scholarly journals Systematic protein complex profiling and differential analysis from co-fractionation mass spectrometry data

2020 ◽  
Author(s):  
Andrea Fossati ◽  
Chen Li ◽  
Peter Sykacek ◽  
Moritz Heusel ◽  
Fabian Frommelt ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Protein Quantification ◽  
Mass Spectrometry Data ◽  
Size Exclusion ◽  
Differential Analysis ◽  
Experimental Conditions ◽  
Systematic Analysis ◽  
Improved Performance ◽  
Novel Protein

AbstractProtein complexes, macro-molecular assemblies of two or more proteins, play vital roles in numerous cellular activities and collectively determine the cellular state. Despite the availability of a range of methods for analysing protein complexes, systematic analysis of complexes under multiple conditions has remained challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise, for instance in the combination of size exclusion chromatography (SEC) with accurate protein quantification by SWATH/DIA-MS. However, most approaches for interpreting co-fractionation datasets to yield complex composition, abundance and rearrangements between samples depend heavily on prior evidence. We introduce PCprophet, a computational framework to identify novel protein complexes from SEC-SWATH-MS data and to characterize their changes across different experimental conditions. We demonstrate accurate prediction of protein complexes (AUC >0.99 and accuracy around 97%) via five-fold cross-validation on SEC-SWATH-MS data, show improved performance over state-of-the-art approaches on multiple annotated co-fractionation datasets, and describe a Bayesian approach to analyse altered protein-protein interactions across conditions. PCprophet is a generic computational tool consisting of modules for data pre-processing, hypothesis generation, machine-learning prediction, post-prediction processing, and differential analysis. It can be applied to any co-fractionation MS dataset, independent of separation or quantitative LC-MS workflow employed, and to support the detection and quantitative tracking of novel protein complexes and their physiological dynamics.

scholarly journals SECAT: Quantifying differential protein-protein interaction states by network-centric analysis

2019 ◽  
Author(s):  
George Rosenberger ◽  
Moritz Heusel ◽  
Isabell Bludau ◽  
Ben Collins ◽  
Claudia Martelli ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Protein Complexes ◽  
Mass Spectrometric ◽  
Mass Spectrometric Analysis ◽  
Size Exclusion ◽  
Spectrometric Analysis ◽  
Differential Analysis ◽  
Ppi Networks

AbstractProtein-protein interactions (PPIs) play critical functional and regulatory roles in virtually all cellular processes. They are essential for the formation of macromolecular complexes, which in turn constitute the basis for extended protein interaction networks that determine the functional state of a cell. We and others have previously shown that chromatographic fractionation of native protein complexes in combination with bottom-up mass spectrometric analysis of consecutive fractions supports the multiplexed characterization and detection of state-specific changes of protein complexes.In this study, we describe a computational approach that extends the analysis of data from the co-fractionation / mass spectrometric analysis of native complexes to the level of PPI networks, thus enabling a qualitative and quantitative comparison of the proteome organization between samples and states. The Size-Exclusion Chromatography Algorithmic Toolkit (SECAT) implements a novel, network-centric strategy for the scalable and robust differential analysis of PPI networks. SECAT and its underlying statistical framework elucidate differential quantitative abundance and stoichiometry attributes of proteins in the context of their PPIs. We validate algorithm predictions using publicly available datasets and demonstrate that SECAT represents a more scalable and effective methodology to assess protein-network state and that our approach thus obviates the need to explicitly infer individual protein complexes. Further, by differential analysis of PPI networks of HeLa cells in interphase and mitotic state, respectively, we demonstrate the ability of the algorithm to detect PPI network differences and to thus suggest molecular mechanisms that differentiate cellular states.

scholarly journals A new computational approach to analyze human protein complexes and predict novel protein interactions

2007 ◽  
Vol 8 (12) ◽  
pp. R256 ◽  
Author(s):  
Sara Zanivan ◽  
Ilaria Cascone ◽  
Chiara Peyron ◽  
Ivan Molineris ◽  
Serena Marchio ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Computational Approach ◽  
Human Protein ◽  
Novel Protein

scholarly journals Systematic Identification of Protein Phosphorylation-Mediated Interactions

