Tumor relevant protein functional interactions identified using bipartite graph analyses

AbstractAn increased surge of -omics data for the diseases such as cancer allows for deriving insights into the affiliated protein interactions. We used bipartite network principles to build protein functional associations of the differentially regulated genes in 18 cancer types. This approach allowed us to combine expression data to functional associations in many cancers simultaneously. Further, graph centrality measures suggested the importance of upregulated genes such as BIRC5, UBE2C, BUB1B, KIF20A and PTH1R in cancer. Pathway analysis of the high centrality network nodes suggested the importance of the upregulation of cell cycle and replication associated proteins in cancer. Some of the downregulated high centrality proteins include actins, myosins and ATPase subunits. Among the transcription factors, mini-chromosome maintenance proteins (MCMs) and E2F family proteins appeared prominently in regulating many differentially regulated genes. The projected unipartite networks of the up and downregulated genes were comprised of 37,411 and 41,756 interactions, respectively. The conclusions obtained by collating these interactions revealed pan-cancer as well as subtype specific protein complexes and clusters. Therefore, we demonstrate that incorporating expression data from multiple cancers into bipartite graphs validates existing cancer associated mechanisms as well as directs to novel interactions and pathways.

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Metallocluster transactions: dynamic protein interactions guide the biosynthesis of Fe–S clusters in bacteria

Biochemical Society Transactions ◽

10.1042/bst20180365 ◽

2018 ◽

Vol 46 (6) ◽

pp. 1593-1603 ◽

Cited By ~ 9

Author(s):

Chenkang Zheng ◽

Patricia C. Dos Santos

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Specific Protein ◽

Biological Synthesis ◽

Cluster Assembly ◽

Sequence Motifs ◽

Protein Protein Interactions ◽

Cysteine Desulfurase ◽

Wide Range ◽

Domains Of Life

Iron–sulfur (Fe–S) clusters are ubiquitous cofactors present in all domains of life. The chemistries catalyzed by these inorganic cofactors are diverse and their associated enzymes are involved in many cellular processes. Despite the wide range of structures reported for Fe–S clusters inserted into proteins, the biological synthesis of all Fe–S clusters starts with the assembly of simple units of 2Fe–2S and 4Fe–4S clusters. Several systems have been associated with the formation of Fe–S clusters in bacteria with varying phylogenetic origins and number of biosynthetic and regulatory components. All systems, however, construct Fe–S clusters through a similar biosynthetic scheme involving three main steps: (1) sulfur activation by a cysteine desulfurase, (2) cluster assembly by a scaffold protein, and (3) guided delivery of Fe–S units to either final acceptors or biosynthetic enzymes involved in the formation of complex metalloclusters. Another unifying feature on the biological formation of Fe–S clusters in bacteria is that these systems are tightly regulated by a network of protein interactions. Thus, the formation of transient protein complexes among biosynthetic components allows for the direct transfer of reactive sulfur and Fe–S intermediates preventing oxygen damage and reactions with non-physiological targets. Recent studies revealed the importance of reciprocal signature sequence motifs that enable specific protein–protein interactions and consequently guide the transactions between physiological donors and acceptors. Such findings provide insights into strategies used by bacteria to regulate the flow of reactive intermediates and provide protein barcodes to uncover yet-unidentified cellular components involved in Fe–S metabolism.

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The Maturation Pathway of Nickel Urease

Inorganics ◽

10.3390/inorganics7070085 ◽

2019 ◽

Vol 7 (7) ◽

pp. 85 ◽

Cited By ~ 3

Author(s):

Yap Shing Nim ◽

Kam-Bo Wong

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Toxic Metal ◽

Specific Protein ◽

Accessory Proteins ◽

Protein Protein Interactions ◽

Nickel Ions ◽

Hydrogenase Maturation ◽

Maturation Factor

Maturation of urease involves post-translational insertion of nickel ions to form an active site with a carbamylated lysine ligand and is assisted by urease accessory proteins UreD, UreE, UreF and UreG. Here, we review our current understandings on how these urease accessory proteins facilitate the urease maturation. The urease maturation pathway involves the transfer of Ni2+ from UreE → UreG → UreF/UreD → urease. To avoid the release of the toxic metal to the cytoplasm, Ni2+ is transferred from one urease accessory protein to another through specific protein–protein interactions. One central theme depicts the role of guanosine triphosphate (GTP) binding/hydrolysis in regulating the binding/release of nickel ions and the formation of the protein complexes. The urease and [NiFe]-hydrogenase maturation pathways cross-talk with each other as UreE receives Ni2+ from hydrogenase maturation factor HypA. Finally, the druggability of the urease maturation pathway is reviewed.

