Analysis of protein-network formation of different vegetable proteins during emulsification to produce solid fat substitutes

AbstractPlant-based emulsion gels can be used as solid animal fat substitutes for vegan sausages. For this reason, commercially available protein isolates with different amino acid profiles from pea, soy and potato (Pea-1, Pea-2, Soy, Potato) have been tested for their ability to form shape stable emulsions gels at neutral pH and upon heating to 72 °C. In order to obtain emulsion gels that are as solid as possible, the protein concentrations in the continuous phase (CPC, 8.0–11.5% (w/w)) and the oil mass fractions (65–80%) were varied. For leguminous proteins, a positive correlation of both parameters on emulsion rigidity was shown, indicating that both, interfacial and protein–protein interactions, are involved in structure reinforcement. Firmness increased with increasing content in cysteine (Pea-1 < Pea-2 < Soy) and the interactions were of electrostatic, hydrophobic and hydrophilic nature. Potato emulsion rigidity was independent of CPC and oil content. The emulsions showed a much higher degree in crosslinking, and very low charge density. Temperature-sweep analysis and CLSM revealed that Potato protein gelled as consequence to low temperature stability. Hence, the structure reinforcement in Potato emulsions mainly contributed to the protein network, with 70% oil and CPC 11.5% forming a hybrid gel with highest firmness. However, gelling of Potato protein also resulted in interfacial adsorption of protein aggregates and reduced interfacial stability with increasing CPC. This was demonstrated in the amount of extractable fat which was 2.0 and 0.6% for Pea-1 and 2 emulsions, 6.4% for Soy and 34.4% of total fat for Potato emulsions.

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Quantification of protein-protein interactions in highly denatured whey and potato protein gels

MethodsX ◽

10.1016/j.mex.2021.101243 ◽

2021 ◽

Vol 8 ◽

pp. 101243

Author(s):

Caren Tanger ◽

David J. Andlinger ◽

Annette Brümmer-Rolf ◽

Julia Engel ◽

Ulrich Kulozik

Keyword(s):

Protein Interactions ◽

Protein Protein Interactions ◽

Potato Protein ◽

Protein Gels

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UniProt-Related Documents (UniReD): assisting wet lab biologists in their quest on finding novel counterparts in a protein network

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqaa005 ◽

2020 ◽

Vol 2 (1) ◽

Cited By ~ 1

Author(s):

Theodosios Theodosiou ◽

Nikolaos Papanikolaou ◽

Maria Savvaki ◽

Giulia Bonetto ◽

Stella Maxouri ◽

...

Keyword(s):

Protein Interactions ◽

Computational Prediction ◽

Biomedical Literature ◽

Protein Network ◽

Protein Protein Interactions ◽

High Coverage ◽

Depth Study ◽

Experimental Approaches ◽

Wet Lab ◽

User Friendly

Abstract The in-depth study of protein–protein interactions (PPIs) is of key importance for understanding how cells operate. Therefore, in the past few years, many experimental as well as computational approaches have been developed for the identification and discovery of such interactions. Here, we present UniReD, a user-friendly, computational prediction tool which analyses biomedical literature in order to extract known protein associations and suggest undocumented ones. As a proof of concept, we demonstrate its usefulness by experimentally validating six predicted interactions and by benchmarking it against public databases of experimentally validated PPIs succeeding a high coverage. We believe that UniReD can become an important and intuitive resource for experimental biologists in their quest for finding novel associations within a protein network and a useful tool to complement experimental approaches (e.g. mass spectrometry) by producing sorted lists of candidate proteins for further experimental validation. UniReD is available at http://bioinformatics.med.uoc.gr/unired/

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Network Protein Interaction in Parkinson's Disease and Periodontitis Interplay: A Bioinformatic Analysis

10.20944/preprints202009.0050.v1 ◽

2020 ◽

Author(s):

João Botelho ◽

Paulo Mascarenhas ◽

José João Mendes ◽

Vanessa Machado

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Molecular Mechanisms ◽

Interaction Analysis ◽

Bioinformatic Analysis ◽

Comprehensive Analysis ◽

Protein Network ◽

Protein Protein Interactions ◽

Network Interaction ◽

Genes Encoding

Recent studies supported a clinical association between Parkinson’s Disease (PD) and periodontitis. Hence, investigating possible protein interactions between these two conditions is of interest. In this study, we conducted a protein-protein network interaction analysis with recognized genes encoding proteins for PD and periodontitis. Genes of interest were collected via GWAS database. Then, we conducted a protein interaction analysis using STRING database, with a highest confidence cut-off of 0.9. Our protein network casted a comprehensive analysis of potential protein-protein interactions between PD and periodontitis. This analysis may underpin valuable information for new candidate molecular mechanisms between PD and periodontitis and may serve new potential targets for research purposes. These results should be carefully interpreted giving the limitations of this approach.

