Studying post-translational modifications with protein interaction networks

doi:10.1016/j.sbi.2013.11.009

Current Opinion in Structural Biology

Volume 24, February 2014, Pages 34-44

https://doi.org/10.1016/j.sbi.2013.11.009 Get rights and content

Highlights

•
Compendium of recent HTP PPI datasets; with a focus on PTM-mediated signaling.
•
Distinct interactome strategies tackle specific biologically focused questions.
•
Combined interaction and PTM measurements reveal conditional interaction patterns.
•
PPI networks prove useful for the functional dissection of PTMs.

At least 46 interactome studies, broad at proteome scale or biologically more focused, have together mapped about 75,000 human protein–protein interactions (PPIs). Many of the studies addressed local interactome data paucity analyzing specific homeostatic and regulatory systems, with recent focus demonstrating the involvement of post-translational protein modification (PTM) enzyme families in a wide range of cellular functions. These datasets provided insight into binding mechanisms, the dynamic modularity of complexes or delineated combinatorial enzymatic cascades. Furthermore, the combined study of PPI and PTM dynamics has begun to reveal conditional rewiring of molecular networks through PTM-mediated recognition events. Taken together these studies highlight the utility of local and global interaction networks to functionally prioritize the many changing PTMs mapped in human cells.

Introduction

Most cellular functions are in large part driven by the coordinated action of multiple macro-molecular assemblies of interacting protein subunits. Defining the molecular architecture of how these individual protein building blocks interact is a major task fundamental to a better understanding of cellular processes in health and disease [1, 2]. Both broad and focused protein–protein interaction (PPI) studies have recently dented interactome data paucity and provided novel insight into a diverse array of cellular systems. Yet, the mapping of conditional interactions, that is, interactions that are strengthened or loosened under specific conditions and thus change with changing conditions, has just started [3]. Here, we collect recent systematically generated human interaction data: focused studies that lead to functional or mechanistic insights as well as broad, proteome wide PPI data resources. We further point out that interaction approaches are particularly useful in understanding of post-translational modification (PTM)-mediated signaling, both in defining modifying enzyme relationships and in delineating PTM-dependent, conditional interactions. Finally, we highlight how collated interactome data can be used in conjugation with PTM data to extract biological signals from PTM collections and drive insight into PTM signaling.

Section snippets

Recent progress in systematic human PPI mapping

Generating datasets broad in scope is fundamental to interactome mapping, providing an increasingly better framework for further analysis. Much of the work to improve data quality focused on determining and improving the specificity of large scale PPI approaches [4, 5, 6]. Given high specificity, it is relatively low coverage large unbiased data sets suffer from. Comprehensive interactome mapping requires both search space and interaction coverage: that is, methods that scale well with the

Characterizing the PTM modifying enzyme interaction space

Many of the recent PPI sets focused on modifying enzymes, that is, kinases, phosphatases, methyltransferases, deacetylases, and E2/E3 ubiquitin ligases (Table 1). PTM systems, as defined through writer/reader/eraser/substrate components [29], are requisite for cellular functioning. Ectopic expression or activity can cause a wide variety of human diseases, reflected in the number of pharmaceuticals targeted at PTM components currently in clinical trials [30]. Despite this, the vast majority of

Conditional/PTMa-dependent protein interactions

As addressed above, mapping of interaction profiles for enzymes and regulators involved in PTM signaling has accelerated in recent years, revealing multiple novel aspects of PTM regulated biology. One functional PTM paradigm provided by small scale studies is their ability to dynamically alter interaction partner preferences in response to stimuli. These conditional interactions can either be mediated through single modification events, or through multiple modifications in short sequence space.

Conclusions

In general, systematic investigation linking specific PTMs to large scale alterations in network structure have lagged behind due to the technical challenges inherent in connecting two large scale measurements, that is, protein interaction data and protein modification data. Combining recent MS studies and literature datasets, over 100,000 modifications across more than 12,000 unique proteins have been identified in human cells. PTM data sets are difficult to normalize and interpret because of

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

• of special interest
•• of outstanding interest

References (96)

