From the journals: MCP

A global proteomics approach to study the influence of COVID-19 on host signaling pathways. One resource to bring all the structural databases together. A new way to automate and optimize protein–protein studies. Read about papers on these topics recently published in the journal Molecular & Cellular Proteomics.

A global proteomics approach to COVID-19 and host signaling pathways

NIAID-RML

This scanning electron microscope image shows SARS-CoV-2 (round blue objects)
emerging from the surface of cells cultured in the lab. SARS-CoV-2 is the virus that
causes COVID-19.

COVID-19 has taken over the world as the largest global pandemic of our time. While the inflammatory implications of SARS-CoV-2 have been well studied, researchers do not yet understand the effect of the virus on signaling pathways. This is crucial, since COVID-19 manifests in severities ranging from asymptomatic infection to multiorgan failure. Immune response to a viral antigen is wide-ranging, from an interferon-mediated antiviral response to downstream events that activate transcription factors. This eventually leads to an inhibition of replication, transcription and translation of the viral genome, followed by its degradation and recruitment of immune cells.

In a recent study in the journal Molecular & Cellular Proteomics, Patrick M.Vanderboom and colleagues at the Mayo Clinic compared SARS-CoV-2 negative and positive patient samples to analyze molecular features of the host response. A global proteomics approach was used to characterize the influence of this infection, and samples were obtained from the nasopharynx due to the proximity to the lungs, where this COVID-19 most often progresses to severity.

When they subjected these samples to mass spectrometry, the researchers found 7,582 proteins, of which 143 were upregulated and 80 were downregulated in patients who had COVID-19. The upregulated proteins were involved mostly in interferon signaling. In particular, the authors monitored two specific molecules, RIG-1 and STAT1, involved in interferon signaling and found that the levels of these proteins correlate with viral loads.

The authors state that while these studies provide definitive information about the signaling pathways that are affected by SARS-CoV-2 infections, they will need to do more research to understand completely the pathogenesis of the virus and its potential outcomes in individual patients.

IMProv brings structural databases together

A popular technique used to validate direct interactions in protein complexes is cross-linking mass spectrometry, or XL-MS, which typically will detect linked residues while integrating these networks with structural techniques to generate accurate models of high-level molecular processes. XL-MS can overcome ambiguity in modest-resolution cryo-EM density maps and add more information to extrapolate X-ray maps into more accurate models.

In a recent paper in the journal Molecular & Cellular Proteomics, Daniel S. Ziemianowicz and colleagues at the University of California, San Francisco, and the University of Calgary, Canada, describe a new tool known as IMProv that can integrate cryo-EM densities, existing structures and cross-linking data. This addresses the effect of underlying protein dynamics on cross-linking. To use this resource, a user provides the sequence information for each protein building block and available partial or homologous structures. IMProv generates models using four steps: building a Python modeling interface, creating corresponding directories, using a SLURM bash script to model on a high-performance cluster and combining all of the above to generate the final model.

The authors show how IMProv could fill some gaps in the current model of the polycomb repressive complex 2. Overall, this resource will serve as an effective tool to develop existing data repositories and enable the use of cross-linking data to interpret and model structural data with greater precision.

Automating and optimizing protein–protein studies

When protein interactions occur in intrinsically disordered regions, it’s often through short linear motifs, known as SLiMs, which are both tedious and challenging to study. Researchers must incubate individual peptide spots with the protein extract on a cellulose membrane and then retrieve them for further analysis. This time-consuming procedure limits the number of samples that can be analyzed at a time.

To overcome these shortcomings, Evelyn Ramberger, Lorena Suarez–Artiles, Daniel Perez–Hernandez and colleagues at Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Germany, have developed an optimized method for using protein interaction screen on a peptide matrix, or PRISMA, in combination with quantitative mass spectrometry.

PRISMA is a new way to study point mutations and post-translational modifications within protein interaction motifs and to map these motifs.

In a recent paper in the journal Molecular & Cellular Proteomics, the authors write that PRISMA can be automated and allow the detection of phosphorylation-dependent interactors of certain proteins or mutation-dependent interactions of certain peptides. The authors propose that the transfer of this method from manual low-throughput procedures to an automated, microwell format with a high-throughput output retrieval will enable researchers to use PRISMA to explore disordered protein functions more efficiently. This method could contribute to deciphering the protein networks dependent on these short motifs that are involved in signaling processes and diseases.