Towards Accurate Protein Structure Predictions with SAXS Technology (TAPESTRY)

利用 SAXS 技术 (TAPESTRY) 实现准确的蛋白质结构预测

基本信息

批准号：
10418659
负责人：
Susan Emiko Tsutakawa
金额：
$ 40.78万
依托单位：
UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
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：
2020-06-01 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10418659
关键词：
Algorithms Amino Acid Sequence Area Basic Science Biology Biomedical Research Bypass Communities Complex Crystallization Data Data Analyses Data Collection Databases Discipline Disease Drug Design Effectiveness Engineering Feedback Funding Future Goals Gold Knowledge Label Lead Light Machine Learning Measures Medical Medicine Methods Modeling Molecular Conformation Nuclear Magnetic Resonance Pathogenesis Patient-Focused Outcomes Positioning Attribute Protein Conformation Protein Region Proteins Provider Publishing Research Personnel Resolution Roentgen Rays Sampling Science Scientist Shapes Signal Transduction Source Structure Synchrotrons Tail Technology Tertiary Protein Structure Testing Validation Work analytical tool base beamline cost data modeling design drug discovery experience experimental study flexibility improved innovation insight prediction algorithm predictive modeling protein complex protein folding protein function protein structure protein structure prediction restraint risk prediction tool

项目摘要

Project Summary There is an unmet need in medicine and basic sciences for accurate atomic structures of proteins. This need surpasses the capabilities of traditional high-resolution experimental methods. With machine learning advances, structure prediction algorithms are poised to provide atomic models for these areas in the near future. Yet, the gaps in prediction algorithms limit accuracy and reliability, particularly for large multi-domain proteins, protein complexes, and flexible proteins. Our proposal, Towards Accurate protein structure Predictions with SAXS TechnologY (TAPESTRY), will create technology to increase reliability and improve accuracy of protein structure predictions through experimental validation, particularly for difficult proteins. TAPESTRY is innovative by combining our strengths in high-throughput synchrotron SAXS (Small Angle X-ray Scattering) data collection and analysis with the Critical Assessment of protein Structure Prediction (CASP), which assesses structure predictions against “gold standard”, not-yet-released crystal structures every two years. Through CASP, we take advantage of the collective protein folding knowledge of the global community of structure prediction scientists. Our approach is strategic. We provide SAXS data for CASP, giving prediction scientists access to experimental data. We develop analytical and experimental tools, designed for prediction scientists to overcome current gaps that limit the use of SAXS data. We test these tools against our TAPESTRY databases of standard proteins, with corresponding crystal structures, SAXS data, and predicted models. Finally, we evaluate the robustness of our technology through CASP and obtain an unbiased assessment of our tools and the state of the field. As a first step, we target well-folded proteins (Aim 1) and proteins with disordered tails (Aim 2) in this proposal. The feasibility of our technology proposal is supported by our current data and proofs-in-concepts, our beamline capabilities, and proven experience in SAXS analysis. We show that experimental SAXS data, which contains distance information that can act as restraints in protein structure prediction algorithms, match crystal structures of well-folded proteins and score predictions based on topological accuracy. We show cases in CASP13 (2018) when SAXS data improved the fold of predicted models. SAXS data collection is rapid (10 seconds), does not require labeling or crystallization, and is available at no cost to the scientific community. We have proven experience in developing informative and effective SAXS analytical tools. Our long-term goal is to enable biomedical researchers to input an amino acid sequence and rapidly obtain an experimentally validated and accurate atomic model(s) that reflects the protein conformation(s) in solution. If TAPESTRY is successful, the increased availability of such atomic models will have strong and broad potential to advance biomedical research and impact all areas of biology in which proteins are involved.

项目概要医学和基础科学对蛋白质的精确原子结构的需求尚未得到满足。随着机器学习的进步，超越了传统高分辨率实验方法的能力。结构预测算法有望在不久的将来为这些领域提供原子模型。预测算法的缺陷限制了准确性和可靠性，特别是对于大型多域蛋白质、蛋白质我们的提案，使用 SAXS 进行准确的蛋白质结构预测。 TechnologY (TAPESTRY)，将创造提高蛋白质可靠性和准确性的技术通过实验验证进行结构预测，特别是对于困难的蛋白质。 TAPESTRY 结合了我们在高通量同步加速器 SAXS（小角度）方面的优势，实现了创新 X 射线散射）数据收集和分析以及蛋白质结构的严格评估预测（CASP），根据“黄金标准”、尚未发布的晶体评估结构预测每两年一次结构，我们利用集体蛋白质折叠知识。全球结构预测科学家界的成员。我们的方法是战略性的。我们为 CASP 提供 SAXS 数据，让预测科学家能够访问这些数据。我们开发分析和实验工具，专为预测科学家设计克服当前限制 SAXS 数据使用的差距，我们根据 TAPESTRY 数据库测试这些工具。标准蛋白质，以及相应的晶体结构、SAXS 数据和预测模型。通过 CASP 评估我们技术的稳健性，并对我们的工具进行公正的评估以及该领域的现状作为第一步，我们的目标是折叠良好的蛋白质（目标 1）和尾部无序的蛋白质。本提案中的（目标 2）。我们的技术提案的可行性得到了我们当前数据和概念验证的支持，我们的我们展示了实验 SAXS 数据、光束线功能和经过验证的经验。其中包含可以作为蛋白质结构预测算法中的约束的距离信息，匹配我们展示了折叠良好的蛋白质的晶体结构和基于拓扑准确性的分数预测。在 CASP13 (2018) 中，SAXS 数据改进了预测模型的倍数，SAXS 数据收集速度很快 (10)。秒），不需要标记或结晶，并且科学界可以免费获得。在开发信息丰富且有效的 SAXS 分析工具方面拥有丰富的经验。我们的长期目标是让生物医学研究人员能够输入氨基酸序列并快速获得经过实验验证且准确的原子模型，反映溶液中的蛋白质构象。 TAPESTRY 是成功的，此类原子模型可用性的增加将具有强大而广泛的潜力推进生物医学研究并影响涉及蛋白质的所有生物学领域。