Procedures for structural modeling of protein-protein complexes (protein docking) produce a number of models which need to be further analyzed and scored. Scoring can be based on independently determined constraints on the structure of the complex, such as knowledge of amino acids essential for the protein interaction. Previously, we showed that text mining of residues in freely available PubMed abstracts of papers on studies of protein-protein interactions may generate such constraints. However, absence of post-processing of the spotted residues reduced usability of the constraints, as a significant number of the residues were not relevant for the binding of the specific proteins.

中文翻译：

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用于通过机器学习增强蛋白质复合物建模的文本挖掘。

蛋白质-蛋白质复合物结构建模程序（蛋白质对接）产生了许多需要进一步分析和评分的模型。评分可以基于对复合物结构的独立确定的限制，例如蛋白质相互作用所必需的氨基酸知识。以前，我们表明，在关于蛋白质-蛋白质相互作用研究的免费 PubMed 论文摘要中，对残基进行文本挖掘可能会产生此类限制。然而，没有对发现的残基进行后处理会降低约束的可用性，因为大量残基与特定蛋白质的结合无关。