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 支持生命的化学变化绘制并解码人类互动基因组， 相互作用发生，它们支持什么功能，以及相互作用如何在疾病中改变是关键目标 生物学。还有一个生物医学的必要性，学会抑制或调节蛋白质相互作用， 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（SLiMs）的蛋白质-蛋白质相互作用的计算和实验研究 与模块化的、结构保守的相互作用结构域结合。SLiM是丰富的，估计更多 在人类蛋白质组中，有超过105个结合基序，它们在信号转导和免疫应答中起着关键作用。 与疾病有关的结构和调节复合物的组装。结合到 SLiM，例如EVH 1、TRAF、SH 3、WW等，在蛋白质组中出现许多拷贝， 旁系同源家族的结构域重复和分歧。该研究计划将解决两个关键问题 问题. (1)旁系特异性问题：旁系同源蛋白质结构域如何相互作用 重叠与不同，以及不同的结合特征如何在相似的序列和结构中编码？ 这将提供目前在相互作用组中缺失的环节，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靶向它们的认识。 (2)SLiM特异性问题：什么样的序列/结构特征决定了SLiM结合，以及这是如何决定的 监管？学习区分真实的交互者与无数的基序匹配误报的特征 在蛋白质组中的研究将揭示SLiM识别的机制，并支持新相互作用的预测。 这项建议的重点是开发新的方法和模型，将应用于生物医学研究 重要的SLiM结合EVH 1和Atg 8样结构域。EVH 1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。 Atg 8样蛋白对自噬至关重要，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。增加或减少自噬有助于 许多疾病都是通过不为人知的机制引起的。拟议的研究将结合联合收割机高通量 使用实验细胞表面显示筛选的相互作用映射，使用深度数据驱动建模 学习以支持新交互的检测、预测和设计。筛选加建模 这种方法将为每个家族揭示新的相互作用伙伴，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技术。 和其他蛋白质间的相互作用。