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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.

[This corrects the article DOI: 10.1371/journal.pone.0060143.].

Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype–phenotype relationships 1 , 2 . Here we present a human ‘all-by-all’ reference interactome map of human binary protein interactions, or ‘HuRI’. With approximately 53,000 protein–protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome 3 , transcriptome 4 and proteome 5 data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein–protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes. A human binary protein interactome map that includes around 53,000 protein–protein interactions involving more than 8,000 proteins provides a reference for the study of human cellular function in health and disease.

Neurodegeneration correlates with Alzheimer’s disease (AD) symptoms, but the molecular identities of pathogenic amyloid β-protein (Aβ) oligomers and their targets, leading to neurodegeneration, remain unclear. Amylospheroids (ASPD) are AD patient-derived 10- to 15-nm spherical Aβ oligomers that cause selective degeneration of mature neurons. Here, we show that the ASPD target is neuronspecific Na⁺/K⁺-ATPase α3 subunit (NAKα3). ASPD-binding to NAKα3 impaired NAKα3-specific activity, activated N-type voltage-gated calcium channels, and caused mitochondrial calcium dyshomeostasis, tau abnormalities, and neurodegeneration. NMR and molecular modeling studies suggested that spherical ASPD contain N-terminal-Aβ–derived “thorns” responsible for target binding, which are distinct from low molecular-weight oligomers and dodecamers. The fourth extracellular loop (Ex4) region of NAKα3 encompassing Asn879and Trp880is essential for ASPD–NAKα3 interaction, because tetrapeptides mimicking this Ex4 region bound to the ASPD surface and blocked ASPD neurotoxicity. Our findings open up new possibilities for knowledge-based design of peptidomimetics that inhibit neurodegeneration in AD by blocking aberrant ASPD–NAKα3 interaction.

As viruses continue to pose risks to global health, having a better understanding of virus–host protein–protein interactions aids in the development of treatments and vaccines. Here, we introduce Viruses.STRING, a protein–protein interaction database specifically catering to virus–virus and virus–host interactions. This database combines evidence from experimental and text-mining channels to provide combined probabilities for interactions between viral and host proteins. The database contains 177,425 interactions between 239 viruses and 319 hosts. The database is publicly available at viruses.string-db.org, and the interaction data can also be accessed through the latest version of the Cytoscape STRING app.

Specific protein–protein interactions are central to all processes that underlie cell physiology. Numerous studies have together identified hundreds of thousands of human protein–protein interactions. However, many interactions remain to be discovered, and low affinity, conditional, and cell type‐specific interactions are likely to be disproportionately underrepresented. Here, we describe an optimized proteomic peptide‐phage display library that tiles all disordered regions of the human proteome and allows the screening of ~ 1,000,000 overlapping peptides in a single binding assay. We define guidelines for processing, filtering, and ranking the results and provide PepTools, a toolkit to annotate the identified hits. We uncovered >2,000 interaction pairs for 35 known short linear motif (SLiM)‐binding domains and confirmed the quality of the produced data by complementary biophysical or cell‐based assays. Finally, we show how the amino acid resolution‐binding site information can be used to pinpoint functionally important disease mutations and phosphorylation events in intrinsically disordered regions of the proteome. The optimized human disorderome library paired with PepTools represents a powerful pipeline for unbiased proteome‐wide discovery of SLiM‐based interactions. Synopsis An optimized phage peptidome that tiles the disordered regions of the human proteome is presented, allowing the field of motif‐based interactions to transition into high‐throughput. Guidelines and tools for data analysis are provided. An optimized second generation human disorderome (HD2) phage library tiles all disordered regions from the human proteome. Different peptide display parameters are tested, including display on the major or minor coat proteins of the M13 phage, and splitting the library design based sub‐cellular localization of the peptide containing proteins. PepTools is a dedicated toolkit to annotate peptides and to identify consensus motifs. > 2,000 motif‐based interactions are presented, together with information on potential disease mutations or phosphorylation sites that might affect the interactions. Graphical Abstract An optimized phage peptidome that tiles the disordered regions of the human proteome is presented, allowing the field of motif‐based interactions to transition into high‐throughput. Guidelines and tools for data analysis are provided.

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

In less than 20 years, three deadly coronaviruses, SARS-CoV, MERS-CoV and SARS-CoV-2, have emerged in human population causing hundreds to hundreds of thousands of deaths. Other coronaviruses are causing epizootic representing a significant threat for both domestic and wild animals. Members of this viral family have the longest genome of all RNA viruses, and express up to 29 proteins establishing complex interactions with the host proteome. Deciphering these interactions is essential to identify cellular pathways hijacked by these viruses to replicate and escape innate immunity. Virus-host interactions also provide key information to select targets for antiviral drug development. Here, we have manually curated the literature to assemble a unique dataset of 1311 coronavirus-host protein–protein interactions. Functional enrichment and network-based analyses showed coronavirus connections to RNA processing and translation, DNA damage and pathogen sensing, interferon production, and metabolic pathways. In particular, this global analysis pinpointed overlooked interactions with translation modulators (GIGYF2-EIF4E2), components of the nuclear pore, proteins involved in mitochondria homeostasis (PHB, PHB2, STOML2), and methylation pathways (MAT2A/B). Finally, interactome data provided a rational for the antiviral activity of some drugs inhibiting coronaviruses replication. Altogether, this work describing the current landscape of coronavirus-host interactions provides valuable hints for understanding the pathophysiology of coronavirus infections and developing effective antiviral therapies.

[This corrects the article DOI: 10.1371/journal.pone.0263792.].