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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. Comprehensive mapping of binary protein-protein interactions requires to combine several complementary assays. Here, the authors show that complete coverage could be reached with a minimal number of assays as long as they explore various experimental conditions.

The nuclear envelope (NE), a protective membrane bordering the nucleus, is composed of highly specialized proteins that are indispensable for normal cellular activity. Lamina-associated polypeptide 1 (LAP1) is a NE protein whose functions are just beginning to be unveiled. The fact that mutations causing LAP1 deficiency are extremely rare and pathogenic is indicative of its paramount importance to preserving human health, anticipating that LAP1 might have a multifaceted role in the cell. Mapping the LAP1 protein interactome is, thus, imperative to achieve an integrated view of its potential biological properties. To this end, we employed in silico- and mass spectrometry-based approaches to identify candidate LAP1-interacting proteins, whose functional attributes were subsequently characterized using bioinformatics tools. Our results reveal the complex and multifunctional network of protein–protein interactions associated to LAP1, evidencing a strong interconnection between LAP1 and cellular processes as diverse as chromatin and cytoskeleton organization, DNA repair, RNA processing and translation, as well as protein biogenesis and turnover, among others. Novel interactions between LAP1 and DNA repair proteins were additionally validated, strengthening the previously proposed involvement of LAP1 in the maintenance of genomic stability. Overall, this study reaffirms the biological relevance of LAP1 and the need to deepen our knowledge about this NE protein, providing new insights about its potential functional partners that will help guiding future research towards a mechanistic understanding of LAP1’s functioning.

This paper reports an approach to measure equilibrium binding affinities for interacting proteins in high throughput, allowing the rapid and quantitative profiling of the specificity of interaction motifs. Many protein interactions are mediated by small linear motifs interacting specifically with defined families of globular domains. Quantifying the specificity of a motif requires measuring and comparing its binding affinities to all its putative target domains. To this end, we developed the high-throughput holdup assay, a chromatographic approach that can measure up to 1,000 domain-motif equilibrium binding affinities per day. After benchmarking the approach on 210 PDZ-peptide pairs with known affinities, we determined the affinities of two viral PDZ-binding motifs derived from human papillomavirus E6 oncoproteins for 209 PDZ domains covering 79% of the human 'PDZome'. We obtained sharply sequence-dependent binding profiles that quantitatively describe the PDZome recognition specificity of each motif. This approach, applicable to many categories of domain-ligand interactions, has wide potential for quantifying the specificities of interactomes.

In cellular systems, biophysical interactions between macromolecules underlie a complex web of functional interactions. How biophysical and functional networks are coordinated, whether all biophysical interactions correspond to functional interactions, and how such biophysical‐versus‐functional network coordination is shaped by evolutionary forces are all largely unanswered questions. Here, we investigate these questions using an “inter‐interactome” approach. We systematically probed the yeast and human proteomes for interactions between proteins from these two species and functionally characterized the resulting inter‐interactome network. After a billion years of evolutionary divergence, the yeast and human proteomes are still capable of forming a biophysical network with properties that resemble those of intra‐species networks. Although substantially reduced relative to intra‐species networks, the levels of functional overlap in the yeast–human inter‐interactome network uncover significant remnants of co‐functionality widely preserved in the two proteomes beyond human–yeast homologs. Our data support evolutionary selection against biophysical interactions between proteins with little or no co‐functionality. Such non‐functional interactions, however, represent a reservoir from which nascent functional interactions may arise. Synopsis An inter‐species “inter‐interactome” was generated by systematic mapping protein–protein interactions between human and yeast proteomes. Comparisons of the inter‐species interactome with the two “parent” intra‐species human and yeast networks reveal evolutionary constraints and plasticity of biological systems. The human and yeast proteomes widely retain the ability to form inter‐species protein–protein interactions. Inter‐species interactions significantly but not exclusively correspond to ancestral binding properties preserved in human and yeast proteins. Ancestral binding properties appear to underlie conserved and species‐specific functions. Graphical Abstract An inter‐species “inter‐interactome” was generated by systematic mapping protein–protein interactions between human and yeast proteomes. Comparisons of the inter‐species interactome with the two “parent” intra‐species human and yeast networks reveal evolutionary constraints and plasticity of biological systems.

A combination of forward and reverse two hybrid screening allows systematic identification of 'edgetic' or edge-specific alleles, which encode proteins that have lost a single physical interaction but for which other interactions remain unperturbed. Genes and gene products do not function in isolation but within highly interconnected 'interactome' networks, modeled as graphs of nodes and edges representing macromolecules and interactions between them, respectively. We propose to investigate genotype-phenotype associations by methodical use of alleles that lack single interactions, while retaining all others, in contrast to genetic approaches designed to eliminate gene products completely. We describe an integrated strategy based on the reverse yeast two-hybrid system to isolate and characterize such edge-specific, or 'edgetic', alleles. We established a proof of concept with CED-9, a Caenorhabditis elegans BCL2 ortholog. Using ced-9 edgetic alleles, we uncovered a new potential functional link between apoptosis and a centrosomal protein. This approach is amenable to higher throughput and is particularly applicable to interactome network analysis in organisms for which transgenesis is straightforward.