2020 ◽  
Author(s):  
Brendan M. Floyd ◽  
Kevin Drew ◽  
Edward M. Marcotte
Keyword(s):  
Mass Spectrometry ◽  
Protein Phosphorylation ◽  
Protein Interactions ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Specific Protein ◽  
Mass Spectrometry Data ◽  
Size Exclusion ◽  
Functional Protein

ABSTRACTProtein phosphorylation is a key regulatory mechanism involved in nearly every eukaryotic cellular process. Increasingly sensitive mass spectrometry approaches have identified hundreds of thousands of phosphorylation sites but the functions of a vast majority of these sites remain unknown, with fewer than 5% of sites currently assigned a function. To increase our understanding of functional protein phosphorylation we developed an approach for identifying the phosphorylation-dependence of protein assemblies in a systematic manner. A combination of non-specific protein phosphatase treatment, size-exclusion chromatography, and mass spectrometry allowed us to identify changes in protein interactions after the removal of phosphate modifications. With this approach we were able to identify 316 proteins involved in phosphorylation-sensitive interactions. We recovered known phosphorylation-dependent interactors such as the FACT complex and spliceosome, as well as identified novel interactions such as the tripeptidyl peptidase TPP2 and the supraspliceosome component ZRANB2. More generally, we find phosphorylation-dependent interactors to be strongly enriched for RNA-binding proteins, providing new insight into the role of phosphorylation in RNA binding. By searching directly for phosphorylated amino acid residues in mass spectrometry data, we identified the likely regulatory phosphosites on ZRANB2 and FACT complex subunit SSRP1. This study provides both a method and resource for obtaining a better understanding of the role of phosphorylation in native macromolecular assemblies.

scholarly journals pyProGA—A PyMOL plugin for protein residue network analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0255167
Author(s):  
Vladimir Sladek ◽  
Yuta Yamamoto ◽  
Ryuhei Harada ◽  
Mitsuo Shoji ◽  
Yasuteru Shigeta ◽  
...  
Keyword(s):  
Open Source Software ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Structural Data ◽  
Pair Interaction ◽  
Differential Analysis ◽  
Protein Protein Interactions ◽  
Network Methods ◽  
Free Open Source ◽  
Pymol Plugin

The field of protein residue network (PRN) research has brought several useful methods and techniques for structural analysis of proteins and protein complexes. Many of these are ripe and ready to be used by the proteomics community outside of the PRN specialists. In this paper we present software which collects an ensemble of (network) methods tailored towards the analysis of protein-protein interactions (PPI) and/or interactions of proteins with ligands of other type, e.g. nucleic acids, oligosaccharides etc. In parallel, we propose the use of the network differential analysis as a method to identify residues mediating key interactions between proteins. We use a model system, to show that in combination with other, already published methods, also included in pyProGA, it can be used to make such predictions. Such extended repertoire of methods allows to cross-check predictions with other methods as well, as we show here. In addition, the possibility to construct PRN models from various kinds of input is so far a unique asset of our code. One can use structural data as defined in PDB files and/or from data on residue pair interaction energies, either from force-field parameters or fragment molecular orbital (FMO) calculations. pyProGA is a free open-source software available from https://gitlab.com/Vlado_S/pyproga.

scholarly journals InterPep2: global peptide–protein docking using interaction surface templates

2020 ◽  
Vol 36 (8) ◽  
pp. 2458-2465 ◽  
Author(s):  
Isak Johansson-Åkhe ◽  
Claudio Mirabello ◽  
Björn Wallner
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Structural Features ◽  
Protein Docking ◽  
Supplementary Information ◽  
Peptide Ligand ◽  
Protein Protein Interactions ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Regions ◽  
Improved Performance

Abstract Motivation Interactions between proteins and peptides or peptide-like intrinsically disordered regions are involved in many important biological processes, such as gene expression and cell life-cycle regulation. Experimentally determining the structure of such interactions is time-consuming and difficult because of the inherent flexibility of the peptide ligand. Although several prediction-methods exist, most are limited in performance or availability. Results InterPep2 is a freely available method for predicting the structure of peptide–protein interactions. Improved performance is obtained by using templates from both peptide–protein and regular protein–protein interactions, and by a random forest trained to predict the DockQ-score for a given template using sequence and structural features. When tested on 252 bound peptide–protein complexes from structures deposited after the complexes used in the construction of the training and templates sets of InterPep2, InterPep2-Refined correctly positioned 67 peptides within 4.0 Å LRMSD among top10, similar to another state-of-the-art template-based method which positioned 54 peptides correctly. However, InterPep2 displays a superior ability to evaluate the quality of its own predictions. On a previously established set of 27 non-redundant unbound-to-bound peptide–protein complexes, InterPep2 performs on-par with leading methods. The extended InterPep2-Refined protocol managed to correctly model 15 of these complexes within 4.0 Å LRMSD among top10, without using templates from homologs. In addition, combining the template-based predictions from InterPep2 with ab initio predictions from PIPER-FlexPepDock resulted in 22% more near-native predictions compared to the best single method (22 versus 18). Availability and implementation The program is available from: http://wallnerlab.org/InterPep2. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Activity-dependent changes in synaptic protein complex composition are consistent in different detergents despite differential solubility

