scholarly journals A Co-Fractionation Mass Spectrometry-based Prediction of Protein Complex Assemblies in the Developing Rice Aleurone-subaleurone

2021 ◽  
Author(s):  
Youngwoo Lee ◽  
Thomas W Okita ◽  
Daniel B Szymanski
Keyword(s):  
Mass Spectrometry ◽  
Protein Complex ◽  
Large Scale ◽  
Rna Binding ◽  
Seed Quality ◽  
Protein Complexes ◽  
Early Stage ◽  
Practical Significance ◽  
Rice Seed ◽  
Complex Composition

Multiprotein complexes execute and coordinate diverse cellular processes such as organelle biogenesis, vesicle trafficking, cell signaling, and metabolism. Knowledge about their composition and localization provides useful clues about the mechanisms of cellular homeostasis and systems-level control. This is of great biological importance and practical significance in heterotrophic rice endosperm and aleurone-subaleurone tissues that are a primary source of seed vitamins and stored energy. Dozens of protein complexes have been implicated in the synthesis, transport, and storage of seed proteins, lipids, vitamins, and minerals. Mutations in protein complexes that control RNA transport result in aberrant endosperm with shrunken and floury phenotypes, significantly reducing seed yield and quality. The purpose of this research is to broadly predict protein complex composition in the aleurone-subaleurone layers of developing rice seeds using co-fractionation mass spectrometry. Following orthogonal chromatographic separations of biological replicates, thousands of protein elution profiles were subjected to distance-based clustering to enable a large-scale determination of multimerization state and complex composition. Predictions included evolutionarily conserved proteins across diverse functional categories, including novel heteromeric RNA binding protein complexes that influence seed quality. This effective and open-ended proteomics pipeline provides useful clues about systems-level controls in the early stage of rice seed development.

Algorithm of combining chromatography mass spectrometry-untargeted profiling and multivariate analysis for identification of marker-substances in samples of complex composition

2020 ◽  
Vol 86 (7) ◽  
pp. 12-19
Author(s):  
I. V. Plyushchenko ◽  
D. G. Shakhmatov ◽  
I. A. Rodin
Keyword(s):  
Mass Spectrometry ◽  
Multivariate Analysis ◽  
Large Scale ◽  
Complex Composition ◽  
Unified Protocol ◽  
Chromatography Mass Spectrometry ◽  
Marker Substances ◽  
Selection Testing ◽  
Untargeted Profiling

A viral development of statistical data processing, computing capabilities, chromatography-mass spectrometry, and omics technologies (technologies based on the achievements of genomics, transcriptomics, proteomics, metabolomics) in recent decades has not led to formation of a unified protocol for untargeted profiling. Systematic errors reduce the reproducibility and reliability of the obtained results, and at the same time hinder consolidation and analysis of data gained in large-scale multi-day experiments. We propose an algorithm for conducting omics profiling to identify potential markers in the samples of complex composition and present the case study of urine samples obtained from different clinical groups of patients. Profiling was carried out by the method of liquid chromatography mass spectrometry. The markers were selected using methods of multivariate analysis including machine learning and feature selection. Testing of the approach was performed using an independent dataset by clustering and projection on principal components.

scholarly journals RNA-Centric Approaches to Profile the RNA–Protein Interaction Landscape on Selected RNAs

2021 ◽  
Vol 7 (1) ◽  
pp. 11 ◽  
Author(s):  
André P. Gerber
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Regulatory Networks ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Protein Complexes ◽  
Cell Protein ◽  
Transcriptional Regulatory Networks ◽  
Technological Advances

RNA–protein interactions frame post-transcriptional regulatory networks and modulate transcription and epigenetics. While the technological advances in RNA sequencing have significantly expanded the repertoire of RNAs, recently developed biochemical approaches combined with sensitive mass-spectrometry have revealed hundreds of previously unrecognized and potentially novel RNA-binding proteins. Nevertheless, a major challenge remains to understand how the thousands of RNA molecules and their interacting proteins assemble and control the fate of each individual RNA in a cell. Here, I review recent methodological advances to approach this problem through systematic identification of proteins that interact with particular RNAs in living cells. Thereby, a specific focus is given to in vivo approaches that involve crosslinking of RNA–protein interactions through ultraviolet irradiation or treatment of cells with chemicals, followed by capture of the RNA under study with antisense-oligonucleotides and identification of bound proteins with mass-spectrometry. Several recent studies defining interactomes of long non-coding RNAs, viral RNAs, as well as mRNAs are highlighted, and short reference is given to recent in-cell protein labeling techniques. These recent experimental improvements could open the door for broader applications and to study the remodeling of RNA–protein complexes upon different environmental cues and in disease.