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Proteomics of host-bacterial interactions: new insights from dual perspectives

Canadian Journal of Microbiology ◽

10.1139/cjm-2020-0324 ◽

2020 ◽

Author(s):

Arjun Sukumaran ◽

Elizabeth Woroszchuk ◽

Taylor Ross ◽

Jennifer Geddes-McAlister

Keyword(s):

Mass Spectrometry ◽

Protein Interactions ◽

Bacterial Infections ◽

Cell Activation ◽

Network Formation ◽

Biological Systems ◽

Protective Measure ◽

Protein Protein Interactions ◽

Novel Therapeutic Strategies ◽

Bacterial Effectors

Mass spectrometry-based proteomics is a powerful and robust platform for studying the interactions between biological systems during health and disease. Bacterial infections represent a significant threat to global health and drive the pursuit of novel therapeutic strategies to combat emerging and resistant pathogens. During infection, the interplay between a host and pathogen determines the ability of the microbe to survive in a hostile environment and promotes an immune response by the host as a protective measure. It is the protein-level changes from either biological system that define the outcome of infection, and mass spectrometry-based proteomics provides a rapid and effective platform to identify such changes. In particular, proteomics detects alterations in protein abundance, quantifies protein secretion and/or release, measures an array of post-translational modifications that influence signaling cascades, and profiles protein-protein interactions through protein complex and/or network formation. Such information provides new insight into the role of known and novel bacterial effectors, as well as the outcome of host cell activation. In this Review, we highlight the diverse applications of mass spectrometry-based proteomics in profiling the relationship between bacterial pathogens and the host. Our work identifies a plethora of strategies for exploring mechanisms of infection from dual perspectives (i.e., host and pathogen) and we suggest opportunities to extrapolate the current knowledgebase to other biological systems for applications in therapeutic discovery.

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Chapman–Kolmogorov equations for global PPIs with Discriminant-EM

International Journal of Biomathematics ◽

10.1142/s1793524514500533 ◽

2014 ◽

Vol 07 (05) ◽

pp. 1450053 ◽

Cited By ~ 2

Author(s):

Md. Sarwar Kamal ◽

Mohammad Ibrahim Khan

Keyword(s):

Protein Interactions ◽

Probabilistic Approach ◽

Biological Data ◽

Protein Network ◽

Data Networks ◽

Kolmogorov Equations ◽

Protein Protein Interactions ◽

Source Codes ◽

Biology Research ◽

Random Method

Ongoing improvements in Computational Biology research have generated massive amounts of Protein–Protein Interactions (PPIs) dataset. In this regard, the availability of PPI data for several organisms provoke the discovery of computational methods for measurements, analysis, modeling, comparisons, clustering and alignments of biological data networks. Nevertheless, fixed network comparison is computationally stubborn and as a result several methods have been used instead. We illustrate a probabilistic approach among proteins nodes that are part of various networks by using Chapman–Kolmogorov (CK) formula. We have compared CK formula with semi-Markov random method, SMETANA. We significantly noticed that CK outperforms the SMETANA in all respects such as efficiency, speed, space and complexity. We have modified the SMETANA source codes available in MATLAB in the light of CK formula. Discriminant-Expectation Maximization (D-EM) accesses the parameters of a protein network datasets and determines a linear transformation to simplify the assumption of probabilistic format of data distributions and find good features dynamically. Our implementation finds that D-EM has a satisfactory performance in protein network alignment applications.

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Partial characterization of bean and maize root peroxidases and their ability to crosslink potato protein

Archives of Biological Sciences ◽

10.2298/abs181016011g ◽

2019 ◽

Vol 71 (2) ◽

pp. 293-303

Author(s):

Jovana Glusac ◽

Sivan Isaschar-Ovdat ◽

Ayelet Fishman ◽

Biljana Kukavica

Keyword(s):

Cell Wall ◽

Caffeic Acid ◽

Protein Interactions ◽

Polyacrylamide Gel Electrophoresis ◽

Sodium Dodecyl ◽

High Specificity ◽

Class Iii ◽

Protein Protein Interactions ◽

Potato Protein ◽

Molecular Weights

Two fractions of Class III peroxidases (POX; EC 1.11.1.7), soluble and ionically bound to the cell wall, were partially purified from bean and maize roots and characterized. According to the measured Km, both the soluble and ionically bound to the cell wall fractions of POX had high affinity for H2O2 and the high specificity for caffeic acid. Approximate molecular weights of POX in their tertiary (native) structure were determined by modified sodium dodecyl sulfate (SDS) polyacrylamide gel electrophoresis (PAGE). Proteomic analysis resolved the identity and pI of different enzyme bands. The ability of maize and bean soluble peroxidase to crosslink native potato proteins was evaluated. The results obtained by SDSPAGE showed that both POX enzymes were capable of crosslinking potato protein, in particular patatin, a globular protein, with and without the presence of H2O2. To investigate the possible role of phenolic compounds in facilitating crosslinking, commercial horseradish peroxidase (HRP) with/without the addition of caffeic acid was used to crosslink potato protein. Information provided here could be useful for the purification of POX from maize and bean roots and for examination of protein-protein interactions.

