Photo Cross-linking/mass spectrometry (CLMS) --- Towards a new technology for protein structure determination

光交联/质谱（CLMS）——迈向蛋白质结构测定新技术

• 批准号: 329673113
• 负责人: Professor Dr. Oliver Brock
• 金额: --
• 依托单位: Fachgebiet Bioanalytik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/329673113?language=en
• 关键词: Photo; Cross; linking; mass; spectrometry

Many protein systems are elusive to structure analysis with established methods. This project aims to develop novel methods for protein structure determination to target this problem class of proteins. The proposed method is based on high-density cross-link/mass spectrometry (CLMS) data and custom-tailored computational algorithms to interpret them. Specifically, this project targets three critical and interdependent endeavors for advancing cross-linking for structure determination: 1) Increasing the density of CLMS data, 2) improving the distribution of CLMS data, and 3) combining high-density CLMS data with customized conformational space search algorithms. To improve the data density of CLMS data, we will test and evaluate different fragmentation methods in combination with high-density cross-linking reagents, such as the photoactivatable diazirine-based cross-linker sulfo-SDA. This requires adjustments in mass spectrometric measurements and settings, as well further developments in the computational interpretation of peptide and fragmentation spectra. We further propose a graph-based analysis to incorporate corroborate information between cross-links to boost the accuracy and density of the links. To improve the distribution of CLMS data, we target a current limitation of trypsin-based digestion protocols. Trypsin-based digestion might create peptides that are too small or too large for mass spectrometric analysis because of uneven trypsin cleavage site distribution of the target protein. We will test alternative proteases that have cleavage sites to trypsin or unspecific cleavage sites. We aim to develop multi-digestion protocols to ensure even distribution of links over the sequence. This includes the modification of mass spectrometric acquisition protocols that are currently optimized for peptide detection of trypsin digested proteins. Furthermore, we will develop custom-tailored computational methods to leverage the cross-linking data in structure modeling. To accomplish this, we will develop algorithms that are able to retrieve structure information from protein structure databases using the CLMS data. In addition, we will develop noise-robust structure modeling algorithms that compensate for the noisy nature of high-density CLMS data. This will be accomplished by a conformational space search algorithm that automatically updates its belief of the used cross-links during. This algorithm will reject noise in CLMS data and therefore improve the quality of the resulting structure models. This project will rigorously evaluate the proposed method by a blind test in the context of the 13th community-wide Critical Assessment of protein Structure Prediction (CASP) experiment. We will cross-link proteins with unknown structure and test our computational algorithms using this data. In addition, the CLMS data will be disseminated in CASP to other structure prediction groups to maximize the impact of the proposed method.

许多蛋白质系统难以用现有的方法进行结构分析。本项目旨在开发新的蛋白质结构测定方法，以针对这类问题的蛋白质。该方法基于高密度交联/质谱（CLMS）数据和定制的计算算法来解释它们。具体而言，该项目针对三个关键且相互依存的工作来推进交联以确定结构：1)增加CLMS数据密度，2)改善CLMS数据分布，3)将高密度CLMS数据与定制构象空间搜索算法相结合。为了提高CLMS数据的数据密度，我们将测试和评估不同的碎片化方法，并结合高密度交联试剂，如光活化重氮嘧啶基交联剂磺基sda。这需要调整质谱测量和设置，以及进一步发展肽和碎片谱的计算解释。我们进一步提出了一种基于图的分析方法来整合交联之间的确证信息，以提高交联的准确性和密度。为了改善CLMS数据的分布，我们针对当前基于胰蛋白酶的消化方案的限制。由于靶蛋白的胰蛋白酶裂解位点分布不均匀，基于胰蛋白酶的消化可能产生过小或过大的肽，无法进行质谱分析。我们将测试具有胰蛋白酶裂解位点或非特异性裂解位点的替代蛋白酶。我们的目标是开发多消化协议，以确保在序列上均匀分布链路。这包括对质谱采集协议的修改，该协议目前优化用于胰蛋白酶消化蛋白的肽检测。此外，我们将开发定制的计算方法，以利用结构建模中的交联数据。为了实现这一目标，我们将开发能够使用CLMS数据从蛋白质结构数据库检索结构信息的算法。此外，我们将开发噪声鲁棒结构建模算法，以补偿高密度CLMS数据的噪声性质。这将通过一种构象空间搜索算法来完成，该算法自动更新其在过程中使用的交联的信念。该算法可以去除CLMS数据中的噪声，从而提高所得结构模型的质量。本项目将在第13届社区蛋白质结构预测关键评估（CASP）实验背景下，通过盲测严格评估所提出的方法。我们将交联未知结构的蛋白质，并使用这些数据测试我们的计算算法。此外，CLMS数据将在CASP中传播给其他结构预测组，以最大限度地发挥所提出方法的影响。