Computational design of novel protein binders based on structure mining and learning from data

基于结构挖掘和数据学习的新型蛋白质结合剂的计算设计

• 批准号: 9887271
• 负责人: Gevorg Grigoryan
• 金额: $ 40.64万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9887271
• 关键词: Address; Affinity; Age; Algorithms; Amino Acid Sequence; Antibodies; Back; Basic Science; Binding; Binding Proteins; Binding Sites; Biological Assay; Biomedical Research; Case Study; Clinical Research; Collection; Communities; Complement; Complex; Computer Models; Computing Methodologies; Custom; Data; Development; Disease; Disease Pathway; Engineering; Failure; Feedback; Generations; Geometry; Image; Label; Learning; Libraries; Life; Ligands; Methodology; Methods; Mining; Modernization; Nature; Outcome; Pathway interactions; Pattern; Peptides; Pharmaceutical Preparations; Physicians; Property; Protein Binding Domain; Protein Engineering; Protein Family; Proteins; Protocols documentation; Publishing; Reagent; Research; Route; Scientist; Sequence Homologs; Signal Transduction; Site; Source; Specific qualifier value; Specificity; Structural Models; Structure; Techniques; Technology; Tertiary Protein Structure; Testing; Time; Update; Work; base; data archive; data warehouse; design; disease diagnosis; empowered; experimental study; extracellular; flexibility; high throughput screening; improved; inhibitor/antagonist; iterative design; lens; model development; novel; novel strategies; protein structure; repaired; research and development; scaffold; screening; small molecule; statistics; structural biology; success; synergism; therapeutic development; tool

Our long-term objective is to turn computational protein design into a disruptive technology platform that will enable the routine and rapid generation of reagents for detecting proteins or perturbing their functions. Currently, research and therapy rely on small molecules and/or antibodies for these tasks. These are powerful tools, but they can be slow and expensive to develop, and they do not meet all needs. Designer peptides or mini-proteins have high potential to bind extracellular or intracellular targets either as labels (e.g., for imaging) or as functional modulators (e.g., interaction inhibitors), for applications in basic and clinical research and in disease diagnosis and treatment. Existing tools for designing such custom proteins rely on experimental library screening, sometimes guided or supported by computational modeling of structure. Despite the immense value such molecules would bring to basic biomedical research and therapeutic development, there are not yet rapid and facile routes to obtaining designed proteins with desired properties. Computational methods can potentially address this need, but existing technology is not sufficiently reliable, flexible or automated for routine use. Compared to the mid-1990’s, when the modern approach to computational protein design was developed, we live in a data and technology-empowered age. The premise of this proposal is that we can increase the range of problems that can be solved using computational design, and also dramatically improve success rates, by making full use of the proven rules of sequence-structure compatibility encoded in known natural structures and their homologous sequences. The Protein Data Bank (the collection of all known protein structures) has grown 10-fold since 2000, placing us at a point where we can design novel proteins by constructing them from building blocks used in nature. We have implemented a new design framework that is based on this principle and that is different in fundamental aspects from all previously published alternatives. Tests on diverse tasks demonstrate outstanding success. To further develop our approach, we propose methodological advances that we will implement, test and then apply to protein design challenges involving detecting or inhibiting protein recognition domains. We will develop and apply methods to: automatically identify design strategies for binding to a target protein, score and rank specific design candidates, design libraries that will be screened to provide rich experimental data about successes and failures, and automatically feed experimental data back into model development in a principled way. Outcomes will include new methodology that will be shared with the community, computational predictions of high-ranked interface design sites that can inform analysis of structures and pathways, and experimentally validated designer molecules that bind to protein domains important for signaling in disease pathways.

我们的长期目标是将计算蛋白设计变成一个破坏性的技术平台 启用常规和快速生成试剂，以检测蛋白质或扰动其功能。现在， 研究和治疗依赖于这些任务的小分子和/或抗体。这些是强大的工具，但是 它们可以缓慢而昂贵，并且无法满足所有需求。设计师Pepperides或迷你蛋白 具有很高的细胞外或细胞内靶标的标签（例如，成像）或功能性具有很高的潜力 调节剂（例如相互作用抑制剂），用于基础和临床研究以及疾病诊断中的应用 和治疗。现有设计这种自定义蛋白质的工具依赖于实验库筛选， 有时由结构的计算建模指导或支持。尽管有巨大的价值 分子会带来基本的生物医学研究和治疗性发展，尚不迅速 可容纳具有所需特性的设计蛋白质的便利路线。计算方法可能可能 满足这种需求，但是现有技术不足以可靠，灵活或自动化供日常使用。 与1990年代中期相比，当开发了现代计算蛋白设计方法时，我们 生活在数据和技术授权的年龄中。该提议的前提是我们可以增加 可以使用计算设计解决的问题，并通过 充分利用已知的自然结构中编码的序列结构兼容性规则 它们的同源序列。蛋白质数据库（所有已知蛋白质结构的收集）已生长 自2000年以来，10倍，将我们置于我们可以通过建造建造新颖蛋白质来设计新颖蛋白质的时刻 自然中使用的块。我们已经实施了一个基于此原则的新设计框架，也就是说 基本方面不同于所有以前发表的替代方案。对潜水员任务的测试证明了 杰出的成功。为了进一步发展我们的方法，我们提出了方法论上的进步，我们将 实施，测试，然后应用于蛋白质设计挑战，涉及检测或抑制蛋白质识别 域。我们将开发和应用方法：自动确定与目标结合的设计策略 蛋白质，得分和排名特定的设计候选者，将筛选以提供丰富的设计库 有关成功和失败的实验数据，并自动将实验数据送回模型 以主要方式发展。成果将包括将与社区共享的新方法， 高排名界面设计站点的计算预测，可以为结构和结构分析提供信息 途径以及实验验证的设计器分子，与蛋白质结构域结合对于信号很重要 在疾病途径中。