Computational and Experimental Investigation and Design of Protein Interaction Specificity

蛋白质相互作用特异性的计算和实验研究与设计

• 批准号: 10621973
• 负责人: AMY E KEATING
• 金额: $ 54.83万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10621973
• 关键词: Autophagocytosis; Autophagosome; Binding; Binding Proteins; Biological Process; Biology; Cell Shape; Cell surface; Cellular Structures; Cellular biology; Chemicals; Complex; Detection; Disease; Disease Pathway; Family; Goals; Human; Invaded; Investigation; Knowledge; Learning; Life; Link; Maps; Methods; Modeling; Neoplasm Metastasis; Play; Proline; Protein Engineering; Proteins; Proteome; Research; Role; Signal Transduction; Specificity; Structure; Techniques; Tertiary Protein Structure; biological research; cancer cell; computer studies; data-driven model; deep learning; design; experimental study; human interactome; improved; inhibitor; member; novel therapeutics; paralogous gene; pharmacologic; programs; protein protein interaction; receptor; recruit; research and development; screening; transmission process; vasodilator-stimulated phosphoprotein

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.

蛋白质-蛋白质相互作用传递信息、塑造细胞结构、组装复合物并使得 支持生命的化学变化。绘制和解码人类相互作用组以确定 相互作用的发生、它们支持哪些功能以及相互作用在疾病中如何改变是关键目标 对于生物学。学习抑制或调节蛋白质相互作用对于生物医学来说也是当务之急。 发现研究和新疗法的开发。该提案提出了一个综合方案 涉及短线性基序 (SLiM) 的蛋白质-蛋白质相互作用的计算和实验研究 结合模块化、结构保守的相互作用域。 SLiM 很丰富，估计还有更多 人类蛋白质组中有超过 105 个结合基序，它们在信号转导和 与疾病有关的结构和调节复合物的组装。绑定到的域 SLiM，如 EVH1、TRAF、SH3、WW 等，由于扩展而在蛋白质组中出现许多拷贝。 通过域重复和分歧形成旁系同源家族。该研究计划将解决两个关键问题 问题。 (1)旁系同源特异性问题：旁系同源蛋白结构域如何相互作用 重叠与不同，不同的结合谱如何以相似的序列和结构编码？ 回答这个问题将提供相互作用组中当前缺失的链接，并支持预测和设计 旁系同源特异性相互作用，这将提高我们对疾病途径以及如何针对它们的了解。 (2) SLiM 特异性问题：什么序列/结构特征决定 SLiM 结合以及这是如何决定的 监管？学习区分真实交互者和无数主题匹配误报的特征 蛋白质组中的研究将揭示 SLiM 识别机制并支持新相互作用的预测。 该提案的重点是开发用于生物医学研究的新方法和模型 重要的 SLiM 结合 EVH1 和 Atg8 样域。 EVH1 结构域存在于与脯氨酸结合的蛋白质中 丰富的基序，包括调节癌细胞侵袭和转移的 Ena/VASP 家族成员。 Atg8 样蛋白对于自噬至关重要，并参与形成自噬体和招募货物 通过与选择性自噬受体结合而降解。自噬的增加或减少有助于 通过知之甚少的机制导致许多疾病。拟议的研究将结合高通量 使用实验细胞表面显示筛选和使用深度数据驱动建模的相互作用映射 学习支持新交互的检测、预测和设计。筛选加建模 该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， 阐明特异性机制，并提供设计这些选择性抑制剂的新技术 以及其他蛋白质-蛋白质相互作用。