Illuminating the dark protein-protein interactome

The hGIDGID4 E3 ubiquitin ligase complex targets ARHGAP11A to regulate cell migration

The human CTLH/GID (hGID) complex emerged as an important E3 ligase regulating multiple cellular processes, including cell cycle progression and metabolism. However, the range of biological functions controlled by hGID remains unexplored. Here, we used proximity-dependent biotinylation (BioID2) to identify proteins interacting with the hGID complex, among them, substrate candidates that bind GID4 in a pocket-dependent manner. Biochemical and cellular assays revealed that the hGIDGID4 E3 ligase binds and ubiquitinates ARHGAP11A, thereby targeting this RhoGAP for proteasomal degradation. Indeed, GID4 depletion or impeding the GID4 substrate binding pocket with the PFI-7 inhibitor stabilizes ARHGAP11A protein amounts, although it carries no functional N-terminal degron. Interestingly, GID4 inactivation impairs cell motility and directed cell movement by increasing ARHGAP11A levels at the cell periphery, where it inactivates RhoA. Together, we identified a wide range of hGIDGID4 E3 ligase substrates and uncovered a unique function of the hGIDGID4 E3 ligase regulating cell migration by targeting ARHGAP11A.

Proximity sequencing for the detection of mRNA, extracellular proteins and extracellular protein complexes in single cells

Complex cellular functions occur via the coordinated formation and dissociation of protein complexes. Functions such as the response to a signaling ligand can incorporate dozens of proteins and hundreds of complexes. Until recently, it has been difficult to measure multiple protein complexes at the single-cell level. Here, we present a step-by-step procedure for proximity sequencing, which enables the simultaneous measurement of proteins, mRNA and hundreds of protein complexes located on the outer membrane of cells. We guide the user through probe creation, sample preparation, staining, sequencing and computational quantification of protein complexes. This protocol empowers researchers to study, for example, the interplay between transcriptional states and cellular functions by coupling measurements of transcription to measurements of linked effector molecules, yet could be generalizable to other paired events. The protocol requires roughly 16 h spread over several days to complete by users with expertise in basic molecular biology and single-cell sequencing.

Guiding questions to avoid data leakage in biological machine learning applications

Machine learning methods for extracting patterns from high-dimensional data are very important in the biological sciences. However, in certain cases, real-world applications cannot confirm the reported prediction performance. One of the main reasons for this is data leakage, which can be seen as the illicit sharing of information between the training data and the test data, resulting in performance estimates that are far better than the performance observed in the intended application scenario. Data leakage can be difficult to detect in biological datasets due to their complex dependencies. With this in mind, we present seven questions that should be asked to prevent data leakage when constructing machine learning models in biological domains. We illustrate the usefulness of our questions by applying them to nontrivial examples. Our goal is to raise awareness of potential data leakage problems and to promote robust and reproducible machine learning-based research in biology.

Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes

While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.

Detection of differential bait proteoforms through immunoprecipitation-mass spectrometry data analysis

Proteins are often referred to as the workhorses of cells, and their interactions are necessary to facilitate specific cellular functions. Despite the recognition that protein-protein interactions, and thus protein functions, are determined by proteoform states, such as mutations and post-translational modifications (PTMs), methods for determining the differential abundance of proteoforms across conditions are very limited. Classically, immunoprecipitation coupled with mass spectrometry (IP-MS) has been used to understand how the interactome (preys) of a given protein (bait) changes between conditions to elicit specific cellular functions. Reversing this concept, we present here a new workflow for IP-MS data analysis that focuses on identifying the differential peptidoforms of the bait protein between conditions. This method can provide detailed information about specific bait proteoforms, potentially revealing pathogenic protein states that can be exploited for the development of targeted therapies.

Mass spectrometry and split luciferase complementation assays reveal the MecA protein interactome of Streptococcus mutans

MecA is a highly conserved adaptor protein encoded by prokaryotes from the Bacillota phylum. MecA mutants exhibit similar pleiotropic defects in a variety of organisms, although most of these phenotypes currently lack a mechanistic basis. MecA mediates ClpCP-dependent proteolysis of its substrates, but only several such substrates have been reported in the literature and there are suggestions that proteolysis-independent regulatory mechanisms may also exist. Here, we provide the first comprehensive characterization of the MecA interactome and further assess its regulatory role in Clp-dependent proteolysis. Untargeted coimmunoprecipitation assays coupled with mass spectrometry revealed that the MecA ortholog from the oral pathobiont Streptococcus mutans likely serves as a major protein interaction network hub by potentially complexing with >100 distinct protein substrates, most of which function in highly conserved metabolic pathways. The interactome results were independently verified using a newly developed prokaryotic split luciferase complementation assay (SLCA) to detect MecA protein-protein interactions in vivo. In addition, we further develop a new application of SLCA to support in vivo measurements of MecA relative protein binding affinities. SLCA results were independently verified using targeted coimmunoprecipitation assays, suggesting the general utility of this approach for prokaryotic protein-protein interaction studies. Our results indicate that MecA indeed regulates its interactome through both Clp-dependent proteolysis as well as through an as-yet undefined proteolysis-independent mechanism that may affect more than half of its protein interactome. This suggests a significant aspect of the MecA regulatory function still has yet to be discovered.IMPORTANCEDespite multiple decades of study, the regulatory mechanism and function of MecA have remained largely a mystery. The current study provides the first detailed roadmap to investigate these functions in other medically significant bacteria. Furthermore, this study developed new genetic approaches to assay prokaryotic protein-protein interactions via the split luciferase complementation assay (SLCA). SLCA technology is commonly employed in eukaryotic genetic research but has not yet been established for studies of bacterial protein-protein interactions. The SLCA protein binding affinity assay described here is a new technological advance exclusive to the current study and has not been reported elsewhere.

Human-virus protein-protein interactions maps assist in revealing the pathogenesis of viral infection

Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host. The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps. The work aims to provide a toolbox for researchers, hoping to better assist in deciphering the relationship between humans and viruses.

30 years of nanobodies - an ongoing success story of small binders in biological research

A milestone in the field of recombinant binding molecules was achieved 30 years ago with the discovery of single-domain antibodies from which antigen-binding variable domains, better known as nanobodies (Nbs), can be derived. Being only one tenth the size of conventional antibodies, Nbs feature high affinity and specificity, while being highly stable and soluble. In addition, they display accessibility to cryptic sites, low off-target accumulation and deep tissue penetration. Efficient selection methods, such as (semi-)synthetic/naïve or immunized cDNA libraries and display technologies, have facilitated the isolation of Nbs against diverse targets, and their single-gene format enables easy functionalization and high-yield production. This Review highlights recent advances in Nb applications in various areas of biological research, including structural biology, proteomics and high-resolution and in vivo imaging. In addition, we provide insights into intracellular applications of Nbs, such as live-cell imaging, biosensors and targeted protein degradation.