scholarly journals ComplexBrowser: a tool for identification and quantification of protein complexes in large scale proteomics datasets

2019 ◽  
Author(s):  
Wojciech Michalak ◽  
Vasileios Tsiamis ◽  
Veit Schwämmle ◽  
Adelina Rogowska-Wrzesińska
Keyword(s):  
T Cell Activation ◽  
Protein Complex ◽  
Quantitative Proteomics ◽  
Large Scale ◽  
Protein Complexes ◽  
Quantitative Measure ◽  
Exploratory Analysis ◽  
List Type ◽  
Link Type ◽  
Complex Components

AbstractWe have developed ComplexBrowser, an open source, online platform for supervised analysis of quantitative proteomics data that focuses on protein complexes. The software uses information from CORUM and Complex Portal databases to identify protein complex components. Based on the expression changes of individual complex subunits across the proteomics experiment it calculates Complex Fold Change (CFC) factor that characterises the overall protein complex expression trend and the level of subunit co-regulation. Thus up- and down-regulated complexes can be identified. It provides interactive visualisation of protein complexes composition and expression for exploratory analysis. It also incorporates a quality control step that includes normalisation and statistical analysis based on Limma test. ComplexBrowser performance was tested on two previously published proteomics studies identifying changes in protein expression in human adenocarcinoma tissue and during activation of mouse T-cells. The analysis revealed 1519 and 332 protein complexes, of which 233 and 41 were found co-ordinately regulated in the respective studies. The adopted approach provided evidence for a shift to glucose-based metabolism and high proliferation in adenocarcinoma tissues and identification of chromatin remodelling complexes involved in mouse T-cell activation. The results correlate with the original interpretation of the experiments and also provide novel biological details about protein complexes affected. ComplexBrowser is, to our knowledge, the first tool to automate quantitative protein complex analysis for high-throughput studies, providing insights into protein complex regulation within minutes of analysis.A fully functional demo version of ComplexBrowser v1.0 is available online via http://computproteomics.bmb.sdu.dk/Apps/ComplexBrowser/The source code can be downloaded from: https://bitbucket.org/michalakw/complexbrowserHighlightsAutomated analysis of protein complexes in proteomics experimentsQuantitative measure of the coordinated changes in protein complex componentsInteractive visualisations for exploratory analysis of proteomics resultsIn briefComplexBrowser is capable of identifying protein complexes in datasets obtained from large scale quantitative proteomics experiments. It provides, in the form of the CFC factor, a quantitative measure of the coordinated changes in complex components. This facilitates assessing the overall trends in the processes governed by the identified protein complexes providing a new and complementary way of interpreting proteomics experiments.

The 5S RNA – protein complex from yeast: a model for the evolution and structure of the eukaryotic ribosome

1982 ◽  
Vol 60 (4) ◽  
pp. 490-496 ◽  
Author(s):  
Ross N. Nazar ◽  
Makoto Yaguchi ◽  
Gordon E. Willick
Keyword(s):  
Binding Site ◽  
Protein Complex ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Protein Complexes ◽  
Basic Structure ◽  
5S Rna ◽  
Chemical Studies ◽  
Physical And Chemical ◽  
Rna Protein Complexes

The ribosomal 5S RNA – protein complex appears to be an excellent model for studies on the evolution and structure of ribosomes. In eukaryotes this complex is composed of two components, the 5S rRNA and a single ribosomal protein which in yeast has a molecular weight of about 38 000. The primary protein-binding site is located in the 3′-end region of the 5S RNA together with a small portion of the 5′ end. The primary RNA-binding site appears to be situated in the C-terminal end of the protein (YL3 in yeast) but the binding specificity requires other structural elements in the N-terminal half of the molecule. When compared with prokaryotic 5S RNA – protein complexes, various physical and chemical studies suggest that the basic structure and interactions have been conserved in the course of evolution, but that the single larger eukaryotic 5S RNA binding protein has evolved through a fusion of genes for the multiple 5S RNA binding proteins in prokaryotes.

