Portelance Reagan, Wu Anqi, Kandoor Alekhya, Naegle Kristen M
Department of Biomedical Engineering and the Department for Genome Sciences, University of Virginia, Charlottesville, Virginia, United States of America.
bioRxiv. 2025 May 13:2025.05.13.653723. doi: 10.1101/2025.05.13.653723.
Tandem SH2 domains occur in key protein mediators of phosphotyrosine signaling and have the capacity to drive high affinity interactions through the avidity that results with bisphosphorylated protein partners. However, challenges have prevented the broad exploration of tandem SH2 domain avidity and here we utilize advances in both computational modeling and experimental approaches to predict and test tandem SH2 domain recruitment. Theoretical model behavior suggests that maximum avidity occurs with closely spaced or flexibly linked phosphotyrosine sites, combined with moderate monovalent affinities - exactly around the affinities of SH2 domains with individual phosphotyrosine sites. Surprisingly, despite sequence diversity, structure-based analysis showed remarkably conserved three-dimensional spacing between SH2 domains across all tandem SH2 families, which we interrogate experimentally, suggesting evolutionary optimization for avidity interactions. The combination of structure-based analysis of domain spacing with available monovalent experimental data appears to be sufficiently accurate to rank order predict high affinity interactions of tandem SH2 domain recruitment to the EGFR C-terminal tail. These approaches lay the groundwork for larger utility in multivalent prediction and testing to help better understand protein interactions that drive cell signaling.
串联SH2结构域存在于磷酸酪氨酸信号传导的关键蛋白介质中,能够通过与双磷酸化蛋白伴侣结合产生的亲和力驱动高亲和力相互作用。然而,一些挑战阻碍了对串联SH2结构域亲和力的广泛探索,在此我们利用计算建模和实验方法的进展来预测和测试串联SH2结构域的募集。理论模型行为表明,最大亲和力出现在紧密间隔或灵活连接的磷酸酪氨酸位点,同时结合适度的单价亲和力——恰好接近SH2结构域与单个磷酸酪氨酸位点的亲和力。令人惊讶的是,尽管存在序列多样性,但基于结构的分析表明,所有串联SH2家族的SH2结构域之间的三维间距显著保守,我们通过实验对此进行了研究,这表明亲和力相互作用存在进化优化。基于结构的结构域间距分析与可用的单价实验数据相结合,似乎足以准确地对串联SH2结构域募集到表皮生长因子受体(EGFR)C末端尾巴的高亲和力相互作用进行排序预测。这些方法为在多价预测和测试中更广泛地应用奠定了基础,以帮助更好地理解驱动细胞信号传导的蛋白质相互作用。