Prospective analysis to determine model accuracy performance and boundaries in the post-AlphaFold2 environment

前瞻性分析以确定 AlphaFold2 后环境中的模型准确性性能和边界

• 批准号: 10672042
• 负责人: KRZYSZTOF A FIDELIS
• 金额: $ 29.79万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10672042
• 关键词: Active Sites; Address; Agreement; Amino Acid Sequence; Amino Acids; Antibodies; Area; Automobile Driving; Biological; Biological Process; Biology; Complementarity Determining Regions; Computer Models; Consumption; Crystallization; Data; Data Analyses; Data Set; Database Management Systems; Deposition; Development; Disease; Environment; Evaluation; Funding; Generations; Goals; Grain; Individual; Infrastructure; Knowledge; Length; Medicine; Methods; Modeling; Modification; Molecular Structure; Performance; Play; Procedures; Proline; Property; Proteins; Research; Rest; Role; Sequence Alignment; Set protein; Side; Structure; System; Techniques; Time; Uncertainty; Variant; Work; cost; data visualization; deep learning; experience; experimental study; falls; genome-wide; grasp; improved; learning strategy; method development; parent grant; programs; prospective; protein complex; protein structure; structural biology; tool

Experimental determination of protein structure often provides atomic accuracy models, but is inherently time- consuming, costly, and not always possible. Computational modeling offers an alternative. Recent application of deep learning methods to structure modeling now allows the generation of highly accurate models of structure for the great majority of single proteins. Progress in modeling accuracy for the modeling of protein complexes is also expected in the near term. These developments have dramatically expanded the utility of models in biology and medicine. However, having even high accuracy models at one’s disposal is not the same as an experimental result. While experimental structures come with established norms and procedures to ascertain accuracy, models are at best annotated with predictions of accuracy. The increased use of models underscores the need for similar knowledge, norms, and procedures for models. The Critical Assessment of Structure Prediction (CASP, funded by the parent grant), is already focused on assessing performance of modeling techniques and the performance of methods for estimating model accuracy, and to this end, extensive additional data are currently being acquired as part of the CASP15 experiment. But a detailed assessment of accuracy and accuracy prediction properties under an adequate range of conditions requires more extensive information than will be available through traditional CASP procedures. The release of close to a million models obtained with the AlphaFold2 method from DeepMind has created an opportunity to perform such an analysis. We will use these data to perform a prospective analysis of model accuracy and accuracy prediction performance. Specifically, we will compare structures released by the PDB with the corresponding previously deposited models. We will assess the agreement between these experimental and computed structures, both overall and at the individual amino acid level, and the agreement between the predicted and actual accuracy. Critically, we will examine these factors as a function of the relevant variables, particularly the quality and type of the experimental information, environmental effects such as crystal packing, function related structural features, rare structural features, protein interface regions, sequence length, and the depth of the available amino acid sequence alignment used to generate the model. We will also build tools to analyze and visualize the relationships and interdependencies between these data, to facilitate and expand our understanding of model accuracy performance. The overall goal of this work is to provide a fine-grained landscape of accuracy obtained with current modeling methods, and confidence limits on accuracy in each situation. Although the present analysis is restricted to AlphaFold2 models, identification of the most important variables should allow us to apply the procedures to the range of methods represented in the smaller data sets obtained in regular CASP experiments, and to new applications of deep learning methods in structural biology, for example the modeling of protein complexes. The infrastructure and expertise we already have in place, including the wide range of accuracy analysis tools, and our extensive experience with accuracy, accuracy estimation methods, and database management, make us the appropriate group to address this task. The proposed research falls well within the scientific scope of the current R01.

蛋白质结构的实验测定通常提供原子精度模型，但本质上是时间- 计算建模耗时、成本高且并不总是可行，但最近的深度应用提供了一种替代方案。 结构建模的学习方法现在可以为伟大的人生成高度准确的结构模型 大多数单一蛋白质的建模准确性也有望取得进展。 短期内，这些发展极大地扩展了模型在生物学和医学中的实用性。 然而，即使拥有高精度模型也与实验结果不同。 实验结构具有确定准确性的既定规范和程序，模型最多有注释 模型使用的增加强调了对类似知识、规范和预测的需求。 结构预测的关键评估（CASP，由母基金资助）已经完成。 重点评估建模技术的性能和估计模型方法的性能 准确性，为此，作为 CASP15 实验的一部分，目前正在获取更多数据。 在适当范围的条件下对准确度和准确度预测特性进行详细评估需要更多 比通过传统 CASP 程序提供的信息更广泛 发布了近一百万个模型。 使用 DeepMind 的 AlphaFold2 方法获得的数据为执行此类分析创造了机会。 我们将使用这些数据对模型准确性和准确性预测性能进行前瞻性分析。 具体来说，我们将发布的 PDB 的结构与相应的先前存储的模型进行比较。 评估这些实验结构和计算结构之间的一致性，包括整体结构和单个氨基结构 重要的是，我们将检查这些因素作为一个因素。 相关变量的函数，特别是实验信息的质量和类型、环境影响 例如晶体堆积、功能相关结构特征、罕见结构特征、蛋白质界面区域、序列 我们还将构建用于生成模型的可用氨基酸序列比对的长度和深度。 分析和可视化这些数据之间的关系和相互依赖性，以促进和扩展我们的 了解模型准确性性能。这项工作的总体目标是提供一个细粒度的景观。 使用当前建模方法获得的准确度，以及每种情况下准确度的置信度限制。 目前的分析仅限于 AlphaFold2 模型，识别出最重要的变量应该允许我们应用 常规 CASP 实验中获得的较小数据集中所代表的方法范围的程序，以及 深度学习方法在结构生物学中的新应用，例如蛋白质复合物的建模。 我们已经具备的基础设施和专业知识，包括广泛的准确性分析工具，以及 我们在准确性、准确性估计方法和数据库管理方面的丰富经验使我们成为 拟议的研究完全属于当前 R01 的科学范围。