Binary Protein-protein Interactions Research Articles

ReLo is a simple and rapid colocalization assay to identify and characterize direct protein–protein interactions

The characterization of protein-protein interactions (PPIs) is fundamental to the understanding of biochemical processes. Many methods have been established to identify and study direct PPIs; however, screening and investigating PPIs involving large or poorly soluble proteins remains challenging. Here, we introduce ReLo, a simple, rapid, and versatile cell culture-based method for detecting and investigating interactions in a cellular context. Our experiments demonstrate that ReLo specifically detects direct binary PPIs. Furthermore, we show that ReLo bridging experiments can also be used to determine the binding topology of subunits within multiprotein complexes. In addition, ReLo facilitates the identification of protein domains that mediate complex formation, allows screening for interfering point mutations, and it is sensitive to drugs that mediate or disrupt an interaction. In summary, ReLo is a simple and rapid alternative for the study of PPIs, especially when studying structurally complex proteins or when established methods fail.

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry.

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.

AI-guided pipeline for protein–protein interaction drug discovery identifies a SARS-CoV-2 inhibitor

Protein–protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

XCAPT5: protein–protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language model

BackgroundPredicting protein–protein interactions (PPIs) from sequence data is a key challenge in computational biology. While various computational methods have been proposed, the utilization of sequence embeddings from protein language models, which contain diverse information, including structural, evolutionary, and functional aspects, has not been fully exploited. Additionally, there is a significant need for a comprehensive neural network capable of efficiently extracting these multifaceted representations.ResultsAddressing this gap, we propose xCAPT5, a novel hybrid classifier that uniquely leverages the T5-XL-UniRef50 protein large language model for generating rich amino acid embeddings from protein sequences. The core of xCAPT5 is a multi-kernel deep convolutional siamese neural network, which effectively captures intricate interaction features at both micro and macro levels, integrated with the XGBoost algorithm, enhancing PPIs classification performance. By concatenating max and average pooling features in a depth-wise manner, xCAPT5 effectively learns crucial features with low computational cost.ConclusionThis study represents one of the initial efforts to extract informative amino acid embeddings from a large protein language model using a deep and wide convolutional network. Experimental results show that xCAPT5 outperforms recent state-of-the-art methods in binary PPI prediction, excelling in cross-validation on several benchmark datasets and demonstrating robust generalization across intra-species, cross-species, inter-species, and stringent similarity contexts.

Reconstructing and analyzing the experimentally-supported binary protein-protein interaction network of Moorella thermoacetica, an anaerobe of industrial interest

There has been a rapidly increasing industrial interest in developing optimized engineering solutions for CO2 capture and subsequent conversion into biofuels and useful chemicals. The investigation of the non-photosynthetic CO2 bioconversion has gained momentum in the field of metabolic engineering and industrial biotechnology in recent years, as an alternative to the biomass-based microbial processes. Acetogens are bacteria, which possess the particular ability, using catabolically the relevant Wood – Ljungdahl (W-L) pathway. The thermophilic obligatory anaerobe, Moorella thermoacetica, has been the model acetogen due to its small fully-sequenced genome. Furthermore, its metabolic network has been reconstructed and relevant metabolic boundaries have been determined. However, its protein-protein interaction (PPI) network remains unexplored. In fact, PPI networks have not been studied extensively in bacteria. However, multi-omic analyses are crucial to further our understanding of bacterial molecular physiology and regulation and the integrated interpretation of omic datasets in the context of multi-level biomolecular networks is of great value and should be increasingly used in microbial research too. In this study, we extend the biomolecular analysis toolbox of M. thermoacetica by reconstructing a high-confidence experimentally-supported protein-protein interaction (PPI) network, based on (a) systematic literature curation, (b) comparative genomic analysis of the experimental binary PPI networks of Bacillus subtilis, an evolutionary adjacent microorganism, and the model bacterium Escherichia coli, and (c) the functional PPI resource STRING. The PPI network was further analyzed for its topology and pathway enrichment, including links to the metabolic network. The acquired results supported the need to pay better attention to the elucidation of the microbial PPI networks, especially of the extremophiles that have gained industrial interest.

