Recent advances in large-scale protein interactome mapping

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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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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Mining Protein Interactome Networks to Measure Interaction Reliability and Select Hub Proteins

Computational Knowledge Discovery for Bioinformatics Research ◽

10.4018/978-1-4666-1785-8.ch013 ◽

2013 ◽

pp. 222-238

Author(s):

Young-Rae Cho ◽

Aidong Zhang

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Functional Characterization ◽

Flow Simulation ◽

Protein Protein Interactions ◽

Systematic Analysis ◽

Graph Theoretic ◽

Interactome Network ◽

Protein Interactome ◽

Hub Proteins

High-throughput techniques involve large-scale detection of protein-protein interactions. This interaction data set from the genome-scale perspective is structured into an interactome network. Since the interaction evidence represents functional linkage, various graph-theoretic computational approaches have been applied to the interactome networks for functional characterization. However, this data is generally unreliable, and the typical genome-wide interactome networks have a complex connectivity. In this paper, the authors explore systematic analysis of protein interactome networks, and propose a $k$-round signal flow simulation algorithm to measure interaction reliability from connection patterns of the interactome networks. This algorithm quantitatively characterizes functional links between proteins by simulating the propagation of information signals through complex connections. In this regard, the algorithm efficiently estimates the strength of alternative paths for each interaction. The authors also present an algorithm for mining the complex interactome network structure. The algorithm restructures the network by hierarchical ordering of nodes, and this structure re-formatting process reveals hub proteins in the interactome networks. This paper demonstrates that two rounds of simulation accurately scores interaction reliability in terms of ontological correlation and functional consistency. Finally, the authors validate that the selected structural hubs represent functional core proteins.

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Conserved Central Intraviral Protein Interactome of the Herpesviridae Family

mSystems ◽

10.1128/msystems.00295-19 ◽

2019 ◽

Vol 4 (5) ◽

Cited By ~ 1

Author(s):

Anna Hernández Durán ◽

Kay Grünewald ◽

Maya Topf

Keyword(s):

Protein Interactions ◽

Computational Models ◽

Functional Organization ◽

Driving Forces ◽

Protein Complexes ◽

Structural Features ◽

System Level ◽

Protein Protein Interactions ◽

Protein Interactome ◽

Essential Components

ABSTRACT Protein interactions are major driving forces behind the functional phenotypes of biological processes. As such, evolutionary footprints are reflected in system-level collections of protein-protein interactions (PPIs), i.e., protein interactomes. We conducted a comparative analysis of intraviral protein interactomes for representative species of each of the three subfamilies of herpesviruses (herpes simplex virus 1, human cytomegalovirus, and Epstein-Barr virus), which are highly prevalent etiologic agents of important human diseases. The intraviral interactomes were reconstructed by combining experimentally supported and computationally predicted protein-protein interactions. Using cross-species network comparison, we then identified family-wise conserved interactions and protein complexes, which we defined as a herpesviral “central” intraviral protein interactome. A large number of widely accepted conserved herpesviral protein complexes are present in this central intraviral interactome, encouragingly supporting the biological coherence of our results. Importantly, these protein complexes represent most, if not all, of the essential steps required during a productive life cycle. Hence the central intraviral protein interactome could plausibly represent a minimal infectious interactome of the herpesvirus family across a variety of hosts. Our data, which have been integrated into our herpesvirus interactomics database, HVint2.0, could assist in creating comprehensive system-level computational models of this viral lineage. IMPORTANCE Herpesviruses are an important socioeconomic burden for both humans and livestock. Throughout their long evolutionary history, individual herpesvirus species have developed remarkable host specificity, while collectively the Herpesviridae family has evolved to infect a large variety of eukaryotic hosts. The development of approaches to fight herpesvirus infections has been hampered by the complexity of herpesviruses’ genomes, proteomes, and structural features. The data and insights generated by our study add to the understanding of the functional organization of herpesvirus-encoded proteins, specifically of family-wise conserved features defining essential components required for a productive infectious cycle across different hosts, which can contribute toward the conceptualization of antiherpetic infection strategies with an effect on a broader range of target species. All of the generated data have been made freely available through our HVint2.0 database, a dedicated resource of curated herpesvirus interactomics purposely created to promote and assist future studies in the field.