2019 ◽  
Author(s):  
Jonathan D. Lautz ◽  
Edward P. Gniffke ◽  
Emily A. Brown ◽  
Karen B. Immendorf ◽  
Ryan D. Mendel ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Synaptic Activity ◽  
Synaptic Proteins ◽  
Size Exclusion ◽  
Synaptic Protein ◽  
Exclusion Chromatography ◽  
Associated Proteins ◽  
Dependent Protein ◽  
Activity Dependent

AbstractAt the post-synaptic density (PSD), large protein complexes dynamically form and dissociate in response to synaptic activity, comprising the biophysical basis for learning and memory. The use of detergents to both isolate the PSD fraction and release its membrane-associated proteins complicates studies of these activity-dependent protein interaction networks, because detergents can simultaneously disrupt the very interactions under study. Despite widespread recognition that different detergents yield different experimental results, the effect of detergent on activity-dependent synaptic protein complexes has not been rigorously examined. Here, we characterize the effect of three detergents commonly used to study synaptic proteins on activity-dependent protein interactions. We first demonstrate that SynGAP-containing interactions are more abundant in 1% Deoxycholate (DOC), while Shank-, Homer-and mGluR5-containing interactions are more abundant in 1% NP-40 or Triton. All interactions were detected preferentially in high molecular weight (HMW) complexes generated by size exclusion chromatography, although the detergent-specific abundance of proteins in HMW fractions did not correlate with the abundance of detected interactions. Activity-dependent changes in protein complexes were consistent across detergent types, suggesting that detergents do not isolate distinct protein pools with unique behaviors. However, detection of activity-dependent changes is more or less feasible in different detergents due to baseline solubility. Collectively, our results demonstrate that detergents affect the solubility of individual proteins, but activity-dependent changes in protein interactions, when detectable, are consistent across detergent types.

scholarly journals Dimensionality reduction by UMAP to visualize physical and genetic interactions

2019 ◽  
Author(s):  
Michael W. Dorrity ◽  
Lauren M. Saunders ◽  
Christine Queitsch ◽  
Stanley Fields ◽  
Cole Trapnell
Keyword(s):  
Dimensionality Reduction ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Single Gene ◽  
Genetic Interactions ◽  
Gene Deletions ◽  
Transcript Profiles ◽  
Low Dimensional ◽  
Complex Expression ◽  
Novel Protein

Dimensionality reduction is often used to visualize complex expression profiling data. Here, we use the Uniform Manifold Approximation and Projection (UMAP) method on published transcript profiles of 1484 single gene deletions of Saccharomyces cerevisiae. Proximity in low-dimensional UMAP space identifies clusters of genes that correspond to protein complexes and pathways, and finds novel protein interactions even within well-characterized complexes. This approach is more sensitive than previous methods and should be broadly useful as additional transcriptome datasets become available for other organisms.

scholarly journals Complexome Profiling—Exploring Mitochondrial Protein Complexes in Health and Disease

2022 ◽  
Vol 9 ◽  
Author(s):  
Alfredo Cabrera-Orefice ◽  
Alisa Potter ◽  
Felix Evers ◽  
Johannes F. Hevler ◽  
Sergio Guerrero-Castillo
Keyword(s):  
Protein Complexes ◽  
Gradient Centrifugation ◽  
Mitochondrial Protein ◽  
Size Exclusion ◽  
Native Proteins ◽  
Mass Spectrometry Identification ◽  
Exclusion Chromatography ◽  
Health And Disease ◽  
The Impact ◽  
Novel Protein

