Article ; Online: A deep learning model for predicting optimal distance range in crosslinking mass spectrometry data.
2023 Volume 23, Issue 17, Page(s) e2200341
Abstract: Macromolecular assemblies play an important role in all cellular processes. While there has recently been significant progress in protein structure prediction based on deep learning, large protein complexes cannot be predicted with these approaches. The ... ...
| Abstract | Macromolecular assemblies play an important role in all cellular processes. While there has recently been significant progress in protein structure prediction based on deep learning, large protein complexes cannot be predicted with these approaches. The integrative structure modeling approach characterizes multi-subunit complexes by computational integration of data from fast and accessible experimental techniques. Crosslinking mass spectrometry is one such technique that provides spatial information about the proximity of crosslinked residues. One of the challenges in interpreting crosslinking datasets is designing a scoring function that, given a structure, can quantify how well it fits the data. Most approaches set an upper bound on the distance between Cα atoms of crosslinked residues and calculate a fraction of satisfied crosslinks. However, the distance spanned by the crosslinker greatly depends on the neighborhood of the crosslinked residues. Here, we design a deep learning model for predicting the optimal distance range for a crosslinked residue pair based on the structures of their neighborhoods. We find that our model can predict the distance range with the area under the receiver-operator curve of 0.86 and 0.7 for intra- and inter-protein crosslinks, respectively. Our deep scoring function can be used in a range of structure modeling applications. |
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| MeSH term(s) | Deep Learning ; Models, Molecular ; Cross-Linking Reagents/chemistry ; Mass Spectrometry ; Proteins/chemistry |
| Chemical Substances | Cross-Linking Reagents ; Proteins |
| Language | English |
| Publishing date | 2023-05-03 |
| Publishing country | Germany |
| Document type | Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 2032093-0 |
| ISSN | 1615-9861 ; 1615-9853 |
| ISSN (online) | 1615-9861 |
| ISSN | 1615-9853 |
| DOI | 10.1002/pmic.202200341 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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