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Protein Network of the Pseudomonas aeruginosa Denitrification Apparatus

Journal of Bacteriology ◽

10.1128/jb.00055-16 ◽

2016 ◽

Vol 198 (9) ◽

pp. 1401-1413 ◽

Cited By ~ 35

Author(s):

José Manuel Borrero-de Acuña ◽

Manfred Rohde ◽

Josef Wissing ◽

Lothar Jänsch ◽

Max Schobert ◽

...

Keyword(s):

Nitric Oxide ◽

Electron Microscopy ◽

Pseudomonas Aeruginosa ◽

Nitrous Oxide ◽

Protein Interactions ◽

Molecular Basis ◽

Protein Network ◽

Protein Protein Interactions ◽

Content Type ◽

Enzyme Complexes

ABSTRACTOxidative phosphorylation using multiple-component, membrane-associated protein complexes is the most effective way for a cell to generate energy. Here, we systematically investigated the multiple protein-protein interactions of the denitrification apparatus of the pathogenic bacteriumPseudomonas aeruginosa. During denitrification, nitrate (Nar), nitrite (Nir), nitric oxide (Nor), and nitrous oxide (Nos) reductases catalyze the reaction cascade of NO3−→ NO2−→ NO → N2O → N2. Genetic experiments suggested that the nitric oxide reductase NorBC and the regulatory protein NosR are the nucleus of the denitrification protein network. We utilized membrane interactomics in combination with electron microscopy colocalization studies to elucidate the corresponding protein-protein interactions. The integral membrane proteins NorC, NorB, and NosR form the core assembly platform that binds the nitrate reductase NarGHI and the periplasmic nitrite reductase NirS via its maturation factor NirF. The periplasmic nitrous oxide reductase NosZ is linked via NosR. The nitrate transporter NarK2, the nitrate regulatory system NarXL, various nitrite reductase maturation proteins, NirEJMNQ, and the Nos assembly lipoproteins NosFL were also found to be attached. A number of proteins associated with energy generation, including electron-donating dehydrogenases, the complete ATP synthase, almost all enzymes of the tricarboxylic acid (TCA) cycle, and the Sec system of protein transport, among many other proteins, were found to interact with the denitrification proteins. This deduced nitrate respirasome is presumably only one part of an extensive cytoplasmic membrane-anchored protein network connecting cytoplasmic, inner membrane, and periplasmic proteins to mediate key activities occurring at the barrier/interface between the cytoplasm and the external environment.IMPORTANCEThe processes of cellular energy generation are catalyzed by large multiprotein enzyme complexes. The molecular basis for the interaction of these complexes is poorly understood. We employed membrane interactomics and electron microscopy to determine the protein-protein interactions involved. The well-investigated enzyme complexes of denitrification of the pathogenic bacteriumPseudomonas aeruginosaserved as a model. Denitrification is one essential step of the universal N cycle and provides the bacterium with an effective alternative to oxygen respiration. This process allows the bacterium to form biofilms, which create low-oxygen habitats and which are a key in the infection mechanism. Our results provide new insights into the molecular basis of respiration, as well as opening a new window into the infection strategies of this pathogen.

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Predicting protein subcellular location using learned distributed representations from a protein-protein network

10.1101/768739 ◽

2019 ◽

Author(s):

Xiaoyong Pan ◽

Lei Chen ◽

Min Liu ◽

Tao Huang ◽

Yu-Dong Cai

Keyword(s):

Protein Interactions ◽

Class Imbalance ◽

Sampling Technique ◽

Subcellular Location ◽

Protein Network ◽

Interacting Proteins ◽

Protein Protein Interactions ◽

Distributed Representations ◽

Protein Subcellular Location ◽

Protein Subcellular Locations

AbstractFunctions of proteins are in general related to their subcellular locations. To identify the functions of a protein, we first need know where this protein is located. Interacting proteins tend to locate in the same subcellular location. Thus, it is imperative to take the protein-protein interactions into account for computational identification of protein subcellular locations.In this study, we present a deep learning-based method, node2loc, to predict protein subcellular location. node2loc first learns distributed representations of proteins in a protein-protein network using node2vec, which acquires representations from unlabeled data for downstream tasks. Then the learned representations are further fed into a recurrent neural network (RNN) to predict subcellular locations. Considering the severe class imbalance of different subcellular locations, Synthetic Minority Over-sampling Technique (SMOTE) is applied to artificially boost subcellular locations with few proteins.We construct a benchmark dataset with 16 subcellular locations and evaluate node2loc on this dataset. node2loc yields a Matthews correlation coefficient (MCC) value of 0.812, which outperforms other baseline methods. The results demonstrate that the learned presentations from a protein-protein network have strong discriminate ability for classifying protein subcellular locations and the RNN is a more powerful classifier than traditional machine learning models. node2loc is freely available at https://github.com/xypan1232/node2loc.

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