CharmeRT: Boosting Peptide Identifications by Chimeric Spectra Identification and Retention Time Prediction.
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ABSTRACT:
SUBMITTER: Dorfer V
PROVIDER: S-EPMC6079931 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Abstract:
Crosslinking mass spectrometry (Crosslinking MS) has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information contained in spectra limits especially the identification of heteromeric protein-protein interactions (PPIs) from the many theoretically possible PPIs. We successfully leveraged here chromatographic retention time (RT) to complement the mass spectrometry-centric identification process. For this, we first made crosslinked peptides amenable to RT prediction, through a Siamese neural network, and then added RT information to the identification process. Our multi-task machine learning model xiRT achieved highly accurate predictions in a multi-dimensional separation experiment of crosslinked E. coli lysate conducted for this study. We combined strong cation exchange (SCX), hydrophilic strong anion exchange (hSAX) and reversed-phase (RP) chromatography and reached R^2 0.94 in RP and a margin of error of 1 fraction for hSAX in 94%, and SCX in 85% of the cases. Importantly, supplementing the search engine score with retention time features led to a 1.4-fold increase in PPIs, at 1% PPI false discovery rate (FDR). We also demonstrated the value of this approach for the more routine analysis of multiprotein complexes. In the Fanconi anaemia monoubiquitin ligase complex, an increase of 1.7-fold in heteromeric residue-pairs was achieved at 1% residue-pair FDR, solely using reversed-phase RT. Retention times therefore proved to be a powerful complement to mass spectrometric information to improve the identification of crosslinked peptides. We envision xiRT to supplement search engines in their scoring routines to increase the sensitivity of Crosslinking MS analyses especially for protein-protein interactions.
Conclusion:
Using a Siamese network architecture, we succeeded in bringing RT prediction into the Crosslinking MS field, independent of separation setup and search software. Our open source application xiRT introduces the concept of multi-task learning to achieve multi-dimensional chromatographic retention time prediction, and may use any peptide sequence-dependent measure including for example collision cross section or isoelectric point. The black-box character of the neural network was reduced by means of interpretable machine learning that revealed individual amino acid contributions towards the separation behavior. The RT predictions – even when using only the RP dimension – complement mass spectrometric information to enhance the identification of heteromeric crosslinks in multiprotein complex and proteome-wide studies. Overfitting does not account for this gain as known false target matches from an entrapment database did not increase. Leveraging additional information sources may help to address the mass-spectrometric identification challenge of heteromeric crosslinks.
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Project description:MotivationLiquid chromatography retention times prediction can assist in metabolite identification, which is a critical task and challenge in nontargeted metabolomics. However, different chromatographic conditions may result in different retention times for the same metabolite. Current retention time prediction methods lack sufficient scalability to transfer from one specific chromatographic method to another.ResultsTherefore, we present RT-Transformer, a novel deep neural network model coupled with graph attention network and 1D-Transformer, which can predict retention times under any chromatographic methods. First, we obtain a pre-trained model by training RT-Transformer on the large small molecule retention time dataset containing 80 038 molecules, and then transfer the resulting model to different chromatographic methods based on transfer learning. When tested on the small molecule retention time dataset, as other authors did, the average absolute error reached 27.30 after removing not retained molecules. Still, it reached 33.41 when no samples were removed. The pre-trained RT-Transformer was further transferred to 5 datasets corresponding to different chromatographic conditions and fine-tuned. According to the experimental results, RT-Transformer achieves competitive performance compared to state-of-the-art methods. In addition, RT-Transformer was applied to 41 external molecular retention time datasets. Extensive evaluations indicate that RT-Transformer has excellent scalability in predicting retention times for liquid chromatography and improves the accuracy of metabolite identification.Availability and implementationThe source code for the model is available at https://github.com/01dadada/RT-Transformer. The web server is available at https://huggingface.co/spaces/Xue-Jun/RT-Transformer.
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