Computational and Experimental Investigation and Design of Protein Interaction Specificity

蛋白质-蛋白质相互作用传递信息，塑造细胞结构，组装复合体，并使 支持生命的化学变化。映射和解码人类交互作用组以确定 相互作用的发生，它们支持什么功能，以及相互作用在疾病中如何改变是至关重要的目标 为了生物学。在生物医学上，也有必要学会抑制或调节蛋白质之间的相互作用 发现、研究和开发新疗法。该提案提出了一项综合计划， 涉及短线状基序的蛋白质-蛋白质相互作用的计算和实验研究 结合到模块化的、结构保守的相互作用域。苗条丰富，估计有更多 在人类蛋白质组中有105多个结合基序，它们在信号转导和细胞周期调控中发挥重要作用。 与疾病有关的结构和调控复合体的组装。绑定到的域 由于EVH1、TRAF、SH3、WW等纤细物质的扩增，在蛋白质组中以许多拷贝形式存在 结构域重复和分化的近缘家系。这项研究计划将解决两个关键问题 问题。(1)Paralog专一性问题：Paralog蛋白质结构域是如何相互作用的 重叠与不同，不同的结合谱是如何在相似的序列和结构中编码的？ 回答这个问题将提供目前互动组中缺失的环节，并支持预测和设计 Paralog特定的相互作用，这将提高我们对疾病途径以及如何针对它们的知识。 (2)细长的特异性问题：什么序列/结构特征决定了细长结合，这是如何决定的 受监管？学习区分真实交互作用者和无数模体匹配假阳性的特征 在蛋白质组中，将揭示SLIM识别的机制，并支持预测新的相互作用。 这项建议的重点是开发新的方法和模型，这些方法和模型将应用于生物医学研究 重要的细小结合EVH1和ATG8样结构域。EVH1结构域在与Pro-1结合的蛋白质中被发现 丰富的基序，包括调控癌细胞侵袭和转移的Ena/Vasp家族成员。 类ATG8蛋白对自噬至关重要，并参与形成自噬小体和招募货物 通过与选择性自噬受体结合进行降解。增加或减少自噬的贡献 通过鲜为人知的机制对许多疾病产生影响。拟议的研究将结合高吞吐量 使用实验细胞-表面显示筛选的交互映射和使用深度的数据驱动建模 学习支持新交互的检测、预测和设计。筛选加建模 这种方法将为每个家庭揭示新的互动伙伴，从而拓宽我们对细胞生物学的理解， 阐明特异性的机制，并为设计这些选择性抑制剂提供新的技术 以及其他蛋白质之间的相互作用。

Protein-protein interactions transmit information, shape cell structure, assemble complexes, and enable chemical transformations that support life. Mapping and decoding the human interactome to establish which interactions occur, what functions they support, and how interactions are altered in disease are critical goals for biology. There is also a biomedical imperative to learn to inhibit or modulate protein interactions for discovery research and the development of new therapies. This proposal presents an integrated program of computational and experimental studies of protein-protein interactions that involve short linear motifs (SLiMs) binding to modular, structurally conserved interaction domains. SLiM are abundant, with estimates of more than 105 binding motifs in the human proteome, and they play critical roles in signal transduction and the assembly of structural and regulatory complexes that are implicated in disease. The domains that bind to SLiMs, such as EVH1, TRAF, SH3, WW, etc., occur in many copies in the proteome due to the expansion of paralogous families by domain duplication and divergence. This research program will address two key questions. (1) The paralog specificity question: How do the interactions made by paralogous protein domains overlap vs. differ, and how are distinct binding profiles encoded in similar sequences and structures? Answering this will provide currently missing links in the interactome and support the prediction and design of paralog-specific interactions, which will improve our knowledge of disease pathways and how to target them. (2) The SLiM specificity question: What sequence/structure features determine SLiM binding and how is this regulated? Learning the features that distinguish real interactors from myriad motif-matching false positives in the proteome will uncover mechanisms of SLiM recognition and support the prediction of new interactions. This proposal focuses on developing new methods and models that will be applied to study biomedically important SLiM-binding EVH1 and Atg8-like domains. EVH1 domains are found in proteins that bind to proline- rich motifs, including members of the Ena/VASP family that regulate cancer cell invasion and metastasis. Atg8-like proteins are critical for autophagy and participate in forming the autophagosome and recruiting cargo for degradation by binding to selective autophagy receptors. Increased or decreased autophagy contributes to many diseases via poorly understood mechanisms. The proposed studies will combine high-throughput interaction mapping using experimental cell-surface display screening with data-driven modeling using deep learning to support the detection, prediction, and design of new interactions. The screening-plus-modeling approach will reveal new interaction partners for each family that broaden our understanding of cell biology, elucidate mechanisms of specificity, and provide new techniques for designing selective inhibitors of these and other protein-protein interactions.