Center for Critical Assessment of Structure Prediction (CASP)

项目摘要 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- 消耗，往往昂贵，并不总是可能的。计算建模目前不太准确，但提供了一个 当实验结果不可用时，可采用替代方法。CASP（Critical Assessment of Structure） 预测）是推进蛋白质结构建模领域进行社区范围的实验，客观地 确定当前方法的优点和缺点，从而促进进步。约100个研究小组 全球参与。在最近的实验（2018年）中，有57，000份提交了9个建模类别， 包括超过35,000个三级结构模型。该中心为CASP提供基础设施，目标1是继续 这一资源的开发和利用。主要任务包括登记和与与会者沟通; 建模目标的征集、表征和管理;模型的收集和验证;以及广泛的 对提交的材料进行数字分析。这些业务得到安全和强大的数据基础设施的支持。中心 还开发评估，分析和显示软件，并提供模型和评估结果的访问。CASP依赖于 独立的评估员，建模或相关实验领域的专家，来解释结果。中心 协调这一进程，提供评价数据，并在必要时采用新的评价方法。 最近的CASP在模型准确性方面显示出了显着的改进，特别是对于最困难的情况， 不能使用同源建模。推动进步的一个主要因素是使用新的机器学习方法， 特别是卷积神经网络。这些方法和相关方法似乎准备在以下方面取得进一步的重大进展： 一些关键的建模领域。项目下一阶段的计划旨在利用这些和其他 进步的机会。更大的成功与建模小蛋白质和结构域决定了重点转移到 仍然具有挑战性的领域的大型多结构域蛋白质和复合物（目标2），其中的进展预计从 机器学习的发展和稀疏实验数据的结合。虽然模型的准确性 虽然已经有所改进，但它仍然很少与实验竞争。目标3是追求能够使模型更加 准确和有用，方法是在改进初始模型方面取得进一步进展，改进估计模型准确性的方法， 以及评估模型的效用。目标4介绍加强CASP与 更广泛的研究社区，提供直接解决当代问题的模型（例如，针对CoV-2 蛋白质结构），并通过会议、网络研讨会、出版物和其他手段促进交流。

PROJECT SUMMARY Experimental determination of protein structure often provides atomic accuracy models, but is inherently time- consuming, often costly, and not always possible. Computational modeling is currently less accurate, but offers an alternative approach when experimental results are not available. The goal of CASP (Critical Assessment of Structure Prediction) is to advance the protein structure modeling field by conducting community-wide experiments that objectively determine the strengths and weaknesses of current methods and so foster progress. Approximately 100 research groups world-wide participate. In the most recent experiment (2018), there were 57,000 submissions in nine modeling categories, including over 35,000 tertiary structure models. The Center provides the infrastructure for CASP and Aim 1 is the continued development and operation of this resource. Principal tasks include registration and communication with participants; solicitation, characterization, and management of modeling targets; collection and validation of models; and extensive numerical analysis of submissions. These operations are supported by a secure and robust data infrastructure. The Center also develops evaluation, analysis, and display software, and provides access to models and evaluation results. CASP relies on independent assessors, experts in modeling or a related experimental field, to interpret the results. The Center coordinates this process, providing evaluation data and, when necessary, implementing new evaluation methods. Recent CASPs have shown dramatic improvements in model accuracy, especially for the most difficult cases where homology modeling cannot be used. A major factor driving progress is the use of new machine learning approaches, particularly convolutional neural networks. These and related methods appear poised to make further major advances in a number of key modeling areas. The plan for the next period of the project is designed to capitalize on these and other opportunities for progress. Greater success with modeling small proteins and domains dictates a shift in emphasis to the still challenging area of large multi-domain proteins and complexes (Aim 2), where progress is expected both from the machine learning developments and from the incorporation of sparse experimental data. Although accuracy of models has improved, it is still seldom competitive with experiment. Aim 3 is to pursue strategies that will make models more accurate and useful, by nurturing further progress in refining initial models, better methods for estimating model accuracy, and assessment of the utility of models. Aim 4 introduces new ways of strengthening interactions between CASP and the broader research community, providing models that directly address contemporary problems (for example, for CoV-2 protein structures) and boosting communications through meetings, webinars, publications and other means.