U. Stelzl et al.
The value of high quality protein–protein interaction networks for systems biology
Curr Opin Chem Biol
(2006)
M. Vidal et al.
Interactome networks and human disease
Cell
(2011)
J.R. Hutchins et al.
Systematic analysis of human protein complexes identifies chromosome segregation proteins
Science
(2010)
A. Vinayagam et al.
Repeated two-hybrid screening detects transient protein–protein interactions
Theor Chem Acc
(2010)
P. Braun
Interactome mapping for analysis of complex phenotypes: insights from benchmarking binary interaction assays
Proteomics
(2012)
R. Mosca et al.
Towards a detailed atlas of protein–protein interactions
Curr Opin Struct Biol
(2013)
M. Brehme et al.
Charting the molecular network of the drug target Bcr-Abl
Proc Natl Acad Sci U S A
(2009)
A. Meixner et al.
A QUICK screen for Lrrk2 interaction partners — leucine-rich repeat kinase 2 is involved in actin cytoskeleton dynamics
Mol Cell Proteomics
(2011)
M. Goudreault et al.
A PP2A phosphatase high density interaction network identifies a novel striatin-interacting phosphatase and kinase complex linked to the cerebral cavernous malformation 3 (CCM3) protein
Mol Cell Proteomics
(2009)
K. Husnjak et al.
Ubiquitin-binding proteins: decoders of ubiquitin-mediated cellular functions
Annu Rev Biochem
(2012)

L.M. Napolitano et al.

Functional interactions between ubiquitin E2 enzymes and TRIM proteins

Biochem J

(2011)

P. Filippakopoulos et al.

Histone recognition and large-scale structural analysis of the human bromodomain family

Cell

(2012)

N. Bisson et al.

Selected reaction monitoring mass spectrometry reveals the dynamics of signaling through the GRB2 adaptor

Nat Biotechnol

(2011)

H. Liu et al.

A method for systematic mapping of protein lysine methylation identifies functions for HP1beta in DNA damage response

Mol Cell

(2013)

Y. Zheng et al.

Temporal regulation of EGF signalling networks by the scaffold protein Shc1

Nature

(2013)

E.D. Levy et al.

Protein abundance is key to distinguish promiscuous from functional phosphorylation based on evolutionary information

Philos Trans R Soc Lond B Biol Sci

(2012)

J.V. Olsen et al.

Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis

Sci Signal

(2010)

P. Beltrao et al.

Systematic functional prioritization of protein posttranslational modifications

Cell

(2012)

P. Mertins et al.

Integrated proteomic analysis of post-translational modifications by serial enrichment

Nat Methods

(2013)

R. Bell et al.

A human protein interaction network shows conservation of aging processes between human and invertebrate species

PLoS Genet

(2009)

A.R. Mazloom et al.

Recovering protein–protein and domain–domain interactions from aggregation of IP-MS proteomics of coregulator complexes

PLoS Comput Biol

(2011)

J. Lim et al.

A protein–protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration

Cell

(2006)

J. Wang et al.

Protein interaction data set highlighted with human Ras-MAPK/PI3K signaling pathways

J Proteome Res

(2008)

L.M. Camargo et al.

Disrupted in Schizophrenia 1 Interactome: evidence for the close connectivity of risk genes and a potential synaptic basis for schizophrenia

Mol Psychiatry

(2007)

T.M. Greco et al.

Nuclear import of histone deacetylase 5 by requisite nuclear localization signal phosphorylation

Mol Cell Proteomics

(2011)

W. Li et al.

Merlin/NF2 suppresses tumorigenesis by inhibiting the E3 ubiquitin ligase CRL4(DCAF1) in the nucleus

Cell

(2010)

K. Tripsianes et al.

Structural basis for dimethylarginine recognition by the Tudor domains of human SMN and SPF30 proteins

Nat Struct Mol Biol

(2011)

T. Ideker et al.

Differential network biology

Mol Syst Biol

(2012)

K. Venkatesan et al.

An empirical framework for binary interactome mapping

Nat Methods

(2009)

H. Choi et al.

SAINT: probabilistic scoring of affinity purification-mass spectrometry data

Nat Methods

(2011)

M. Varjosalo et al.

Interlaboratory reproducibility of large-scale human protein-complex analysis by standardized AP-MS

Nat Methods

(2013)

M. Weimann et al.

A Y2H-seq approach defines the human protein methyltransferase interactome

Nat Methods

(2013)

H. Yu et al.

Next-generation sequencing to generate interactome datasets

Nat Methods

(2011)

P.C. Havugimana et al.

A census of human soluble protein complexes

Cell

(2012)

A.R. Kristensen et al.

A high-throughput approach for measuring temporal changes in the interactome

Nat Methods

(2012)

A. Malovannaya et al.

Analysis of the human endogenous coregulator complexome

Cell

(2011)

J.D. Ellis et al.

