A new strategy of studying protein–protein interactions: Integrated strong anion exchange/reversed‐phase chromatography/immunoprecipitation coupled with mass spectrometry for large‐scale identification of proteins interact with immunoglobulin G in HeLa cells

2020 ◽  
Vol 43 (20) ◽  
pp. 3913-3920
Author(s):  
Feng Qin ◽  
Xuantang Wang ◽  
Guoquan Yan ◽  
Mingxia Gao ◽  
Xiangmin Zhang
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Hela Cells ◽  
Immunoglobulin G ◽  
Large Scale ◽  
Reversed Phase ◽  
Protein Protein Interactions ◽  
Reversed Phase Chromatography ◽  
New Strategy ◽  
Strong Anion Exchange

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 Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sun Sook Chung ◽  
Joseph C F Ng ◽  
Anna Laddach ◽  
N Shaun B Thomas ◽  
Franca Fraternali
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Short Loop ◽  
New Strategy ◽  
Loop Network ◽  
Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

scholarly journals Reliable identification of protein-protein interactions by crosslinking mass spectrometry

2020 ◽  
Author(s):  
Swantje Lenz ◽  
Ludwig R. Sinn ◽  
Francis J. O’Reilly ◽  
Lutz Fischer ◽  
Fritz Wegner ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Large Scale ◽  
Protein Complexes ◽  
Estimation Procedure ◽  
Scale Analysis ◽  
Protein Protein Interactions ◽  
False Discovery ◽  
Large Scale Analysis ◽  
False Discovery Rate Estimation

Crosslinking mass spectrometry is widening its scope from structural analyzes of purified multi-protein complexes towards systems-wide analyzes of protein-protein interactions. Assessing the error in these large datasets is currently a challenge. Using a controlled large-scale analysis of Escherichia coli cell lysate, we demonstrate a reliable false-discovery rate estimation procedure for protein-protein interactions identified by crosslinking mass spectrometry.

scholarly journals Development of Large-scale Cross-linking Mass Spectrometry

2017 ◽  
Vol 17 (6) ◽  
pp. 1055-1066 ◽  
Author(s):  
Helena Maria Barysz ◽  
Johan Malmström
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Protein Function ◽  
Large Scale ◽  
Unmet Need ◽  
Large Data ◽  
Cross Linking ◽  
Data Sets ◽  
Protein Protein Interactions ◽  
Model Complex

Cross-linking mass spectrometry (CLMS) provides distance constraints to study the structure of proteins, multiprotein complexes and protein-protein interactions which are critical for the understanding of protein function. CLMS is an attractive technology to bridge the gap between high-resolution structural biology techniques and proteomic-based interactome studies. However, as outlined in this review there are still several bottlenecks associated with CLMS which limit its application on a proteome-wide level. Specifically, there is an unmet need for comprehensive software that can reliably identify cross-linked peptides from large data sets. In this review we provide supporting information to reason that targeted proteomics of cross-links may provide the required sensitivity to reliably detect and quantify cross-linked peptides and that a reporter ion signature for cross-linked peptides may become a useful approach to increase confidence in the identification process of cross-linked peptides. In addition, the review summarizes the recent advances in CLMS workflows using the analysis of condensin complex in intact chromosomes as a model complex.

scholarly journals Keeping in Touch with Type-III Secretion System Effectors: Mass Spectrometry-Based Proteomics to Study Effector–Host Protein–Protein Interactions

2020 ◽  
Vol 21 (18) ◽  
pp. 6891
Author(s):  
Margaux De Meyer ◽  
Joren De Ryck ◽  
Sofie Goormachtig ◽  
Petra Van Damme
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Type Iii Secretion ◽  
Large Scale ◽  
Type Iii Secretion System ◽  
Secretion System ◽  
Host Protein ◽  
Protein Protein Interactions ◽  
Type Iii ◽  
Recent Advances

Manipulation of host cellular processes by translocated bacterial effectors is key to the success of bacterial pathogens and some symbionts. Therefore, a comprehensive understanding of effectors is of critical importance to understand infection biology. It has become increasingly clear that the identification of host protein targets contributes invaluable knowledge to the characterization of effector function during pathogenesis. Recent advances in mapping protein–protein interaction networks by means of mass spectrometry-based interactomics have enabled the identification of host targets at large-scale. In this review, we highlight mass spectrometry-driven proteomics strategies and recent advances to elucidate type-III secretion system effector–host protein–protein interactions. Furthermore, we highlight approaches for defining spatial and temporal effector–host interactions, and discuss possible avenues for studying natively delivered effectors in the context of infection. Overall, the knowledge gained when unravelling effector complexation with host factors will provide novel opportunities to control infectious disease outcomes.

scholarly journals Large‐scale mapping of human protein–protein interactions by mass spectrometry

2007 ◽  
Vol 3 (1) ◽  
pp. 89 ◽  
Author(s):  
Rob M Ewing ◽  
Peter Chu ◽  
Fred Elisma ◽  
Hongyan Li ◽  
Paul Taylor ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Large Scale ◽  
Human Protein ◽  
Protein Protein Interactions

scholarly journals Reliable identification of protein-protein interactions by crosslinking mass spectrometry

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Swantje Lenz ◽  
Ludwig R. Sinn ◽  
Francis J. O’Reilly ◽  
Lutz Fischer ◽  
Fritz Wegner ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Large Scale ◽  
Structural Information ◽  
Protein Complexes ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
False Discovery Rates ◽  
Large Scale Analysis ◽  
Exit Tunnel

AbstractProtein-protein interactions govern most cellular pathways and processes, and multiple technologies have emerged to systematically map them. Assessing the error of interaction networks has been a challenge. Crosslinking mass spectrometry is currently widening its scope from structural analyses of purified multi-protein complexes towards systems-wide analyses of protein-protein interactions (PPIs). Using a carefully controlled large-scale analysis of Escherichia coli cell lysate, we demonstrate that false-discovery rates (FDR) for PPIs identified by crosslinking mass spectrometry can be reliably estimated. We present an interaction network comprising 590 PPIs at 1% decoy-based PPI-FDR. The structural information included in this network localises the binding site of the hitherto uncharacterised protein YacL to near the DNA exit tunnel on the RNA polymerase.

Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

2020 ◽  
Author(s):  
Salvador Guardiola ◽  
Monica Varese ◽  
Xavier Roig ◽  
Jesús Garcia ◽  
Ernest Giralt
Keyword(s):  
Protein Interactions ◽  
Cyclic Peptides ◽  
General Framework ◽  
Large Scale ◽  
De Novo ◽  
Inhibitory Effect ◽  
Original Text ◽  
Protein Protein Interactions ◽  
Retraction Notice ◽  
Pharmaceutical Properties

<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our <i>de novo </i>design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.<i></i></p>

Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

2020 ◽  
Author(s):  
Salvador Guardiola ◽  
Monica Varese ◽  
Xavier Roig ◽  
Jesús Garcia ◽  
Ernest Giralt
Keyword(s):  
Protein Interactions ◽  
Cyclic Peptides ◽  
General Framework ◽  
Large Scale ◽  
De Novo ◽  
Inhibitory Effect ◽  
Original Text ◽  
Protein Protein Interactions ◽  
Retraction Notice ◽  
Pharmaceutical Properties

<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our <i>de novo </i>design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.<i></i></p>

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