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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

基本信息

批准号：
10541909
负责人：
Gevorg Grigoryan
金额：
$ 35.46万
依托单位：
DARTMOUTH COLLEGE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-02-01 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10541909
关键词：
Acceleration 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 data library design disease diagnosis empowerment 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 technology platform therapeutic development tool

项目摘要

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

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Neural network-derived Potts models for structure-based protein design using backbone atomic coordinates and tertiary motifs.

DOI：
10.1002/pro.4554
发表时间：
2023-03
期刊：
PROTEIN SCIENCE
影响因子：
8
作者：
Li, Alex J.;Lu, Mindren;Desta, Israel;Sundar, Vikram;Grigoryan, Gevorg;Keating, Amy E.
通讯作者：
Keating, Amy E.

Tertiary motifs as building blocks for the design of protein-binding peptides.