scholarly journals Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sven H. Giese ◽  
Ludwig R. Sinn ◽  
Fritz Wegner ◽  
Juri Rappsilber
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Retention Time ◽  
Mass Spectra ◽  
Chromatographic Retention ◽  
Protein Protein Interactions ◽  
E Coli ◽  
Noisy Information ◽  
Machine Learning Model ◽  
Time Information

AbstractCrosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits the numbers of protein–protein interactions that can be confidently identified. Here, we leverage chromatographic retention time information to aid the identification of crosslinked peptides from mass spectra. Our Siamese machine learning model xiRT achieves highly accurate retention time predictions of crosslinked peptides in a multi-dimensional separation of crosslinked E. coli lysate. Importantly, supplementing the search engine score with retention time features leads to a substantial increase in protein–protein interactions without affecting confidence. This approach is not limited to cell lysates and multi-dimensional separation but also improves considerably the analysis of crosslinked multiprotein complexes with a single chromatographic dimension. Retention times are a powerful complement to mass spectrometric information to increase the sensitivity of crosslinking mass spectrometry analyses.

scholarly journals Retention Time Prediction Using Neural Networks Increases Identifications in Crosslinking Mass Spectrometry

2021 ◽  
Author(s):  
Sven H. Giese ◽  
Ludwig R. Sinn ◽  
Fritz Wegner ◽  
Juri Rappsilber
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Retention Time ◽  
Fold Increase ◽  
Reversed Phase ◽  
Fanconi Anaemia ◽  
Chromatographic Retention ◽  
Protein Protein Interactions ◽  
Noisy Information ◽  
Strong Anion Exchange

AbstractCrosslinking 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 in the spectra limits the numbers of protein-protein interactions (PPIs) that can be confidently identified. Here, we successfully leveraged chromatographic retention time (RT) information to aid the identification of crosslinked peptides from spectra. Our Siamese machine learning model xiRT achieved highly accurate RT predictions of crosslinked peptides in a multi-dimensional separation of crosslinked E. coli lysate. We combined strong cation exchange (SCX), hydrophilic strong anion exchange (hSAX) and reversed-phase (RP) chromatography and reached R2 0.94 in RP and a margin of error of 1 fraction for hSAX in 94%, and SCX in 85% of the predictions. Importantly, supplementing the search engine score with retention time features led to a 1.4-fold increase in PPIs at a 1% false discovery rate. We also demonstrate the value of this approach for the more routine analysis of a crosslinked multiprotein complexes. An increase of 1.7-fold in heteromeric crosslinked residue-pairs was achieved at 1% residue-pair FDR for Fanconi anaemia monoubiquitin ligase complex, solely using reversed-phase RT. Retention times are a powerful complement to mass spectrometric information to increase the sensitivity of Crosslinking MS analyses.

scholarly journals E. coli primase and DNA polymerase III holoenzyme are able to bind concurrently to a primed template during DNA replication

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Andrea Bogutzki ◽  
Natalie Naue ◽  
Lidia Litz ◽  
Andreas Pich ◽  
Ute Curth
Keyword(s):  
Dna Replication ◽  
Dna Polymerase ◽  
Protein Interactions ◽  
Dna Polymerase Iii ◽  
Protein Protein Interactions ◽  
Single Stranded Dna ◽  
E Coli ◽  
Polymerase Iii ◽  
C Terminus ◽  
Pol Iii

