Very large datasets and new models to predict and design protein interactions

描述(申请人提供)：特定的蛋白质-蛋白质相互作用负责组织细胞，处理生物信号和信息，以及生命的化学。因此，了解生物机制依赖于了解蛋白质之间发生的相互作用。一个重要的长期目标是开发可靠地预测和合理修改蛋白质相互作用的方法。这种能力将提供对病理学的分子细节的洞察，并突出疾病治疗的机会。这项提案描述了一个综合的实验/计算技术平台，它将提供蛋白质相互作用特异性的预测模型。实验部分包括构建随机的蛋白质或多肽文库，这些文库将根据它们与特定受体结合的亲和力进行排序。大量文库成员的身份和结合亲和力将使用高通量测序方法进行解码。这些数据由每次测序运行最多107对{序列，亲和力}组成，将被用作计算机器学习方法的输入。将生成捕捉序列和交互之间关系的模型，并将通过实验测试这些模型的预测能力。这项建议中描述的工作重点是研究两种一般类型的蛋白质复合体的新平台的技术开发和应用。首先是短螺旋配体与中等大小球状蛋白的相互作用，这里使用抗凋亡的Bcl-2和钙结合的EF-Hand蛋白进行了研究。第二类是短线性多肽与模块相互作用域的相互作用，这里是PDZ和SH3结构域。这四个蛋白质家族在人类细胞中介导了大量重要的分子识别事件，所建立的模型将为研究它们的生物学功能提供有价值的支持。这项工作还将对拟议技术的能力进行严格的测试，然后将其应用于更广泛的分子复合体，例如蛋白质-蛋白质、蛋白质-小分子和蛋白质-核酸组件。考虑到缺乏精确测量蛋白质-蛋白质相互作用的高通量方法，以及大多数预测蛋白质结合的计算模型的原始能力，所提出的技术平台有可能极大地改变蛋白质相互作用特异性的研究。 公共卫生相关性：特定的蛋白质-蛋白质相互作用是所有生物过程的基础。了解健康组织与患病组织之间的相互作用，再加上抑制这种相互作用的方法，将极大地扩大治疗人类疾病的机会。这项建议描述了一种新的技术，以促进蛋白质复合体的测量、预测和设计。

DESCRIPTION (provided by applicant): Specific protein-protein interactions are responsible for organizing the cell, for processing biological signals and information, and for the chemistry of life. Thus, understanding biological mechanism relies on understanding the interactions that occur between proteins. An important long-term goal is to develop methods for reliably predicting and rationally modifying protein-protein interactions. Such capabilities would provide insight into the molecular details of pathology and highlight opportunities for disease treatment. This proposal describes an integrated experimental/computational technology platform that will provide predictive models of protein interaction specificity. The experimental component involves constructing randomized libraries of proteins or peptides that will be sorted according to their affinities for binding a particular receptor. The identities and binding affinities for very large numbers of library members will be decoded using high-throughput sequencing methods. The data, consisting of up to 107 {sequence, affinity} pairs per sequencing run, will be used as input to computational machine learning methods. Models will be generated that capture the relationship between sequence and interactions, and the predictive power of these models will be tested experimentally. The work described in this proposal emphasizes technology development and application of the new platform to study two general types of protein complexes. First are interactions of short helical ligands with mid-sized globular proteins, here studied using anti-apoptotic Bcl-2 and Ca2+ binding EF-hand proteins. Second are interactions of short linear peptides with modular interaction domains, here PDZ and SH3 domains. These four protein families mediate an enormous number of important molecular recognition events in human cells, and the resulting models will provide valuable support to study of their biological functions. This work will also provide a stringent test of the capabilities of the proposed technology, which can then be applied to a much wider variety of molecular complexes, e.g., protein-protein, protein-small molecule and protein-nucleic acid assemblies. Given the paucity of high- throughput methods for accurately measuring protein-protein interactions, and the primitive capabilities of most computational models for predicting protein binding, the proposed technology platform has the potential to dramatically transform the study of protein interaction specificity. PUBLIC HEALTH RELEVANCE: Specific protein-protein interactions underlie all biological processes. Knowledge of interactions that occur in healthy vs. diseased tissues, coupled with methods for inhibiting such interactions, would dramatically expand opportunities to treat human disease. This proposal describes a new technology for advancing the measurement, prediction and design of protein complexes.