Smart Cutter: An Efficient Strategy for Increasing the Coverage of Chemical Cross-Linking Analysis

SpotLink enables sensitive and precise identification of site nonspecific cross-links at the proteome scale

Nonspecific cross-linker can provide distance restraints between surface residues of any type, which could be used to investigate protein structure construction and protein-protein interaction (PPI). However, the vast number of potential combinations of cross-linked residues or sites obtained with such a cross-linker makes the data challenging to analyze, especially for the proteome-wide applications. Here, we developed SpotLink software for identifying site nonspecific cross-links at the proteome scale. Contributed by the dual pointer dynamic pruning algorithm and the quality control of cross-linking sites, SpotLink identified > 3000 cross-links from human cell samples within a short period of days. We demonstrated that SpotLink outperformed other approaches in terms of sensitivity and precision on the datasets of the simulated succinimidyl 4,4'-azipentanoate dataset and the condensin complexes with known structures. In addition, some valuable PPI were discovered in the datasets of the condensin complexes and the HeLa dataset, indicating the unique identification advantages of site nonspecific cross-linking. These findings reinforce the importance of SpotLink as a fundamental characteristic of site nonspecific cross-linking technologies.

[Mirror cutting-assisted orthogonal digestion enabling large-scale and accurate protein complex characterization]

Protein complexes are involved in a variety of biological activities. Accurate and comprehensive characterization of the structures and interactions of protein complexes is crucial in determining their biological functions. Chemical cross-linking coupled with mass spectrometry (CXMS) is an emerging investigative technique for protein complexes. CXMS enables the sensitive high-throughput analysis of protein complexes without the requirements of molecular weight and purification. These attributes have spurred the increased use of CXMS for the structure and interaction characterization of purified protein complexes and complicated cell lysate samples. CXMS utilizes chemical cross-linking reagents to covalently connect two reactive amino acids in or between proteins that are spatially close to each other. Subsequently, the cross-linked proteins are digested into cross-linked peptides, followed by LC-MS/MS analysis, as well as database searching to provide cross-linking information for the composition, interaction, and structural site distance restrictions of protein complex identification. Therefore, identification of cross-linked sites has a decisive influence on the characterization of protein complexes. This identification is limited by the unsatisfactory quality of the cross-linked peptide spectrum. Insufficient b/y fragment ions and poor continuity of amino acid sequence matching lead to low coverage and accuracy of cross-linked site identification. Based on the complementary feature of mirror-cutting digestion, an orthogonal digestion strategy based on LysargiNase combined with trypsin was developed in this study. Trypsin is the most commonly utilized digestion enzyme in proteomics, with extremely high enzyme activity and specificity. Trypsin generates C-terminally charged peptides after lysine (K) and arginine (R). LysargiNase is a mirror protease complementary to trypsin that cleaves before the K and R residues. This generates peptides with an N-terminal positively-charged residue. Owing to the different physical and chemical micro-environments of the cross-linked peptides digested by LysargiNase and trypsin, the behavior of their detection ability in MS analysis is diverse. Using the orthogonal digestion strategy, both simple and complicated cross-linked samples were analyzed in this study. For the analysis of bovine serum albumin (BSA), 291 pairs of non-redundant cross-linked sites were obtained, of which 216 pairs of cross-linked sites were provided by trypsin digestion, whereas 75 pairs of cross-linked sites were exclusively supplied by LysargiNase digestion. Except for the 35% increase in the number of identified cross-linked sites, 32% of the spectra of the commonly identified cross-linked peptides have better quality with more b-type fragment ions and consecutive sequence matching. Furthermore, for the Escherichia coli sample, 726 pairs of cross-linked sites were obtained in total, among which, 624 and 274 pairs were identified from trypsin and LysargiNase digestion, respectively. LysargiNase digestion yielded 120 individual cross-linked sites, which resulted in a 16% increase in single trypsin digestion. Consistent with the BSA sample, the quality was improved in 35% of the spectra of commonly identified cross-linked peptides. Corresponding to the identified cross-linked peptides, 242 structural constraints with 607 pairs of intra-cross-linked sites and 29 sets of protein-protein interactions with 119 pairs of inter-cross-linked sites were obtained. The collective results demonstrated that, mirror cutting-assisted orthogonal digestion strategy could significantly increase the number of identified fragment ions and amino acid sequences matching the continuity of the spectra by contributing b-and y-type ions, respectively. This improved the accuracy and coverage of cross-linked peptide identification. The findings additionally demonstrate the superiority of our method in the accurate identification of the cross-linked peptide spectra and the increased number of identified cross-linked sites. In a word, this method is expected to provide new insights for the large-scale and highly accurate characterization of protein complexes.

pDeepXL: MS/MS Spectrum Prediction for Cross-Linked Peptide Pairs by Deep Learning

In cross-linking mass spectrometry, the identification of cross-linked peptide pairs heavily relies on the ability of a database search engine to measure the similarities between experimental and theoretical MS/MS spectra. However, the lack of accurate ion intensities in theoretical spectra impairs the performance of search engines, in particular, on proteome scales. Here we introduce pDeepXL, a deep neural network to predict MS/MS spectra of cross-linked peptide pairs. To train pDeepXL, we used the transfer-learning technique because it facilitated the training with limited benchmark data of cross-linked peptide pairs. Test results on more than ten data sets showed that pDeepXL accurately predicted the spectra of both noncleavable DSS/BS3/Leiker cross-linked peptide pairs (>80% of predicted spectra have Pearson's r values higher than 0.9) and cleavable DSSO/DSBU cross-linked peptide pairs (>75% of predicted spectra have Pearson's r values higher than 0.9). pDeepXL also achieved the accurate prediction on unseen data sets using an online fine-tuning technique. Lastly, integrating pDeepXL into a database search engine increased the number of identified cross-link spectra by 18% on average.