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Inferring interaction partners from protein sequences

10.1101/050732 ◽

2016 ◽

Cited By ~ 2

Author(s):

Anne-Florence Bitbol ◽

Robert S. Dwyer ◽

Lucy J. Colwell ◽

Ned S. Wingreen

Keyword(s):

Protein Interactions ◽

Sequence Data ◽

Protein Complexes ◽

A Priori ◽

Specific Interaction ◽

Three Dimensional ◽

Specific Protein ◽

Protein Protein Interactions ◽

Entropy Model ◽

Interaction Partners

Specific protein-protein interactions are crucial in the cell, both to ensure the formation and stability of multi-protein complexes, and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners. Hence, the sequences of interacting partners are correlated. Here we exploit these correlations to accurately identify which proteins are specific interaction partners from sequence data alone. Our general approach, which employs a pairwise maximum entropy model to infer direct couplings between residues, has been successfully used to predict the three-dimensional structures of proteins from sequences. Building on this approach, we introduce an iterative algorithm to predict specific interaction partners from among the members of two protein families. We assess the algorithm's performance on histidine kinases and response regulators from bacterial two-component signaling systems. The algorithm proves successful without any a priori knowledge of interaction partners, yielding a striking 0.93 true positive fraction on our complete dataset, and we uncover the origin of this surprising success. Finally, we discuss how our method could be used to predict novel protein-protein interactions.

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Recent advances in large-scale protein interactome mapping

F1000Research ◽

10.12688/f1000research.7629.1 ◽

2016 ◽

Vol 5 ◽

pp. 782 ◽

Cited By ~ 23

Author(s):

Virja Mehta ◽

Laura Trinkle-Mulcahy

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Protein Complexes ◽

Specific Protein ◽

Quality Data ◽

Protein Protein Interactions ◽

Cellular Functions ◽

Biological Studies ◽

Protein Interactome ◽

Literature Curation

Protein-protein interactions (PPIs) underlie most, if not all, cellular functions. The comprehensive mapping of these complex networks of stable and transient associations thus remains a key goal, both for systems biology-based initiatives (where it can be combined with other ‘omics’ data to gain a better understanding of functional pathways and networks) and for focused biological studies. Despite the significant challenges of such an undertaking, major strides have been made over the past few years. They include improvements in the computation prediction of PPIs and the literature curation of low-throughput studies of specific protein complexes, but also an increase in the deposition of high-quality data from non-biased high-throughput experimental PPI mapping strategies into publicly available databases.

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Inferring interaction partners from protein sequences using mutual information

10.1101/378042 ◽

2018 ◽

Author(s):

Anne-Florence Bitbol

Keyword(s):

Mutual Information ◽

Protein Interactions ◽

Sequence Data ◽

Protein Complexes ◽

Three Dimensional ◽

Specific Protein ◽

Amino Acid Residues ◽

Protein Protein Interactions ◽

Cellular Processes ◽

Interaction Partners

AbstractSpecific protein-protein interactions are crucial in most cellular processes. They enable multiprotein complexes to assemble and to remain stable, and they allow signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interacting partners, and thus in correlations between their sequences. Pairwise maximum-entropy based models have enabled successful inference of pairs of amino-acid residues that are in contact in the three-dimensional structure of multi-protein complexes, starting from the correlations in the sequence data of known interaction partners. Recently, algorithms inspired by these methods have been developed to identify which proteins are specific interaction partners among the paralogous proteins of two families, starting from sequence data alone. Here, we demonstrate that a slightly higher performance for partner identification can be reached by an approximate maximization of the mutual information between the sequence alignments of the two protein families. This stands in contrast with structure prediction of proteins and of multiprotein complexes from sequence data, where pairwise maximum-entropy based global statistical models substantially improve performance compared to mutual information. Our findings entail that the statistical dependences allowing interaction partner prediction from sequence data are not restricted to the residue pairs that are in direct contact at the interface between the partner proteins.Author summarySpecific protein-protein interactions are at the heart of most intra-cellular processes. Mapping these interactions is thus crucial to a systems-level understanding of cells, and has broad applications to areas such as drug targeting. Systematic experimental identification of protein interaction partners is still challenging. However, a large and rapidly growing amount of sequence data is now available. Recently, algorithms have been proposed to identify which proteins interact from their sequences alone, thanks to the co-variation of the sequences of interacting proteins. These algorithms build upon inference methods that have been used with success to predict the three-dimensional structures of proteins and multi-protein complexes, and their focus is on the amino-acid residues that are in direct contact. Here, we propose a simpler method to identify which proteins interact among the paralogous proteins of two families, starting from their sequences alone. Our method relies on an approximate maximization of mutual information between the sequences of the two families, without specifically emphasizing the contacting residue pairs. We demonstrate that this method slightly outperforms the earlier one. This result highlights that partner prediction does not only rely on the identities and interactions of directly contacting amino-acids.