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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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Mass spectrometry-based methods for analysing the mitochondrial interactome in mammalian cells

The Journal of Biochemistry ◽

10.1093/jb/mvz090 ◽

2019 ◽

Vol 167 (3) ◽

pp. 225-231 ◽

Cited By ~ 2

Author(s):

Takumi Koshiba ◽

Hidetaka Kosako

Keyword(s):

Mass Spectrometry ◽

Protein Interactions ◽

Mammalian Cells ◽

Protein Complexes ◽

Living Cells ◽

Protein Protein Interactions ◽

Cellular Functions ◽

Protein Protein Interaction ◽

Membrane Protein Complexes ◽

Protein Protein Interaction Networks

Abstract Protein–protein interactions are essential biologic processes that occur at inter- and intracellular levels. To gain insight into the various complex cellular functions of these interactions, it is necessary to assess them under physiologic conditions. Recent advances in various proteomic technologies allow to investigate protein–protein interaction networks in living cells. The combination of proximity-dependent labelling and chemical cross-linking will greatly enhance our understanding of multi-protein complexes that are difficult to prepare, such as organelle-bound membrane proteins. In this review, we describe our current understanding of mass spectrometry-based proteomics mapping methods for elucidating organelle-bound membrane protein complexes in living cells, with a focus on protein–protein interactions in mitochondrial subcellular compartments.

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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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iSUMO - integrative prediction of functionally relevant SUMOylation events

10.1101/056564 ◽

2016 ◽

Author(s):

Xiaotong Yao ◽

Shuvadeep Maity ◽

Shashank Gandhi ◽

Marcin Imielenski ◽

Christine Vogel

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Rna Binding ◽

Rna Binding Proteins ◽

False Positive Rate ◽

Protein Protein Interactions ◽

Cellular Functions ◽

Positive Rate ◽

Protein Nucleic Acid ◽

Scale Experiment

AbstractPost-translational modifications by the Small Ubiquitin-like Modifier (SUMO) are essential for diverse cellular functions. Large-scale experiment and sequence-based predictions have identified thousands of SUMOylated proteins. However, the overlap between the datasets is small, suggesting many false positives with low functional relevance. Therefore, we integrated ~800 sequence features and protein characteristics such as cellular function and protein-protein interactions in a machine learning approach to score likely functional SUMOylation events (iSUMO). iSUMO is trained on a total of 24 large-scale datasets, and it predicts 2,291 and 706 SUMO targets in human and yeast, respectively. These estimates are five times higher than what existing sequence-based tools predict at the same 5% false positive rate. Protein-protein and protein-nucleic acid interactions are highly predictive of protein SUMOylation, supporting a role of the modification in protein complex formation. We note the marked prevalence of SUMOylation amongst RNA-binding proteins. We validate iSUMO predictions by experimental or other evidence. iSUMO therefore represents a comprehensive tool to identify high-confidence, functional SUMOylation events for human and yeast.

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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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Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling

10.1101/2021.09.14.460228 ◽

2021 ◽

Author(s):

Jimin Pei ◽

Jing Zhang ◽

Qian Cong

Keyword(s):

Deep Learning ◽

Protein Interactions ◽

Large Scale ◽

Protein Complexes ◽

Mitochondrial Protein ◽

Protein Structures ◽

Complex Structures ◽

Protein Protein Interactions ◽

Learning Methods ◽

Contact Probability

AbstractRecent development of deep-learning methods has led to a breakthrough in the prediction accuracy of 3-dimensional protein structures. Extending these methods to protein pairs is expected to allow large-scale detection of protein-protein interactions and modeling protein complexes at the proteome level. We applied RoseTTAFold and AlphaFold2, two of the latest deep-learning methods for structure predictions, to analyze coevolution of human proteins residing in mitochondria, an organelle of vital importance in many cellular processes including energy production, metabolism, cell death, and antiviral response. Variations in mitochondrial proteins have been linked to a plethora of human diseases and genetic conditions. RoseTTAFold, with high computational speed, was used to predict the coevolution of about 95% of mitochondrial protein pairs. Top-ranked pairs were further subject to the modeling of the complex structures by AlphaFold2, which also produced contact probability with high precision and in many cases consistent with RoseTTAFold. Most of the top ranked pairs with high contact probability were supported by known protein-protein interactions and/or similarities to experimental structural complexes. For high-scoring pairs without experimental complex structures, our coevolution analyses and structural models shed light on the details of their interfaces, including CHCHD4-AIFM1, MTERF3-TRUB2, FMC1-ATPAF2, ECSIT-NDUFAF1 and COQ7-COQ9, among others. We also identified novel PPIs (PYURF-NDUFAF5, LYRM1-MTRF1L and COA8-COX10) for several proteins without experimentally characterized interaction partners, leading to predictions of their molecular functions and the biological processes they are involved in.

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Reliable identification of protein-protein interactions by crosslinking mass spectrometry

10.1101/2020.05.25.114256 ◽

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.

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