Quantitative Assessment of the Effects of Trypsin Digestion Methods on Affinity Purification–Mass Spectrometry-based Protein–Protein Interaction Analysis

Affinity purification-mass spectrometry (AP–MS) has become the method of choice for discovering protein–protein interactions (PPIs) under native conditions. The success of AP-MS depends on the efficiency of trypsin digestion and the recovery of the tryptic peptides for MS analysis. Several different protocols have been used for trypsin digestion of protein complexes in AP-MS studies, but no systematic studies have been conducted on the impact of trypsin digestion conditions on the identification of PPIs. Here, we used NFκB/RelA and Bromodomain-containing protein 4 (BRD4) as baits and test five distinct trypsin digestion methods (two using “on-beads,” three using “elution-digestion” protocols). Although the performance of the trypsin digestion protocols change slightly depending on the different baits, antibodies and cell lines used, we found that elution-digestion methods consistently outperformed on-beads digestion methods. The high-abundance interactors can be identified universally by all five methods, but the identification of low-abundance RelA interactors is significantly affected by the choice of trypsin digestion method. We also found that different digestion protocols influence the selected reaction monitoring (SRM)–MS quantification of PPIs, suggesting that optimization of trypsin digestion conditions may be required for robust targeted analysis of PPIs

Selective Affinity Enrichment of Nitrotyrosine-Containing Peptides for Quantitative Analysis in Complex Samples

Protein tyrosine nitration by oxidative and nitrate stress is important in the pathogenesis of many inflammatory or aging-related diseases. Mass spectrometry analysis of protein nitrotyrosine is very challenging because the non-nitrated peptides suppress the signals of the low-abundance nitrotyrosine (NT) peptides. No validated methods for enrichment of NT-peptides are currently available. Here we report an immunoaffinity enrichment of NT-peptides for proteomics analysis. The effectiveness of this approach was evaluated using nitrated protein standards and whole-cell lysates in vitro. A total of 1881 NT sites were identified from a nitrated whole-cell extract, indicating that this immunoaffinity-MS method is a valid approach for the enrichment of NT-peptides, and provides a significant advance for characterizing the nitrotyrosine proteome. We noted that this method had higher affinity to peptides with N-terminal nitrotyrosine relative to peptides with other nitrotyrosine locations, which raises the need for future study to develop a pan-specific nitrotyrosine antibody for unbiased, proteome-wide analysis of tyrosine nitration. We applied this method to quantify the changes in protein tyrosine nitration in mouse lungs after intranasal poly­(I:C) treatment and quantified 237 NT sites. This result indicates that the immunoaffinity-MS method can be used for quantitative analysis of protein nitrotyrosines in complex samples

MolTaut: A Tool for the Rapid Generation of Favorable Tautomer in Aqueous Solution

Fast and proper treatment of the tautomeric states for drug-like molecules is critical in computer-aided drug discovery since the major tautomer of a molecule determines its pharmacophore features and physical properties. We present MolTaut, a tool for the rapid generation of favorable states of drug-like molecules in water. MolTaut works by enumerating possible tautomeric states with tautomeric transformation rules, ranking tautomers with their relative internal energies and solvation energies calculated by AI-based models, and generating preferred ionization states according to predicted microscopic pKa. Our test shows that the ranking ability of the AI-based tautomer scoring approach is comparable to the DFT method (wB97X/6-31G*//M062X/6-31G*/SMD) from which the AI models try to learn. We find that the substitution effect on tautomeric equilibrium is well predicted by MolTaut, which is helpful in computer-aided ligand design. The source code of MolTaut is freely available to researchers and can be accessed at https://github.com/xundrug/moltaut. To facilitate the usage of MolTaut by medicinal chemists, we made a free web server, which is available at http://moltaut.xundrug.cn. MolTaut is a handy tool for investigating the tautomerization issue in drug discovery

Appendix A. Tables showing calculations of thermodynamic analysis indicators and of network analysis indicators.

Tables showing calculations of thermodynamic analysis indicators and of network analysis indicators