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首页> 外文期刊>Journal of computational biology: A journal of computational molecular cell biology >Learning Sequence Determinants of Protein: Protein Interaction Specificity with Sparse Graphical Models
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Learning Sequence Determinants of Protein: Protein Interaction Specificity with Sparse Graphical Models

机译:学习蛋白质的序列决定因素:具有稀疏图形模型的蛋白质相互作用特异性

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摘要

In studying the strength and specificity of interaction between members of two protein families, key questions center on which pairs of possible partners actually interact, how well they interact, and why they interact while others do not. The advent of large-scale experimental studies of interactions between members of a target family and a diverse set of possible interaction partners offers the opportunity to address these questions. We develop here a method, DgSpi (data-driven graphical models of specificity in protein:protein interactions), for learning and using graphical models that explicitly represent the amino acid basis for interaction specificity (why) and extend earlier classification-oriented approaches (which) to predict the G of binding (how well). We demonstrate the effectiveness of our approach in analyzing and predicting interactions between a set of 82 PDZ recognition modules against a panel of 217 possible peptide partners, based on data from MacBeath and colleagues. Our predicted G values are highly predictive of the experimentally measured ones, reaching correlation coefficients of 0.69 in 10-fold cross-validation and 0.63 in leave-one-PDZ-out cross-validation. Furthermore, the model serves as a compact representation of amino acid constraints underlying the interactions, enabling protein-level G predictions to be naturally understood in terms of residue-level constraints. Finally, the model DgSpi readily enables the design of new interacting partners, and we demonstrate that designed ligands are novel and diverse.
机译:在研究两个蛋白质家族成员之间相互作用的强度和特异性时,关键问题集中于可能的一对配对实际上相互作用,它们相互作用的程度以及为什么它们却不相互作用的原因。目标家庭成员与各种可能的相互作用伙伴之间相互作用的大规模实验研究的出现为解决这些问题提供了机会。我们在这里开发一种方法DgSpi(蛋白质:蛋白质相互作用的数据驱动的特异性图形模型),用于学习和使用可明确表示相互作用特异性氨基酸基础的图形模型(为什么)并扩展早期的面向分类的方法(该方法)以预测结合的G(水平)。我们基于MacBeath及其同事的数据,证明了我们的方法在分析和预测82种PDZ识别模块与217种可能的肽伴侣组成的组之间的相互作用时的有效性。我们的预测G值高度预测了实验测量的G值,在10倍交叉验证中达到了0.69的相关系数,在无一PDZ交叉验证中达到了0.63的相关系数。此外,该模型可作为相互作用背后氨基酸限制的紧凑表示形式,从而使蛋白质水平的G预测可以自然地理解为残基水平的限制。最后,模型DgSpi可以轻松实现新的相互作用伙伴的设计,并且我们证明了设计的配体是新颖且多样的。

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