Next-generation large-scale binary protein interaction network for Drosophila melanogaster

Generating reference maps of interactome networks illuminates genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. We apply state-of-the-art methods to identify binary protein-protein interactions (PPIs) for Drosophila melanogaster. Four all-by-all yeast two-hybrid (Y2H) screens of > 10,000 Drosophila proteins result in the ‘FlyBi’ dataset of 8723 PPIs among 2939 proteins. Testing subsets of data from FlyBi and previous PPI studies using an orthogonal assay allows for normalization of data quality; subsequent integration of FlyBi and previous data results in an expanded binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6511 proteins. We use FlyBi data to generate an autophagy network, then validate in vivo using autophagy-related assays. The deformed wings (dwg) gene encodes a protein that is both a regulator and a target of autophagy. Altogether, these resources provide a foundation for building new hypotheses regarding protein networks and function.

Using PlaPPISite to Predict and Analyze Plant Protein-Protein Interaction Sites.

Proteome-wide characterization of protein-protein interactions (PPIs) is crucial to understand the functional roles of protein machinery within cells systematically. With the accumulation of PPI data in different plants, the interaction details of binary PPIs, such as the three-dimensional (3D) structural contexts of interaction sites/interfaces, are urgently demanded. To meet this requirement, we have developed a comprehensive and easy-to-use database called PlaPPISite ( http://zzdlab.com/plappisite/index.php ) to present interaction details for 13 plant interactomes. Here, we provide a clear guide on how to search and view protein interaction details through the PlaPPISite database. Firstly, the running environment of our database is introduced. Secondly, the input file format is briefly introduced. Moreover, we discussed which information related to interaction sites can be achieved through several examples. In addition, some notes about PlaPPISite are also provided. More importantly, we would like to emphasize the importance of interaction site information in plant systems biology through this user guide of PlaPPISite. In particular, the easily accessible 3D structures of PPIs in the coming post-AlphaFold2 era will definitely boost the application of plant interactome to decipher the molecular mechanisms of many fundamental biological issues.

Reconstruction and analysis of a large-scale binary Ras-effector signaling network

BackgroundRas is a key cellular signaling hub that controls numerous cell fates via multiple downstream effector pathways. While pathways downstream of effectors such as Raf, PI3K and RalGDS are extensively described in the literature, how other effectors signal downstream of Ras is often still enigmatic.MethodsA comprehensive and unbiased Ras-effector network was reconstructed downstream of 43 effector proteins (converging onto 12 effector classes) using public pathway and protein–protein interaction (PPI) databases. The output is an oriented graph of pairwise interactions defining a 3-layer signaling network downstream of Ras. The 2290 proteins comprising the network were studied for their implication in signaling crosstalk and feedbacks, their subcellular localizations, and their cellular functions.ResultsThe final Ras-effector network consists of 2290 proteins that are connected via 19,080 binary PPIs, increasingly distributed across the downstream layers, with 441 PPIs in layer 1, 1660 in layer 2, and 16,979 in layer 3. We identified a high level of crosstalk among proteins of the 12 effector classes. A class-specific Ras sub-network was generated in CellDesigner (.xml file) and a functional enrichment analysis thereof shows that 58% of the processes have previously been associated to a respective effector pathway, with the remaining providing insights into novel and unexplored functions of specific effector pathways.ConclusionsOur large-scale and cell general Ras-effector network is a crucial steppingstone towards defining the network boundaries. It constitutes a ‘reference interactome’ and can be contextualized for specific conditions, e.g. different cell types or biopsy material obtained from cancer patients. Further, it can serve as a basis for elucidating systems properties, such as input–output relationships, crosstalk, and pathway redundancy.Graphical abstract3uuXA8noaAjvhTVsANJAr5Video .

Barcode fusion genetics-protein-fragment complementation assay (BFG-PCA): tools and resources that expand the potential for binary protein interaction discovery.

Barcode fusion genetics (BFG) utilizes deep sequencing to improve the throughput of protein–protein interaction (PPI) screening in pools. BFG has been implemented in Yeast two-hybrid (Y2H) screens (BFG-Y2H). While Y2H requires test protein pairs to localize in the nucleus for reporter reconstruction, dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA) allows proteins to localize in broader subcellular contexts and proves to be largely orthogonal to Y2H. Here, we implemented BFG to DHFR-PCA (BFG-PCA). This plasmid-based system can leverage ORF collections across model organisms to perform comparative analysis, unlike the original DHFR-PCA that requires yeast genomic integration. The scalability and quality of BFG-PCA were demonstrated by screening human and yeast interactions for >11 000 bait-prey pairs. BFG-PCA showed high-sensitivity and high-specificity for capturing known interactions for both species. BFG-Y2H and BFG-PCA capture distinct sets of PPIs, which can partially be explained based on the domain orientation of the reporter tags. BFG-PCA is a high-throughput protein interaction technology to interrogate binary PPIs that exploits clone collections from any species of interest, expanding the scope of PPI assays.

