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: 10326369
• 负责人: Gevorg Grigoryan
• 金额: $ 35.44万
• 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/10326369
• 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; design; disease diagnosis; empowered; experimental study; extracellular; flexibility; high throughput screening; improved; inhibitor; iterative design; lens; model development; novel; novel strategies; protein data bank; 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.

我们的长期目标是把计算蛋白设计变成一个颠覆性的技术平台