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 Using Coarse-Grained Simulations to Characterize the Mechanisms of Protein–Protein Association

Biomolecules ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 1056 ◽  
Author(s):  
Kalyani Dhusia ◽  
Zhaoqian Su ◽  
Yinghao Wu
Keyword(s):  
Protein Complex ◽  
Large Scale ◽  
Protein Complexes ◽  
Kinetic Property ◽  
Simulation Method ◽  
Coarse Grained ◽  
Structural Level ◽  
Simulation Approach ◽  
Protein Association ◽  
Order Of Magnitude

The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can be measured through various experimental techniques. To complement these time-consuming and labor-intensive approaches, we developed a coarse-grained simulation approach to study the physical processes of protein–protein association. We systematically calibrated our simulation method against a large-scale benchmark set. By combining a physics-based force field with a statistically-derived potential in the simulation, we found that the association rates of more than 80% of protein complexes can be correctly predicted within one order of magnitude relative to their experimental measurements. We further showed that a mixture of force fields derived from complementary sources was able to describe the process of protein–protein association with mechanistic details. For instance, we show that association of a protein complex contains multiple steps in which proteins continuously search their local binding orientations and form non-native-like intermediates through repeated dissociation and re-association. Moreover, with an ensemble of loosely bound encounter complexes observed around their native conformation, we suggest that the transition states of protein–protein association could be highly diverse on the structural level. Our study also supports the idea in which the association of a protein complex is driven by a “funnel-like” energy landscape. In summary, these results shed light on our understanding of how protein–protein recognition is kinetically modulated, and our coarse-grained simulation approach can serve as a useful addition to the existing experimental approaches that measure protein–protein association rates.

Illustration of SID-IM-SID (surface-induced dissociation-ion mobility-SID) mass spectrometry: homo and hetero model protein complexes

The Analyst ◽  
2015 ◽  
Vol 140 (20) ◽  
pp. 7012-7019 ◽  
Author(s):  
Royston S. Quintyn ◽  
Sophie R. Harvey ◽  
Vicki H. Wysocki
Keyword(s):  
Mass Spectrometry ◽  
Ion Mobility ◽  
Protein Complex ◽  
Protein Complexes ◽  
Second Stage ◽  
Surface Induced Dissociation ◽  
Model Protein ◽  
Complex Architecture

Surface collisions generate subcomplexes, which are then separated by ion mobility and dissociated into their individual subunitsviaa second stage of surface collisions to elucidate protein complex architecture and assembly.

scholarly journals Protein complex similarity based on Weisfeiler-Lehman labeling

2018 ◽  
Author(s):  
Bianca K Stöcker ◽  
Till Schäfer ◽  
Petra Mutzel ◽  
Johannes Köster ◽  
Nils Kriege ◽  
...  
Keyword(s):  
Protein Complex ◽  
Large Scale ◽  
Protein Complexes ◽  
Similarity Measures ◽  
Interaction Networks ◽  
Protein Protein Interaction ◽  
Protein Complex Prediction ◽  
Protein Protein Interaction Networks ◽  
Human Integrin ◽  
Good Agreement

Being able to quantify the similarity between two protein complexes is essential for numerous applications. Prominent examples are database searches for known complexes with a given query complex, comparison of the output of different protein complex prediction algorithms, or summarizing and clustering protein complexes, e.g., for visualization. While the corresponding problems have received much attention on single proteins and protein families, the question about how to model and compute similarity between protein complexes has not yet been systematically studied. Because protein complexes can be naturally modeled as graphs, in principle general graph similarity measures may be used, but these are often computationally hard to obtain and do not take typical properties of protein complexes into account. Here we propose a parametric family of similarity measures based on Weisfeiler-Lehman labeling. We evaluate it on simulated complexes of the extended human integrin adhesome network. Because the connectivity (graph topology) of real complexes is often unknown and hard to obtain experimentally, we use both known protein-protein interaction networks and known interdependencies (constraints) between interactions to simulate more realistic complexes than from interaction networks alone. We empirically show that the defined family of similarity measures is in good agreement with edit similarity, a similarity measure derived from graph edit distance, but can be much more efficiently computed. It can therefore be used in large-scale studies and simulations and serve as a basis for further refinements of modeling protein complex similarity.