Tissue-specific alternative splicing remodels protein–protein interaction networks

Mol Cell

(2012)

B.W. Miller et al.

Application of an integrated physical and functional screening approach to identify inhibitors of the Wnt pathway

Mol Syst Biol

(2009)

M. Taipale et al.

Quantitative analysis of HSP90–client interactions reveals principles of substrate recognition

Cell

(2012)

S.J. Wodak et al.

Protein–protein interaction networks: the puzzling riches

Curr Opin Struct Biol

(2013)

R. Hosur et al.

Coev2Net: a computational framework for boosting confidence in high-throughput protein–protein interaction datasets

Genome Biol

(2012)

L. Royer et al.

Network compression as a quality measure for protein interaction networks

PLoS One

(2012)

A. Kamburov et al.

Cluster-based assessment of protein–protein interaction confidence

BMC Bioinformatics

(2012)

A. Califano et al.

Leveraging models of cell regulation and GWAS data in integrative network-based association studies

Nat Genet

(2012)

E.E. Schadt et al.

NEW: network-enabled wisdom in biology, medicine, and health care

Sci Transl Med

(2012)

K. Lee et al.

Proteome-wide discovery of mislocated proteins in cancer

Genome Res

(2013)

A. Hegele et al.

Dynamic protein–protein interaction wiring of the human spliceosome

Mol Cell

(2012)

D. Perez-Hernandez et al.

The intracellular interactome of tetraspanin-enriched microdomains reveals their function as sorting machineries toward exosomes

J Biol Chem

(2013)

Cited by (45)