Scaling-up a fragment-based protein-protein interaction method using a human reference interaction set.

Protein–protein interactions (PPIs) are essential in understanding numerous aspects of protein function. Here, we significantly scaled and modified analyses of the recently developed all‐vs‐all sequencing (AVA‐Seq) approach using a gold‐standard human protein interaction set (hsPRS‐v2) containing 98 proteins. Binary interaction analyses recovered 20 of 47 (43%) binary PPIs from this positive reference set (PRS), comparing favorably with other methods. However, the increase of 20× in the interaction search space for AVA‐Seq analysis in this manuscript resulted in numerous changes to the method required for future use in genome‐wide interaction studies. We show that standard sequencing analysis methods must be modified to consider the possible recovery of thousands of positives among millions of tested interactions in a single sequencing run. The PRS data were used to optimize data scaling, auto‐activator removal, rank interaction features (such as orientation and unique fragment pairs), and statistical cutoffs. Using these modifications to the method, AVA‐Seq recovered >500 known and novel PPIs, including interactions between wild‐type fragments of tumor protein p53 and minichromosome maintenance complex proteins 2 and 5 (MCM2 and MCM5) that could be of interest in human disease.

Yeast Two-Hybrid System for Mapping Novel Dengue Protein Interactions.

Yeast two-hybrid (Y2H) systems are one of the principal choices for identifying novel binary protein-protein interactions (PPIs). Since its development, it has contributed for the discovery of several PPIs between pathogens and host, allowing not only a comprehensive look at the disease pathogenesis but also for therapeutic strategies. Identification of viral-host PPIs that impact on viral replication and pathogenesis can lead to new advances in antiviral therapies such as the development of drug candidates and vaccine design. In this chapter, we revise the Y2H key parameters necessary for screening PPIs and discuss the possible approaches for using this technique to identify novel dengue-host protein interactions.

Validation of AVA‐Seq using a human reference protein‐protein interaction set

Here is an in-depth characterization of a gold-standard human protein interaction set (HsPRS-v2) using AVA-Seq's (all-vs-all sequencing) convergently fused fragment design. Selected protein pairs were sheared into ~500 bp fragments, selected for the open reading frame, assembled into the pAVA plasmid and integrated for protein interactions. Initially, these data were interpreted strictly from a binary perspective in order to compare this method to other binary protein-protein interaction (PPI) methods which utilize the same HsPRS-v2. AVA-Seq was able to recapitulate 20 of 43 (46.5%) known binary PPIs from this positive reference set; 5 of these interactions were not captured by other methods previously reported. The use of this gold-standard set allows for a better understanding of the limitations and advantages of AVA-Seq. For example, there are PPIs which were not detected even though 95% of the amino acids between the two proteins were represented. Conversely, interactions were detected with as little as 16% protein coverage. The average protein coverage of all protein pairs used in this study was 60% while the average coverage for a detected interaction pair was 68%. By increasing the coverage of the proteins to >70%, the detection of expected PPIs in this data set is expected to increase to >50%. The same experimental data allowed for the determination of other interactions within this known interacting set. To that end, AVA-Seq has detected >500 PPIs from the HsPRS-v2 proteins tested; many of which are found with multiple unique fragment pairs, in multiple screening libraries, in multiple growth conditions and in both orientations. Ongoing work aims to make enhancements to this system for application of small, focused protein interaction sets (such as this study), as well as determining the entire interactomes, among others. The ease, swiftness, and reliability of AVA-Seq allows the user to determine PPIs in a protein pool of their choosing and also interrogate the active site regions of proteins with high-resolution by taking advantage of the convergent fragment feature of this method.

