方法都是在蛋白质水平上操作的，其中基于总体相似性将功能分配给全长蛋白质。然而，大多数蛋白质的功能是通过与其他蛋白质或分子的相互作用来实现的，许多功能关联应该局限于特定区域，而不是整个蛋白质长度。大多数以领域为中心的功能预测方法依赖于精确的领域族分配来推断领域和功能之间的关系，而未分配给已知领域族的区域则被排除在功能评估之外。鉴于目前可用的残基级注释的丰富性，我们提出了一种功能预测方法，该方法使用蛋白质级注释和多种类型的区域特异性特征自动推断特定蛋白质区域的功能标签。结果将该方法应用于从InterPro、UniProtKB和氨基酸序列中获得的局部特征，通过对人和酵母蛋白质组的测试表明，该方法提高了蛋白质功能转移和预测的准确性和区域特异性。我们使用具有结构验证结合位点的蛋白质保留数据集，将我们的方法与全蛋白基线方法的区域级预测性能进行了比较，并比较了蛋白质水平的时间保留预测性能，以扩大我们可以评估的氧化石墨烯术语的多样性和特异性。我们的结果也可以作为将氧化石墨烯术语分类为位点特异性术语和全蛋白术语的起点，并为不同类别的氧化石墨烯术语选择预测方法。代码可在https://github.com/ek1203/region_spec_func_pred免费获得
Motivation Due to the nature of experimental annotation, most protein function prediction methods operate at the protein-level, where functions are assigned to full-length proteins based on overall similarities. However, most proteins function by interacting with other proteins or molecules, and many functional associations should be limited to specific regions rather than the entire protein length. Most domain-centric function prediction methods depend on accurate domain family assignments to infer relationships between domains and functions, with regions that are unassigned to a known domain-family left out of functional evaluation. Given the abundance of residue-level annotations currently available, we present a function prediction methodology that automatically infers function labels of specific protein regions using protein-level annotations and multiple types of region-specific features. Results We apply this method to local features obtained from InterPro, UniProtKB and amino acid sequences and show that this method improves both the accuracy and region-specificity of protein function transfer and prediction by testing on both human and yeast proteomes. We compare region-level predictive performance of our method against that of a whole-protein baseline method using a held-out dataset of proteins with structurally-verified binding sites and also compare protein-level temporal holdout predictive performances to expand the variety and specificity of GO terms we could evaluate. Our results can also serve as a starting point to categorize GO terms into site-specific and whole-protein terms and select prediction methods for different classes of GO terms. Availability The code is freely available at: https://github.com/ek1203/region_spec_func_pred