Identification of serum exosomal miRNA biomarkers for diagnosis of Rheumatoid arthritis
2024, International Immunopharmacology
Rheumatoid arthritis (RA) is an autoimmune disorder characterized by inflammation-induced joint damage, which can cause lasting disability. Therefore, early diagnosis and treatment of RA are crucial. Herein, we evaluated whether exosomal microRNAs (miRNAs) could be served as promising biomarkers that can accelerate the diagnosis of RA and development of therapies for RA.
First, we performed small RNA sequencing to determine the miRNA profiles of serum exosomes within a screening cohort comprised of 18 untreated active RA patients, along with 18 age and gender-matched healthy controls (HCs). Subsequently, the miRNA profiles were then validated in a training cohort consisting of 24 RA patients and 24 HCs by RT-qPCR. Finally, the selected exosomal miRNAs were validated in a larger cohort comprising 108 RA patients and 103 HCs. The diagnostic efficacy of the exosomal miRNAs was evaluated by receiver operating characteristic (ROC) curve analysis. Biological functions of the miRNAs were determined by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses.
Our results first demonstrated a noteworthy upregulation of three candidate miRNAs (miR-885-5p, miR-6894-3p, and miR-1268a) in the RA patients’ serum exosomes compared to HCs. The combination of three miRNAs along with anti- citrullinated peptide antibodies (ACPA) exhibited excellent diagnostic accuracy, yielding an area under the curve (AUC) of 0.963 (95 % CI : 0.941–0.984), sensitivity of 87.96 %, and specificity of 93.20 %. Notably, miR-885-5p exhibited remarkable discriminatory capacity by itself in indistinguishing ACPA- negative RA patients from HCs, with an AUC of 0.993 (95 % CI : 0.978–1.000), sensitivity of 96.67 %, and specificity of 100 %. Moreover, the expression of miR-1268a in the assessment of therapeutic effectiveness displayed significant reduction on 29th day of Methotrexate (MTX) treatment in RA patients. This decreased expression paralleled with trends observed in tender 28-joint count (TJC28), swollen 28-joint count (SJC28), and disease activity score with 28-joint count using C-reactive protein (DAS28-CRP), all of which are indicative of RA disease activity. Finally, predictive analysis indicated that, these three exosomal miRNAs target pivotal signaling molecules involved in inflammatory pathways, thereby demonstrating effective modulation of the immune system.
In this study, we successfully demonstrated the promising potential for serum exosomal miRNAs, particularly miR-885-5p, miR-6894-3p and miR-1268a as biomarkers for early diagnosis and prediction of RA for the first time.
Deciphering the functional landscape of phosphosites with deep neural network
2023, Cell Reports
Current biochemical approaches have only identified the most well-characterized kinases for a tiny fraction of the phosphoproteome, and the functional assignments of phosphosites are almost negligible. Herein, we analyze the substrate preference catalyzed by a specific kinase and present a novel integrated deep neural network model named FuncPhos-SEQ for functional assignment of human proteome-level phosphosites. FuncPhos-SEQ incorporates phosphosite motif information from a protein sequence using multiple convolutional neural network (CNN) channels and network features from protein-protein interactions (PPIs) using network embedding and deep neural network (DNN) channels. These concatenated features are jointly fed into a heterogeneous feature network to prioritize functional phosphosites. Combined with a series of in vitro and cellular biochemical assays, we confirm that NADK-S48/50 phosphorylation could activate its enzymatic activity. In addition, ERK1/2 are discovered as the primary kinases responsible for NADK-S48/50 phosphorylation. Moreover, FuncPhos-SEQ is developed as an online server.
Decoding the cellular effects of genetic variation through interaction proteomics
2022, Current Opinion in Chemical Biology
Citation Excerpt :
Therefore, it is crucial to assay the direct effects of genetic variation on physical protein networks as well, with identified sets of reliable PPIs as prerequisite complementary information. Over the past 15 years, high-quality human PPIs have been recorded in systematic large-scale approaches, including yeast two-hybrid (Y2H), affinity purification coupled to mass spectrometry (AP-MS), co-fractionation coupled to mass spectrometry (CF-MS) and proximity-dependent biotin identification coupled to mass spectrometry (BioID-MS) approaches [16–18]. Likewise, luminescence-based Co-IP in mammalian cells (Lumier) [19] and crosslinking mass spectrometry (Xlink-MS) [20] approaches are going to contribute substantially toward building a human interactome framework map.
It is often unclear how genetic variation translates into cellular phenotypes, including how much of the coding variation can be recovered in the proteome. Proteogenomic analyses of heterogenous cell lines revealed that the genetic differences impact mostly the abundance and stoichiometry of protein complexes, with the effects propagating post-transcriptionally via protein interactions onto other subunits. Conversely, large scale binary interaction analyses of missense variants revealed that loss of interaction is widespread and caused by about 50% disease-associated mutations, while deep scanning mutagenesis of binary interactions identified thousands of interaction-deficient variants per interaction. The idea that phenotypes arise from genetic variation through protein–protein interaction is therefore substantiated by both forward and reverse interaction proteomics. With improved methodologies, these two approaches combined can close the knowledge gap between nucleotide sequence variation and its functional consequences on the cellular proteome.
Interactome Mapping Provides a Network of Neurodegenerative Disease Proteins and Uncovers Widespread Protein Aggregation in Affected Brains
2020, Cell Reports
Interactome maps are valuable resources to elucidate protein function and disease mechanisms. Here, we report on an interactome map that focuses on neurodegenerative disease (ND), connects ∼5,000 human proteins via ∼30,000 candidate interactions and is generated by systematic yeast two-hybrid interaction screening of ∼500 ND-related proteins and integration of literature interactions. This network reveals interconnectivity across diseases and links many known ND-causing proteins, such as α-synuclein, TDP-43, and ATXN1, to a host of proteins previously unrelated to NDs. It facilitates the identification of interacting proteins that significantly influence mutant TDP-43 and HTT toxicity in transgenic flies, as well as of ARF-GEP₁₀₀ that controls misfolding and aggregation of multiple ND-causing proteins in experimental model systems. Furthermore, it enables the prediction of ND-specific subnetworks and the identification of proteins, such as ATXN1 and MKL1, that are abnormally aggregated in postmortem brains of Alzheimer’s disease patients, suggesting widespread protein aggregation in NDs.
Probing Surfaces in Dynamic Protein Interactions
2020, Journal of Molecular Biology
Proteins and their interactions control a plethora of biological functions and enable life. Protein–protein interactions can be highly dynamic, involve proteins with different degrees of “foldedness,” and are often regulated through an intricate network of post-translational modifications. Central parts of protein–protein networks are intrinsically disordered proteins (IDPs). IDPs act as regulatory interaction hubs, enabled by their flexible nature. They employ various modes of binding mechanisms, from folding upon ligand binding to formation of highly dynamic “fuzzy” protein–protein complexes. Mutations or perturbations in regulation of IDPs are hallmarks of many diseases. Protein surfaces play key roles in protein–protein interactions. However, protein surfaces and protein surface accessibility are difficult to study experimentally. NMR-based solvent paramagnetic relaxation enhancement (sPRE) can provide quantitative experimental information on protein surface accessibility, which can be further used to obtain distance information for structure determination, identification of interaction surfaces, conformational changes, and identification of low-populated transient structure and long-range contacts in IDPs and dynamic protein–protein interactions. In this review, we present and discuss state-of the art sPRE techniques and their applications to investigate structure and dynamics of IDPs and protein–protein interactions. Finally, we provide an outline for potential future applications of the sPRE approach in combination with complementary techniques and modeling, to study novel paradigms, such as liquid–liquid phase separation, regulation of IDPs and protein–protein interactions by post-translational modifications, and targeting of disordered proteins.
Structural Basis for EPC1-Mediated Recruitment of MBTD1 into the NuA4/TIP60 Acetyltransferase Complex
2020, Cell Reports
MBTD1, a H4K20me reader, has recently been identified as a component of the NuA4/TIP60 acetyltransferase complex, regulating gene expression and DNA repair. NuA4/TIP60 inhibits 53BP1 binding to chromatin through recognition of the H4K20me mark by MBTD1 and acetylation of H2AK15, blocking the ubiquitination mark required for 53BP1 localization at DNA breaks. The NuA4/TIP60 non-catalytic subunit EPC1 enlists MBTD1 into the complex, but the detailed molecular mechanism remains incompletely explored. Here, we present the crystal structure of the MBTD1-EPC1 complex, revealing a hydrophobic C-terminal fragment of EPC1 engaging the MBT repeats of MBTD1 in a site distinct from the H4K20me binding site. Different cellular assays validate the physiological significance of the key residues involved in the MBTD1-EPC1 interaction. Our study provides a structural framework for understanding the mechanism by which MBTD1 recruits the NuA4/TIP60 acetyltransferase complex to influence transcription and DNA repair pathway choice.

