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连续自动模型评估(CAMEO)对蛋白质结构预测关键评估(CASP12)的补充

Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12.

作者信息

Haas Jürgen, Barbato Alessandro, Behringer Dario, Studer Gabriel, Roth Steven, Bertoni Martino, Mostaguir Khaled, Gumienny Rafal, Schwede Torsten

机构信息

Biozentrum, University of Basel, Switzerland.

SIB Swiss Institute of Bioinformatics, Computational Structural Biology, Basel, Switzerland.

出版信息

Proteins. 2018 Mar;86 Suppl 1(Suppl 1):387-398. doi: 10.1002/prot.25431. Epub 2017 Dec 17.

Abstract

Every second year, the community experiment "Critical Assessment of Techniques for Structure Prediction" (CASP) is conducting an independent blind assessment of structure prediction methods, providing a framework for comparing the performance of different approaches and discussing the latest developments in the field. Yet, developers of automated computational modeling methods clearly benefit from more frequent evaluations based on larger sets of data. The "Continuous Automated Model EvaluatiOn (CAMEO)" platform complements the CASP experiment by conducting fully automated blind prediction assessments based on the weekly pre-release of sequences of those structures, which are going to be published in the next release of the PDB Protein Data Bank. CAMEO publishes weekly benchmarking results based on models collected during a 4-day prediction window, on average assessing ca. 100 targets during a time frame of 5 weeks. CAMEO benchmarking data is generated consistently for all participating methods at the same point in time, enabling developers to benchmark and cross-validate their method's performance, and directly refer to the benchmarking results in publications. In order to facilitate server development and promote shorter release cycles, CAMEO sends weekly email with submission statistics and low performance warnings. Many participants of CASP have successfully employed CAMEO when preparing their methods for upcoming community experiments. CAMEO offers a variety of scores to allow benchmarking diverse aspects of structure prediction methods. By introducing new scoring schemes, CAMEO facilitates new development in areas of active research, for example, modeling quaternary structure, complexes, or ligand binding sites.

摘要

每两年,“蛋白质结构预测技术关键评估”(CASP)社区实验都会对结构预测方法进行一次独立的盲评,为比较不同方法的性能以及讨论该领域的最新进展提供一个框架。然而,自动化计算建模方法的开发者显然能从基于更大数据集的更频繁评估中受益。“连续自动模型评估(CAMEO)”平台通过基于那些即将在蛋白质数据银行(PDB)的下一次发布中公布的结构序列的每周预发布进行全自动盲预测评估,对CASP实验起到补充作用。CAMEO会根据在4天预测窗口期间收集的模型每周发布基准测试结果,平均在5周的时间框架内评估约100个目标。CAMEO基准测试数据是在同一时间点为所有参与方法一致生成的,使开发者能够对其方法的性能进行基准测试和交叉验证,并在出版物中直接引用基准测试结果。为了便于服务器开发并促进更短的发布周期,CAMEO每周发送包含提交统计信息和低性能警告的电子邮件。许多CASP参与者在为即将到来的社区实验准备他们的方法时成功使用了CAMEO。CAMEO提供了多种分数,以允许对结构预测方法的不同方面进行基准测试。通过引入新的评分方案,CAMEO促进了活跃研究领域的新发展,例如,对四级结构、复合物或配体结合位点进行建模。

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