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

许多蛋白质系统很难用现有的方法进行结构分析。该项目旨在开发新的蛋白质结构确定方法，以解决这类蛋白质的问题。提出的方法是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。具体地说，该项目的目标是推进用于结构确定的交联的三个关键和相互依存的努力：1)增加CLMS数据的密度，2)改善CLMS数据的分布，以及3)将高密度CLMS数据与定制的构象空间搜索算法相结合。为了提高CLMS数据的数据密度，我们将结合高密度交联剂对不同的裂解方法进行测试和评估，如可光活化的二氮杂基交联剂磺基SDA。这需要在质谱学测量和设置方面进行调整，以及在多肽和裂解光谱的计算解释方面进一步发展。我们进一步提出了一种基于图的分析方法，以包含交叉链接之间的确证信息，以提高链接的准确性和密度。为了改善CLMS数据的分布，我们的目标是目前基于胰酶的消化方案的限制。基于胰酶的消化可能会产生太小或太大而不能进行质谱分析的多肽，这是因为目标蛋白的胰酶裂解位点分布不均匀。我们将测试具有胰酶切割位点或非特定切割位点的替代蛋白酶。我们的目标是开发多酶切协议，以确保连接在序列上的均匀分布。这包括修改目前优化的用于胰酶消化蛋白质的多肽检测的质谱学采集方案。此外，我们将开发定制的计算方法，以在结构建模中利用交叉链接数据。为了实现这一点，我们将开发能够使用CLMS数据从蛋白质结构数据库中检索结构信息的算法。此外，我们将开发噪声稳健的结构建模算法，以补偿高密度CLMS数据的噪声性质。这将通过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叉链接的信念。该算法将抑制CLMS数据中的噪声，从而提高所得到的结构模型的质量。本项目将在第13届全社区蛋白质结构预测关键评估(CASP)实验的背景下，通过盲法测试来严格评估所提出的方法。我们将对未知结构的蛋白质进行交联，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小组，以最大限度地发挥拟议方法的影响。

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.