Conjoint Feature Representation of GO and Protein Sequence for PPI Prediction Based on an Inception RNN Attention Network

Protein-protein interactions (PPIs) are pivotal for cellular functions and biological processes. In the past years, computational methods using amino acid sequences and gene ontology (GO) annotations of proteins for prioritizing PPIs have provided important references for biological experiments in the wet lab. Despite the current success, sequence information and ontological annotation in semantic representation have not been integrated into current methods. We propose a deep-learning-based PPI prediction methodology conjointly featuring sequence information and GO annotation. First, we adopt a word-embedding tool, the NCBI-blueBERT model pre-trained on PubMed, to map the GO terms into their semantic vectors. Then, the GO semantic vectors and protein sequence vector serve as the input of the proposed inception recurrent neural network (RNN) attention network (IRAN). The IRAN captures the spatial relationship and the potential sequential feature of the protein sequence and ontological annotation semantics. The extensive experimental results on 12 benchmarks demonstrate that our method achieves superiority over state-of-the-art baselines. In the yeast dataset of a binary PPI prediction, our method improved the performance with the Matthews correlation coefficient increasing from 94.2% to 98.2% and the accuracy from 97.1% to 98.2%. The analogous results were also obtained in other comparison evaluations.

Extensive signal integration by the phytohormone protein network.

Plant hormones coordinate responses to environmental cues with developmental programs1, and are fundamental for stress resilience and agronomic yield2. The core signalling pathways underlying the effects of phytohormones have been elucidated by genetic screens and hypothesis-driven approaches, and extended by interactome studies of select pathways3. However, fundamental questions remain about how information from different pathways is integrated. Genetically, most phenotypes seem to be regulated by several hormones, but transcriptional profiling suggests that hormones trigger largely exclusive transcriptional programs4. We hypothesized that protein-protein interactions have an important role in phytohormone signal integration. Here, we experimentally generated a systems-level map of the Arabidopsis phytohormone signalling network, consisting of more than 2,000 binary protein-protein interactions. In the highly interconnected network, we identify pathway communities and hundreds of previously unknown pathway contacts that represent potential points of crosstalk. Functional validation of candidates in seven hormone pathways reveals new functions for 74% of tested proteins in 84% of candidate interactions, and indicates that a large majority of signalling proteins function pleiotropically in several pathways. Moreover, we identify several hundred largely small-molecule-dependent interactions of hormone receptors. Comparison with previous reports suggests that noncanonical and nontranscription-mediated receptor signalling is more common than hitherto appreciated.

Cell-based analysis of pairwise interactions between the components of the multi-tRNA synthetase complex.

Cytoplasmic aminoacyl-tRNA synthetases (ARSs) are organized into multi-tRNA synthetase complexes (MSCs), from Archaea to mammals. An evolutionary conserved role of the MSCs is enhancement of aminoacylation by forming stable associations of the ARSs and tRNAs. In mammals, a single macromolecular MSC exists, which is composed of eight cytoplasmic ARSs, for nine amino acids, and three scaffold proteins. Consequently, nearly half of aminoacyl-tRNA efflux becomes concentrated at the MSC. Stable supply of aminoacyl-tRNA to the ribosome is, therefore, considered to be a major role of the mammalian MSC. Furthermore, the mammalian MSC also serves as a reservoir for releasable components with noncanonical functions. In this study, a split-luciferase complementation system was applied to investigate the configuration of the MSC in live mammalian cells. Multiplex interconnections between the components were simplified into binary protein-protein interactions, and pairwise comparison of the interactions reconstituted a framework consistent with previous in vitro studies. Reversibility of the split-luciferase reporter binding demonstrated convertible organization of the mammalian MSC, including interferon gamma (IFNγ)-stimulated glutamyl-prolyl-tRNA synthetase 1 (EPRS1) release, as well as the cooperation with the ribosome bridged by the tRNAs. The cell-based analysis provided an improved understanding of the flexible framework of the mammalian MSC in physiological conditions.

Biosensor selection of small compounds, modulating a complex formation between cytochrome P450s and NADPH-dependent P450 oxidoreductase