Abstract During DNA replication in E. coli, a switch between DnaG primase and DNA polymerase III holoenzyme (pol III) activities has to occur every time when the synthesis of a new Okazaki fragment starts. As both primase and the χ subunit of pol III interact with the highly conserved C-terminus of single-stranded DNA-binding protein (SSB), it had been proposed that the binding of both proteins to SSB is mutually exclusive. Using a replication system containing the origin of replication of the single-stranded DNA phage G4 (G4ori) saturated with SSB, we tested whether DnaG and pol III can bind concurrently to the primed template. We found that the addition of pol III does not lead to a displacement of primase, but to the formation of higher complexes. Even pol III-mediated primer elongation by one or several DNA nucleotides does not result in the dissociation of DnaG. About 10 nucleotides have to be added in order to displace one of the two primase molecules bound to SSB-saturated G4ori. The concurrent binding of primase and pol III is highly plausible, since even the SSB tetramer situated directly next to the 3′-terminus of the primer provides four C-termini for protein-protein interactions.

scholarly journals The Protein Interactome of Glycolysis in Escherichia coli

Proteomes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 16
Author(s):  
Shomeek Chowdhury ◽  
Stephen Hepper ◽  
Mudassir K. Lodi ◽  
Milton H. Saier ◽  
Peter Uetz
Keyword(s):  
Escherichia Coli ◽  
Protein Interactions ◽  
Allosteric Regulation ◽  
Glycolytic Enzymes ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Interacting Protein ◽  
E Coli ◽  
Protein Interactome ◽  
The Impact

Glycolysis is regulated by numerous mechanisms including allosteric regulation, post-translational modification or protein-protein interactions (PPI). While glycolytic enzymes have been found to interact with hundreds of proteins, the impact of only some of these PPIs on glycolysis is well understood. Here we investigate which of these interactions may affect glycolysis in E. coli and possibly across numerous other bacteria, based on the stoichiometry of interacting protein pairs (from proteomic studies) and their conservation across bacteria. We present a list of 339 protein-protein interactions involving glycolytic enzymes but predict that ~70% of glycolytic interactors are not present in adequate amounts to have a significant impact on glycolysis. Finally, we identify a conserved but uncharacterized subset of interactions that are likely to affect glycolysis and deserve further study.

Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Gabriel L. Streun ◽  
Andrea E. Steuer ◽  
Lars C. Ebert ◽  
Akos Dobay ◽  
Thomas Kraemer
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
High Resolution ◽  
Retention Time ◽  
Drug Testing ◽  
High Resolution Mass Spectrometry ◽  
Urine Samples ◽  
Machine Learning Model ◽  
Resolution Mass

Abstract Objectives Urine sample manipulation including substitution, dilution, and chemical adulteration is a continuing challenge for workplace drug testing, abstinence control, and doping control laboratories. The simultaneous detection of sample manipulation and prohibited drugs within one single analytical measurement would be highly advantageous. Machine learning algorithms are able to learn from existing datasets and predict outcomes of new data, which are unknown to the model. Methods Authentic human urine samples were treated with pyridinium chlorochromate, potassium nitrite, hydrogen peroxide, iodine, sodium hypochlorite, and water as control. In total, 702 samples, measured with liquid chromatography coupled to quadrupole time-of-flight mass spectrometry, were used. After retention time alignment within Progenesis QI, an artificial neural network was trained with 500 samples, each featuring 33,448 values. The feature importance was analyzed with the local interpretable model-agnostic explanations approach. Results Following 10-fold cross-validation, the mean sensitivity, specificity, positive predictive value, and negative predictive value was 88.9, 92.0, 91.9, and 89.2%, respectively. A diverse test set (n=202) containing treated and untreated urine samples could be correctly classified with an accuracy of 95.4%. In addition, 14 important features and four potential biomarkers were extracted. Conclusions With interpretable retention time aligned liquid chromatography high-resolution mass spectrometry data, a reliable machine learning model could be established that rapidly uncovers chemical urine manipulation. The incorporation of our model into routine clinical or forensic analysis allows simultaneous LC-MS analysis and sample integrity testing in one run, thus revolutionizing this field of drug testing.

scholarly journals Artificial Septal Targeting of Bacillus subtilis Cell Division Proteins in Escherichia coli: an Interspecies Approach to the Study of Protein-Protein Interactions in Multiprotein Complexes