Many biological processes are executed and regulated through the molecular interactions of proteins and nucleic acids. Proximity labeling (PL) is a technology for tagging the endogenous interaction partners of specific protein ‘baits’, via genetic fusion to promiscuous enzymes that catalyze the generation of diffusible reactive species in living cells. Tagged molecules that interact with baits can then be enriched and identified by mass spectrometry or nucleic acid sequencing. Here we review the development of PL technologies and highlight studies that have applied PL to the discovery and analysis of molecular interactions. In particular, we focus on the use of PL for mapping protein–protein, protein–RNA and protein–DNA interactions in living cells and organisms.This Review describes proximity labeling methods that make use of peroxidases (APEX) or biotin ligases (TurboID, BioID), and their applications to studying protein–protein and protein–nucleic acid interactions in living systems.

This protocol describes the use of TurboID and split-TurboID in proximity labeling applications for mapping protein–protein interactions and subcellular proteomes in live mammalian cells. TurboID is an engineered biotin ligase that uses ATP to convert biotin into biotin–AMP, a reactive intermediate that covalently labels proximal proteins. Optimized using directed evolution, TurboID has substantially higher activity than previously described biotin ligase–related proximity labeling methods, such as BioID, enabling higher temporal resolution and broader application in vivo. Split-TurboID consists of two inactive fragments of TurboID that can be reconstituted through protein–protein interactions or organelle–organelle interactions, which can facilitate greater targeting specificity than full-length enzymes alone. Proteins biotinylated by TurboID or split-TurboID are then enriched with streptavidin beads and identified by mass spectrometry. Here, we describe fusion construct design and characterization (variable timing), proteomic sample preparation (5–7 d), mass spectrometric data acquisition (2 d), and proteomic data analysis (1 week). This protocol describes proximity labeling approaches using TurboID and split-TurboID, which can be used for mapping protein–protein interactions and organelle proteomes in live mammalian cells with nanometer spatial resolution.

Protein-Protein Interactions (PPIs) are fundamental means of functions and signalings in biological systems. The massive growth in demand and cost associated with experimental PPI studies calls for computational tools for automated prediction and understanding of PPIs. Despite recent progress, in silico methods remain inadequate in modeling the natural PPI hierarchy. Here we present a double-viewed hierarchical graph learning model, HIGH-PPI, to predict PPIs and extrapolate the molecular details involved. In this model, we create a hierarchical graph, in which a node in the PPI network (top outside-of-protein view) is a protein graph (bottom inside-of-protein view). In the bottom view, a group of chemically relevant descriptors, instead of the protein sequences, are used to better capture the structure-function relationship of the protein. HIGH-PPI examines both outside-of-protein and inside-of-protein of the human interactome to establish a robust machine understanding of PPIs. This model demonstrates high accuracy and robustness in predicting PPIs. Moreover, HIGH-PPI can interpret the modes of action of PPIs by identifying important binding and catalytic sites precisely. Overall, “HIGH-PPI [ https://github.com/zqgao22/HIGH-PPI ]” is a domain-knowledge-driven and interpretable framework for PPI prediction studies. Despite recent progress, machine learning methods remain inadequate in modeling the natural protein-protein interaction (PPI) hierarchy for PPI prediction. Here, the authors present a double-viewed hierarchical graph learning model, HIGH-PPI, to predict PPIs and extrapolate the molecular details involved.

Studying protein interaction networks of all proteins in an organism (“interactomes”) remains one of the major challenges in modern biomedicine. Such information is crucial to understanding cellular pathways and developing effective therapies for the treatment of human diseases. Over the past two decades, diverse biochemical, genetic, and cell biological methods have been developed to map interactomes. In this review, we highlight basic principles of interactome mapping. Specifically, we discuss the strengths and weaknesses of individual assays, how to select a method appropriate for the problem being studied, and provide general guidelines for carrying out the necessary follow‐up analyses. In addition, we discuss computational methods to predict, map, and visualize interactomes, and provide a summary of some of the most important interactome resources. We hope that this review serves as both a useful overview of the field and a guide to help more scientists actively employ these powerful approaches in their research. Graphical Abstract A practical guide to the fundamentals of protein interaction network mapping, providing information to help researchers make effective use of proteomics approaches. A range of new and well‐established experimental and computational methods and resources are covered.

ClusPro is a web server that performs rigid-body docking of two proteins by sampling billions of conformations. Low-energy docked structures are clustered, and centers of the largest clusters are used as likely models of the complex. The ClusPro server ( https://cluspro.org ) is a widely used tool for protein–protein docking. The server provides a simple home page for basic use, requiring only two files in Protein Data Bank (PDB) format. However, ClusPro also offers a number of advanced options to modify the search; these include the removal of unstructured protein regions, application of attraction or repulsion, accounting for pairwise distance restraints, construction of homo-multimers, consideration of small-angle X-ray scattering (SAXS) data, and location of heparin-binding sites. Six different energy functions can be used, depending on the type of protein. Docking with each energy parameter set results in ten models defined by centers of highly populated clusters of low-energy docked structures. This protocol describes the use of the various options, the construction of auxiliary restraints files, the selection of the energy parameters, and the analysis of the results. Although the server is heavily used, runs are generally completed in <4 h.