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. 2021 May 28;12:3237. doi: 10.1038/s41467-021-23441-0

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a Experimental and data analysis workflow. The soluble high-molecular-weight proteome of E. coli lysate was crosslinked and the digest sequentially fractionated by strong-cation exchange chromatography (SCX) (9 fractions collected), hydrophilic strong-anion exchange chromatography (hSAX) (10 pools collected), and finally by reversed-phase chromatography (RP) coupled to the MS. The protein database for the crosslink search was created by a linear peptide search with Comet and a sequence-based filter using BLAST. For each E. coli protein in the final database (green) a human protein was added as a control (pale orange). b Potential for false-negative PPI identifications. Verified PPIs are estimated from matches to the STRING/APID databases. PPIs are computed based on CSM-level FDR. Estimated random hits correspond to the average number of semi-randomly drawn pairs (first protein was randomly selected from the STRING/APID database and the second protein was drawn from the FASTA file). Gained PPIs accentuate the additional information that is available in the data at higher FDR. c Decrease of heteromeric CSM scores based on spectral evidence with increasing CSM-FDR. Boxenplot shows the median and 50% of the data in the central boxes while each successive level outward represents half of the remaining data. The sample size for each FDR category is given below the boxes. d xiRT network architecture to predict multi-dimensional retention times. A crosslinked peptide is represented as two individual inputs to xiRT. xiRT uses a Siamese network architecture that shares the weights of the embedding and recurrent layers. Individual layers for the prediction tasks are added with custom activation functions (sigmoid/linear functions for fractionation/regression tasks, respectively). e Rescoring workflow. The predictions from xiRT are combined with xiSCORE’s output to rescore CSMs using a linear support vector machine (SVM), consequently leading to more matches at constant confidence. Source data are provided as a Source Data file.