Exploring protein-protein interactions at the proteome level

Elhabashy, Hadeer; Merino, Felipe; Alva, Vikram; Kohlbacher, Oliver; Lupas, Andrei N.

doi:10.1016/j.str.2022.02.004

Cited by 26 publications

(10 citation statements)

References 212 publications

(199 reference statements)

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“…Recent developments have further improved our ability to unravel this connectivity, through techniques such as high-throughput genetic screens (3)(4)(5), systematic co-fractionation of proteomes (6,7), in-vivo proteome-wide cross-linking of proteins (8)(9)(10), and deep learning-based computational prediction frameworks such as AlphaFold (11,12). These efforts complement earlier results based on focused, small-scale laboratory studies, yeast two-hybrid screens, affinity purifications, co-crystallization and computational prediction algorithms (reviewed in (13)). Together, the many approaches have begun to reveal a large part of the interaction landscape of cellular proteins, but they each have strengths and weaknesses, including potential biases, false negatives and noise.…”

Section: Introductionmentioning

confidence: 80%

The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest

Szklarczyk

Kirsch

Koutrouli

et al. 2022

Nucleic Acids Research

2,627

1,014

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Much of the complexity within cells arises from functional and regulatory interactions among proteins. The core of these interactions is increasingly known, but novel interactions continue to be discovered, and the information remains scattered across different database resources, experimental modalities and levels of mechanistic detail. The STRING database (https://string-db.org/) systematically collects and integrates protein–protein interactions—both physical interactions as well as functional associations. The data originate from a number of sources: automated text mining of the scientific literature, computational interaction predictions from co-expression, conserved genomic context, databases of interaction experiments and known complexes/pathways from curated sources. All of these interactions are critically assessed, scored, and subsequently automatically transferred to less well-studied organisms using hierarchical orthology information. The data can be accessed via the website, but also programmatically and via bulk downloads. The most recent developments in STRING (version 12.0) are: (i) it is now possible to create, browse and analyze a full interaction network for any novel genome of interest, by submitting its complement of encoded proteins, (ii) the co-expression channel now uses variational auto-encoders to predict interactions, and it covers two new sources, single-cell RNA-seq and experimental proteomics data and (iii) the confidence in each experimentally derived interaction is now estimated based on the detection method used, and communicated to the user in the web-interface. Furthermore, STRING continues to enhance its facilities for functional enrichment analysis, which are now fully available also for user-submitted genomes.

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Section: Introductionmentioning

confidence: 80%

The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest

Szklarczyk

Kirsch

Koutrouli

et al. 2022

Nucleic Acids Research

2,627

1,014

View full text Add to dashboard Cite

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“…The processes occurring in the ribosome are so complex that "astronomical" is astronomically too small a word. They involve many distinct short-lived RNAs (Shi et al, 2022) with context-dependent functionality (Plawgo & Raczynska, 2022), and uncountably many protein-protein interactions yet to be explored (Elhabashy et al, 2022). The body's MRD systems are also notably susceptible to the states of mind and concommitant emotions of the host (D'Acquisto, 2017), not to mention the DNA, RNA, and protein products of the host that interact with those of the host's gut biota (Vos et al, 2022).…”

Section: Irreducible Complexitymentioning

confidence: 99%

All Cause Mortality and COVID-19 Injections: Evidence from 28 Weeks of Public Health England “COVID-19 Vaccine Surveillance Reports”

Oller

Santiago

2022

Vaccine Theory Prac & Res

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According to data collected in the UK for weeks 34-52 in 2021 (excluding week 51 which was not covered) and in weeks 1-12 of 2022 — a period of 28 weeks during which the genetic “vaccines” from Pfizer, Moderna, and AstraZeneca were being pressed upon the public — deaths reported to Public Health England (PHE) from their hospital “trusts” were being tabulated in a series of vaccine surveillance reports. Each source for the data analyzed here covered a four-week period. Deaths were reported to PHE from under age 18, progressing in increments of 10 years to 80 or older. In addition to deaths, the UK National Immunization Management System also recorded the date of a positive COVID-19 test result, and dates of any vaccinations. To obtain non-overlapping records, the 7 surveillance reports relied on here appeared in weeks 38, 42, 47, and 51 in 2021 and 4, 9, and 13 in 2022. Assuming that dying was not reported more than once for any individual, and, that dates of positive COVID-19 tests and COVID-19 vaccinations were reasonably reliable, we believe there is no good reason to doubt the data or the analyses we are reporting. During the 28 weeks we are referring to, we found an almost perfect correlation between the total deaths within 60 days of a positive COVID-19 test, across the various age groups (a) from any cause whatever and (b) deaths after the persons who died had received one, two, or three doses of at least one of the COVID-19 vaccines. We simply collated the data from the various UK Health Security Agency vaccine surveillance reports to discover a correlation of 0.99881, coefficient of determination at 0.99762, between (a) and (b). The shots account for almost exactly 100% of the variance in death-from-all-causes in the UK data set.

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“…Quantitative high-throughput (HTP) approaches are needed to explore the human protein-protein affinity interactome (1)(2)(3). Commonly, protein complexes are identified from cellular extracts with pull-down-based approaches involving a bait immobilized on beads via antibodies (immunoprecipitation) or affinity tags (affinity purification).…”

Section: Introductionmentioning

confidence: 99%

Native holdup (nHU) to measure binding affinities from cell extracts

et al. 2022

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Characterizing macromolecular interactions is essential for understanding cellular processes, yet most methods currently used to detect protein interactions from cells are qualitative. Here, we introduce the native holdup (nHU) approach to estimate equilibrium binding constants of protein interactions directly from cell extracts. Compared to other pull-down–based assays, nHU requires less sample preparation and can be coupled to any analytical methods as readouts, such as Western blotting or mass spectrometry. We use nHU to explore interactions of SNX27, a cargo adaptor of the retromer complex and find good agreement between in vitro affinities and those measured directly from cell extracts using nHU. We discuss the strengths and limitations of nHU and provide simple protocols that can be implemented in most laboratories.

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Exploring protein-protein interactions at the proteome level

Cited by 26 publications

References 212 publications

The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest

The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest

All Cause Mortality and COVID-19 Injections: Evidence from 28 Weeks of Public Health England “COVID-19 Vaccine Surveillance Reports”

Native holdup (nHU) to measure binding affinities from cell extracts

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