Center for Critical Assessment of Structure Prediction (CASP)

结构预测关键评估中心 (CASP)

基本信息

批准号：
10624259
负责人：
KRZYSZTOF A FIDELIS
金额：
$ 63.9万
依托单位：
UNIVERSITY OF CALIFORNIA AT DAVIS
依托单位国家：
美国
项目类别：
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/10624259
关键词：
2019-nCoV Address Area Automobile Driving Benchmarking Biological Biological Process Biology Categories Chemicals Collaborations Collection Communication Communities Community Developments Complex Computer Models Computer software Consumption Cryoelectron Microscopy Crystallography Data Data Set Dedications Development Disease Double-Blind Method Drug Design Epitopes Evaluation Fostering Future Goals Homology Modeling Information Dissemination Infrastructure International Journals Knowledge Laboratories Life Machine Learning Medicine Methods Modeling Molecular Biology Molecular Structure Paper Participant Performance Play Procedures Process Proteins Provider Publications Reporting Research Resources Rest Role Sampling Seasons Secure Sequence Alignment Series Structure Techniques Tertiary Protein Structure Time Validation Visualization Work biological research convolutional neural network cost crosslink data acquisition data handling data infrastructure deep learning design experimental study improved learning strategy meetings member method development model building online resource operation programs protein structure structural biology success symposium vaccine development webinar

项目摘要

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.

项目概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，通常成本高昂，而且并不总是可行。计算模型目前不太准确，但提供了当实验结果不可用时的替代方法。 CASP（结构批判性评估）的目标预测）是通过进行社区范围的客观实验来推进蛋白质结构建模领域确定当前方法的优点和缺点，从而促进进步。大约100个研究小组全球参与。在最近的实验（2018 年）中，九个建模类别中有 57,000 份提交，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 是持续该资源的开发和运营。主要任务包括注册和与参与者的沟通；建模目标的征求、表征和管理；模型的收集和验证；和广泛的对提交的材料进行数值分析。这些操作由安全且强大的数据基础设施支持。中心还开发评估、分析和显示软件，并提供对模型和评估结果的访问。 CASP 依赖由独立评估员、建模或相关实验领域的专家来解释结果。中心协调这一过程，提供评估数据，并在必要时实施新的评估方法。最近的 CASP 在模型准确性方面显示出显着的改进，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新机器学习方法的使用，特别是卷积神经网络。这些和相关方法似乎有望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内容进步的机会。小蛋白质和结构域建模取得的更大成功决定了重点转向大型多结构域蛋白质和复合物领域仍然具有挑战性（目标 2），预计该领域将取得进展机器学习的发展以及稀疏实验数据的结合。尽管模型的准确性虽然有所改善，但与实验相比仍然很少有竞争力。目标 3 是寻求使模型更强大的策略准确且有用，通过在完善初始模型、更好的估计模型准确性的方法方面取得进一步进展，和评估模型的效用。目标 4 引入了加强 CASP 与企业之间互动的新方法更广泛的研究界，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、出版物和其他方式促进沟通。

项目成果

期刊论文数量（58）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Evaluation of the template-based modeling in CASP12.

DOI：
10.1002/prot.25425
发表时间：
2018-03
期刊：
Proteins
影响因子：
2.9
作者：
Kryshtafovych A;Monastyrskyy B;Fidelis K;Moult J;Schwede T;Tramontano A
通讯作者：
Tramontano A

Assessing the accuracy of contact and distance predictions in CASP14.

DOI：
10.1002/prot.26248
发表时间：
2021-12
期刊：
Proteins
影响因子：
2.9
作者：
Ruiz-Serra V;Pontes C;Milanetti E;Kryshtafovych A;Lepore R;Valencia A
通讯作者：
Valencia A

Assessment of model accuracy estimations in CASP12.

DOI：
10.1002/prot.25371
发表时间：
2018-03
期刊：
Proteins
影响因子：
2.9
作者：
Kryshtafovych A;Monastyrskyy B;Fidelis K;Schwede T;Tramontano A
通讯作者：
Tramontano A

Assessment of protein disorder region predictions in CASP10.

DOI：
10.1002/prot.24391
发表时间：
2014-02
期刊：
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
影响因子：
2.9
作者：
Monastyrskyy, Bohdan;Kryshtafovych, Andriy;Moult, John;Tramontano, Anna;Fidelis, Krzysztof
通讯作者：
Fidelis, Krzysztof

Evaluation of model quality predictions in CASP9.