2008 ◽  
Vol 190 (18) ◽  
pp. 6048-6059 ◽  
Author(s):  
Carine Robichon ◽  
Glenn F. King ◽  
Nathan W. Goehring ◽  
Jon Beckwith
Keyword(s):  
Escherichia Coli ◽  
Bacillus Subtilis ◽  
Cell Division ◽  
Protein Interactions ◽  
Fluorescent Protein ◽  
Bacterial Cell Division ◽  
Protein Protein Interactions ◽  
Positive Interaction ◽  
E Coli ◽  
Multiprotein Complexes

ABSTRACT Bacterial cell division is mediated by a set of proteins that assemble to form a large multiprotein complex called the divisome. Recent studies in Bacillus subtilis and Escherichia coli indicate that cell division proteins are involved in multiple cooperative binding interactions, thus presenting a technical challenge to the analysis of these interactions. We report here the use of an E. coli artificial septal targeting system for examining the interactions between the B. subtilis cell division proteins DivIB, FtsL, DivIC, and PBP 2B. This technique involves the fusion of one of the proteins (the “bait”) to ZapA, an E. coli protein targeted to mid-cell, and the fusion of a second potentially interacting partner (the “prey”) to green fluorescent protein (GFP). A positive interaction between two test proteins in E. coli leads to septal localization of the GFP fusion construct, which can be detected by fluorescence microscopy. Using this system, we present evidence for two sets of strong protein-protein interactions between B. subtilis divisomal proteins in E. coli, namely, DivIC with FtsL and DivIB with PBP 2B, that are independent of other B. subtilis cell division proteins and that do not disturb the cytokinesis process in the host cell. Our studies based on the coexpression of three or four of these B. subtilis cell division proteins suggest that interactions among these four proteins are not strong enough to allow the formation of a stable four-protein complex in E. coli in contrast to previous suggestions. Finally, our results demonstrate that E. coli artificial septal targeting is an efficient and alternative approach for detecting and characterizing stable protein-protein interactions within multiprotein complexes from other microorganisms. A salient feature of our approach is that it probably only detects the strongest interactions, thus giving an indication of whether some interactions suggested by other techniques may either be considerably weaker or due to false positives.

scholarly journals PIPINO: A Software Package to Facilitate the Identification of Protein-Protein Interactions from Affinity Purification Mass Spectrometry Data

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Stefan Kalkhof ◽  
Stefan Schildbach ◽  
Conny Blumert ◽  
Friedemann Horn ◽  
Martin von Bergen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Isotope Labeling ◽  
Affinity Purification ◽  
Interaction Network ◽  
Mass Spectrometry Data ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Binding Behavior ◽  
Bona Fide

The functionality of most proteins is regulated by protein-protein interactions. Hence, the comprehensive characterization of the interactome is the next milestone on the path to understand the biochemistry of the cell. A powerful method to detect protein-protein interactions is a combination of coimmunoprecipitation or affinity purification with quantitative mass spectrometry. Nevertheless, both methods tend to precipitate a high number of background proteins due to nonspecific interactions. To address this challenge the software Protein-Protein-Interaction-Optimizer (PIPINO) was developed to perform an automated data analysis, to facilitate the selection of bona fide binding partners, and to compare the dynamic of interaction networks. In this study we investigated the STAT1 interaction network and its activation dependent dynamics. Stable isotope labeling by amino acids in cell culture (SILAC) was applied to analyze the STAT1 interactome after streptavidin pull-down of biotagged STAT1 from human embryonic kidney 293T cells with and without activation. Starting from more than 2,000 captured proteins 30 potential STAT1 interaction partners were extracted. Interestingly, more than 50% of these were already reported or predicted to bind STAT1. Furthermore, 16 proteins were found to affect the binding behavior depending on STAT1 phosphorylation such as STAT3 or the importin subunits alpha 1 and alpha 6.

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