scholarly journals Towards a systematic characterization of protein complex function: a natural language processing and machine-learning framework

2021 ◽  
Author(s):  
Varun S. Sharma ◽  
Andrea Fossati ◽  
Rodolfo Ciuffa ◽  
Marija Buljan ◽  
Evan G. Williams ◽  
...  
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Protein Complex ◽  
Large Scale ◽  
Protein Complexes ◽  
Complex Function ◽  
Word Embedding ◽  
Biological Processes ◽  
Go Terms

SummaryIt is a general assumption of molecular biology that the ensemble of expressed molecules, their activities and interactions determine biological processes, cellular states and phenotypes. Quantitative abundance of transcripts, proteins and metabolites are now routinely measured with considerable depth via an array of “OMICS” technologies, and recently a number of methods have also been introduced for the parallel analysis of the abundance, subunit composition and cell state specific changes of protein complexes. In comparison to the measurement of the molecular entities in a cell, the determination of their function remains experimentally challenging and labor-intensive. This holds particularly true for determining the function of protein complexes, which constitute the core functional assemblies of the cell. Therefore, the tremendous progress in multi-layer molecular profiling has been slow to translate into increased functional understanding of biological processes, cellular states and phenotypes. In this study we describe PCfun, a computational framework for the systematic annotation of protein complex function using Gene Ontology (GO) terms. This work is built upon the use of word embedding— natural language text embedded into continuous vector space that preserves semantic relationships— generated from the machine reading of 1 million open access PubMed Central articles. PCfun leverages the embedding for rapid annotation of protein complex function by integrating two approaches: (1) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector, and (2) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing the statistical test for the enrichment of the top NN GO terms within the child terms of the predicted GO terms by RF models. Thus, PCfun amalgamates information learned from the gold-standard protein-complex database, CORUM, with the unbiased predictions obtained directly from the word embedding, thereby enabling PCfun to identify the potential functions of putative protein complexes. The documentation and examples of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.

scholarly journals Rapid profiling of protein complex re-organization in perturbed systems

2021 ◽  
Author(s):  
Isabell Bludau ◽  
Charlotte Nicod ◽  
Claudia Martelli ◽  
Peng Xue ◽  
Moritz Heusel ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Protein Complex ◽  
Protein Complexes ◽  
Experimental Designs ◽  
Biological Information ◽  
Condition Numbers ◽  
Complex Organization ◽  
Biological Insight ◽  
Large Measurement

Protein complexes constitute the primary functional modules of cellular activity. To respond to perturbations, complexes undergo changes in their abundance, subunit composition or state of modification. Understanding the function of biological systems requires global strategies to capture this contextual state information on protein complexes and interaction networks. Methods based on co-fractionation paired with mass spectrometry have demonstrated the capability for deep biological insight but the scope of studies using this approach has been limited by the large measurement time per biological sample and challenges with data analysis. As such, there has been little uptake of this strategy beyond a few expert labs into the broader life science community despite rich biological information content. We present a rapid integrated experimental and computational workflow to assess the re-organization of protein complexes across multiple cellular states. It enables complex experimental designs requiring increased sample/condition numbers. The workflow combines short gradient chromatography and DIA/SWATH mass spectrometry with a data analysis toolset to quantify changes in complex organization. We applied the workflow to study the global protein complex rearrangements of THP-1 cells undergoing monocyte to macrophage differentiation and a subsequent stimulation of macrophage cells with lipopolysaccharide. We observed massive proteome organization in functions related to signaling, cell adhesion, and extracellular matrix during differentiation, and less pronounced changes in processes related to innate immune response induced by the macrophage stimulation. We therefore establish our integrated differential pipeline for rapid and state-specific profiling of protein complex organization with broad utility in complex experimental designs.

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