Simulation of Energy-Resolved Mass Spectrometry Distributions from Surface-Induced Dissociation

Abstract

Understanding the relationship between protein structure and experimental data is crucial for utilizing experiments to solve biochemical problems and optimizing the use of sparse experimental data for structural interpretation. Tandem mass spectrometry (MS/MS) can be used with a variety of methods to collect structural data for proteins. One example is surface-induced dissociation (SID), which is used to break apart protein complexes (via a surface collision) into intact subcomplexes and can be performed at multiple laboratory frame SID collision energies. These energy-resolved MS/MS experiments have shown that the profile of the breakages depends on the acceleration energy of the collision. It is possible to extract an appearance energy (AE) from energy-resolved mass spectrometry (ERMS) data, which shows the relative intensity of each type of subcomplex as a function of SID acceleration energy. We previously determined that these AE values for specific interfaces correlated with structural features related to interface strength. In this study, we further examined the structural relationships by developing a method to predict the full ERMS plot from the structure, rather than extracting a single value. First, we noted that for proteins with multiple interface types, we could reproduce the correct shapes of breakdown curves, further confirming previous structural hypotheses. Next, we demonstrated that interface size and energy density (measured using Rosetta) correlated with data derived from the ERMS plot (R 2 = 0.71). Furthermore, based on this trend, we used native crystal structures to predict ERMS. The majority of predictions resulted in good agreement, and the average root-mean-square error was 0.20 for the 20 complexes in our data set. We also show that if additional information on cleavage as a function of collision energy could be obtained, the accuracy of predictions improved further. Finally, we demonstrated that ERMS prediction results were better for the native than for inaccurate models in 17/20 cases. An application to run this simulation has been developed in Rosetta, which is freely available for use.

Protein complex prediction using Rosetta, AlphaFold, and mass spectrometry covalent labeling

“…RosettaDock has had many successes in modeling quaternary protein structure 58 . And its docking predictive capabilities can be further enhanced with the inclusion of sparse experimental data [59][60][61][62] . The benefit of using integrative modeling is that the results depend on the combination of Rosetta score and experimental data correlation, not one individually.…”

Deep-Learning Structure Elucidation from Single-Mutant Deep Mutational Scanning

“…Previous work demonstrated that incorporating sparse data from techniques such as structural mass spectrometry and nuclear magnetic resonance into conventional physics/knowledge-based algorithms, such as Rosetta, led to more accurate predictions. [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] When combined with deep learning, sparse data can be used to develop models which outperform AlphaFold2, such as using experimental contacts from photocrosslinking mass spectrometry 27,28 or density maps from cryo-electron microscopy 29 .…”