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

用于预测和设计蛋白质相互作用的非常大的数据集和新模型

• 批准号: 8328742
• 负责人: AMY E KEATING
• 金额: $ 39.59万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8328742
• 关键词: Affinity; Apoptotic; Binding; Biological; Biological Assay; Biological Process; Calcium Binding; Cell physiology; Cells; Chemistry; Color; Complex; Computer Simulation; Computing Methodologies; Coupled; Data; Data Set; Disease; EF Hand Motifs; Event; Family; Fluorescence-Activated Cell Sorting; Goals; Human; Knowledge; Label; Laboratories; Learning; Libraries; Life; Ligands; Machine Learning; Measurement; Measures; Mediating; Methodology; Methods; Modeling; Molecular; Nucleic Acids; Pathology; Peptides; Performance; Phenotype; Property; Protein Binding; Protein Family; Proteins; Randomized; Running; SH3 Domains; Screening procedure; Signal Transduction; Sorting - Cell Movement; Specificity; Staging; Surface; Technology; Testing; Tissues; Training; Work; Yeasts; base; combinatorial; design; globular protein; human disease; indexing; insight; member; molecular recognition; new technology; novel; novel strategies; predictive modeling; protein complex; protein protein interaction; receptor; small molecule; technology development

PROJECT SUMMARY 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.

项目摘要 特定的蛋白质 - 蛋白质相互作用负责组织细胞，处理生物学信号和信息以及生命的化学。因此，理解生物学机制取决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预测和合理修改蛋白质 - 蛋白质相互作用的方法。这样的功能会 提供有关病理学分子细节的洞察力，并突出疾病治疗的机会。该建议描述了一个集成的实验/计算技术平台，该平台将提供蛋白质相互作用特异性的预测模型。实验成分涉及构建蛋白质或肽的随机文库，这些库会根据其结合的亲和力进行分类 受体。大量图书馆成员的身份和结合亲和力将使用高通量测序方法解码。每个测序运行的数据最多由107个{序列，亲和力}对组成，将用作计算机学习方法的输入。将生成捕获序列与互动之间关系的模型，以及这些模型的预测能力 将通过实验进行测试。该提案中描述的工作强调了新平台的技术开发和应用两种一般类型的蛋白质复合物。首先是短螺旋配体与中型球状蛋白的相互作用，在这里使用抗凋亡Bcl-2和Ca2+结合EF的EF手蛋白进行了研究。第二是短线性肽与模块化相互作用的相互作用 域，这里是PDZ和SH3域。这四个蛋白质家族介导了人类细胞中大量重要的分子识别事件，所得模型将为研究其生物学功能提供宝贵的支持。这项工作还将对所提出的技术的能力进行严格的测试，然后可以将其应用于更广泛的分子络合物，例如蛋白质 - 蛋白质蛋白，蛋白质 - 蛋白质分子和蛋白质核酸组件。考虑到高 - 用于准确测量蛋白质 - 蛋白质相互作用的吞吐量方法，以及大多数计算模型预测蛋白质结合的原始能力，提出的技术平台具有显着改变蛋白质相互作用特异性的研究。