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Proximity labeling of protein complexes and cell-type-specific organellar proteomes in Arabidopsis enabled by TurboID

eLife ◽

10.7554/elife.47864 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 22

Author(s):

Andrea Mair ◽

Shou-Ling Xu ◽

Tess C Branon ◽

Alice Y Ting ◽

Dominique C Bergmann

Keyword(s):

Transcription Factor ◽

Protein Interactions ◽

Protein Complexes ◽

Cell Types ◽

Specific Protein ◽

Cell Type ◽

Subcellular Compartment ◽

Cellular Processes ◽

Highly Active

Defining specific protein interactions and spatially or temporally restricted local proteomes improves our understanding of all cellular processes, but obtaining such data is challenging, especially for rare proteins, cell types, or events. Proximity labeling enables discovery of protein neighborhoods defining functional complexes and/or organellar protein compositions. Recent technological improvements, namely two highly active biotin ligase variants (TurboID and miniTurbo), allowed us to address two challenging questions in plants: (1) what are in vivo partners of a low abundant key developmental transcription factor and (2) what is the nuclear proteome of a rare cell type? Proteins identified with FAMA-TurboID include known interactors of this stomatal transcription factor and novel proteins that could facilitate its activator and repressor functions. Directing TurboID to stomatal nuclei enabled purification of cell type- and subcellular compartment-specific proteins. Broad tests of TurboID and miniTurbo in Arabidopsis and Nicotiana benthamiana and versatile vectors enable customization by plant researchers.

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Stitching the synapse: Cross-linking mass spectrometry into resolving synaptic protein interactions

Science Advances ◽

10.1126/sciadv.aax5783 ◽

2020 ◽

Vol 6 (8) ◽

pp. eaax5783 ◽

Cited By ~ 10

Author(s):

M. A. Gonzalez-Lozano ◽

F. Koopmans ◽

P. F. Sullivan ◽

J. Protze ◽

G. Krause ◽

...

Keyword(s):

Mass Spectrometry ◽

Protein Interactions ◽

Protein Complexes ◽

Conformational Dynamics ◽

Synaptic Proteins ◽

Cross Linking ◽

Synaptic Protein ◽

Protein Partners ◽

Associated Proteins ◽

Model Protein

Synaptic transmission is the predominant form of communication in the brain. It requires functionally specialized molecular machineries constituted by thousands of interacting synaptic proteins. Here, we made use of recent advances in cross-linking mass spectrometry (XL-MS) in combination with biochemical and computational approaches to reveal the architecture and assembly of synaptic protein complexes from mouse brain hippocampus and cerebellum. We obtained 11,999 unique lysine-lysine cross-links, comprising connections within and between 2362 proteins. This extensive collection was the basis to identify novel protein partners, to model protein conformational dynamics, and to delineate within and between protein interactions of main synaptic constituents, such as Camk2, the AMPA-type glutamate receptor, and associated proteins. Using XL-MS, we generated a protein interaction resource that we made easily accessible via a web-based platform (http://xlink.cncr.nl) to provide new entries into exploration of all protein interactions identified.

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Contacts-based prediction of binding affinity in protein–protein complexes

eLife ◽

10.7554/elife.07454 ◽

2015 ◽

Vol 4 ◽

Cited By ~ 119

Author(s):

Anna Vangone ◽

Alexandre MJJ Bonvin

Keyword(s):

Binding Affinity ◽

Conformational Changes ◽

Protein Interactions ◽

Prediction Accuracy ◽

Protein Complexes ◽

Structural Features ◽

Specific Protein ◽

Strong Impact ◽

Protein Protein Interactions ◽

Almost All

Almost all critical functions in cells rely on specific protein–protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein–protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein–protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.

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

10.1101/576074 ◽

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.

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Proximity labeling of protein complexes and cell type-specific organellar proteomes in Arabidopsis enabled by TurboID

10.1101/629675 ◽

2019 ◽

Cited By ~ 3

Author(s):

Andrea Mair ◽

Shou-Ling Xu ◽

Tess C. Branon ◽

Alice Y. Ting ◽

Dominique C. Bergmann

Keyword(s):

Transcription Factor ◽

Protein Interactions ◽

Protein Complexes ◽

Cell Types ◽

Specific Protein ◽

Cell Type ◽

Subcellular Compartment ◽

Cellular Processes ◽

Highly Active

AbstractDefining specific protein interactions and spatially or temporally restricted local proteomes improves our understanding of all cellular processes, but obtaining such data is challenging, especially for rare proteins, cell types, or events. Proximity labeling enables discovery of protein neighborhoods defining functional complexes and/or organellar protein compositions. Recent technological improvements, namely two highly active biotin ligase variants (TurboID and miniTurboID), allowed us to address two challenging questions in plants: (1) what are in vivo partners of a low abundant key developmental transcription factor and (2) what is the nuclear proteome of a rare cell type? Proteins identified with FAMA-TurboID include known interactors of this stomatal transcription factor and novel proteins that could facilitate its activator and repressor functions. Directing TurboID to stomatal nuclei enabled purification of cell type- and subcellular compartment-specific proteins. Broad tests of TurboID and miniTurboID in Arabidopsis and N. benthamiana and versatile vectors enable customization by plant researchers.

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