The study of the effect of low-molecular-weight compounds (substrates, endogenous metabolites, drugs and xenobiotics) on the kinetic and equilibrium parameters of functionally significant binary protein-protein interactions (PPIs) is of both fundamental and clinical importance. The surface plasmon resonance (SPR) is the method of the first choice for studying PPIs. Earlier, SPR analysis revealed the modulating effect of steroidal substrates on the affinity of interactions between steroidogenic microsomal cytochromes P450 (CYP) and their redox partner (cytochrome b5). In this work, we have shown the suitability of the experimental approach for assessing the selective effect of the cofactor NADPH on the interaction between cytochromes CYP3A4 or CYP2E1 with NADPH-dependent P450 oxidoreductase (CPR). Experiments have shown that the CYP3A4/CPR complex is not modulated by NADPH, while the dissociation rate of the CYP2E1/CPR complex in the presence of NADPH significantly decreased: the koff values in the absence and presence of NADPH were (3.6 ± 0.2) • 10-3 s-1 and (3.8 ± 0.2) • 10-4 s-1, respectively. Thus, in the presence of NADPH, an increase in the affinity of CYP2E1/CPR complex formation by approximately one order of magnitude was observed, while NADPH did not affect the kon value of this complex. Co-injection of NADPH at the CYP2E1/CPR complex preformed in the absence of NADPH had minor influence on the koff values (<10%). This suggests a stabilizing role of NADPH for the CYP2E1/CPR complex formation. Thus, the use of our approach made it possible to assess the effect of the main electron supplier for the microsomal cytochrome P450 monooxygenase system on the kinetic rate constants of CYP/CPR complexes.

Maximizing binary interactome mapping with a minimal number of assays

Complementary assays are required to comprehensively map complex biological entities such as genomes, proteomes and interactome networks. However, how various assays can be optimally combined to approach completeness while maintaining high precision often remains unclear. Here, we propose a framework for binary protein-protein interaction (PPI) mapping based on optimally combining assays and/or assay versions to maximize detection of true positive interactions, while avoiding detection of random protein pairs. We have engineered a novel NanoLuc two-hybrid (N2H) system that integrates 12 different versions, differing by protein expression systems and tagging configurations. The resulting union of N2H versions recovers as many PPIs as 10 distinct assays combined. Thus, to further improve PPI mapping, developing alternative versions of existing assays might be as productive as designing completely new assays. Our findings should be applicable to systematic mapping of other biological landscapes.

Machine-learning techniques for the prediction of protein–protein interactions

Protein-protein interactions (PPIs) are important for the study of protein functions and pathways involved in different biological processes, as well as for understanding the cause and progression of diseases. Several high-throughput experimental techniques have been employed for the identification of PPIs in a few model organisms, but still, there is a huge gap in identifying all possible binary PPIs in an organism. Therefore, PPI prediction using machine-learning algorithms has been used in conjunction with experimental methods for discovery of novel protein interactions. The two most popular supervised machine-learning techniques used in the prediction of PPIs are support vector machines and random forest classifiers. Bayesian-probabilistic inference has also been used but mainly for the scoring of high-throughput PPI dataset confidence measures. Recently, deep-learning algorithms have been used for sequence-based prediction of PPIs. Several clustering methods such as hierarchical and k-means are useful as unsupervised machine-learning algorithms for the prediction of interacting protein pairs without explicit data labelling. In summary, machine-learning techniques have been widely used for the prediction of PPIs thus allowing experimental researchers to study cellular PPI networks.

Multifaceted protein-protein interaction prediction based on Siamese residual RCNN.

MotivationSequence-based protein–protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information.ResultsWe present an end-to-end framework, PIPR (Protein–Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences. PIPR incorporates a deep residual recurrent convolutional neural network in the Siamese architecture, which leverages both robust local features and contextualized information, which are significant for capturing the mutual influence of proteins sequences. PIPR relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that PIPR outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short.Availability and implementationThe implementation is available at https://github.com/muhaochen/seq_ppi.git.Supplementary information Supplementary data are available at Bioinformatics online.

A Comprehensive Drosophila melanogaster Transcription Factor Interactome

SUMMARYCombinatorial interactions among transcription factors (TFs) play essential roles in generating gene expression specificity and diversity in metazoans. Using yeast 2-hybrid (Y2H) assays on nearly all sequence-specific Drosophila TFs, we identified 1,983 protein-protein interactions (PPIs), more than doubling the number of currently known PPIs among Drosophila TFs. For quality assessment, we validated a subset of our interactions using MITOMI and bimolecular fluorescence complementation assays. We combined our interactome with prior PPI data to generate an integrated Drosophila TF-TF binary interaction network. Our analysis of ChIP-seq data, integrating PPI and gene expression information, uncovered different modes by which interacting TFs are recruited to DNA. We further demonstrate the utility of our Drosophila interactome in shedding light on human TF-TF interactions. This study reveals how TFs interact to bind regulatory elements in vivo and serves as a resource of Drosophila TF-TF binary PPIs for understanding tissue-specific gene regulation.