View all citing articles on Scopus

View full text

Studying post-translational modifications with protein interaction networks

Highlights

Introduction

Section snippets

Recent progress in systematic human PPI mapping

Characterizing the PTM modifying enzyme interaction space

Conditional/PTMa-dependent protein interactions

Conclusions

References and recommended reading

Curr Opin Chem Biol

Cell

Science

Theor Chem Acc

Proteomics

Curr Opin Struct Biol

Proc Natl Acad Sci U S A

Mol Cell Proteomics

Mol Cell Proteomics

Annu Rev Biochem

Biochem J

Cell

Nat Biotechnol

Mol Cell

Nature

Philos Trans R Soc Lond B Biol Sci

Sci Signal

Cell

Nat Methods

PLoS Genet

PLoS Comput Biol

Cell

J Proteome Res

Mol Psychiatry

Mol Cell Proteomics

Cell

Nat Struct Mol Biol

Differential network biology

Mol Syst Biol

An empirical framework for binary interactome mapping

Nat Methods

SAINT: probabilistic scoring of affinity purification-mass spectrometry data

Nat Methods

Interlaboratory reproducibility of large-scale human protein-complex analysis by standardized AP-MS

Nat Methods

A Y2H-seq approach defines the human protein methyltransferase interactome

Nat Methods

Next-generation sequencing to generate interactome datasets

Nat Methods

A census of human soluble protein complexes

Cell

A high-throughput approach for measuring temporal changes in the interactome

Nat Methods

Analysis of the human endogenous coregulator complexome

Cell

Tissue-specific alternative splicing remodels protein–protein interaction networks

Mol Cell

Application of an integrated physical and functional screening approach to identify inhibitors of the Wnt pathway

Mol Syst Biol

Quantitative analysis of HSP90–client interactions reveals principles of substrate recognition

Cell

Protein–protein interaction networks: the puzzling riches

Curr Opin Struct Biol

Coev2Net: a computational framework for boosting confidence in high-throughput protein–protein interaction datasets

Genome Biol

Network compression as a quality measure for protein interaction networks

PLoS One

Cluster-based assessment of protein–protein interaction confidence

BMC Bioinformatics

Leveraging models of cell regulation and GWAS data in integrative network-based association studies

Nat Genet

NEW: network-enabled wisdom in biology, medicine, and health care

Sci Transl Med

Proteome-wide discovery of mislocated proteins in cancer

Genome Res

Dynamic protein–protein interaction wiring of the human spliceosome

Mol Cell

The intracellular interactome of tetraspanin-enriched microdomains reveals their function as sorting machineries toward exosomes

J Biol Chem