Complexome profiling (CP) is a state-of-the-art approach that combines separation of native proteins by electrophoresis, size exclusion chromatography or density gradient centrifugation with tandem mass spectrometry identification and quantification. Resulting data are computationally clustered to visualize the inventory, abundance and arrangement of multiprotein complexes in a biological sample. Since its formal introduction a decade ago, this method has been mostly applied to explore not only the composition and abundance of mitochondrial oxidative phosphorylation (OXPHOS) complexes in several species but also to identify novel protein interactors involved in their assembly, maintenance and functions. Besides, complexome profiling has been utilized to study the dynamics of OXPHOS complexes, as well as the impact of an increasing number of mutations leading to mitochondrial disorders or rearrangements of the whole mitochondrial complexome. Here, we summarize the major findings obtained by this approach; emphasize its advantages and current limitations; discuss multiple examples on how this tool could be applied to further investigate pathophysiological mechanisms and comment on the latest advances and opportunity areas to keep developing this methodology.

scholarly journals Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants

2021 ◽  
Author(s):  
Rui Yin ◽  
Brandon Y Feng ◽  
Amitabh Varshney ◽  
Brian G Pierce
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Protein Docking ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Future Developments ◽  
Massive Number ◽  
Improved Performance

High resolution experimental structural determination of protein-protein interactions has led to valuable mechanistic insights, yet due to the massive number of interactions and experimental limitations there is a need for computational methods that can accurately model their structures. Here we explore the use of the recently developed deep learning method, AlphaFold, to predict structures of protein complexes from sequence. With a benchmark of 152 diverse heterodimeric protein complexes, multiple implementations and parameters of AlphaFold were tested for accuracy. Remarkably, many cases had highly accurate models generated as top-ranked predictions, greatly surpassing the performance of unbound protein-protein docking, whereas antibody-antigen docking was largely unsuccessful. While AlphaFold-generated accuracy predictions were able to discriminate near-native models, previously developed scoring protocols improved performance. Our study demonstrates that end-to-end deep learning can accurately model transient protein complexes, and identifies areas for improvement to guide future developments to reliably model any protein-protein interaction of interest.

scholarly journals Butyrylcholinesterase–Protein Interactions in Human Serum

2021 ◽  
Vol 22 (19) ◽  
pp. 10662
Author(s):  
Jacek Jasiecki ◽  
Anna Szczoczarz ◽  
Dominik Cysewski ◽  
Krzysztof Lewandowski ◽  
Piotr Skowron ◽  
...  
Keyword(s):  
Human Serum ◽  
Protein Interactions ◽  
Serum Proteins ◽  
Protein Complexes ◽  
Density Lipoprotein ◽  
Specific Protein ◽  
Size Exclusion ◽  
Strong Interactions ◽  
Binding Studies ◽  
Exclusion Chromatography

Measuring various biochemical and cellular components in the blood is a routine procedure in clinical practice. Human serum contains hundreds of diverse proteins secreted from all cells and tissues in healthy and diseased states. Moreover, some serum proteins have specific strong interactions with other blood components, but most interactions are probably weak and transient. One of the serum proteins is butyrylcholinesterase (BChE), an enzyme existing mainly as a glycosylated soluble tetramer that plays an important role in the metabolism of many drugs. Our results suggest that BChE interacts with plasma proteins and forms much larger complexes than predicted from the molecular weight of the BChE tetramer. To investigate and isolate such complexes, we developed a two-step strategy to find specific protein–protein interactions by combining native size-exclusion chromatography (SEC) with affinity chromatography with the resin that specifically binds BChE. Second, to confirm protein complexes′ specificity, we fractionated blood serum proteins by density gradient ultracentrifugation followed by co-immunoprecipitation with anti-BChE monoclonal antibodies. The proteins coisolated in complexes with BChE were identified by mass spectroscopy. These binding studies revealed that BChE interacts with a number of proteins in the human serum. Some of these interactions seem to be more stable than transient. BChE copurification with ApoA-I and the density of some fractions containing BChE corresponding to high-density lipoprotein cholesterol (HDL) during ultracentrifugation suggest its interactions with HDL. Moreover, we observed lower BChE plasma activity in individuals with severely reduced HDL levels (≤20 mg/dL). The presented two-step methodology for determination of the BChE interactions can facilitate further analysis of such complexes, especially from the brain tissue, where BChE could be involved in the pathogenesis and progression of AD.

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