Schneider Michael, Belsom Adam, Rappsilber Juri, Brock Oliver
Robotics and Biology Laboratory, Technische Universität Berlin, 10587, Berlin, Germany.
Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, EH9 3BF, United Kingdom.
Proteins. 2016 Sep;84 Suppl 1(Suppl Suppl 1):152-63. doi: 10.1002/prot.25028. Epub 2016 Mar 28.
Hybrid approaches combine computational methods with experimental data. The information contained in the experimental data can be leveraged to probe the structure of proteins otherwise elusive to computational methods. Compared with computational methods, the structures produced by hybrid methods exhibit some degree of experimental validation. In spite of these advantages, most hybrid methods have not yet been validated in blind tests, hampering their development. Here, we describe the first blind test of a specific cross-link based hybrid method in CASP. This blind test was coordinated by the CASP organizers and utilized a novel, high-density cross-linking/mass-spectrometry (CLMS) approach that is able to collect high-density CLMS data in a matter of days. This experimental protocol was developed in the Rappsilber laboratory. This approach exploits the chemistry of a highly reactive, photoactivatable cross-linker to produce an order of magnitude more cross-links than homobifunctional cross-linkers. The Rappsilber laboratory generated experimental CLMS data based on this protocol, submitted the data to the CASP organizers which then released this data to the CASP11 prediction groups in a separate, CLMS assisted modeling experiment. We did not observe a clear improvement of assisted models, presumably because the properties of the CLMS data-uncertainty in cross-link identification and residue-residue assignment, and uneven distribution over the protein-were largely unknown to the prediction groups and their approaches were not yet tailored to this kind of data. We also suggest modifications to the CLMS-CASP experiment and discuss the importance of rigorous blind testing in the development of hybrid methods. Proteins 2016; 84(Suppl 1):152-163. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.
混合方法将计算方法与实验数据相结合。实验数据中包含的信息可用于探究蛋白质结构,而这些结构用计算方法难以捉摸。与计算方法相比,混合方法产生的结构具有一定程度的实验验证。尽管有这些优点,但大多数混合方法尚未在盲测中得到验证,这阻碍了它们的发展。在此,我们描述了在蛋白质结构预测技术关键评估(CASP)中对一种基于特定交联的混合方法的首次盲测。这次盲测由CASP组织者协调,并采用了一种新颖的、高密度交联/质谱(CLMS)方法,该方法能够在数天内收集高密度的CLMS数据。这个实验方案是在拉普西尔伯实验室开发的。这种方法利用了一种高反应性、可光活化交联剂的化学性质,比同型双功能交联剂产生的交联数量多一个数量级。拉普西尔伯实验室基于此方案生成了实验性的CLMS数据,并将数据提交给CASP组织者,然后组织者在一个单独的、CLMS辅助建模实验中将这些数据发布给CASP11预测小组。我们没有观察到辅助模型有明显改进,可能是因为预测小组对CLMS数据的特性——交联识别和残基-残基分配中的不确定性以及在蛋白质上的不均匀分布——了解甚少,而且他们的方法尚未针对这类数据进行调整。我们还建议对CLMS-CASP实验进行改进,并讨论了严格的盲测在混合方法开发中的重要性。《蛋白质》2016年;84(增刊1):152 - 163。© 2016作者。《蛋白质:结构、功能与生物信息学》由威利期刊公司出版