Towards Accurate Protein Structure Predictions with SAXS Technology (TAPESTRY)

项目摘要 在医学和基础科学中，对蛋白质的精确原子结构的需求尚未得到满足。这一需求 超越了传统高分辨率实验方法的能力。随着机器学习的进步， 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。然而， 预测算法中的空白限制了准确性和可靠性，特别是对于大的多结构域蛋白质、蛋白质 复合体和柔性蛋白质。我们的建议，用SAXS进行准确的蛋白质结构预测 技术(Tapestry)，将创造技术来提高蛋白质的可靠性和准确性 通过实验验证的结构预测，特别是对困难蛋白质的预测。 Tapestry通过结合我们在高通量同步加速器SAXS(小角度)方面的优势而创新 X射线散射)数据收集和分析与蛋白质结构的临界评估 预测(CASP)，评估结构预测与“黄金标准”，尚未发布的晶体 每两年建造一次。通过CASP，我们利用了蛋白质折叠的集体知识 全球结构预测科学家社区的成员。 我们的做法是战略性的。我们为CASP提供SAXS数据，使预测科学家能够访问 实验数据。我们开发分析和实验工具，专为预测科学家设计 克服目前限制SAXS数据使用的差距。我们在我们的Tapestry数据库中测试这些工具 标准蛋白质，具有相应的晶体结构、SAXS数据和预测模型。最后，我们 通过CASP评估我们技术的健壮性，并对我们的工具进行公正的评估 以及田野的状态。作为第一步，我们针对折叠良好的蛋白质(目标1)和带有无序尾巴的蛋白质 (目标2)在本建议中。 我们的技术方案的可行性得到了我们目前的数据和概念证明的支持，我们的 光束线能力，以及在SAXS分析方面的成熟经验。我们展示了实验SAXS数据， 它包含可以在蛋白质结构预测算法中充当约束的距离信息，Match 折叠良好蛋白质的晶体结构和基于拓扑精确度的分数预测。我们展示案例 在CASP13(2018)中，当SAXS数据改进了预测模型的折叠。SAXS数据收集速度很快(10 秒)，不需要标记或结晶，科学界免费提供。我们 在开发信息丰富和有效的SAXS分析工具方面有成熟的经验。 我们的长期目标是使生物医学研究人员能够输入氨基酸序列并快速获得 经过实验验证的准确的原子模型(S)，反映了蛋白质在溶液中的构象(S)。如果 Tapestry是成功的，这种原子模型的可用性增加将具有强大和广阔的潜力 推进生物医学研究，影响所有涉及蛋白质的生物学领域。

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.